release v1.4.0

* Add Japanese README

* minor fix

.gitattributes

@@ -2,5 +2,8 @@ backend-python/rwkv_pip/** linguist-vendored
 backend-python/wkv_cuda_utils/** linguist-vendored
 backend-python/get-pip.py linguist-vendored
 backend-python/convert_model.py linguist-vendored
+backend-python/utils/midi.py linguist-vendored
 build/** linguist-vendored
+finetune/lora/** linguist-vendored
+finetune/json2binidx_tool/** linguist-vendored
 frontend/wailsjs/** linguist-generated

.github/workflows/release.yml

@@ -67,7 +67,7 @@ jobs:
       - run: gh release upload ${{github.ref_name}} build/bin/RWKV-Runner_windows_x64.exe
 
   linux:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-20.04
     needs: create-draft
     steps:
       - uses: actions/checkout@v3
@@ -81,6 +81,11 @@ jobs:
           sudo apt-get install upx
           sudo apt-get install build-essential libgtk-3-dev libwebkit2gtk-4.0-dev
           go install github.com/wailsapp/wails/v2/cmd/wails@latest
+          rm -rf ./backend-python/wkv_cuda_utils
+          rm ./backend-python/get-pip.py
+          sed -i '1,2d' ./backend-golang/wsl_not_windows.go
+          rm ./backend-golang/wsl.go
+          mv ./backend-golang/wsl_not_windows.go ./backend-golang/wsl.go
           make
           mv build/bin/RWKV-Runner build/bin/RWKV-Runner_linux_x64
 
@@ -98,6 +103,11 @@ jobs:
           go-version: '1.20.5'
       - run: |
           go install github.com/wailsapp/wails/v2/cmd/wails@latest
+          rm -rf ./backend-python/wkv_cuda_utils
+          rm ./backend-python/get-pip.py
+          sed -i '' '1,2d' ./backend-golang/wsl_not_windows.go
+          rm ./backend-golang/wsl.go
+          mv ./backend-golang/wsl_not_windows.go ./backend-golang/wsl.go
           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

.gitignore

@@ -8,6 +8,7 @@ __pycache__
 *.bin
 /config.json
 /cache.json
+/presets.json
 /frontend/stats.html
 /frontend/package.json.md5
 /py310
@@ -19,4 +20,7 @@ __pycache__
 *.old
 .DS_Store
 *.log.*
-*.log
+*.log
+train_log.txt
+finetune/json2binidx_tool/data
+/wsl.state

CURRENT_CHANGE.md

@@ -1,7 +1,17 @@
 ## Changes
 
-- exact avoidOverflow
-- adjust MoreUtilsButton
+- add Composition Page (RWKV-Music)
+- improve RunButton prompt
+- support for `stop` array api params
+- improve embeddings API results
+- improve python backend startup speed
+- add support for MIDI RWKV
+- add midi api
+- add CPU-120M-Music config
+- improve sse fetch
+- update manifest (a lot of new models)
+- update presets
+- remove LoraFinetunePrecision fp32
 
 ## Install
 

README.md

@@ -13,7 +13,7 @@ compatible with the OpenAI API, which means that every ChatGPT client is an RWKV
 [![license][license-image]][license-url]
 [![release][release-image]][release-url]
 
-English | [简体中文](README_ZH.md)
+English | [简体中文](README_ZH.md) | [日本語](README_JA.md)
 
 ### Install
 
@@ -49,7 +49,7 @@ English | [简体中文](README_ZH.md)
 
 #### Default configs has enabled custom CUDA kernel acceleration, which is much faster and consumes much less VRAM. If you encounter possible compatibility issues, go to the Configs page and turn off `Use Custom CUDA kernel to Accelerate`.
 
-#### If Windows Defender claims this is a virus, you can try downloading [v1.0.8](https://github.com/josStorer/RWKV-Runner/releases/tag/v1.0.8)/[v1.0.9](https://github.com/josStorer/RWKV-Runner/releases/tag/v1.0.9) and letting it update automatically to the latest version, or add it to the trusted list.
+#### If Windows Defender claims this is a virus, you can try downloading [v1.3.7_win.zip](https://github.com/josStorer/RWKV-Runner/releases/download/v1.3.7/RWKV-Runner_win.zip) and letting it update automatically to the latest version, or add it to the trusted list (`Windows Security` -> `Virus & threat protection` -> `Manage settings` -> `Exclusions` -> `Add or remove exclusions` -> `Add an exclusion` -> `Folder` -> `RWKV-Runner`).
 
 #### For different tasks, adjusting API parameters can achieve better results. For example, for translation tasks, you can try setting Temperature to 1 and Top_P to 0.3.
 
@@ -64,6 +64,8 @@ English | [简体中文](README_ZH.md)
 - Easy-to-understand and operate parameter configuration
 - Built-in model conversion tool
 - Built-in download management and remote model inspection
+- Built-in one-click LoRA Finetune
+- Can also be used as an OpenAI ChatGPT and GPT-Playground client
 - Multilingual localization
 - Theme switching
 - Automatic updates
@@ -89,6 +91,9 @@ body.json:
 
 ## Embeddings API Example
 
+Note: v1.4.0 has improved the quality of embeddings API. The generated results are not compatible
+with previous versions. If you are using embeddings API to generate knowledge bases or similar, please regenerate.
+
 If you are using langchain, just use `OpenAIEmbeddings(openai_api_base="http://127.0.0.1:8000", openai_api_key="sk-")`
 
 ```python
@@ -126,46 +131,45 @@ for i in np.argsort(embeddings_cos_sim)[::-1]:
     print(f"{embeddings_cos_sim[i]:.10f} - {values[i]}")
 ```
 
-## Todo
-
-- [ ] Model training functionality
-- [x] CUDA operator int8 acceleration
-- [x] macOS support
-- [x] Linux support
-- [ ] Local State Cache DB
-
 ## Related Repositories:
 
+- RWKV-4-World: https://huggingface.co/BlinkDL/rwkv-4-world/tree/main
 - RWKV-4-Raven: https://huggingface.co/BlinkDL/rwkv-4-raven/tree/main
 - ChatRWKV: https://github.com/BlinkDL/ChatRWKV
 - RWKV-LM: https://github.com/BlinkDL/RWKV-LM
+- RWKV-LM-LoRA: https://github.com/Blealtan/RWKV-LM-LoRA
+- MIDI-LLM-tokenizer: https://github.com/briansemrau/MIDI-LLM-tokenizer
 
 ## Preview
 
 ### Homepage
 
-![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/60efbb65-29e3-4346-a597-5bdcd099251c)
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/d7f24d80-f382-428d-8b28-edf87e1549e2)
 
 ### Chat
 
-![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/6cde9c45-51bb-4dee-b1fe-746862448520)
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/80009872-528f-4932-aeb2-f724fa892e7c)
 
 ### Completion
 
-![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/52f47f92-d21d-4cd7-b04e-d6f9af937a97)
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/bf49de8e-3b89-4543-b1ef-7cd4b19a1836)
 
 ### Configuration
 
-![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/93270a68-9d6d-4247-b6a3-e543c65a876b)
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/48befdc6-e03c-4851-9bee-22f77ee2640e)
 
 ### Model Management
 
-![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/6f96fdd3-fdf5-4b78-af80-2afbd1ad173b)
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/367fe4f8-cc12-475f-9371-3cf62cdbf293)
 
 ### Download Management
 
-![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/6982e7ee-bace-4a88-bb47-92379185bf9d)
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/c8153cf9-c8cb-4618-8268-60c82a5be539)
+
+### LoRA Finetune
+
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/4715045a-683e-4d2a-9b0e-090c7a5df63f)
 
 ### Settings
 
-![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/b3b2ab46-344c-4f04-b066-1503f776eeb9)
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/1067e635-8c07-4217-86a8-e48a5fcbb075)

README_JA.md

@@ -0,0 +1,176 @@
+<p align="center">
+    <img src="https://github.com/josStorer/RWKV-Runner/assets/13366013/d24834b0-265d-45f5-93c0-fac1e19562af">
+</p>
+
+<h1 align="center">RWKV Runner</h1>
+
+<div align="center">
+
+このプロジェクトは、すべてを自動化することで、大規模な言語モデルを使用する際の障壁をなくすことを目的としています。必要なのは、
+わずか数メガバイトの軽量な実行プログラムだけです。さらに、このプロジェクトは OpenAI API と互換性のあるインターフェイスを提供しており、
+すべての ChatGPT クライアントは RWKV クライアントであることを意味します。
+
+[![license][license-image]][license-url]
+[![release][release-image]][release-url]
+
+[English](README.md) | [简体中文](README_ZH.md) | 日本語
+
+### インストール
+
+[![Windows][Windows-image]][Windows-url]
+[![MacOS][MacOS-image]][MacOS-url]
+[![Linux][Linux-image]][Linux-url]
+
+[FAQs](https://github.com/josStorer/RWKV-Runner/wiki/FAQs) | [プレビュー](#Preview) | [ダウンロード][download-url] | [サーバーデプロイ例](https://github.com/josStorer/RWKV-Runner/tree/master/deploy-examples)
+
+[license-image]: http://img.shields.io/badge/license-MIT-blue.svg
+
+[license-url]: https://github.com/josStorer/RWKV-Runner/blob/master/LICENSE
+
+[release-image]: https://img.shields.io/github/release/josStorer/RWKV-Runner.svg
+
+[release-url]: https://github.com/josStorer/RWKV-Runner/releases/latest
+
+[download-url]: https://github.com/josStorer/RWKV-Runner/releases
+
+[Windows-image]: https://img.shields.io/badge/-Windows-blue?logo=windows
+
+[Windows-url]: https://github.com/josStorer/RWKV-Runner/blob/master/build/windows/Readme_Install.txt
+
+[MacOS-image]: https://img.shields.io/badge/-MacOS-black?logo=apple
+
+[MacOS-url]: https://github.com/josStorer/RWKV-Runner/blob/master/build/darwin/Readme_Install.txt
+
+[Linux-image]: https://img.shields.io/badge/-Linux-black?logo=linux
+
+[Linux-url]: https://github.com/josStorer/RWKV-Runner/blob/master/build/linux/Readme_Install.txt
+
+</div>
+
+#### デフォルトの設定はカスタム CUDA カーネルアクセラレーションを有効にしています。互換性の問題が発生する可能性がある場合は、コンフィグページに移動し、`Use Custom CUDA kernel to Accelerate` をオフにしてください。
+
+#### Windows Defender がこれをウイルスだと主張する場合は、[v1.3.7_win.zip](https://github.com/josStorer/RWKV-Runner/releases/download/v1.3.7/RWKV-Runner_win.zip) をダウンロードして最新版に自動更新させるか、信頼済みリストに追加してみてください (`Windows Security` -> `Virus & threat protection` -> `Manage settings` -> `Exclusions` -> `Add or remove exclusions` -> `Add an exclusion` -> `Folder` -> `RWKV-Runner`)。
+
+#### 異なるタスクについては、API パラメータを調整することで、より良い結果を得ることができます。例えば、翻訳タスクの場合、Temperature を 1 に、Top_P を 0.3 に設定してみてください。
+
+## 特徴
+
+- RWKV モデル管理とワンクリック起動
+- OpenAI API と完全に互換性があり、すべての ChatGPT クライアントを RWKV クライアントにします。モデル起動後、
+  http://127.0.0.1:8000/docs を開いて詳細をご覧ください。
+- 依存関係の自動インストールにより、軽量な実行プログラムのみを必要とします
+- 2G から 32G の VRAM のコンフィグが含まれており、ほとんどのコンピュータで動作します
+- ユーザーフレンドリーなチャットと完成インタラクションインターフェースを搭載
+- 分かりやすく操作しやすいパラメータ設定
+- 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
+- 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
+- 多言語ローカライズ
+- テーマ切り替え
+- 自動アップデート
+
+## API 同時実行ストレステスト
+
+```bash
+ab -p body.json -T application/json -c 20 -n 100 -l http://127.0.0.1:8000/chat/completions
+```
+
+body.json:
+
+```json
+{
+  "messages": [
+    {
+      "role": "user",
+      "content": "Hello"
+    }
+  ]
+}
+```
+
+## 埋め込み API の例
+
+Note: v1.4.0 has improved the quality of embeddings API. The generated results are not compatible
+with previous versions. If you are using embeddings API to generate knowledge bases or similar, please regenerate.
+
+LangChain を使用している場合は、`OpenAIEmbeddings(openai_api_base="http://127.0.0.1:8000", openai_api_key="sk-")`
+を使用してください
+
+```python
+import numpy as np
+import requests
+
+
+def cosine_similarity(a, b):
+    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
+
+
+values = [
+    "I am a girl",
+    "我是个女孩",
+    "私は女の子です",
+    "广东人爱吃福建人",
+    "我是个人类",
+    "I am a human",
+    "that dog is so cute",
+    "私はねこむすめです、にゃん♪",
+    "宇宙级特大事件！号外号外！"
+]
+
+embeddings = []
+for v in values:
+    r = requests.post("http://127.0.0.1:8000/embeddings", json={"input": v})
+    embedding = r.json()["data"][0]["embedding"]
+    embeddings.append(embedding)
+
+compared_embedding = embeddings[0]
+
+embeddings_cos_sim = [cosine_similarity(compared_embedding, e) for e in embeddings]
+
+for i in np.argsort(embeddings_cos_sim)[::-1]:
+    print(f"{embeddings_cos_sim[i]:.10f} - {values[i]}")
+```
+
+## 関連リポジトリ:
+
+- RWKV-4-World: https://huggingface.co/BlinkDL/rwkv-4-world/tree/main
+- RWKV-4-Raven: https://huggingface.co/BlinkDL/rwkv-4-raven/tree/main
+- ChatRWKV: https://github.com/BlinkDL/ChatRWKV
+- RWKV-LM: https://github.com/BlinkDL/RWKV-LM
+- RWKV-LM-LoRA: https://github.com/Blealtan/RWKV-LM-LoRA
+- MIDI-LLM-tokenizer: https://github.com/briansemrau/MIDI-LLM-tokenizer
+
+## プレビュー
+
+### ホームページ
+
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/d7f24d80-f382-428d-8b28-edf87e1549e2)
+
+### チャット
+
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/80009872-528f-4932-aeb2-f724fa892e7c)
+
+### 補完
+
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/bf49de8e-3b89-4543-b1ef-7cd4b19a1836)
+
+### コンフィグ
+
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/48befdc6-e03c-4851-9bee-22f77ee2640e)
+
+### モデル管理
+
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/367fe4f8-cc12-475f-9371-3cf62cdbf293)
+
+### ダウンロード管理
+
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/c8153cf9-c8cb-4618-8268-60c82a5be539)
+
+### LoRA Finetune
+
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/4715045a-683e-4d2a-9b0e-090c7a5df63f)
+
+### 設定
+
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/1067e635-8c07-4217-86a8-e48a5fcbb075)

README_ZH.md

@@ -12,7 +12,7 @@ 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]
 
-[English](README.md) | 简体中文
+[English](README.md) | 简体中文 | [日本語](README_JA.md)
 
 ### 安装
 
@@ -20,7 +20,7 @@ API兼容的接口，这意味着一切ChatGPT客户端都是RWKV客户端。
 [![MacOS][MacOS-image]][MacOS-url]
 [![Linux][Linux-image]][Linux-url]
 
-[视频演示](https://www.bilibili.com/video/BV1hM4y1v76R) | [疑难解答](https://www.bilibili.com/read/cv23921171) | [预览](#Preview) | [下载][download-url] | [懒人包](https://pan.baidu.com/s/1wchIUHgne3gncIiLIeKBEQ?pwd=1111) | [服务器部署示例](https://github.com/josStorer/RWKV-Runner/tree/master/deploy-examples)
+[视频演示](https://www.bilibili.com/video/BV1hM4y1v76R) | [疑难解答](https://www.bilibili.com/read/cv23921171) | [预览](#Preview) | [下载][download-url] | [懒人包](https://pan.baidu.com/s/1zdzZ_a0uM3gDqi6pXIZVAA?pwd=1111) | [服务器部署示例](https://github.com/josStorer/RWKV-Runner/tree/master/deploy-examples)
 
 [license-image]: http://img.shields.io/badge/license-MIT-blue.svg
 
@@ -46,11 +46,9 @@ API兼容的接口，这意味着一切ChatGPT客户端都是RWKV客户端。
 
 </div>
 
-#### 注意 目前RWKV中文模型质量一般，推荐使用英文模型或World(全球语言)体验实际RWKV能力
-
 #### 预设配置已经开启自定义CUDA算子加速，速度更快，且显存消耗更少。如果你遇到可能的兼容性问题，前往配置页面，关闭`使用自定义CUDA算子加速`
 
-#### 如果Windows Defender说这是一个病毒，你可以尝试下载[v1.0.8](https://github.com/josStorer/RWKV-Runner/releases/tag/v1.0.8)/[v1.0.9](https://github.com/josStorer/RWKV-Runner/releases/tag/v1.0.9)然后让其自动更新到最新版，或添加信任
+#### 如果Windows Defender说这是一个病毒，你可以尝试下载[v1.3.7_win.zip](https://github.com/josStorer/RWKV-Runner/releases/download/v1.3.7/RWKV-Runner_win.zip)，然后让其自动更新到最新版，或添加信任 (`Windows Security` -> `Virus & threat protection` -> `Manage settings` -> `Exclusions` -> `Add or remove exclusions` -> `Add an exclusion` -> `Folder` -> `RWKV-Runner`)
 
 #### 对于不同的任务，调整API参数会获得更好的效果，例如对于翻译任务，你可以尝试设置Temperature为1，Top_P为0.3
 
@@ -60,10 +58,12 @@ API兼容的接口，这意味着一切ChatGPT客户端都是RWKV客户端。
 - 与OpenAI API完全兼容，一切ChatGPT客户端，都是RWKV客户端。启动模型后，打开 http://127.0.0.1:8000/docs 查看详细内容
 - 全自动依赖安装，你只需要一个轻巧的可执行程序
 - 预设了2G至32G显存的配置，几乎在各种电脑上工作良好
-- 自带用户友好的聊天和补全交互页面
+- 自带用户友好的聊天和续写交互页面
 - 易于理解和操作的参数配置
 - 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
+- 也可用作 OpenAI ChatGPT 和 GPT Playground 客户端
 - 多语言本地化
 - 主题切换
 - 自动更新
@@ -89,6 +89,8 @@ body.json:
 
 ## Embeddings API 示例
 
+注意: 1.4.0 版本对embeddings API质量进行了改善，生成结果与之前的版本不兼容，如果你正在使用此API生成知识库等，请重新生成
+
 如果你在用langchain, 直接使用 `OpenAIEmbeddings(openai_api_base="http://127.0.0.1:8000", openai_api_key="sk-")`
 
 ```python
@@ -126,46 +128,45 @@ for i in np.argsort(embeddings_cos_sim)[::-1]:
     print(f"{embeddings_cos_sim[i]:.10f} - {values[i]}")
 ```
 
-## Todo
-
-- [ ] 模型训练功能
-- [x] CUDA算子int8提速
-- [x] macOS支持
-- [x] linux支持
-- [ ] 本地状态缓存数据库
-
 ## 相关仓库:
 
+- RWKV-4-World: https://huggingface.co/BlinkDL/rwkv-4-world/tree/main
 - RWKV-4-Raven: https://huggingface.co/BlinkDL/rwkv-4-raven/tree/main
 - ChatRWKV: https://github.com/BlinkDL/ChatRWKV
 - RWKV-LM: https://github.com/BlinkDL/RWKV-LM
+- RWKV-LM-LoRA: https://github.com/Blealtan/RWKV-LM-LoRA
+- MIDI-LLM-tokenizer: https://github.com/briansemrau/MIDI-LLM-tokenizer
 
 ## Preview
 
 ### 主页
 
-![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/9d25380a-a17b-443f-b823-86c754ebebf0)
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/ff2b1eef-dd3b-4cbf-98fb-b5a1ecee43e1)
 
 ### 聊天
 
-![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/0e66d5fa-f34a-409f-9cd4-d880815733f3)
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/9570e73b-dca2-4316-9e92-09961f3c48c4)
 
-### 补全
+### 续写
 
-![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/d4178ee9-a188-4878-9777-25c916872c29)
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/69f9ba7a-2fe8-4a5e-94cb-aa655aa409e2)
 
 ### 配置
 
-![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/ad9921fc-7248-40a3-9e18-03445b86e4bf)
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/59460f69-b172-4c7a-86cb-573262543076)
 
 ### 模型管理
 
-![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/7c36f15f-3e77-49cd-a16d-99a29f870bdf)
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/551121ee-1bfe-421b-a9d1-24125126ab4b)
 
 ### 下载管理
 
-![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/32fde30b-11dd-43b9-9667-ad6975be2106)
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/cc076038-2a91-4d36-bd39-266020e8ea87)
+
+### LoRA微调
+
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/31939b8f-9546-4f44-b434-295b492ec625)
 
 ### 设置
 
-![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/e8a0f746-9da7-48e3-b3fc-e1453ac50de2)
+![image](https://github.com/josStorer/RWKV-Runner/assets/13366013/9652d7cc-ac33-4587-a8fb-03e5a6f5ea77)

assets/sound-font/sound_fetch.py

@@ -0,0 +1,116 @@
+# https://github.com/magenta/magenta-js/issues/164
+
+import json
+import os
+import urllib.request
+
+
+def get_pitches_array(min_pitch, max_pitch):
+    return list(range(min_pitch, max_pitch + 1))
+
+
+base_url = 'https://storage.googleapis.com/magentadata/js/soundfonts'
+soundfont_path = 'sgm_plus'
+soundfont_json_url = f"{base_url}/{soundfont_path}/soundfont.json"
+
+# Download soundfont.json
+soundfont_json = ""
+
+if not os.path.exists('soundfont.json'):
+    try:
+        with urllib.request.urlopen(soundfont_json_url) as response:
+            soundfont_json = response.read()
+
+        # Save soundfont.json
+        with open('soundfont.json', 'wb') as file:
+            file.write(soundfont_json)
+
+    except:
+        print("Failed to download soundfont.json")
+
+else:
+    # If file exists, get it from the file system
+    with open('soundfont.json', 'rb') as file:
+        soundfont_json = file.read()
+
+# Parse soundfont.json
+soundfont_data = json.loads(soundfont_json)
+
+if soundfont_data is not None:
+
+    # Iterate over each instrument
+    for instrument_id, instrument_name in soundfont_data['instruments'].items():
+
+        if not os.path.isdir(instrument_name):
+
+            # Create instrument directory if it doesn't exist
+            os.makedirs(instrument_name)
+
+        instrument_json = ""
+
+        instrument_path = f"{soundfont_path}/{instrument_name}"
+
+        if not os.path.exists(f"{instrument_name}/instrument.json"):
+
+            # Download instrument.json
+            instrument_json_url = f"{base_url}/{instrument_path}/instrument.json"
+
+            try:
+                with urllib.request.urlopen(instrument_json_url) as response:
+                    instrument_json = response.read()
+
+                # Save instrument.json
+                with open(f"{instrument_name}/instrument.json", 'wb') as file:
+                    file.write(instrument_json)
+
+            except:
+                print(f"Failed to download {instrument_name}/instrument.json")
+
+        else:
+
+            # If file exists, get it from the file system
+            with open(f"{instrument_name}/instrument.json", 'rb') as file:
+                instrument_json = file.read()
+
+        # Parse instrument.json
+        instrument_data = json.loads(instrument_json)
+
+        if instrument_data is not None:
+            # Iterate over each pitch and velocity
+            for velocity in instrument_data['velocities']:
+
+                pitches = get_pitches_array(instrument_data['minPitch'], instrument_data['maxPitch'])
+
+                for pitch in pitches:
+
+                    # Create the file name
+                    file_name = f'p{pitch}_v{velocity}.mp3'
+
+                    # Check if the file already exists
+                    if os.path.exists(f"{instrument_name}/{file_name}"):
+                        pass
+                        #print(f"Skipping {instrument_name}/{file_name} - File already exists")
+
+                    else:
+
+                        # Download pitch/velocity file
+                        file_url = f"{base_url}/{instrument_path}/{file_name}"
+
+                        try:
+                            with urllib.request.urlopen(file_url) as response:
+                                file_contents = response.read()
+
+                            # Save pitch/velocity file
+                            with open(f"{instrument_name}/{file_name}", 'wb') as file:
+                                file.write(file_contents)
+
+                            print(f"Downloaded {instrument_name}/{file_name}")
+
+                        except:
+                            print(f"Failed to download {instrument_name}/{file_name}")
+
+        else:
+            print(f"Failed to parse instrument.json for {instrument_name}")
+
+else:
+    print('Failed to parse soundfont.json')

assets/sound-font/soundfont.json

@@ -0,0 +1,134 @@
+{
+  "name": "sgm_plus",
+  "instruments": {
+    "0": "acoustic_grand_piano",
+    "1": "bright_acoustic_piano",
+    "2": "electric_grand_piano",
+    "3": "honkytonk_piano",
+    "4": "electric_piano_1",
+    "5": "electric_piano_2",
+    "6": "harpsichord",
+    "7": "clavichord",
+    "8": "celesta",
+    "9": "glockenspiel",
+    "10": "music_box",
+    "11": "vibraphone",
+    "12": "marimba",
+    "13": "xylophone",
+    "14": "tubular_bells",
+    "15": "dulcimer",
+    "16": "drawbar_organ",
+    "17": "percussive_organ",
+    "18": "rock_organ",
+    "19": "church_organ",
+    "20": "reed_organ",
+    "21": "accordion",
+    "22": "harmonica",
+    "23": "tango_accordion",
+    "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_finger",
+    "34": "electric_bass_pick",
+    "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",
+    "49": "string_ensemble_2",
+    "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",
+    "81": "lead_2_sawtooth",
+    "82": "lead_3_calliope",
+    "83": "lead_4_chiff",
+    "84": "lead_5_charang",
+    "85": "lead_6_voice",
+    "86": "lead_7_fifths",
+    "87": "lead_8_bass_lead",
+    "88": "pad_1_new_age",
+    "89": "pad_2_warm",
+    "90": "pad_3_polysynth",
+    "91": "pad_4_choir",
+    "92": "pad_5_bowed",
+    "93": "pad_6_metallic",
+    "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",
+    "103": "fx_8_scifi",
+    "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",
+    "drums": "percussion"
+  }
+}

assets/soundfont_builder.rb

@@ -0,0 +1,469 @@
+#!/usr/bin/env ruby
+#
+# JavaScript Soundfont Builder for MIDI.js
+# Author: 0xFE <mohit@muthanna.com>
+# edited by Valentijn Nieman <valentijnnieman@gmail.com>
+#
+# Requires:
+#
+#   FluidSynth
+#   Lame
+#   Ruby Gems: midilib parallel
+#
+#   $ brew install fluidsynth lame (on OSX)
+#   $ gem install midilib parallel
+#
+# You'll need to download a GM soundbank to generate audio.
+#
+# Usage:
+#
+# 1) Install the above dependencies.
+# 2) Edit BUILD_DIR, SOUNDFONT, and INSTRUMENTS as required.
+# 3) Run without any argument.
+
+require 'base64'
+require 'digest/sha1'
+require 'etc'
+require 'fileutils'
+require 'midilib'
+require 'parallel'
+require 'zlib'
+require 'json'
+
+include FileUtils
+
+BUILD_DIR = "./sound-font" # Output path
+SOUNDFONT = "./default_sound_font.sf2" # Soundfont file path
+
+# This script will generate MIDI.js-compatible instrument JS files for
+# all instruments in the below array. Add or remove as necessary.
+INSTRUMENTS = [
+  0,
+  1,
+  2,
+  3,
+  4,
+  5,
+  6,
+  7,
+  8,
+  9,
+  10,
+  11,
+  12,
+  13,
+  14,
+  15,
+  16,
+  17,
+  18,
+  19,
+  20,
+  21,
+  22,
+  23,
+  24,
+  25,
+  26,
+  27,
+  28,
+  29,
+  30,
+  31,
+  32,
+  33,
+  34,
+  35,
+  36,
+  37,
+  38,
+  39,
+  40,
+  41,
+  42,
+  43,
+  44,
+  45,
+  46,
+  47,
+  48,
+  49,
+  50,
+  51,
+  52,
+  53,
+  54,
+  55,
+  56,
+  57,
+  58,
+  59,
+  60,
+  61,
+  62,
+  63,
+  64,
+  65,
+  66,
+  67,
+  68,
+  69,
+  70,
+  71,
+  72,
+  73,
+  74,
+  75,
+  76,
+  77,
+  78,
+  79,
+  80,
+  81,
+  82,
+  83,
+  84,
+  85,
+  86,
+  87,
+  88,
+  89,
+  90,
+  91,
+  92,
+  93,
+  94,
+  95,
+  96,
+  97,
+  98,
+  99,
+  100,
+  101,
+  102,
+  103,
+  104,
+  105,
+  106,
+  107,
+  108,
+  109,
+  110,
+  111,
+  112,
+  113,
+  114,
+  115,
+  116,
+  117,
+  118,
+  119,
+  120,
+  121,
+  122,
+  123,
+  124,
+  125,
+  126,
+  127
+]
+
+# It was found that midilib uses names that are incompatible with MIDI.js
+# For example, midilib uses "SynthBrass 1" -> https://github.com/jimm/midilib/blob/6c8e481ae72cd9f00a38eb3700ddfca6b549f153/lib/midilib/consts.rb#L280
+# and the MIDI association uses "SynthBrass 1" -> https://www.midi.org/specifications-old/item/gm-level-1-sound-set
+# but the MIDI.js calls this "Synth Brass 1" -> https://github.com/mudcube/MIDI.js/blob/a8a84257afa70721ae462448048a87301fc1554a/js/midi/gm.js#L44
+# there are others like "Bag pipe" vs "Bagpipe", etc.
+# here, we use the MIDI.js definitions because that is how most users will interact with the generated soundfonts.
+MIDIJS_PATCH_NAMES = [
+  "Acoustic Grand Piano",
+  "Bright Acoustic Piano",
+  "Electric Grand Piano",
+  "Honky-tonk Piano",
+  "Electric Piano 1",
+  "Electric Piano 2",
+  "Harpsichord",
+  "Clavinet",
+  "Celesta",
+  "Glockenspiel",
+  "Music Box",
+  "Vibraphone",
+  "Marimba",
+  "Xylophone",
+  "Tubular Bells",
+  "Dulcimer",
+  "Drawbar Organ",
+  "Percussive Organ",
+  "Rock Organ",
+  "Church Organ",
+  "Reed Organ",
+  "Accordion",
+  "Harmonica",
+  "Tango Accordion",
+  "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 (finger)",
+  "Electric Bass (pick)",
+  "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",
+  "String Ensemble 2",
+  "Synth Strings 1",
+  "Synth Strings 2",
+  "Choir Aahs",
+  "Voice Oohs",
+  "Synth Choir",
+  "Orchestra Hit",
+  "Trumpet",
+  "Trombone",
+  "Tuba",
+  "Muted Trumpet",
+  "French Horn",
+  "Brass Section",
+  "Synth Brass 1",
+  "Synth Brass 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)",
+  "Lead 2 (sawtooth)",
+  "Lead 3 (calliope)",
+  "Lead 4 (chiff)",
+  "Lead 5 (charang)",
+  "Lead 6 (voice)",
+  "Lead 7 (fifths)",
+  "Lead 8 (bass + lead)",
+  "Pad 1 (new age)",
+  "Pad 2 (warm)",
+  "Pad 3 (polysynth)",
+  "Pad 4 (choir)",
+  "Pad 5 (bowed)",
+  "Pad 6 (metallic)",
+  "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)",
+  "FX 8 (sci-fi)",
+  "Sitar",
+  "Banjo",
+  "Shamisen",
+  "Koto",
+  "Kalimba",
+  "Bagpipe",
+  "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"
+]
+
+# The encoders and tools are expected in your PATH. You can supply alternate
+# paths by changing the constants below.
+LAME = "lame" # `which lame`.chomp
+FLUIDSYNTH = "fluidsynth" # `which fluidsynth`.chomp
+
+puts "Building the following instruments using font: " + SOUNDFONT
+
+# Display instrument names.
+INSTRUMENTS.each do |i|
+  puts "    #{i}: " + MIDIJS_PATCH_NAMES[i]
+end
+
+puts
+puts "Using MP3 encoder: " + LAME
+puts "Using FluidSynth encoder: " + FLUIDSYNTH
+puts
+puts "Sending output to: " + BUILD_DIR
+puts
+
+raise "Can't find soundfont: #{SOUNDFONT}" unless File.exist? SOUNDFONT
+raise "Can't find 'lame' command" if LAME.empty?
+raise "Can't find 'fluidsynth' command" if FLUIDSYNTH.empty?
+raise "Output directory does not exist: #{BUILD_DIR}" unless File.exist?(BUILD_DIR)
+
+puts "Hit return to begin."
+$stdin.readline
+
+NOTES = {
+  "C"  => 0,
+  "Db" => 1,
+  "D"  => 2,
+  "Eb" => 3,
+  "E"  => 4,
+  "F"  => 5,
+  "Gb" => 6,
+  "G"  => 7,
+  "Ab" => 8,
+  "A"  => 9,
+  "Bb" => 10,
+  "B"  => 11
+}
+
+MIDI_C0 = 12
+VELOCITY = 100
+DURATION = Integer(3000)
+TEMP_FILE = "#{BUILD_DIR}/%s%stemp.midi"
+FLUIDSYNTH_RAW = "%s.wav"
+
+def deflate(string, level)
+  z = Zlib::Deflate.new(level)
+  dst = z.deflate(string, Zlib::FINISH)
+  z.close
+  dst
+end
+
+def note_to_int(note, octave)
+  value = NOTES[note]
+  increment = MIDI_C0 * octave
+  return value + increment
+end
+
+def int_to_note(value)
+  raise "Bad Value" if value < MIDI_C0
+  reverse_notes = NOTES.invert
+  value -= MIDI_C0
+  octave = value / 12
+  note = value % 12
+  return { key: reverse_notes[note],
+           octave: octave }
+end
+
+# Run a quick table validation
+MIDI_C0.upto(100) do |x|
+  note = int_to_note x
+  #raise "Broken table" unless note_to_int(note[:key], note[:octave]) == x
+end
+
+def generate_midi(program, note_value, file)
+  include MIDI
+  seq = Sequence.new()
+  track = Track.new(seq)
+
+  seq.tracks << track
+  track.events << ProgramChange.new(0, Integer(program))
+  track.events << NoteOn.new(0, note_value, VELOCITY, 0) # channel, note, velocity, delta
+  track.events << NoteOff.new(0, note_value, VELOCITY, DURATION)
+
+  File.open(file, 'wb') { | file | seq.write(file) }
+end
+
+def run_command(cmd)
+  puts "Running: " + cmd
+  `#{cmd}`
+end
+
+def midi_to_audio(source, target)
+  run_command "#{FLUIDSYNTH} -C no -R no -g 0.5 -F #{target} #{SOUNDFONT} #{source}"
+  run_command "#{LAME} -v -b 8 -B 64 #{target}"
+  rm target
+end
+
+def open_js_file(instrument_key, type)
+  js_file = File.open("#{BUILD_DIR}/#{instrument_key}-#{type}.js", "w")
+  js_file.write(
+"""
+if (typeof(MIDI) === 'undefined') var MIDI = {};
+if (typeof(MIDI.Soundfont) === 'undefined') MIDI.Soundfont = {};
+MIDI.Soundfont.#{instrument_key} = {
+""")
+  return js_file
+end
+
+def close_js_file(file)
+  file.write("\n}\n")
+  file.close
+end
+
+def base64js(note, file, type)
+  output = '"' + note + '": '
+  output += '"' + "data:audio/#{type};base64,"
+  output += Base64.strict_encode64(File.read(file)) + '"'
+  return output
+end
+
+def generate_audio(program)
+  instrument = MIDIJS_PATCH_NAMES[program]
+  instrument_key = instrument.downcase.gsub(/[^a-z0-9 ]/, "").gsub(/[ ]/, "_")
+
+  puts "Generating audio for: " + instrument + "(#{instrument_key})"
+
+  mkdir_p "#{BUILD_DIR}/#{instrument_key}"
+
+
+  note_to_int("A", 0).upto(note_to_int("C", 8)) do |note_value|
+    output_name = "p#{note_value}_v#{VELOCITY}"
+    output_path_prefix = BUILD_DIR + "/#{instrument_key}" + output_name
+
+    puts "Generating: #{output_name}"
+    temp_file_specific = TEMP_FILE % [output_name, instrument_key]
+    generate_midi(program, note_value, temp_file_specific)
+    midi_to_audio(temp_file_specific, output_path_prefix + ".wav")
+
+    mv output_path_prefix + ".mp3", "#{BUILD_DIR}/#{instrument_key}/#{output_name}.mp3"
+    rm temp_file_specific
+  end
+
+  tempHash = {
+    "name" => instrument_key,
+    "minPitch" => 0,
+    "maxPitch" => 127,
+    "durationSeconds" => 3.0,
+    "releaseSeconds" => 1.0,
+    "percussive": false,
+    "velocities": [100]
+  }
+
+  File.open("#{BUILD_DIR}/#{instrument_key}/instrument.json", "w") do |f|
+    f.write(tempHash.to_json)
+  end
+end
+
+Parallel.each(INSTRUMENTS, :in_processes=>Etc.nprocessors){|i| generate_audio(i)}

backend-golang/app.go

@@ -9,6 +9,7 @@ import (
 	"path/filepath"
 	"runtime"
 
+	"github.com/fsnotify/fsnotify"
 	"github.com/minio/selfupdate"
 	wruntime "github.com/wailsapp/wails/v2/pkg/runtime"
 )
@@ -40,7 +41,36 @@ func (a *App) OnStartup(ctx context.Context) {
 		a.cmdPrefix = "cd " + a.exDir + " && "
 	}
 
+	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)
+	f, err := os.Create(a.exDir + "lora-models/train_log.txt")
+	if err == nil {
+		f.Close()
+	}
+
 	a.downloadLoop()
+
+	watcher, err := fsnotify.NewWatcher()
+	if err == nil {
+		watcher.Add("./lora-models")
+		watcher.Add("./models")
+		go func() {
+			for {
+				select {
+				case event, ok := <-watcher.Events:
+					if !ok {
+						return
+					}
+					wruntime.EventsEmit(ctx, "fsnotify", event.Name)
+				case _, ok := <-watcher.Errors:
+					if !ok {
+						return
+					}
+				}
+			}
+		}()
+	}
 }
 
 func (a *App) UpdateApp(url string) (broken bool, err error) {

backend-golang/file.go

@@ -122,6 +122,10 @@ func (a *App) CopyFile(src string, dst string) error {
 }
 
 func (a *App) OpenSaveFileDialog(filterPattern string, defaultFileName string, savedContent string) (string, error) {
+	return a.OpenSaveFileDialogBytes(filterPattern, defaultFileName, []byte(savedContent))
+}
+
+func (a *App) OpenSaveFileDialogBytes(filterPattern string, defaultFileName string, savedContent []byte) (string, error) {
 	path, err := wruntime.SaveFileDialog(a.ctx, wruntime.SaveDialogOptions{
 		DefaultFilename: defaultFileName,
 		Filters: []wruntime.FileFilter{{
@@ -135,7 +139,7 @@ func (a *App) OpenSaveFileDialog(filterPattern string, defaultFileName string, s
 	if path == "" {
 		return "", nil
 	}
-	if err := os.WriteFile(path, []byte(savedContent), 0644); err != nil {
+	if err := os.WriteFile(path, savedContent, 0644); err != nil {
 		return "", err
 	}
 	return path, nil

backend-golang/rwkv.go

@@ -1,6 +1,7 @@
 package backend_golang
 
 import (
+	"encoding/json"
 	"errors"
 	"os"
 	"os/exec"
@@ -31,6 +32,71 @@ func (a *App) ConvertModel(python string, modelPath string, strategy string, out
 	return Cmd(python, "./backend-python/convert_model.py", "--in", modelPath, "--out", outPath, "--strategy", strategy)
 }
 
+func (a *App) ConvertData(python string, input string, outputPrefix string, vocab string) (string, error) {
+	var err error
+	if python == "" {
+		python, err = GetPython()
+	}
+	if err != nil {
+		return "", err
+	}
+	tokenizerType := "HFTokenizer"
+	if strings.Contains(vocab, "rwkv_vocab_v20230424") {
+		tokenizerType = "RWKVTokenizer"
+	}
+
+	input = strings.TrimSuffix(input, "/")
+	if fi, err := os.Stat(input); err == nil && fi.IsDir() {
+		files, err := os.ReadDir(input)
+		if err != nil {
+			return "", err
+		}
+		jsonlFile, err := os.Create(outputPrefix + ".jsonl")
+		if err != nil {
+			return "", err
+		}
+		defer jsonlFile.Close()
+		for _, file := range files {
+			if file.IsDir() || !strings.HasSuffix(file.Name(), ".txt") {
+				continue
+			}
+			textContent, err := os.ReadFile(input + "/" + file.Name())
+			if err != nil {
+				return "", err
+			}
+			textJson, err := json.Marshal(map[string]string{"text": strings.ReplaceAll(strings.ReplaceAll(string(textContent), "\r\n", "\n"), "\r", "\n")})
+			if err != nil {
+				return "", err
+			}
+			if _, err := jsonlFile.WriteString(string(textJson) + "\n"); err != nil {
+				return "", err
+			}
+		}
+		input = outputPrefix + ".jsonl"
+	} else if err != nil {
+		return "", err
+	}
+
+	return Cmd(python, "./finetune/json2binidx_tool/tools/preprocess_data.py", "--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
+	if python == "" {
+		python, err = GetPython()
+	}
+	if err != nil {
+		return "", err
+	}
+	args := []string{python, "./finetune/lora/merge_lora.py"}
+	if useGpu {
+		args = append(args, "--use-gpu")
+	}
+	args = append(args, strconv.Itoa(loraAlpha), baseModel, loraPath, outputPath)
+	return Cmd(args...)
+}
+
 func (a *App) DepCheck(python string) error {
 	var err error
 	if python == "" {
@@ -81,3 +147,11 @@ func (a *App) InstallPyDep(python string, cnMirror bool) (string, error) {
 		return Cmd(python, "-m", "pip", "install", "-r", "./backend-python/requirements_without_cyac.txt")
 	}
 }
+
+func (a *App) GetPyError() string {
+	content, err := os.ReadFile("./error.txt")
+	if err != nil {
+		return ""
+	}
+	return string(content)
+}

backend-golang/wsl.go

@@ -0,0 +1,181 @@
+//go:build windows
+
+package backend_golang
+
+import (
+	"bufio"
+	"context"
+	"errors"
+	"io"
+	"os"
+	"os/exec"
+	"path/filepath"
+	"strings"
+	"time"
+
+	su "github.com/nyaosorg/go-windows-su"
+	wsl "github.com/ubuntu/gowsl"
+	wruntime "github.com/wailsapp/wails/v2/pkg/runtime"
+)
+
+var distro *wsl.Distro
+var stdin io.WriteCloser
+var cmd *exec.Cmd
+
+func isWslRunning() (bool, error) {
+	if distro == nil {
+		return false, nil
+	}
+	state, err := distro.State()
+	if err != nil {
+		return false, err
+	}
+	if state != wsl.Running {
+		distro = nil
+		return false, nil
+	}
+	return true, nil
+}
+
+func (a *App) WslStart() error {
+	running, err := isWslRunning()
+	if err != nil {
+		return err
+	}
+	if running {
+		return nil
+	}
+	distros, err := wsl.RegisteredDistros(context.Background())
+	if err != nil {
+		return err
+	}
+	for _, d := range distros {
+		if strings.Contains(d.Name(), "Ubuntu") {
+			distro = &d
+			break
+		}
+	}
+	if distro == nil {
+		return errors.New("ubuntu not found")
+	}
+
+	cmd = exec.Command("wsl", "-d", distro.Name(), "-u", "root")
+
+	stdin, err = cmd.StdinPipe()
+	if err != nil {
+		return err
+	}
+
+	stdout, err := cmd.StdoutPipe()
+	cmd.Stderr = cmd.Stdout
+	if err != nil {
+		// stdin.Close()
+		stdin = nil
+		return err
+	}
+
+	go func() {
+		reader := bufio.NewReader(stdout)
+		for {
+			if stdin == nil {
+				break
+			}
+			line, _, err := reader.ReadLine()
+			if err != nil {
+				wruntime.EventsEmit(a.ctx, "wslerr", err.Error())
+				break
+			}
+			wruntime.EventsEmit(a.ctx, "wsl", string(line))
+		}
+		// stdout.Close()
+	}()
+
+	if err := cmd.Start(); err != nil {
+		return err
+	}
+	return nil
+}
+
+func (a *App) WslCommand(command string) error {
+	running, err := isWslRunning()
+	if err != nil {
+		return err
+	}
+	if !running {
+		return errors.New("wsl not running")
+	}
+	_, err = stdin.Write([]byte(command + "\n"))
+	if err != nil {
+		return err
+	}
+	return nil
+}
+
+func (a *App) WslStop() error {
+	running, err := isWslRunning()
+	if err != nil {
+		return err
+	}
+	if !running {
+		return errors.New("wsl not running")
+	}
+	if cmd != nil {
+		err = cmd.Process.Kill()
+		cmd = nil
+	}
+	// stdin.Close()
+	stdin = nil
+	distro = nil
+	if err != nil {
+		return err
+	}
+	return nil
+}
+
+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")
+	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)
+	if err != nil {
+		return err
+	}
+	time.Sleep(2 * time.Second)
+	data, err = os.ReadFile(exDir + "/wsl.state")
+	if err != nil {
+		return err
+	}
+	if strings.Contains(string(data), "Enabled") {
+		return nil
+	} else {
+		return errors.New("wsl is not enabled")
+	}
+}
+
+func (a *App) WslEnable(forceMode bool) error {
+	cmd := `/online /enable-feature /featurename:Microsoft-Windows-Subsystem-Linux`
+	_, err := su.ShellExecute(su.RUNAS, "dism", cmd, `C:\`)
+	if err != nil {
+		return err
+	}
+	if forceMode {
+		os.WriteFile("./wsl.state", []byte("Enabled"), 0644)
+	}
+	return nil
+}
+
+func (a *App) WslInstallUbuntu() error {
+	_, err := Cmd("ms-windows-store://pdp/?ProductId=9PN20MSR04DW")
+	return err
+}

backend-golang/wsl_not_windows.go

@@ -0,0 +1,31 @@
+//go:build darwin || linux
+
+package backend_golang
+
+import (
+	"errors"
+)
+
+func (a *App) WslStart() error {
+	return errors.New("wsl not supported")
+}
+
+func (a *App) WslCommand(command string) error {
+	return errors.New("wsl not supported")
+}
+
+func (a *App) WslStop() error {
+	return errors.New("wsl not supported")
+}
+
+func (a *App) WslIsEnabled() error {
+	return errors.New("wsl not supported")
+}
+
+func (a *App) WslEnable(forceMode bool) error {
+	return errors.New("wsl not supported")
+}
+
+func (a *App) WslInstallUbuntu() error {
+	return errors.New("wsl not supported")
+}

backend-python/convert_model.py

@@ -219,13 +219,17 @@ def get_args():
     return p.parse_args()
 
 
-args = get_args()
-if not args.quiet:
-    print(f"** {args}")
+try:
+    args = get_args()
+    if not args.quiet:
+        print(f"** {args}")
 
-RWKV(
-    getattr(args, "in"),
-    args.strategy,
-    verbose=not args.quiet,
-    convert_and_save_and_exit=args.out,
-)
+    RWKV(
+        getattr(args, "in"),
+        args.strategy,
+        verbose=not args.quiet,
+        convert_and_save_and_exit=args.out,
+    )
+except Exception as e:
+    with open("error.txt", "w") as f:
+        f.write(str(e))

backend-python/dep_check.py

@@ -1,8 +1,15 @@
+import midi2audio
+import mido
+import lm_dataformat
+import ftfy
+import tqdm
 import tiktoken
 import GPUtil
 
 import torch
 import rwkv
+import numpy
+import tokenizers
 import fastapi
 import uvicorn
 import sse_starlette

backend-python/main.py

@@ -12,7 +12,7 @@ from utils.rwkv import *
 from utils.torch import *
 from utils.ngrok import *
 from utils.log import log_middleware
-from routes import completion, config, state_cache
+from routes import completion, config, state_cache, midi
 import global_var
 
 app = FastAPI(dependencies=[Depends(log_middleware)])
@@ -27,6 +27,7 @@ app.add_middleware(
 
 app.include_router(completion.router)
 app.include_router(config.router)
+app.include_router(midi.router)
 app.include_router(state_cache.router)
 
 
@@ -41,12 +42,12 @@ def init():
         ngrok_connect()
 
 
-@app.get("/")
+@app.get("/", tags=["Root"])
 def read_root():
     return {"Hello": "World!"}
 
 
-@app.post("/exit")
+@app.post("/exit", tags=["Root"])
 def exit():
     parent_pid = os.getpid()
     parent = psutil.Process(parent_pid)
@@ -55,20 +56,9 @@ def exit():
     parent.kill()
 
 
-def debug():
-    model = RWKV(
-        model="../models/RWKV-4-Raven-7B-v11-Eng49%-Chn49%-Jpn1%-Other1%-20230430-ctx8192.pth",
-        strategy="cuda fp16",
-        tokens_path="20B_tokenizer.json",
-    )
-    d = model.pipeline.decode([])
-    print(d)
-
-
 if __name__ == "__main__":
     uvicorn.run(
         "main:app",
         port=8000 if len(sys.argv) < 2 else int(sys.argv[1]),
         host="127.0.0.1" if len(sys.argv) < 3 else sys.argv[2],
     )
-    # debug()

backend-python/routes/completion.py

@@ -1,7 +1,7 @@
 import asyncio
 import json
 from threading import Lock
-from typing import List
+from typing import List, Union
 import base64
 
 from fastapi import APIRouter, Request, status, HTTPException
@@ -25,7 +25,7 @@ class ChatCompletionBody(ModelConfigBody):
     messages: List[Message]
     model: str = "rwkv"
     stream: bool = False
-    stop: str = None
+    stop: str | List[str] = None
 
     class Config:
         schema_extra = {
@@ -44,10 +44,10 @@ class ChatCompletionBody(ModelConfigBody):
 
 
 class CompletionBody(ModelConfigBody):
-    prompt: str
+    prompt: Union[str, List[str]]
     model: str = "rwkv"
     stream: bool = False
-    stop: str = None
+    stop: str | List[str] = None
 
     class Config:
         schema_extra = {
@@ -72,7 +72,7 @@ requests_num = 0
 
 
 async def eval_rwkv(
-    model: RWKV,
+    model: AbstractRWKV,
     request: Request,
     body: ModelConfigBody,
     prompt: str,
@@ -95,27 +95,64 @@ async def eval_rwkv(
             return
         await asyncio.sleep(0.1)
     else:
-        completion_lock.acquire()
-        if await request.is_disconnected():
-            completion_lock.release()
+        with completion_lock:
+            if await request.is_disconnected():
+                requests_num = requests_num - 1
+                print(f"{request.client} Stop Waiting (Lock)")
+                quick_log(
+                    request,
+                    None,
+                    "Stop Waiting (Lock). RequestsNum: " + str(requests_num),
+                )
+                return
+            set_rwkv_config(model, global_var.get(global_var.Model_Config))
+            set_rwkv_config(model, body)
+
+            response, prompt_tokens, completion_tokens = "", 0, 0
+            for response, delta, prompt_tokens, completion_tokens in model.generate(
+                prompt,
+                stop=stop,
+            ):
+                if await request.is_disconnected():
+                    break
+                if stream:
+                    yield json.dumps(
+                        {
+                            "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,
+                                }
+                            ],
+                        }
+                    )
+            # torch_gc()
             requests_num = requests_num - 1
-            print(f"{request.client} Stop Waiting (Lock)")
+            if await request.is_disconnected():
+                print(f"{request.client} Stop Waiting")
+                quick_log(
+                    request,
+                    body,
+                    response + "\nStop Waiting. RequestsNum: " + str(requests_num),
+                )
+                return
             quick_log(
                 request,
-                None,
-                "Stop Waiting (Lock). RequestsNum: " + str(requests_num),
+                body,
+                response + "\nFinished. RequestsNum: " + str(requests_num),
             )
-            return
-        set_rwkv_config(model, global_var.get(global_var.Model_Config))
-        set_rwkv_config(model, body)
-
-        response, prompt_tokens, completion_tokens = "", 0, 0
-        for response, delta, prompt_tokens, completion_tokens in model.generate(
-            prompt,
-            stop=stop,
-        ):
-            if await request.is_disconnected():
-                break
             if stream:
                 yield json.dumps(
                     {
@@ -126,92 +163,53 @@ async def eval_rwkv(
                         "model": model.name,
                         "choices": [
                             {
-                                "delta": {"content": delta},
+                                "delta": {},
                                 "index": 0,
-                                "finish_reason": None,
+                                "finish_reason": "stop",
                             }
                             if chat_mode
                             else {
-                                "text": delta,
+                                "text": "",
                                 "index": 0,
-                                "finish_reason": None,
+                                "finish_reason": "stop",
                             }
                         ],
                     }
                 )
-        # torch_gc()
-        requests_num = requests_num - 1
-        completion_lock.release()
-        if await request.is_disconnected():
-            print(f"{request.client} Stop Waiting")
-            quick_log(
-                request,
-                body,
-                response + "\nStop Waiting. RequestsNum: " + str(requests_num),
-            )
-            return
-        quick_log(
-            request,
-            body,
-            response + "\nFinished. RequestsNum: " + str(requests_num),
-        )
-        if stream:
-            yield json.dumps(
-                {
-                    "object": "chat.completion.chunk"
-                    if chat_mode
-                    else "text_completion",
+                yield "[DONE]"
+            else:
+                yield {
+                    "object": "chat.completion" if chat_mode else "text_completion",
                     "response": response,
                     "model": model.name,
+                    "usage": {
+                        "prompt_tokens": prompt_tokens,
+                        "completion_tokens": completion_tokens,
+                        "total_tokens": prompt_tokens + completion_tokens,
+                    },
                     "choices": [
                         {
-                            "delta": {},
+                            "message": {
+                                "role": "assistant",
+                                "content": response,
+                            },
                             "index": 0,
                             "finish_reason": "stop",
                         }
                         if chat_mode
                         else {
-                            "text": "",
+                            "text": response,
                             "index": 0,
                             "finish_reason": "stop",
                         }
                     ],
                 }
-            )
-            yield "[DONE]"
-        else:
-            yield {
-                "object": "chat.completion" if chat_mode else "text_completion",
-                "response": response,
-                "model": model.name,
-                "usage": {
-                    "prompt_tokens": prompt_tokens,
-                    "completion_tokens": completion_tokens,
-                    "total_tokens": prompt_tokens + completion_tokens,
-                },
-                "choices": [
-                    {
-                        "message": {
-                            "role": "assistant",
-                            "content": response,
-                        },
-                        "index": 0,
-                        "finish_reason": "stop",
-                    }
-                    if chat_mode
-                    else {
-                        "text": response,
-                        "index": 0,
-                        "finish_reason": "stop",
-                    }
-                ],
-            }
 
 
-@router.post("/v1/chat/completions")
-@router.post("/chat/completions")
+@router.post("/v1/chat/completions", tags=["Completions"])
+@router.post("/chat/completions", tags=["Completions"])
 async def chat_completions(body: ChatCompletionBody, request: Request):
-    model: RWKV = global_var.get(global_var.Model)
+    model: TextRWKV = global_var.get(global_var.Model)
     if model is None:
         raise HTTPException(status.HTTP_400_BAD_REQUEST, "model not loaded")
 
@@ -301,16 +299,19 @@ The following is a coherent verbose detailed conversation between a girl named {
             return None
 
 
-@router.post("/v1/completions")
-@router.post("/completions")
+@router.post("/v1/completions", tags=["Completions"])
+@router.post("/completions", tags=["Completions"])
 async def completions(body: CompletionBody, request: Request):
-    model: RWKV = global_var.get(global_var.Model)
+    model: AbstractRWKV = global_var.get(global_var.Model)
     if model is None:
         raise HTTPException(status.HTTP_400_BAD_REQUEST, "model not loaded")
 
-    if body.prompt is None or body.prompt == "":
+    if body.prompt is None or body.prompt == "" or body.prompt == []:
         raise HTTPException(status.HTTP_400_BAD_REQUEST, "prompt not found")
 
+    if type(body.prompt) == list:
+        body.prompt = body.prompt[0]  # TODO: support multiple prompts
+
     if body.stream:
         return EventSourceResponse(
             eval_rwkv(model, request, body, body.prompt, body.stream, body.stop, False)
@@ -325,7 +326,7 @@ async def completions(body: CompletionBody, request: Request):
 
 
 class EmbeddingsBody(BaseModel):
-    input: str | List[str] | List[List[int]]
+    input: Union[str, List[str], List[List[int]]]
     model: str = "rwkv"
     encoding_format: str = None
     fast_mode: bool = False
@@ -345,12 +346,12 @@ def embedding_base64(embedding: List[float]) -> str:
     return base64.b64encode(np.array(embedding).astype(np.float32)).decode("utf-8")
 
 
-@router.post("/v1/embeddings")
-@router.post("/embeddings")
-@router.post("/v1/engines/text-embedding-ada-002/embeddings")
-@router.post("/engines/text-embedding-ada-002/embeddings")
+@router.post("/v1/embeddings", tags=["Embeddings"])
+@router.post("/embeddings", tags=["Embeddings"])
+@router.post("/v1/engines/text-embedding-ada-002/embeddings", tags=["Embeddings"])
+@router.post("/engines/text-embedding-ada-002/embeddings", tags=["Embeddings"])
 async def embeddings(body: EmbeddingsBody, request: Request):
-    model: RWKV = global_var.get(global_var.Model)
+    model: AbstractRWKV = global_var.get(global_var.Model)
     if model is None:
         raise HTTPException(status.HTTP_400_BAD_REQUEST, "model not loaded")
 
@@ -372,81 +373,88 @@ async def embeddings(body: EmbeddingsBody, request: Request):
             return
         await asyncio.sleep(0.1)
     else:
-        completion_lock.acquire()
-        if await request.is_disconnected():
-            completion_lock.release()
-            requests_num = requests_num - 1
-            print(f"{request.client} Stop Waiting (Lock)")
-            quick_log(
-                request,
-                None,
-                "Stop Waiting (Lock). RequestsNum: " + str(requests_num),
-            )
-            return
+        with completion_lock:
+            if await request.is_disconnected():
+                requests_num = requests_num - 1
+                print(f"{request.client} Stop Waiting (Lock)")
+                quick_log(
+                    request,
+                    None,
+                    "Stop Waiting (Lock). RequestsNum: " + str(requests_num),
+                )
+                return
 
-        base64_format = False
-        if body.encoding_format == "base64":
-            base64_format = True
+            base64_format = False
+            if body.encoding_format == "base64":
+                base64_format = True
 
-        embeddings = []
-        prompt_tokens = 0
-        if type(body.input) == list:
-            if type(body.input[0]) == list:
-                encoding = tiktoken.model.encoding_for_model("text-embedding-ada-002")
-                for i in range(len(body.input)):
-                    if await request.is_disconnected():
-                        break
-                    input = encoding.decode(body.input[i])
-                    embedding, token_len = model.get_embedding(input, body.fast_mode)
-                    prompt_tokens = prompt_tokens + token_len
-                    if base64_format:
-                        embedding = embedding_base64(embedding)
-                    embeddings.append(embedding)
-            else:
-                for i in range(len(body.input)):
-                    if await request.is_disconnected():
-                        break
-                    embedding, token_len = model.get_embedding(
-                        body.input[i], body.fast_mode
+            embeddings = []
+            prompt_tokens = 0
+            if type(body.input) == list:
+                if type(body.input[0]) == list:
+                    encoding = tiktoken.model.encoding_for_model(
+                        "text-embedding-ada-002"
                     )
-                    prompt_tokens = prompt_tokens + token_len
-                    if base64_format:
-                        embedding = embedding_base64(embedding)
-                    embeddings.append(embedding)
-        else:
-            embedding, prompt_tokens = model.get_embedding(body.input, body.fast_mode)
-            if base64_format:
-                embedding = embedding_base64(embedding)
-            embeddings.append(embedding)
+                    for i in range(len(body.input)):
+                        if await request.is_disconnected():
+                            break
+                        input = encoding.decode(body.input[i])
+                        embedding, token_len = model.get_embedding(
+                            input, body.fast_mode
+                        )
+                        prompt_tokens = prompt_tokens + token_len
+                        if base64_format:
+                            embedding = embedding_base64(embedding)
+                        embeddings.append(embedding)
+                else:
+                    for i in range(len(body.input)):
+                        if await request.is_disconnected():
+                            break
+                        embedding, token_len = model.get_embedding(
+                            body.input[i], body.fast_mode
+                        )
+                        prompt_tokens = prompt_tokens + token_len
+                        if base64_format:
+                            embedding = embedding_base64(embedding)
+                        embeddings.append(embedding)
+            else:
+                embedding, prompt_tokens = model.get_embedding(
+                    body.input, body.fast_mode
+                )
+                if base64_format:
+                    embedding = embedding_base64(embedding)
+                embeddings.append(embedding)
 
-        requests_num = requests_num - 1
-        completion_lock.release()
-        if await request.is_disconnected():
-            print(f"{request.client} Stop Waiting")
+            requests_num = requests_num - 1
+            if await request.is_disconnected():
+                print(f"{request.client} Stop Waiting")
+                quick_log(
+                    request,
+                    None,
+                    "Stop Waiting. RequestsNum: " + str(requests_num),
+                )
+                return
             quick_log(
                 request,
                 None,
-                "Stop Waiting. RequestsNum: " + str(requests_num),
+                "Finished. RequestsNum: " + str(requests_num),
             )
-            return
-        quick_log(
-            request,
-            None,
-            "Finished. RequestsNum: " + str(requests_num),
-        )
 
-        ret_data = [
-            {
-                "object": "embedding",
-                "index": i,
-                "embedding": embedding,
+            ret_data = [
+                {
+                    "object": "embedding",
+                    "index": i,
+                    "embedding": embedding,
+                }
+                for i, embedding in enumerate(embeddings)
+            ]
+
+            return {
+                "object": "list",
+                "data": ret_data,
+                "model": model.name,
+                "usage": {
+                    "prompt_tokens": prompt_tokens,
+                    "total_tokens": prompt_tokens,
+                },
             }
-            for i, embedding in enumerate(embeddings)
-        ]
-
-        return {
-            "object": "list",
-            "data": ret_data,
-            "model": model.name,
-            "usage": {"prompt_tokens": prompt_tokens, "total_tokens": prompt_tokens},
-        }

backend-python/routes/config.py

@@ -13,13 +13,16 @@ router = APIRouter()
 
 def get_tokens_path(model_path: str):
     model_path = model_path.lower()
-    default_tokens_path = (
-        f"{pathlib.Path(__file__).parent.parent.resolve()}/rwkv_pip/20B_tokenizer.json"
-    )
+    tokenizer_dir = f"{pathlib.Path(__file__).parent.parent.resolve()}/rwkv_pip/"
+
+    default_tokens_path = tokenizer_dir + "20B_tokenizer.json"
+
     if "raven" in model_path:
         return default_tokens_path
     elif "world" in model_path:
         return "rwkv_vocab_v20230424"
+    elif "midi" in model_path:
+        return tokenizer_dir + "tokenizer-midi.json"
     else:
         return default_tokens_path
 
@@ -39,7 +42,7 @@ class SwitchModelBody(BaseModel):
         }
 
 
-@router.post("/switch-model")
+@router.post("/switch-model", tags=["Configs"])
 def switch_model(body: SwitchModelBody, response: Response, request: Request):
     if global_var.get(global_var.Model_Status) is global_var.ModelStatus.Loading:
         response.status_code = Status.HTTP_304_NOT_MODIFIED
@@ -52,13 +55,27 @@ def switch_model(body: SwitchModelBody, response: Response, request: Request):
     if body.model == "":
         return "success"
 
+    if "->" in body.strategy:
+        state_cache.disable_state_cache()
+    else:
+        try:
+            state_cache.enable_state_cache()
+        except HTTPException:
+            pass
+
     os.environ["RWKV_CUDA_ON"] = "1" if body.customCuda else "0"
 
     global_var.set(global_var.Model_Status, global_var.ModelStatus.Loading)
     try:
         global_var.set(
             global_var.Model,
-            RWKV(
+            TextRWKV(
+                model=body.model,
+                strategy=body.strategy,
+                tokens_path=get_tokens_path(body.model),
+            )
+            if "midi" not in body.model.lower()
+            else MusicRWKV(
                 model=body.model,
                 strategy=body.strategy,
                 tokens_path=get_tokens_path(body.model),
@@ -81,7 +98,7 @@ def switch_model(body: SwitchModelBody, response: Response, request: Request):
     return "success"
 
 
-@router.post("/update-config")
+@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
@@ -93,7 +110,7 @@ def update_config(body: ModelConfigBody):
     return "success"
 
 
-@router.get("/status")
+@router.get("/status", tags=["Configs"])
 def status():
     gpus = GPUtil.getGPUs()
     if len(gpus) == 0:

backend-python/routes/midi.py

@@ -0,0 +1,131 @@
+import io
+from fastapi import APIRouter, HTTPException, status
+from starlette.responses import StreamingResponse
+from pydantic import BaseModel
+from utils.midi import *
+from midi2audio import FluidSynth
+
+router = APIRouter()
+
+
+class TextToMidiBody(BaseModel):
+    text: str
+
+    class Config:
+        schema_extra = {
+            "example": {
+                "text": "p:24:a p:2a:a p:31:a p:39:a p:3b:a p:45:a b:26:a g:3e:a g:3e:a g:42:a g:42:a g:45:a g:45:a pi:3e:a pi:42:a pi:45:a t14 p:24:0 p:2a:0 p:31:0 p:39:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a t14 p:2a:0 p:3b:0 p:45:0 b:26:0 g:3e:0 g:3e:0 g:42:0 g:42:0 g:45:0 g:45:0 pi:3e:0 pi:42:0 pi:45:0 t2 p:2e:a p:3b:a p:45:a b:26:a g:3e:a g:3e:a g:42:a g:42:a g:45:a g:45:a pi:3e:a pi:42:a pi:45:a t14 p:2e:0 p:3b:0 p:45:0 g:3e:0 g:3e:0 g:42:0 g:42:0 g:45:0 g:45:0 pi:3e:0 pi:42:0 pi:45:0 t2 p:2e:a p:3b:a p:45:a g:3e:a g:3e:a g:42:a g:42:a g:45:a g:45:a pi:3e:a pi:42:a pi:45:a t14 p:2e:0 p:3b:0 p:45:0 b:26:0 g:3e:0 g:3e:0 g:42:0 g:42:0 g:45:0 g:45:0 pi:3e:0 pi:42:0 pi:45:0 t2 p:26:a p:2a:a p:3b:a p:45:a t14 p:26:0 p:2a:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a b:26:a g:3e:a g:3e:a g:42:a g:42:a g:45:a g:45:a pi:3e:a pi:42:a pi:45:a t14 p:2a:0 p:3b:0 p:45:0 b:26:0 t2 p:24:a p:2a:a p:3b:a p:45:a b:2d:a t14 p:24:0 p:2a:0 p:3b:0 p:45:0 b:2d:0 g:3e:0 g:3e:0 g:42:0 g:42:0 g:45:0 g:45:0 pi:3e:0 pi:42:0 pi:45:0 t2 p:24:a p:2a:a p:3b:a p:45:a b:21:a g:39:a g:39:a g:3d:a g:3d:a g:40:a g:40:a pi:39:a pi:3d:a pi:40:a t14 p:24:0 p:2a:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a t14 p:2a:0 p:3b:0 p:45:0 b:21:0 g:39:0 g:39:0 g:3d:0 g:3d:0 g:40:0 g:40:0 pi:39:0 pi:3d:0 pi:40:0 t2 p:24:a p:2e:a p:3b:a p:45:a b:21:a g:39:a g:39:a g:3d:a g:3d:a g:40:a g:40:a pi:39:a pi:3d:a pi:40:a t14 p:24:0 p:2e:0 p:3b:0 p:45:0 b:21:0 g:39:0 g:39:0 g:3d:0 g:3d:0 g:40:0 g:40:0 pi:39:0 pi:3d:0 pi:40:0 t2 p:24:a p:2a:a p:3b:a p:45:a b:21:a g:39:a g:39:a g:3d:a g:3d:a g:40:a g:40:a pi:39:a pi:3d:a pi:40:a t14 p:24:0 p:2a:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a t14 p:2a:0 p:3b:0 p:45:0 b:21:0 g:39:0 g:39:0 g:3d:0 g:3d:0 g:40:0 g:40:0 pi:39:0 pi:3d:0 pi:40:0 t2 p:26:a p:2a:a p:3b:a p:45:a b:21:a g:39:a g:39:a g:3d:a g:3d:a g:40:a g:40:a pi:39:a pi:3d:a pi:40:a t14 p:26:0 p:2a:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a t14 p:2a:0 p:3b:0 p:45:0 b:21:0 g:39:0 g:39:0 g:3d:0 g:3d:0 g:40:0 g:40:0 pi:39:0 pi:3d:0 pi:40:0 t2 p:26:a p:2e:a p:31:a p:39:a p:3b:a p:45:a b:21:a g:39:a g:39:a g:3d:a g:3d:a g:40:a g:40:a pi:39:a pi:3d:a pi:40:a t14 p:26:0 p:2e:0 p:31:0 p:39:0 p:3b:0 p:45:0 b:21:0 t2 p:26:a p:2e:a p:31:a p:39:a p:3b:a p:45:a b:21:a t14 p:26:0 p:2e:0 p:31:0 p:39:0 p:3b:0 p:45:0 b:21:0 g:39:0 g:39:0 g:3d:0 g:3d:0 g:40:0 g:40:0 pi:39:0 pi:3d:0 pi:40:0 t2 p:24:a p:2a:a p:31:a p:39:a p:3b:a p:45:a b:1f:a g:3b:a g:3b:a g:3e:a g:3e:a g:43:a g:43:a pi:3b:a pi:3e:a pi:43:a t14 p:24:0 p:2a:0 p:31:0 p:39:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a t14 p:2a:0 p:3b:0 p:45:0 b:1f:0 g:3b:0 g:3b:0 g:3e:0 g:3e:0 g:43:0 g:43:0 pi:3b:0 pi:3e:0 pi:43:0 t2 p:2e:a p:3b:a p:45:a b:1f:a g:3b:a g:3b:a g:3e:a g:3e:a g:43:a g:43:a pi:3b:a pi:3e:a pi:43:a t14 p:2e:0 p:3b:0 p:45:0 g:3b:0 g:3b:0 g:3e:0 g:3e:0 g:43:0 g:43:0 pi:3b:0 pi:3e:0 pi:43:0 t2 p:2e:a p:3b:a p:45:a g:3b:a g:3b:a g:3e:a g:3e:a g:43:a g:43:a pi:3b:a pi:3e:a pi:43:a t14 p:2e:0 p:3b:0 p:45:0 b:1f:0 g:3b:0 g:3b:0 g:3e:0 g:3e:0 g:43:0 g:43:0 pi:3b:0 pi:3e:0 pi:43:0 t2 p:26:a p:2a:a p:3b:a p:45:a t14 p:26:0 p:2a:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a b:1f:a g:3b:a g:3b:a g:3e:a g:3e:a g:43:a g:43:a pi:3b:a pi:3e:a pi:43:a t14 p:2a:0 p:3b:0 p:45:0 b:1f:0 t2 p:24:a p:2a:a p:3b:a p:45:a b:1f:a t14 p:24:0 p:2a:0 p:3b:0 p:45:0 b:1f:0 g:3b:0 g:3b:0 g:3e:0 g:3e:0 g:43:0 g:43:0 pi:3b:0 pi:3e:0 pi:43:0 t2 p:24:a p:2e:a p:3b:a p:45:a b:26:a g:39:a g:39:a g:3e:a g:3e:a g:42:a g:42:a pi:39:a pi:3e:a pi:42:a t14 p:24:0 p:2e:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a t14 p:2a:0 p:3b:0",
+            }
+        }
+
+
+@router.post("/text-to-midi", tags=["MIDI"])
+def text_to_midi(body: TextToMidiBody):
+    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()
+    mid.save(None, mid_data)
+    mid_data.seek(0)
+
+    return StreamingResponse(mid_data, media_type="audio/midi")
+
+
+class TxtToMidiBody(BaseModel):
+    txt_path: str
+    midi_path: str
+
+    class Config:
+        schema_extra = {
+            "example": {
+                "txt_path": "midi/sample.txt",
+                "midi_path": "midi/sample.mid",
+            }
+        }
+
+
+@router.post("/txt-to-midi", tags=["MIDI"])
+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"
+    cfg = VocabConfig.from_json(vocab_config)
+    with open(body.txt_path, "r") as f:
+        text = f.read()
+    text = text.strip()
+    mid = convert_str_to_midi(cfg, text)
+    mid.save(body.midi_path)
+
+    return "success"
+
+
+class MidiToWavBody(BaseModel):
+    midi_path: str
+    wav_path: str
+    sound_font_path: str = "assets/default_sound_font.sf2"
+
+    class Config:
+        schema_extra = {
+            "example": {
+                "midi_path": "midi/sample.mid",
+                "wav_path": "midi/sample.wav",
+                "sound_font_path": "assets/default_sound_font.sf2",
+            }
+        }
+
+
+@router.post("/midi-to-wav", tags=["MIDI"])
+def midi_to_wav(body: MidiToWavBody):
+    """
+    Install fluidsynth first, see more: https://github.com/FluidSynth/fluidsynth/wiki/Download#distributions
+    """
+
+    if not body.wav_path.startswith("midi/"):
+        raise HTTPException(status.HTTP_400_BAD_REQUEST, "bad output path")
+
+    fs = FluidSynth(body.sound_font_path)
+    fs.midi_to_audio(body.midi_path, body.wav_path)
+
+    return "success"
+
+
+class TextToWavBody(BaseModel):
+    text: str
+    wav_name: str
+    sound_font_path: str = "assets/default_sound_font.sf2"
+
+    class Config:
+        schema_extra = {
+            "example": {
+                "text": "p:24:a p:2a:a p:31:a p:39:a p:3b:a p:45:a b:26:a g:3e:a g:3e:a g:42:a g:42:a g:45:a g:45:a pi:3e:a pi:42:a pi:45:a t14 p:24:0 p:2a:0 p:31:0 p:39:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a t14 p:2a:0 p:3b:0 p:45:0 b:26:0 g:3e:0 g:3e:0 g:42:0 g:42:0 g:45:0 g:45:0 pi:3e:0 pi:42:0 pi:45:0 t2 p:2e:a p:3b:a p:45:a b:26:a g:3e:a g:3e:a g:42:a g:42:a g:45:a g:45:a pi:3e:a pi:42:a pi:45:a t14 p:2e:0 p:3b:0 p:45:0 g:3e:0 g:3e:0 g:42:0 g:42:0 g:45:0 g:45:0 pi:3e:0 pi:42:0 pi:45:0 t2 p:2e:a p:3b:a p:45:a g:3e:a g:3e:a g:42:a g:42:a g:45:a g:45:a pi:3e:a pi:42:a pi:45:a t14 p:2e:0 p:3b:0 p:45:0 b:26:0 g:3e:0 g:3e:0 g:42:0 g:42:0 g:45:0 g:45:0 pi:3e:0 pi:42:0 pi:45:0 t2 p:26:a p:2a:a p:3b:a p:45:a t14 p:26:0 p:2a:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a b:26:a g:3e:a g:3e:a g:42:a g:42:a g:45:a g:45:a pi:3e:a pi:42:a pi:45:a t14 p:2a:0 p:3b:0 p:45:0 b:26:0 t2 p:24:a p:2a:a p:3b:a p:45:a b:2d:a t14 p:24:0 p:2a:0 p:3b:0 p:45:0 b:2d:0 g:3e:0 g:3e:0 g:42:0 g:42:0 g:45:0 g:45:0 pi:3e:0 pi:42:0 pi:45:0 t2 p:24:a p:2a:a p:3b:a p:45:a b:21:a g:39:a g:39:a g:3d:a g:3d:a g:40:a g:40:a pi:39:a pi:3d:a pi:40:a t14 p:24:0 p:2a:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a t14 p:2a:0 p:3b:0 p:45:0 b:21:0 g:39:0 g:39:0 g:3d:0 g:3d:0 g:40:0 g:40:0 pi:39:0 pi:3d:0 pi:40:0 t2 p:24:a p:2e:a p:3b:a p:45:a b:21:a g:39:a g:39:a g:3d:a g:3d:a g:40:a g:40:a pi:39:a pi:3d:a pi:40:a t14 p:24:0 p:2e:0 p:3b:0 p:45:0 b:21:0 g:39:0 g:39:0 g:3d:0 g:3d:0 g:40:0 g:40:0 pi:39:0 pi:3d:0 pi:40:0 t2 p:24:a p:2a:a p:3b:a p:45:a b:21:a g:39:a g:39:a g:3d:a g:3d:a g:40:a g:40:a pi:39:a pi:3d:a pi:40:a t14 p:24:0 p:2a:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a t14 p:2a:0 p:3b:0 p:45:0 b:21:0 g:39:0 g:39:0 g:3d:0 g:3d:0 g:40:0 g:40:0 pi:39:0 pi:3d:0 pi:40:0 t2 p:26:a p:2a:a p:3b:a p:45:a b:21:a g:39:a g:39:a g:3d:a g:3d:a g:40:a g:40:a pi:39:a pi:3d:a pi:40:a t14 p:26:0 p:2a:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a t14 p:2a:0 p:3b:0 p:45:0 b:21:0 g:39:0 g:39:0 g:3d:0 g:3d:0 g:40:0 g:40:0 pi:39:0 pi:3d:0 pi:40:0 t2 p:26:a p:2e:a p:31:a p:39:a p:3b:a p:45:a b:21:a g:39:a g:39:a g:3d:a g:3d:a g:40:a g:40:a pi:39:a pi:3d:a pi:40:a t14 p:26:0 p:2e:0 p:31:0 p:39:0 p:3b:0 p:45:0 b:21:0 t2 p:26:a p:2e:a p:31:a p:39:a p:3b:a p:45:a b:21:a t14 p:26:0 p:2e:0 p:31:0 p:39:0 p:3b:0 p:45:0 b:21:0 g:39:0 g:39:0 g:3d:0 g:3d:0 g:40:0 g:40:0 pi:39:0 pi:3d:0 pi:40:0 t2 p:24:a p:2a:a p:31:a p:39:a p:3b:a p:45:a b:1f:a g:3b:a g:3b:a g:3e:a g:3e:a g:43:a g:43:a pi:3b:a pi:3e:a pi:43:a t14 p:24:0 p:2a:0 p:31:0 p:39:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a t14 p:2a:0 p:3b:0 p:45:0 b:1f:0 g:3b:0 g:3b:0 g:3e:0 g:3e:0 g:43:0 g:43:0 pi:3b:0 pi:3e:0 pi:43:0 t2 p:2e:a p:3b:a p:45:a b:1f:a g:3b:a g:3b:a g:3e:a g:3e:a g:43:a g:43:a pi:3b:a pi:3e:a pi:43:a t14 p:2e:0 p:3b:0 p:45:0 g:3b:0 g:3b:0 g:3e:0 g:3e:0 g:43:0 g:43:0 pi:3b:0 pi:3e:0 pi:43:0 t2 p:2e:a p:3b:a p:45:a g:3b:a g:3b:a g:3e:a g:3e:a g:43:a g:43:a pi:3b:a pi:3e:a pi:43:a t14 p:2e:0 p:3b:0 p:45:0 b:1f:0 g:3b:0 g:3b:0 g:3e:0 g:3e:0 g:43:0 g:43:0 pi:3b:0 pi:3e:0 pi:43:0 t2 p:26:a p:2a:a p:3b:a p:45:a t14 p:26:0 p:2a:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a b:1f:a g:3b:a g:3b:a g:3e:a g:3e:a g:43:a g:43:a pi:3b:a pi:3e:a pi:43:a t14 p:2a:0 p:3b:0 p:45:0 b:1f:0 t2 p:24:a p:2a:a p:3b:a p:45:a b:1f:a t14 p:24:0 p:2a:0 p:3b:0 p:45:0 b:1f:0 g:3b:0 g:3b:0 g:3e:0 g:3e:0 g:43:0 g:43:0 pi:3b:0 pi:3e:0 pi:43:0 t2 p:24:a p:2e:a p:3b:a p:45:a b:26:a g:39:a g:39:a g:3e:a g:3e:a g:42:a g:42:a pi:39:a pi:3e:a pi:42:a t14 p:24:0 p:2e:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a t14 p:2a:0 p:3b:0",
+                "wav_name": "sample",
+                "sound_font_path": "assets/default_sound_font.sf2",
+            }
+        }
+
+
+@router.post("/text-to-wav", tags=["MIDI"])
+def text_to_wav(body: TextToWavBody):
+    """
+    Install fluidsynth first, see more: https://github.com/FluidSynth/fluidsynth/wiki/Download#distributions
+    """
+
+    text = body.text.strip()
+    if not text.startswith("<start>"):
+        text = "<start> " + text
+    if not text.endswith("<end>"):
+        text = text + " <end>"
+    txt_path = f"midi/{body.wav_name}.txt"
+    midi_path = f"midi/{body.wav_name}.mid"
+    wav_path = f"midi/{body.wav_name}.wav"
+    with open(txt_path, "w") as f:
+        f.write(text)
+    txt_to_midi(TxtToMidiBody(txt_path=txt_path, midi_path=midi_path))
+    midi_to_wav(
+        MidiToWavBody(
+            midi_path=midi_path, wav_path=wav_path, sound_font_path=body.sound_font_path
+        )
+    )
+
+    return "success"

backend-python/routes/state_cache.py

@@ -4,8 +4,6 @@ from fastapi import APIRouter, HTTPException, Request, Response, status
 from pydantic import BaseModel
 import gc
 import copy
-import sys
-import torch
 
 router = APIRouter()
 
@@ -34,6 +32,32 @@ def init():
         print("cyac not found")
 
 
+@router.post("/disable-state-cache", tags=["State Cache"])
+def disable_state_cache():
+    global trie, dtrie
+
+    trie = None
+    dtrie = {}
+    gc.collect()
+
+    return "success"
+
+
+@router.post("/enable-state-cache", tags=["State Cache"])
+def enable_state_cache():
+    global trie, dtrie
+    try:
+        import cyac
+
+        trie = cyac.Trie()
+        dtrie = {}
+        gc.collect()
+
+        return "success"
+    except ModuleNotFoundError:
+        raise HTTPException(status.HTTP_400_BAD_REQUEST, "cyac not found")
+
+
 class AddStateBody(BaseModel):
     prompt: str
     tokens: List[str]
@@ -41,12 +65,14 @@ class AddStateBody(BaseModel):
     logits: Any
 
 
-@router.post("/add-state")
+@router.post("/add-state", tags=["State Cache"])
 def add_state(body: AddStateBody):
     global trie, dtrie, loop_del_trie_id
     if trie is None:
         raise HTTPException(status.HTTP_400_BAD_REQUEST, "trie not loaded")
 
+    import torch
+
     try:
         id: int = trie.insert(body.prompt)
         device: torch.device = body.state[0].device
@@ -79,12 +105,14 @@ def add_state(body: AddStateBody):
         )
 
 
-@router.post("/reset-state")
+@router.post("/reset-state", tags=["State Cache"])
 def reset_state():
     global trie, dtrie
     if trie is None:
         raise HTTPException(status.HTTP_400_BAD_REQUEST, "trie not loaded")
 
+    import cyac
+
     trie = cyac.Trie()
     dtrie = {}
     gc.collect()
@@ -113,12 +141,14 @@ def _get_a_dtrie_buff_size(dtrie_v):
     return 54 * len(dtrie_v["tokens"]) + 491520 + 262144 + 28  # TODO
 
 
-@router.post("/longest-prefix-state")
+@router.post("/longest-prefix-state", tags=["State Cache"])
 def longest_prefix_state(body: LongestPrefixStateBody, request: Request):
     global trie
     if trie is None:
         raise HTTPException(status.HTTP_400_BAD_REQUEST, "trie not loaded")
 
+    import torch
+
     id = -1
     try:
         for id, len in trie.prefix(body.prompt):
@@ -150,7 +180,7 @@ def longest_prefix_state(body: LongestPrefixStateBody, request: Request):
         }
 
 
-@router.post("/save-state")
+@router.post("/save-state", tags=["State Cache"])
 def save_state():
     global trie
     if trie is None:

backend-python/utils/midi.py

@@ -0,0 +1,685 @@
+# https://github.com/briansemrau/MIDI-LLM-tokenizer
+
+# MIT License
+
+# Copyright (c) 2023 Brian Semrau
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+import json
+import random
+from dataclasses import dataclass
+from functools import lru_cache
+from math import ceil, floor, log
+from typing import Dict, Iterator, List, Optional, Tuple
+
+import mido
+
+
+@dataclass
+class VocabConfig:
+    # Number of note events. Should be 128.
+    note_events: int
+    # Number of wait events. Configurable, must evenly divide max_wait_time.
+    wait_events: int
+    # Max wait time in milliseconds to be represented by a single token.
+    max_wait_time: int
+    # Number of velocity events. Should be 128 (or 100? need to check midi standard)
+    velocity_events: int
+    # Number of bins to quantize velocity into. Should evenly divide velocity_events.
+    velocity_bins: int
+    # Exponential scaling factor for velocity bin sizes. 1.0 = linear scaling.
+    velocity_exp: float
+    # Whether to sort tokens by instrument, note. This should improve data reducibility.
+    do_token_sorting: bool
+    # Whether tokens should be represented as combined instrument/note/velocity tokens, or separate tokens for each.
+    unrolled_tokens: bool
+    # If non-zero, notes held for this many seconds will be automatically released during str->midi decoding.
+    decode_end_held_note_delay: float
+    # If true, repeated notes will be automatically released before playing again during str->midi decoding.
+    decode_fix_repeated_notes: bool
+    # List of instrument names to use for binning. Must have at most 16 values.
+    bin_instrument_names: List[str]
+    # Indicates which bin name represents percussion instruments on MIDI channel 10.
+    ch10_instrument_bin_name: str
+    # Mapping from instrument name to bin name.
+    program_name_to_bin_name: Dict[str, str]
+    # Mapping from bin name to program name.
+    bin_name_to_program_name: Dict[str, str]
+    # Mapping from program number to instrument name.
+    instrument_names: Dict[str, str]
+
+    def __post_init__(self):
+        self.validate()
+
+        self._instrument_names_str_to_int = {
+            name: int(i) for i, name in self.instrument_names.items()
+        }
+        self._instrument_names_int_to_str = {
+            int(i): name for i, name in self.instrument_names.items()
+        }
+
+        self._bin_str_to_int = {
+            name: int(i) for i, name in enumerate(self.bin_instrument_names)
+        }
+
+        self._bin_int_to_instrument_int = [
+            self._instrument_names_str_to_int[self.bin_name_to_program_name[name]]
+            if name != self.ch10_instrument_bin_name
+            else 0
+            for name in self.bin_instrument_names
+        ]
+        self._instrument_int_to_bin_int = [
+            self._bin_str_to_int[self.program_name_to_bin_name[instr]]
+            if self.program_name_to_bin_name[instr] != ""
+            else -1
+            for instr in self.program_name_to_bin_name.keys()
+        ]
+
+        self._ch10_bin_int = (
+            self._bin_str_to_int[self.ch10_instrument_bin_name]
+            if self.ch10_instrument_bin_name
+            else -1
+        )
+
+        self.short_instr_bin_names = []
+        for instr in self.bin_instrument_names:
+            i = min(1, len(instr))
+            while instr[:i] in self.short_instr_bin_names:
+                i += 1
+            self.short_instr_bin_names.append(instr[:i])
+        self._short_instrument_names_str_to_int = {
+            name: int(i) for i, name in enumerate(self.short_instr_bin_names)
+        }
+
+        range_excluding_ch10 = [
+            (i if i < 9 else i + 1) for i in range(len(self.bin_instrument_names))
+        ]
+        bins_excluding_ch10 = [
+            n for n in self.bin_instrument_names if n != self.ch10_instrument_bin_name
+        ]
+        self.bin_channel_map = {
+            bin: channel
+            for channel, bin in zip(range_excluding_ch10, bins_excluding_ch10)
+        }
+        if self.ch10_instrument_bin_name:
+            self.bin_channel_map[self.ch10_instrument_bin_name] = 9
+
+    def validate(self):
+        if self.max_wait_time % self.wait_events != 0:
+            raise ValueError("max_wait_time must be exactly divisible by wait_events")
+        if self.velocity_bins < 2:
+            raise ValueError("velocity_bins must be at least 2")
+        if len(self.bin_instrument_names) > 16:
+            raise ValueError("bin_instruments must have at most 16 values")
+        if (
+            self.ch10_instrument_bin_name
+            and self.ch10_instrument_bin_name not in self.bin_instrument_names
+        ):
+            raise ValueError("ch10_instrument_bin_name must be in bin_instruments")
+        if self.velocity_exp <= 0:
+            raise ValueError("velocity_exp must be greater than 0")
+
+    @classmethod
+    def from_json(cls, path: str):
+        with open(path, "r") as f:
+            config = json.load(f)
+        return cls(**config)
+
+
+class VocabUtils:
+    def __init__(self, cfg: VocabConfig) -> None:
+        self.cfg = cfg
+
+    @lru_cache(maxsize=128)
+    def format_wait_token(self, wait: int) -> str:
+        return f"t{wait}"
+
+    @lru_cache(maxsize=128)
+    def format_note_token(
+        self, instrument_bin: int, note: int, velocity_bin: int
+    ) -> str:
+        return f"{self.cfg.short_instr_bin_names[instrument_bin]}:{note:x}:{velocity_bin:x}"
+
+    def format_unrolled_note(self, note: int) -> str:
+        return f"n{note:x}"
+
+    def format_unrolled_velocity(self, velocity_bin: int) -> str:
+        return f"v{velocity_bin:x}"
+
+    def format_unrolled_instrument_bin(self, instrument_bin: int) -> str:
+        return f"i{self.cfg.short_instr_bin_names[instrument_bin]}"
+
+    def velocity_to_bin(self, velocity: float) -> int:
+        velocity = max(0, min(velocity, self.cfg.velocity_events - 1))
+        binsize = self.cfg.velocity_events / (self.cfg.velocity_bins - 1)
+        if self.cfg.velocity_exp == 1.0:
+            return ceil(velocity / binsize)
+        else:
+            return ceil(
+                (
+                    self.cfg.velocity_events
+                    * (
+                        (
+                            self.cfg.velocity_exp
+                            ** (velocity / self.cfg.velocity_events)
+                            - 1.0
+                        )
+                        / (self.cfg.velocity_exp - 1.0)
+                    )
+                )
+                / binsize
+            )
+
+    def bin_to_velocity(self, bin: int) -> int:
+        binsize = self.cfg.velocity_events / (self.cfg.velocity_bins - 1)
+        if self.cfg.velocity_exp == 1.0:
+            return max(0, ceil(bin * binsize - 1))
+        else:
+            return max(
+                0,
+                ceil(
+                    self.cfg.velocity_events
+                    * log(
+                        ((self.cfg.velocity_exp - 1) * binsize * bin)
+                        / self.cfg.velocity_events
+                        + 1,
+                        self.cfg.velocity_exp,
+                    )
+                    - 1
+                ),
+            )
+
+    def delta_to_wait_ids(self, delta_ms: float) -> Iterator[int]:
+        def roundi(f: float):
+            return ceil(f - 0.5)
+
+        max_wait_ms = self.cfg.max_wait_time
+        div = max_wait_ms / self.cfg.wait_events
+
+        # if delta_ms // max_wait_ms > 512:  # arbitrary limit to avoid excessive time_shifts
+        #    raise ValueError("delta_time is too large")
+        if delta_ms > max_wait_ms * 10:
+            delta_ms = max_wait_ms * 10  # truncate time
+
+        for _ in range(floor(delta_ms / max_wait_ms)):
+            yield roundi(max_wait_ms / div)
+        leftover_time_shift = roundi((delta_ms % max_wait_ms) / div)
+        if leftover_time_shift > 0:
+            yield leftover_time_shift
+
+    def prog_data_to_token_data(
+        self, program: int, channel: int, note: int, velocity: float
+    ) -> Optional[Tuple[int, int, int]]:
+        if channel == 9:
+            if self.cfg._ch10_bin_int == -1:
+                return None
+            return self.cfg._ch10_bin_int, note, self.velocity_to_bin(velocity)
+
+        instrument_bin = self.cfg._instrument_int_to_bin_int[program]
+        if instrument_bin != -1:
+            return instrument_bin, note, self.velocity_to_bin(velocity)
+        return None
+
+    def prog_data_list_to_token_data_list(
+        self, data: List[Tuple[int, int, int, float]]
+    ) -> Iterator[Tuple[int, int, int]]:
+        for d in data:
+            token_data = self.prog_data_to_token_data(*d)
+            if token_data is not None:
+                yield token_data
+
+    def sort_token_data(
+        self, data: List[Tuple[int, int, int]]
+    ) -> List[Tuple[int, int, int]]:
+        # ensure order is preserved for tokens with the same instrument, note
+        data = [(i, n, v, x) for x, (i, n, v) in enumerate(data)]
+        data.sort(key=lambda x: (x[0] != self.cfg._ch10_bin_int, x[0], x[1], x[3]))
+        return [(i, n, v) for i, n, v, _ in data]
+
+    def data_to_wait_tokens(self, delta_ms: float) -> List[str]:
+        if delta_ms == 0.0:
+            return []
+        return [self.format_wait_token(i) for i in self.delta_to_wait_ids(delta_ms)]
+
+    def wait_token_to_delta(self, token: str) -> float:
+        return self.cfg.max_wait_time / self.cfg.wait_events * int(token[1:])
+
+    def note_token_to_data(self, token: str) -> Tuple[int, int, int]:
+        instr_str, note_str, velocity_str = token.strip().split(":")
+        instr_bin = self.cfg._short_instrument_names_str_to_int[instr_str]
+        note = int(note_str, base=16)
+        velocity = self.bin_to_velocity(int(velocity_str, base=16))
+        return instr_bin, note, velocity
+
+
+@dataclass
+class AugmentValues:
+    instrument_bin_remap: Dict[int, int]
+    velocity_mod_factor: float
+    transpose_semitones: int
+    time_stretch_factor: float
+
+    @classmethod
+    def default(cls) -> "AugmentValues":
+        return cls(
+            instrument_bin_remap={},
+            velocity_mod_factor=1.0,
+            transpose_semitones=0,
+            time_stretch_factor=1.0,
+        )
+
+
+@dataclass
+class AugmentConfig:
+    # The number of times to augment each MIDI file. The dataset size will be multiplied by this number.
+    augment_data_factor: int
+    # A list of instrument names to randomly swap with each other.
+    instrument_mixups: List[List[str]]
+    # A list of percentages to change the note velocity by. 0.0 = no change. 0 is included by default.
+    velocity_mod_pct: List[float]
+    # A list of semitones to transpose by. 0 is included by default.
+    transpose_semitones: List[int]
+    # A list of percentages to stretch the tempo by. 0.0 = no stretch. 0 is included by default.
+    time_stretch_pct: List[float]
+    # Random seed to use for reproducibility.
+    seed: int
+
+    cfg: VocabConfig
+
+    def __post_init__(self):
+        self.validate()
+        if len(self.velocity_mod_pct) == 0:
+            self.velocity_mod_pct = [0.0]
+        if len(self.transpose_semitones) == 0:
+            self.transpose_semitones = [0]
+        if len(self.time_stretch_pct) == 0:
+            self.time_stretch_pct = [0.0]
+
+        self._instrument_mixups_int = [
+            [self.cfg._bin_str_to_int[i] for i in l if i in self.cfg._bin_str_to_int]
+            for l in self.instrument_mixups
+        ]
+        self._instrument_mixups_int = [
+            l for l in self._instrument_mixups_int if len(l) > 0
+        ]  # remove empty lists
+        self._instrument_pool_assignments = {}
+        self._mixup_pools = []
+        for pool_i, mixup_list in enumerate(self._instrument_mixups_int):
+            pool = set()
+            for i in mixup_list:
+                pool.add(i)
+                self._instrument_pool_assignments[i] = pool_i
+            self._mixup_pools.append(pool)
+
+    def validate(self):
+        if self.augment_data_factor < 1:
+            raise ValueError("augment_data_factor must be at least 1")
+        used_instruments = set()
+        for mixup_list in self.instrument_mixups:
+            for n in mixup_list:
+                if n in used_instruments:
+                    raise ValueError(f"Duplicate instrument name: {n}")
+                used_instruments.add(n)
+
+    @classmethod
+    def from_json(cls, path: str, cfg: VocabConfig):
+        with open(path, "r") as f:
+            config = json.load(f)
+        config["cfg"] = cfg
+        if "seed" not in config:
+            config["seed"] = random.randint(0, 2**32 - 1)
+        return cls(**config)
+
+    def get_augment_values(self, filename: str) -> Iterator[AugmentValues]:
+        # first yield default values
+        yield AugmentValues.default()
+
+        rng = random.Random(self.seed + hash(filename))
+        for _ in range(int(self.augment_data_factor - 1)):
+            # randomize order for each pool
+            randomized_pools = [list(pool) for pool in self._mixup_pools]
+            for pool in randomized_pools:
+                rng.shuffle(pool)
+            # distribute reassignments
+            instrument_bin_remap = {}
+            for i, pool in enumerate(randomized_pools):
+                for j, instrument in enumerate(pool):
+                    instrument_bin_remap[instrument] = randomized_pools[i - 1][j]
+            yield AugmentValues(
+                instrument_bin_remap=instrument_bin_remap,
+                velocity_mod_factor=1.0 + rng.choice(self.velocity_mod_pct),
+                transpose_semitones=rng.choice(self.transpose_semitones),
+                time_stretch_factor=1.0 + rng.choice(self.time_stretch_pct),
+            )
+
+
+def mix_volume(velocity: int, volume: int, expression: int) -> float:
+    return velocity * (volume / 127.0) * (expression / 127.0)
+
+
+def convert_midi_to_str(
+    cfg: VocabConfig, mid: mido.MidiFile, augment: AugmentValues = None
+) -> str:
+    utils = VocabUtils(cfg)
+    if augment is None:
+        augment = AugmentValues.default()
+
+    # filter out unknown meta messages before merge (https://github.com/mido/mido/pull/286)
+    for i in range(len(mid.tracks)):
+        mid.tracks[i] = [msg for msg in mid.tracks[i] if msg.type != "unknown_meta"]
+
+    if len(mid.tracks) > 1:
+        mid.tracks = [mido.merge_tracks(mid.tracks)]
+
+    delta_time_ms = 0.0
+    tempo = 500000
+    channel_program = {i: 0 for i in range(16)}
+    channel_volume = {i: 127 for i in range(16)}
+    channel_expression = {
+        i: 127 for i in range(16)
+    }  # unlikely to be useful. expression usually modifies an already played note.
+    channel_notes = {i: {} for i in range(16)}
+    channel_pedal_on = {i: False for i in range(16)}
+    channel_pedal_events = {
+        i: {} for i in range(16)
+    }  # {channel: {(note, program) -> True}}
+    started_flag = False
+
+    output = ["<start>"]
+    token_data_buffer: List[
+        Tuple[int, int, int, float]
+    ] = []  # need to sort notes between wait tokens
+
+    def flush_token_data_buffer():
+        nonlocal token_data_buffer, output, cfg, utils, augment
+        token_data = [
+            x for x in utils.prog_data_list_to_token_data_list(token_data_buffer)
+        ]
+        if augment.instrument_bin_remap or augment.transpose_semitones:
+            # TODO put transpose in a real function
+            raw_transpose = (
+                lambda bin, n: n + augment.transpose_semitones
+                if bin != cfg._ch10_bin_int
+                else n
+            )
+            octave_shift_if_oob = (
+                lambda n: n + 12 if n < 0 else n - 12 if n >= cfg.note_events else n
+            )
+            # TODO handle ranges beyond 12
+            # octave_shift_if_oob = lambda n: 0 if n < 0 else (n - cfg.note_events) % 12 + cfg.note_events if n >= cfg.note_events else n
+            transpose = lambda bin, n: octave_shift_if_oob(raw_transpose(bin, n))
+
+            token_data = [
+                (augment.instrument_bin_remap.get(i, i), transpose(i, n), v)
+                for i, n, v in token_data
+            ]
+        if cfg.do_token_sorting:
+            token_data = utils.sort_token_data(token_data)
+        if cfg.unrolled_tokens:
+            for t in token_data:
+                output += [
+                    utils.format_unrolled_instrument_bin(t[0]),
+                    utils.format_unrolled_note(t[1]),
+                    utils.format_unrolled_velocity(t[2]),
+                ]
+        else:
+            output += [utils.format_note_token(*t) for t in token_data]
+        token_data_buffer = []
+
+    def consume_note_program_data(prog: int, chan: int, note: int, vel: float):
+        nonlocal output, started_flag, delta_time_ms, cfg, utils, token_data_buffer
+        is_token_valid = (
+            utils.prog_data_to_token_data(prog, chan, note, vel) is not None
+        )
+        if not is_token_valid:
+            return
+        if started_flag:
+            wait_tokens = utils.data_to_wait_tokens(delta_time_ms)
+            if len(wait_tokens) > 0:
+                flush_token_data_buffer()
+                output += wait_tokens
+        delta_time_ms = 0.0
+        token_data_buffer.append((prog, chan, note, vel * augment.velocity_mod_factor))
+        started_flag = True
+
+    for msg in mid.tracks[0]:
+        time_ms = mido.tick2second(msg.time, mid.ticks_per_beat, tempo) * 1000.0
+        delta_time_ms += time_ms
+        t = msg.type
+
+        if msg.is_meta:
+            if t == "set_tempo":
+                tempo = msg.tempo * augment.time_stretch_factor
+            continue
+
+        def handle_note_off(ch, prog, n):
+            if channel_pedal_on[ch]:
+                channel_pedal_events[ch][(n, prog)] = True
+            else:
+                consume_note_program_data(prog, ch, n, 0)
+                if n in channel_notes[ch]:
+                    del channel_notes[ch][n]
+
+        if t == "program_change":
+            channel_program[msg.channel] = msg.program
+        elif t == "note_on":
+            if msg.velocity == 0:
+                handle_note_off(msg.channel, channel_program[msg.channel], msg.note)
+            else:
+                if (msg.note, channel_program[msg.channel]) in channel_pedal_events[
+                    msg.channel
+                ]:
+                    del channel_pedal_events[msg.channel][
+                        (msg.note, channel_program[msg.channel])
+                    ]
+                consume_note_program_data(
+                    channel_program[msg.channel],
+                    msg.channel,
+                    msg.note,
+                    mix_volume(
+                        msg.velocity,
+                        channel_volume[msg.channel],
+                        channel_expression[msg.channel],
+                    ),
+                )
+                channel_notes[msg.channel][msg.note] = True
+        elif t == "note_off":
+            handle_note_off(msg.channel, channel_program[msg.channel], msg.note)
+        elif t == "control_change":
+            if msg.control == 7 or msg.control == 39:  # volume
+                channel_volume[msg.channel] = msg.value
+            elif msg.control == 11:  # expression
+                channel_expression[msg.channel] = msg.value
+            elif msg.control == 64:  # sustain pedal
+                channel_pedal_on[msg.channel] = msg.value >= 64
+                if not channel_pedal_on[msg.channel]:
+                    for note, program in channel_pedal_events[msg.channel]:
+                        handle_note_off(msg.channel, program, note)
+                    channel_pedal_events[msg.channel] = {}
+            elif msg.control == 123:  # all notes off
+                for channel in channel_notes.keys():
+                    for note in list(channel_notes[channel]).copy():
+                        handle_note_off(channel, channel_program[channel], note)
+        else:
+            pass
+
+    flush_token_data_buffer()
+    output.append("<end>")
+    return " ".join(output)
+
+
+def generate_program_change_messages(cfg: VocabConfig):
+    for bin_name, channel in cfg.bin_channel_map.items():
+        if channel == 9:
+            continue
+        program = cfg._instrument_names_str_to_int[
+            cfg.bin_name_to_program_name[bin_name]
+        ]
+        yield mido.Message("program_change", program=program, time=0, channel=channel)
+    yield mido.Message("program_change", program=0, time=0, channel=9)
+
+
+@dataclass
+class DecodeState:
+    total_time: float  # milliseconds
+    delta_accum: float  # milliseconds
+    current_bin: int
+    current_note: int
+    active_notes: Dict[Tuple[int, int], float]  # { (channel, note): time started, ... }
+
+
+def token_to_midi_message(
+    utils: VocabUtils, token: str, state: DecodeState, end_token_pause: float = 3.0
+) -> Iterator[Tuple[Optional[mido.Message], DecodeState]]:
+    if state is None:
+        state = DecodeState(
+            total_time=0.0,
+            delta_accum=0.0,
+            current_bin=utils.cfg._short_instrument_names_str_to_int[
+                utils.cfg.short_instr_bin_names[0]
+            ],
+            current_note=0,
+            active_notes={},
+        )
+    token = token.strip()
+    if not token:
+        yield None, state
+        return
+    if token == "<end>":
+        d = end_token_pause * 1000.0
+        state.delta_accum += d
+        state.total_time += d
+        if utils.cfg.decode_end_held_note_delay != 0.0:
+            # end held notes
+            for (channel, note), start_time in list(state.active_notes.items()).copy():
+                ticks = int(mido.second2tick(state.delta_accum / 1000.0, 480, 500000))
+                state.delta_accum = 0.0
+                del state.active_notes[(channel, note)]
+                yield mido.Message(
+                    "note_off", note=note, time=ticks, channel=channel
+                ), state
+        yield None, state
+        return
+    if token.startswith("<"):
+        yield None, state
+        return
+
+    if utils.cfg.unrolled_tokens:
+        if token[0] == "t":
+            d = utils.wait_token_to_delta(token)
+            state.delta_accum += d
+            state.total_time += d
+        elif token[0] == "n":
+            state.current_note = int(token[1:], base=16)
+        elif token[0] == "i":
+            state.current_bin = utils.cfg._short_instrument_names_str_to_int[token[1:]]
+        elif token[0] == "v":
+            current_velocity = utils.bin_to_velocity(int(token[1:], base=16))
+            channel = utils.cfg.bin_channel_map[
+                utils.cfg.bin_instrument_names[state.current_bin]
+            ]
+            ticks = int(mido.second2tick(state.delta_accum / 1000.0, 480, 500000))
+            state.delta_accum = 0.0
+            if current_velocity > 0:
+                yield mido.Message(
+                    "note_on",
+                    note=state.current_note,
+                    velocity=current_velocity,
+                    time=ticks,
+                    channel=channel,
+                ), state
+            else:
+                yield mido.Message(
+                    "note_off",
+                    note=state.current_note,
+                    velocity=0,
+                    time=ticks,
+                    channel=channel,
+                ), state
+    else:
+        if token[0] == "t" and token[1].isdigit():  # wait token
+            d = utils.wait_token_to_delta(token)
+            state.delta_accum += d
+            state.total_time += d
+            if utils.cfg.decode_end_held_note_delay != 0.0:
+                # remove notes that have been held for too long
+                for (channel, note), start_time in list(
+                    state.active_notes.items()
+                ).copy():
+                    if (
+                        state.total_time - start_time
+                        > utils.cfg.decode_end_held_note_delay * 1000.0
+                    ):
+                        ticks = int(
+                            mido.second2tick(state.delta_accum / 1000.0, 480, 500000)
+                        )
+                        state.delta_accum = 0.0
+                        del state.active_notes[(channel, note)]
+                        yield mido.Message(
+                            "note_off", note=note, time=ticks, channel=channel
+                        ), state
+                        return
+        else:  # note token
+            bin, note, velocity = utils.note_token_to_data(token)
+            channel = utils.cfg.bin_channel_map[utils.cfg.bin_instrument_names[bin]]
+            ticks = int(mido.second2tick(state.delta_accum / 1000.0, 480, 500000))
+            state.delta_accum = 0.0
+            if velocity > 0:
+                if utils.cfg.decode_fix_repeated_notes:
+                    if (channel, note) in state.active_notes:
+                        del state.active_notes[(channel, note)]
+                    yield mido.Message(
+                        "note_off", note=note, time=ticks, channel=channel
+                    ), state
+                    ticks = 0
+                state.active_notes[(channel, note)] = state.total_time
+                yield mido.Message(
+                    "note_on", note=note, velocity=velocity, time=ticks, channel=channel
+                ), state
+                return
+            else:
+                if (channel, note) in state.active_notes:
+                    del state.active_notes[(channel, note)]
+                yield mido.Message(
+                    "note_off", note=note, time=ticks, channel=channel
+                ), state
+                return
+    yield None, state
+
+
+def str_to_midi_messages(utils: VocabUtils, data: str) -> Iterator[mido.Message]:
+    state = None
+    for token in data.split(" "):
+        for msg, new_state in token_to_midi_message(utils, token, state):
+            state = new_state
+            if msg is not None:
+                yield msg
+
+
+def convert_str_to_midi(
+    cfg: VocabConfig, data: str, meta_text: str = "Generated by MIDI-LLM-tokenizer"
+) -> mido.MidiFile:
+    utils = VocabUtils(cfg)
+    mid = mido.MidiFile()
+    track = mido.MidiTrack()
+    mid.tracks.append(track)
+
+    tempo = 500000
+    if meta_text:
+        track.append(mido.MetaMessage("text", text=meta_text, time=0))
+    track.append(mido.MetaMessage("set_tempo", tempo=tempo, time=0))
+    for msg in generate_program_change_messages(cfg):
+        track.append(msg)
+
+    # data = data.replace("<start>", "").replace("<end>", "").replace("<pad>", "").strip()
+    for msg in str_to_midi_messages(utils, data):
+        track.append(msg)
+
+    track.append(mido.MetaMessage("end_of_track", time=0))
+
+    return mid

backend-python/utils/midi_vocab_config.json

@@ -0,0 +1,303 @@
+{
+    "note_events": 128,
+    "wait_events": 125,
+    "max_wait_time": 1000,
+    "velocity_events": 128,
+    "velocity_bins": 12,
+    "velocity_exp": 0.5,
+    "do_token_sorting": true,
+    "unrolled_tokens": false,
+    "decode_end_held_note_delay": 5.0,
+    "decode_fix_repeated_notes": true,
+    "bin_instrument_names": [
+        "percussion",
+        "drum",
+        "tuba",
+        "marimba",
+        "bass",
+        "guitar",
+        "violin",
+        "trumpet",
+        "piano",
+        "sax",
+        "flute",
+        "lead",
+        "pad"
+    ],
+    "ch10_instrument_bin_name": "percussion",
+    "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": "marimba",
+        "Glockenspiel": "marimba",
+        "Music Box": "marimba",
+        "Vibraphone": "marimba",
+        "Marimba": "marimba",
+        "Xylophone": "marimba",
+        "Tubular Bells": "marimba",
+        "Dulcimer (Santur)": "marimba",
+        "Drawbar Organ (Hammond)": "marimba",
+        "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)": "guitar",
+        "Acoustic Guitar (steel)": "guitar",
+        "Electric Guitar (jazz)": "guitar",
+        "Electric Guitar (clean)": "guitar",
+        "Electric Guitar (muted)": "guitar",
+        "Overdriven Guitar": "guitar",
+        "Distortion Guitar": "guitar",
+        "Guitar harmonics": "guitar",
+        "Acoustic Bass": "bass",
+        "Electric Bass (fingered)": "bass",
+        "Electric Bass (picked)": "bass",
+        "Fretless Bass": "bass",
+        "Slap Bass 1": "bass",
+        "Slap Bass 2": "bass",
+        "Synth Bass 1": "bass",
+        "Synth Bass 2": "bass",
+        "Violin": "violin",
+        "Viola": "violin",
+        "Cello": "bass",
+        "Contrabass": "bass",
+        "Tremolo Strings": "violin",
+        "Pizzicato Strings": "violin",
+        "Orchestral Harp": "violin",
+        "Timpani": "drum",
+        "String Ensemble 1 (strings)": "violin",
+        "String Ensemble 2 (slow strings)": "violin",
+        "SynthStrings 1": "violin",
+        "SynthStrings 2": "violin",
+        "Choir Aahs": "violin",
+        "Voice Oohs": "violin",
+        "Synth Voice": "violin",
+        "Orchestra Hit": "",
+        "Trumpet": "trumpet",
+        "Trombone": "tuba",
+        "Tuba": "tuba",
+        "Muted Trumpet": "trumpet",
+        "French Horn": "trumpet",
+        "Brass Section": "trumpet",
+        "SynthBrass 1": "trumpet",
+        "SynthBrass 2": "trumpet",
+        "Soprano Sax": "sax",
+        "Alto Sax": "sax",
+        "Tenor Sax": "sax",
+        "Baritone Sax": "sax",
+        "Oboe": "sax",
+        "English Horn": "trumpet",
+        "Bassoon": "sax",
+        "Clarinet": "sax",
+        "Piccolo": "flute",
+        "Flute": "flute",
+        "Recorder": "flute",
+        "Pan Flute": "flute",
+        "Blown Bottle": "flute",
+        "Shakuhachi": "flute",
+        "Whistle": "flute",
+        "Ocarina": "flute",
+        "Lead 1 (square wave)": "lead",
+        "Lead 2 (sawtooth wave)": "lead",
+        "Lead 3 (calliope)": "lead",
+        "Lead 4 (chiffer)": "lead",
+        "Lead 5 (charang)": "lead",
+        "Lead 6 (voice solo)": "violin",
+        "Lead 7 (fifths)": "lead",
+        "Lead 8 (bass + lead)": "lead",
+        "Pad 1 (new age Fantasia)": "pad",
+        "Pad 2 (warm)": "pad",
+        "Pad 3 (polysynth)": "pad",
+        "Pad 4 (choir space voice)": "violin",
+        "Pad 5 (bowed glass)": "pad",
+        "Pad 6 (metallic pro)": "pad",
+        "Pad 7 (halo)": "pad",
+        "Pad 8 (sweep)": "pad",
+        "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": "guitar",
+        "Banjo": "guitar",
+        "Shamisen": "guitar",
+        "Koto": "guitar",
+        "Kalimba": "guitar",
+        "Bag pipe": "sax",
+        "Fiddle": "violin",
+        "Shanai": "sax",
+        "Tinkle Bell": "marimba",
+        "Agogo": "marimba",
+        "Steel Drums": "marimba",
+        "Woodblock": "marimba",
+        "Taiko Drum": "drum",
+        "Melodic Tom": "drum",
+        "Synth Drum": "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",
+        "marimba": "Marimba",
+        "drum": "Synth Drum",
+        "guitar": "Acoustic Guitar (steel)",
+        "bass": "Acoustic Bass",
+        "violin": "Violin",
+        "percussion": "",
+        "trumpet": "Trumpet",
+        "tuba": "Tuba",
+        "sax": "Tenor Sax",
+        "flute": "Flute",
+        "lead": "Lead 1 (square wave)",
+        "pad": "Pad 1 (new age Fantasia)"
+    },
+    "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"
+    }
+}

backend-python/utils/rwkv.py

@@ -1,27 +1,27 @@
+from abc import ABC, abstractmethod
 import os
 import pathlib
 import copy
-from typing import Dict, List, Tuple
+import re
+from typing import Dict, Iterable, List, Tuple
 from utils.log import quick_log
 from fastapi import HTTPException
 from pydantic import BaseModel, Field
-import torch
 import numpy as np
-from rwkv_pip.utils import PIPELINE
 from routes import state_cache
 
 
 END_OF_TEXT = 0
-END_OF_LINE = 187
 END_OF_LINE_DOUBLE = 535
 
 
 os.environ["TORCH_EXTENSIONS_DIR"] = f"{pathlib.Path(__file__).parent.parent.resolve()}"
 
 
-class RWKV:
-    def __init__(self, model: str, strategy: str, tokens_path: str) -> None:
+class AbstractRWKV(ABC):
+    def __init__(self, model: str, strategy: str, tokens_path: str):
         from rwkv.model import RWKV as Model  # dynamic import to make RWKV_CUDA_ON work
+        from rwkv_pip.utils import PIPELINE
 
         filename, _ = os.path.splitext(os.path.basename(model))
         self.name = filename
@@ -30,97 +30,52 @@ class RWKV:
         self.model_state = None
         self.model_tokens = []
 
-        self.CHUNK_LEN = 256
-
         self.max_tokens_per_generation = 500
         self.temperature = 1
-        self.top_p = 0.5
-        self.penalty_alpha_presence = 0.4
-        self.penalty_alpha_frequency = 0.4
+        self.top_p = 0.3
+        self.top_k = 0
+        self.penalty_alpha_presence = 0
+        self.penalty_alpha_frequency = 1
 
-        self.interface = ":"
-        if "rwkv_vocab" in tokens_path:
-            self.user = "Question"
-            self.bot = "Answer"
-        else:
-            self.user = "Bob"
-            self.bot = "Alice"
+    @abstractmethod
+    def adjust_occurrence(self, occurrence: Dict, token: int):
+        pass
 
-        self.AVOID_REPEAT_TOKENS = []
-        AVOID_REPEAT = "，：？！"
-        for i in AVOID_REPEAT:
-            dd = self.pipeline.encode(i)
-            assert len(dd) == 1
-            self.AVOID_REPEAT_TOKENS += dd
-
-        self.preload()
-
-    def preload(self):
-        interface = self.interface
-        user = self.user
-        bot = self.bot
-        preset_system = (
-            f"""
-The following is a coherent verbose detailed conversation between a girl named {bot} and her friend {user}. \
-{bot} is very intelligent, creative and friendly. \
-{bot} is unlikely to disagree with {user}, and {bot} doesn't like to ask {user} questions. \
-{bot} likes to tell {user} a lot about herself and her opinions. \
-{bot} usually gives {user} kind, helpful and informative advices.\n
-"""
-            if self.user == "Bob"
-            else f"{user}{interface} hi\n\n{bot}{interface} Hi. "
-            + "I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.\n\n"
-        )
-        logits, _ = self.run_rnn(self.fix_tokens(self.pipeline.encode(preset_system)))
-        try:
-            state_cache.add_state(
-                state_cache.AddStateBody(
-                    prompt=preset_system,
-                    tokens=self.model_tokens,
-                    state=self.model_state,
-                    logits=logits,
-                )
-            )
-        except HTTPException:
-            pass
+    @abstractmethod
+    def adjust_forward_logits(self, logits: List[float], occurrence: Dict, i: int):
+        pass
 
     # Model only saw '\n\n' as [187, 187] before, but the tokenizer outputs [535] for it at the end
-    def fix_tokens(self, tokens):
-        if len(tokens) > 0 and tokens[-1] == END_OF_LINE_DOUBLE:
-            tokens = tokens[:-1] + [END_OF_LINE, END_OF_LINE]
-        return tokens
+    @abstractmethod
+    def fix_tokens(self, tokens) -> List[int]:
+        pass
 
-    def run_rnn(self, _tokens: List[str], newline_adj: int = 0):
-        tokens = [int(x) for x in _tokens]
-        token_len = len(tokens)
-        self.model_tokens += tokens
+    @abstractmethod
+    def run_rnn(
+        self, _tokens: List[str], newline_adj: int = 0
+    ) -> Tuple[List[float], int]:
+        pass
 
-        while len(tokens) > 0:
-            out, self.model_state = self.model.forward(
-                tokens[: self.CHUNK_LEN], self.model_state
-            )
-            tokens = tokens[self.CHUNK_LEN :]
-
-        out[END_OF_LINE] += newline_adj  # adjust \n probability
-
-        if self.model_tokens[-1] in self.AVOID_REPEAT_TOKENS:
-            out[self.model_tokens[-1]] = -999999999
-        return out, token_len
+    @abstractmethod
+    def delta_postprocess(self, delta: str) -> str:
+        pass
 
     def get_embedding(self, input: str, fast_mode: bool) -> Tuple[List[float], int]:
         if fast_mode:
-            embedding, token_len = self.fast_embedding(
+            embedding, token_len = self.__fast_embedding(
                 self.fix_tokens(self.pipeline.encode(input)), None
             )
         else:
             self.model_state = None
             self.model_tokens = []
             _, token_len = self.run_rnn(self.fix_tokens(self.pipeline.encode(input)))
-            embedding = self.model_state[-5].tolist()
+            embedding = self.model_state[-11].tolist()
         embedding = (embedding / np.linalg.norm(embedding)).tolist()
         return embedding, token_len
 
-    def fast_embedding(self, tokens: List[str], state):
+    def __fast_embedding(self, tokens: List[str], state):
+        import torch
+
         tokens = [int(x) for x in tokens]
         token_len = len(tokens)
         self = self.model
@@ -257,7 +212,9 @@ The following is a coherent verbose detailed conversation between a girl named {
 
                 return state[0].tolist(), token_len
 
-    def generate(self, prompt: str, stop: str = None):
+    def generate(
+        self, prompt: str, stop: str | List[str] = None
+    ) -> Iterable[Tuple[str, str, int, int]]:
         quick_log(None, None, "Generation Prompt:\n" + prompt)
         cache = None
         delta_prompt = prompt
@@ -301,44 +258,60 @@ The following is a coherent verbose detailed conversation between a girl named {
         completion_token_len = 0
         response = ""
         for i in range(self.max_tokens_per_generation):
-            for n in occurrence:
-                logits[n] -= (
-                    self.penalty_alpha_presence
-                    + occurrence[n] * self.penalty_alpha_frequency
-                )
+            self.adjust_forward_logits(logits, occurrence, i)
+
             token = self.pipeline.sample_logits(
-                logits, temperature=self.temperature, top_p=self.top_p
+                logits, temperature=self.temperature, top_p=self.top_p, top_k=self.top_k
             )
 
             if token == END_OF_TEXT:
                 yield response, "", prompt_token_len, completion_token_len
                 break
-            if token not in occurrence:
-                occurrence[token] = 1
-            else:
-                occurrence[token] += 1
+
+            self.adjust_occurrence(occurrence, token)
 
             logits, _ = self.run_rnn([token])
             completion_token_len = completion_token_len + 1
-            delta: str = self.pipeline.decode(self.model_tokens[out_last:])
+            delta: str = self.delta_postprocess(
+                self.pipeline.decode(self.model_tokens[out_last:])
+            )
             if "\ufffd" not in delta:  # avoid utf-8 display issues
                 response += delta
                 if stop is not None:
-                    if stop in response:
-                        try:
-                            state_cache.add_state(
-                                state_cache.AddStateBody(
-                                    prompt=prompt + response,
-                                    tokens=self.model_tokens,
-                                    state=self.model_state,
-                                    logits=logits,
+                    if type(stop) == str:
+                        if stop 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(stop)[0]
-                        yield response, "", prompt_token_len, completion_token_len
-                        break
+                            except HTTPException:
+                                pass
+                            response = response.split(stop)[0]
+                            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,
+                                    )
+                                )
+                            except HTTPException:
+                                pass
+                            response = response.split(matched.group())[0]
+                            yield response, "", prompt_token_len, completion_token_len
+                            break
                 out_last = begin + i + 1
                 if i == self.max_tokens_per_generation - 1:
                     try:
@@ -355,6 +328,153 @@ The following is a coherent verbose detailed conversation between a girl named {
                 yield response, delta, prompt_token_len, completion_token_len
 
 
+class TextRWKV(AbstractRWKV):
+    def __init__(self, model: str, strategy: str, tokens_path: str) -> None:
+        super().__init__(model, strategy, tokens_path)
+
+        self.CHUNK_LEN = 256
+
+        self.max_tokens_per_generation = 500
+        self.temperature = 1
+        self.top_p = 0.3
+        self.top_k = 0
+        self.penalty_alpha_presence = 0
+        self.penalty_alpha_frequency = 1
+
+        self.interface = ":"
+        if "world" in self.name.lower():
+            self.user = "Question"
+            self.bot = "Answer"
+            self.END_OF_LINE = 11
+        else:
+            self.user = "Bob"
+            self.bot = "Alice"
+            self.END_OF_LINE = 187
+
+        self.AVOID_REPEAT_TOKENS = []
+        AVOID_REPEAT = "，：？！"
+        for i in AVOID_REPEAT:
+            dd = self.pipeline.encode(i)
+            assert len(dd) == 1
+            self.AVOID_REPEAT_TOKENS += dd
+
+        self.__preload()
+
+    def adjust_occurrence(self, occurrence: Dict, token: int):
+        for xxx in occurrence:
+            occurrence[xxx] *= 0.996
+        if token not in occurrence:
+            occurrence[token] = 1
+        else:
+            occurrence[token] += 1
+
+    def adjust_forward_logits(self, logits: List[float], occurrence: Dict, i: int):
+        for n in occurrence:
+            logits[n] -= (
+                self.penalty_alpha_presence
+                + occurrence[n] * self.penalty_alpha_frequency
+            )
+
+    # Model only saw '\n\n' as [187, 187] before, but the tokenizer outputs [535] for it at the end
+    def fix_tokens(self, tokens) -> List[int]:
+        if "world" in self.name.lower():
+            return tokens
+        if len(tokens) > 0 and tokens[-1] == END_OF_LINE_DOUBLE:
+            tokens = tokens[:-1] + [self.END_OF_LINE, self.END_OF_LINE]
+        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
+
+        while len(tokens) > 0:
+            out, self.model_state = self.model.forward(
+                tokens[: self.CHUNK_LEN], self.model_state
+            )
+            tokens = tokens[self.CHUNK_LEN :]
+
+        out[self.END_OF_LINE] += newline_adj  # adjust \n probability
+
+        if self.model_tokens[-1] in self.AVOID_REPEAT_TOKENS:
+            out[self.model_tokens[-1]] = -999999999
+        return out, token_len
+
+    def delta_postprocess(self, delta: str) -> str:
+        return delta
+
+    def __preload(self):
+        interface = self.interface
+        user = self.user
+        bot = self.bot
+        preset_system = (
+            f"""
+The following is a coherent verbose detailed conversation between a girl named {bot} and her friend {user}. \
+{bot} is very intelligent, creative and friendly. \
+{bot} is unlikely to disagree with {user}, and {bot} doesn't like to ask {user} questions. \
+{bot} likes to tell {user} a lot about herself and her opinions. \
+{bot} usually gives {user} kind, helpful and informative advices.\n
+"""
+            if self.user == "Bob"
+            else f"{user}{interface} hi\n\n{bot}{interface} Hi. "
+            + "I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.\n\n"
+        )
+        logits, _ = self.run_rnn(self.fix_tokens(self.pipeline.encode(preset_system)))
+        try:
+            state_cache.add_state(
+                state_cache.AddStateBody(
+                    prompt=preset_system,
+                    tokens=self.model_tokens,
+                    state=self.model_state,
+                    logits=logits,
+                )
+            )
+        except HTTPException:
+            pass
+
+
+class MusicRWKV(AbstractRWKV):
+    def __init__(self, model: str, strategy: str, tokens_path: str):
+        super().__init__(model, strategy, tokens_path)
+
+        self.max_tokens_per_generation = 500
+        self.temperature = 1
+        self.top_p = 0.8
+        self.top_k = 8
+
+    def adjust_occurrence(self, occurrence: Dict, token: int):
+        for n in occurrence:
+            occurrence[n] *= 0.997  #### decay repetition penalty
+        if token >= 128 or token == 127:
+            occurrence[token] = 1 + (occurrence[token] if token in occurrence else 0)
+        else:
+            occurrence[token] = 0.3 + (occurrence[token] if token in occurrence else 0)
+
+    def adjust_forward_logits(self, logits: List[float], occurrence: Dict, i: int):
+        for n in occurrence:
+            logits[n] -= 0 + occurrence[n] * 0.5
+
+        logits[0] += (i - 2000) / 500  # try not to be too short or too long
+        logits[127] -= 1  # avoid "t125"
+
+    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
+
+
 class ModelConfigBody(BaseModel):
     max_tokens: int = Field(default=None, gt=0, le=102400)
     temperature: float = Field(default=None, ge=0, le=2)
@@ -374,7 +494,7 @@ class ModelConfigBody(BaseModel):
         }
 
 
-def set_rwkv_config(model: RWKV, body: ModelConfigBody):
+def set_rwkv_config(model: AbstractRWKV, body: ModelConfigBody):
     if body.max_tokens is not None:
         model.max_tokens_per_generation = body.max_tokens
     if body.temperature is not None:
@@ -390,7 +510,7 @@ def set_rwkv_config(model: RWKV, body: ModelConfigBody):
         model.penalty_alpha_frequency = body.frequency_penalty
 
 
-def get_rwkv_config(model: RWKV) -> ModelConfigBody:
+def get_rwkv_config(model: AbstractRWKV) -> ModelConfigBody:
     return ModelConfigBody(
         max_tokens=model.max_tokens_per_generation,
         temperature=model.temperature,

finetune/data/sample.jsonl

@@ -0,0 +1,7 @@
+{"text": "1:This is the first document."}
+{"text": "2:Hello\nWorld"}
+{"text": "3:1+1=2\n1+2=3\n2+2=4"}
+{"text": "4:You will be training the GPT version because it's paralleziable and faster to train."}
+{"text": "5:Read the inference code in src/model.py and try using the final hidden state(.xx .aa .bb)"}
+{"text": "6:You can fine-tune the model with longer ctxLen and it can quickly adapt to longer ctxLens."}
+{"text": "7:Consider RWKV 14B. The state has 200 vectors, that is, 5 vectors for each block: fp16 (xx), fp32 (aa), fp32 (bb), fp32 (pp), fp16 (xx)."}

finetune/get_layer_and_embd.py

@@ -0,0 +1,41 @@
+import torch
+import sys
+import time
+import os
+import threading
+import gc
+
+
+def file_cleaner(file):
+    last_pos = 0
+
+    def cleaner():
+        nonlocal last_pos
+        while True:
+            time.sleep(0.1)
+            pos = file.tell()
+            if pos > last_pos:
+                os.posix_fadvise(
+                    file.fileno(), last_pos, pos - last_pos, os.POSIX_FADV_DONTNEED
+                )
+            last_pos = pos
+
+    return cleaner
+
+
+model_file = open(sys.argv[1], "rb")
+cleaner = file_cleaner(model_file)
+cleaner_thread = threading.Thread(target=cleaner, daemon=True)
+cleaner_thread.start()
+
+w = torch.load(model_file, map_location="cpu")
+gc.collect()
+
+n_embd = w["emb.weight"].shape[1]
+n_layer = 0
+keys = list(w.keys())
+for x in keys:
+    layer_id = int(x.split(".")[1]) if ("blocks." in x) else 0
+    n_layer = max(n_layer, layer_id + 1)
+
+print(f"--n_layer {n_layer} --n_embd {n_embd}", end="")

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

@@ -0,0 +1,52 @@
+if [[ ${cnMirror} == 1 ]]; then
+  export PIP_INDEX_URL="https://pypi.tuna.tsinghua.edu.cn/simple"
+  if grep -q "mirrors.aliyun.com" /etc/apt/sources.list; then
+    echo "apt cnMirror already set"
+  else
+    sudo sed -i 's/http:\/\/archive.ubuntu.com\/ubuntu\//http:\/\/mirrors.aliyun.com\/ubuntu\//g' /etc/apt/sources.list
+    sudo apt update
+  fi
+fi
+
+if dpkg -s "gcc" >/dev/null 2>&1; then
+  echo "gcc installed"
+else
+  sudo apt -y install gcc
+fi
+
+if dpkg -s "python3-pip" >/dev/null 2>&1; then
+  echo "pip installed"
+else
+  sudo apt -y install python3-pip
+fi
+
+if dpkg -s "ninja-build" >/dev/null 2>&1; then
+  echo "ninja installed"
+else
+  sudo apt -y install ninja-build
+fi
+
+if dpkg -s "cuda" >/dev/null 2>&1 && dpkg -s "cuda" | grep Version | awk '{print $2}' | grep -q "12"; then
+  echo "cuda 12 installed"
+else
+  wget -N https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-wsl-ubuntu.pin
+  sudo mv cuda-wsl-ubuntu.pin /etc/apt/preferences.d/cuda-repository-pin-600
+  wget -N https://developer.download.nvidia.com/compute/cuda/12.2.0/local_installers/cuda-repo-wsl-ubuntu-12-2-local_12.2.0-1_amd64.deb
+  sudo dpkg -i cuda-repo-wsl-ubuntu-12-2-local_12.2.0-1_amd64.deb
+  sudo cp /var/cuda-repo-wsl-ubuntu-12-2-local/cuda-*-keyring.gpg /usr/share/keyrings/
+  sudo apt-get update
+  sudo apt-get -y install cuda
+fi
+
+if python3 -c "import pkg_resources; pkg_resources.require(open('./finetune/requirements.txt',mode='r'))" &>/dev/null; then
+  echo "requirements satisfied"
+else
+  python3 -m pip install -r ./finetune/requirements.txt
+fi
+
+echo "loading $loadModel"
+modelInfo=$(python3 ./finetune/get_layer_and_embd.py $loadModel)
+echo $modelInfo
+
+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

finetune/json2binidx_tool/tools/indexed_dataset.py

@@ -0,0 +1,597 @@
+# Copyright (c) 2021, EleutherAI
+# This file is based on code by the authors denoted below and has been modified from its original version.
+#
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+# copied from fairseq/fairseq/data/indexed_dataset.py
+# Removed IndexedRawTextDataset since it relied on Fairseq dictionary
+# other slight modifications to remove fairseq dependencies
+# Added document index to index file and made it accessible.
+#    An empty sentence no longer separates documents.
+
+import os
+import shutil
+import struct
+from functools import lru_cache
+from itertools import accumulate
+
+import numpy as np
+import torch
+
+
+
+
+def __best_fitting_dtype(vocab_size=None):
+    if vocab_size is not None and vocab_size < 65500:
+        return np.uint16
+    else:
+        return np.int32
+
+
+def infer_dataset_impl(path):
+    if IndexedDataset.exists(path):
+        with open(index_file_path(path), "rb") as f:
+            magic = f.read(8)
+            if magic == IndexedDataset._HDR_MAGIC:
+                return "cached"
+            elif magic == MMapIndexedDataset.Index._HDR_MAGIC[:8]:
+                return "mmap"
+            else:
+                return None
+    else:
+        print(f"Dataset does not exist: {path}")
+        print(
+            "Path should be a basename that both .idx and .bin can be appended to get full filenames."
+        )
+        return None
+
+
+def make_builder(out_file, impl, vocab_size=None):
+    if impl == "mmap":
+        return MMapIndexedDatasetBuilder(
+            out_file, dtype=__best_fitting_dtype(vocab_size)
+        )
+    else:
+        return IndexedDatasetBuilder(out_file)
+
+
+def make_dataset(path, impl, skip_warmup=False):
+    if not IndexedDataset.exists(path):
+        print(f"Dataset does not exist: {path}")
+        print(
+            "Path should be a basename that both .idx and .bin can be appended to get full filenames."
+        )
+        return None
+    if impl == "infer":
+        impl = infer_dataset_impl(path)
+    if impl == "lazy" and IndexedDataset.exists(path):
+        return IndexedDataset(path)
+    elif impl == "cached" and IndexedDataset.exists(path):
+        return IndexedCachedDataset(path)
+    elif impl == "mmap" and MMapIndexedDataset.exists(path):
+        return MMapIndexedDataset(path, skip_warmup)
+    print(f"Unknown dataset implementation: {impl}")
+    return None
+
+
+def dataset_exists(path, impl):
+    if impl == "mmap":
+        return MMapIndexedDataset.exists(path)
+    else:
+        return IndexedDataset.exists(path)
+
+
+def read_longs(f, n):
+    a = np.empty(n, dtype=np.int64)
+    f.readinto(a)
+    return a
+
+
+def write_longs(f, a):
+    f.write(np.array(a, dtype=np.int64))
+
+
+dtypes = {
+    1: np.uint8,
+    2: np.int8,
+    3: np.int16,
+    4: np.int32,
+    5: np.int64,
+    6: np.float32,
+    7: np.float64,
+    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"
+
+
+def create_doc_idx(sizes):
+    doc_idx = [0]
+    for i, s in enumerate(sizes):
+        if s == 0:
+            doc_idx.append(i + 1)
+    return doc_idx
+
+
+class IndexedDataset(torch.utils.data.Dataset):
+    """Loader for IndexedDataset"""
+
+    _HDR_MAGIC = b"TNTIDX\x00\x00"
+
+    def __init__(self, path):
+        super().__init__()
+        self.path = path
+        self.data_file = None
+        self.read_index(path)
+
+    def read_index(self, path):
+        with open(index_file_path(path), "rb") as f:
+            magic = f.read(8)
+            assert magic == self._HDR_MAGIC, (
+                "Index file doesn't match expected format. "
+                "Make sure that --dataset-impl is configured properly."
+            )
+            version = f.read(8)
+            assert struct.unpack("<Q", version) == (1,)
+            code, self.element_size = struct.unpack("<QQ", f.read(16))
+            self.dtype = dtypes[code]
+            self._len, self.s = struct.unpack("<QQ", f.read(16))
+            self.doc_count = struct.unpack("<Q", f.read(8))
+            self.dim_offsets = read_longs(f, self._len + 1)
+            self.data_offsets = read_longs(f, self._len + 1)
+            self.sizes = read_longs(f, self.s)
+            self.doc_idx = read_longs(f, self.doc_count)
+
+    def read_data(self, path):
+        self.data_file = open(data_file_path(path), "rb", buffering=0)
+
+    def check_index(self, i):
+        if i < 0 or i >= self._len:
+            raise IndexError("index out of range")
+
+    def __del__(self):
+        if self.data_file:
+            self.data_file.close()
+
+    # @lru_cache(maxsize=8)
+    def __getitem__(self, idx):
+        if not self.data_file:
+            self.read_data(self.path)
+        if isinstance(idx, int):
+            i = idx
+            self.check_index(i)
+            tensor_size = self.sizes[self.dim_offsets[i] : self.dim_offsets[i + 1]]
+            a = np.empty(tensor_size, dtype=self.dtype)
+            self.data_file.seek(self.data_offsets[i] * self.element_size)
+            self.data_file.readinto(a)
+            return a
+        elif isinstance(idx, slice):
+            start, stop, step = idx.indices(len(self))
+            if step != 1:
+                raise ValueError("Slices into indexed_dataset must be contiguous")
+            sizes = self.sizes[self.dim_offsets[start] : self.dim_offsets[stop]]
+            size = sum(sizes)
+            a = np.empty(size, dtype=self.dtype)
+            self.data_file.seek(self.data_offsets[start] * self.element_size)
+            self.data_file.readinto(a)
+            offsets = list(accumulate(sizes))
+            sents = np.split(a, offsets[:-1])
+            return sents
+
+    def __len__(self):
+        return self._len
+
+    def num_tokens(self, index):
+        return self.sizes[index]
+
+    def size(self, index):
+        return self.sizes[index]
+
+    @staticmethod
+    def exists(path):
+        return os.path.exists(index_file_path(path)) and os.path.exists(
+            data_file_path(path)
+        )
+
+    @property
+    def supports_prefetch(self):
+        return False  # avoid prefetching to save memory
+
+
+class IndexedCachedDataset(IndexedDataset):
+    def __init__(self, path):
+        super().__init__(path)
+        self.cache = None
+        self.cache_index = {}
+
+    @property
+    def supports_prefetch(self):
+        return True
+
+    def prefetch(self, indices):
+        if all(i in self.cache_index for i in indices):
+            return
+        if not self.data_file:
+            self.read_data(self.path)
+        indices = sorted(set(indices))
+        total_size = 0
+        for i in indices:
+            total_size += self.data_offsets[i + 1] - self.data_offsets[i]
+        self.cache = np.empty(total_size, dtype=self.dtype)
+        ptx = 0
+        self.cache_index.clear()
+        for i in indices:
+            self.cache_index[i] = ptx
+            size = self.data_offsets[i + 1] - self.data_offsets[i]
+            a = self.cache[ptx : ptx + size]
+            self.data_file.seek(self.data_offsets[i] * self.element_size)
+            self.data_file.readinto(a)
+            ptx += size
+        if self.data_file:
+            # close and delete data file after prefetch so we can pickle
+            self.data_file.close()
+            self.data_file = None
+
+    # @lru_cache(maxsize=8)
+    def __getitem__(self, idx):
+        if isinstance(idx, int):
+            i = idx
+            self.check_index(i)
+            tensor_size = self.sizes[self.dim_offsets[i] : self.dim_offsets[i + 1]]
+            a = np.empty(tensor_size, dtype=self.dtype)
+            ptx = self.cache_index[i]
+            np.copyto(a, self.cache[ptx : ptx + a.size])
+            return a
+        elif isinstance(idx, slice):
+            # Hack just to make this work, can optimizer later if necessary
+            sents = []
+            for i in range(*idx.indices(len(self))):
+                sents.append(self[i])
+            return sents
+
+
+class IndexedDatasetBuilder(object):
+    element_sizes = {
+        np.uint8: 1,
+        np.int8: 1,
+        np.int16: 2,
+        np.int32: 4,
+        np.int64: 8,
+        np.float32: 4,
+        np.float64: 8,
+    }
+
+    def __init__(self, out_file, dtype=np.int32):
+        self.out_file = open(out_file, "wb")
+        self.dtype = dtype
+        self.data_offsets = [0]
+        self.dim_offsets = [0]
+        self.sizes = []
+        self.element_size = self.element_sizes[self.dtype]
+        self.doc_idx = [0]
+
+    def add_item(self, np_array):
+        assert isinstance(np_array, np.ndarray) and np_array.dtype == self.dtype
+        bytes = self.out_file.write(np_array)
+        self.data_offsets.append(self.data_offsets[-1] + bytes / self.element_size)
+        for s in np_array.shape:
+            self.sizes.append(s)
+        self.dim_offsets.append(self.dim_offsets[-1] + len(np_array.shape))
+
+    def end_document(self):
+        self.doc_idx.append(len(self.sizes))
+
+    def merge_file_(self, another_file):
+        index = IndexedDataset(another_file)
+        assert index.dtype == self.dtype
+
+        begin = self.data_offsets[-1]
+        for offset in index.data_offsets[1:]:
+            self.data_offsets.append(begin + offset)
+        self.sizes.extend(index.sizes)
+        begin = self.dim_offsets[-1]
+        for dim_offset in index.dim_offsets[1:]:
+            self.dim_offsets.append(begin + dim_offset)
+
+        with open(data_file_path(another_file), "rb") as f:
+            while True:
+                data = f.read(1024)
+                if data:
+                    self.out_file.write(data)
+                else:
+                    break
+
+    def finalize(self, index_file):
+        self.out_file.close()
+        index = open(index_file, "wb")
+        index.write(b"TNTIDX\x00\x00")
+        index.write(struct.pack("<Q", 1))
+        index.write(struct.pack("<QQ", code(self.dtype), self.element_size))
+        index.write(struct.pack("<QQ", len(self.data_offsets) - 1, len(self.sizes)))
+        index.write(struct.pack("<Q", len(self.doc_idx)))
+        write_longs(index, self.dim_offsets)
+        write_longs(index, self.data_offsets)
+        write_longs(index, self.sizes)
+        write_longs(index, self.doc_idx)
+        index.close()
+
+
+def _warmup_mmap_file(path):
+    with open(path, "rb") as stream:
+        while stream.read(100 * 1024 * 1024):
+            pass
+
+
+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):
+                    pointers = np.zeros(len(sizes), dtype=np.int64)
+                    sizes = np.array(sizes, dtype=np.int64)
+
+                    np.cumsum(sizes[:-1], out=pointers[1:])
+                    pointers = pointers * dtype().itemsize
+                    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("    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("    reading sizes...")
+            self._sizes = np.frombuffer(
+                self._bin_buffer, dtype=np.int32, count=self._len, offset=offset
+            )
+            print("    reading pointers...")
+            self._pointers = np.frombuffer(
+                self._bin_buffer,
+                dtype=np.int64,
+                count=self._len,
+                offset=offset + self._sizes.nbytes,
+            )
+            print("    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("    warming up data mmap file...")
+            _warmup_mmap_file(data_file_path(self._path))
+        print("    creating numpy buffer of mmap...")
+        self._bin_buffer_mmap = np.memmap(
+            data_file_path(self._path), mode="r", order="C"
+        )
+        print("    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
+
+    @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)
+        )
+
+
+class MMapIndexedDatasetBuilder(object):
+    def __init__(self, out_file, dtype=np.int64):
+        self._data_file = open(out_file, "wb")
+        self._dtype = dtype
+        self._sizes = []
+        self._doc_idx = [0]
+
+    @property
+    def dtype(self):
+        return self._dtype
+
+    def add_item(self, np_array):
+        assert isinstance(np_array, np.ndarray) and np_array.dtype == self.dtype
+        self._data_file.write(np_array.tobytes(order="C"))
+        self._sizes.append(np_array.size)
+
+    def end_document(self):
+        self._doc_idx.append(len(self._sizes))
+
+    def merge_file_(self, another_file):
+        # Concatenate index
+        index = MMapIndexedDataset.Index(index_file_path(another_file))
+        assert index.dtype == self._dtype
+
+        for size in index.sizes:
+            self._sizes.append(size)
+
+        # Concatenate data
+        with open(data_file_path(another_file), "rb") as f:
+            shutil.copyfileobj(f, self._data_file)
+
+    def finalize(self, index_file):
+        self._data_file.close()
+
+        with MMapIndexedDataset.Index.writer(index_file, self._dtype) as index:
+            index.write(self._sizes, self._doc_idx)

finetune/json2binidx_tool/tools/preprocess_data.py

@@ -0,0 +1,250 @@
+# Copyright (c) 2021, EleutherAI
+# This file is based on code by the authors denoted below and has been modified from its original version.
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Processing data for pretraining."""
+
+import os
+import sys
+
+sys.path.append(os.path.dirname(os.path.realpath(__file__)))
+
+import argparse
+import multiprocessing
+
+import lm_dataformat as lmd
+import numpy as np
+
+sys.path.append(
+    os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))
+)
+import time
+import tqdm
+import ftfy
+
+from tokenizer import build_tokenizer
+import indexed_dataset
+from threading import Semaphore
+
+
+class Encoder(object):
+    def __init__(self, args):
+        self.args = args
+
+    def initializer(self):
+        # Use Encoder class as a container for global data
+        Encoder.tokenizer = build_tokenizer(self.args)
+
+    def encode(self, text):
+        if self.args.ftfy:
+            text = ftfy.fix_text(text)
+        ids = {}
+        for key in self.args.jsonl_keys:
+            doc_ids = []
+            text_ids = Encoder.tokenizer.tokenize(text)
+            if len(text_ids) > 0:
+                doc_ids.append(text_ids)
+            if self.args.append_eod:
+                doc_ids[-1].append(Encoder.tokenizer.eod)
+            ids[key] = doc_ids
+        return ids, len(text)
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    group = parser.add_argument_group(title="input data")
+    group.add_argument(
+        "--input",
+        type=str,
+        required=True,
+        help="Path to input jsonl files or lmd archive(s) - if using multiple archives, put them in a comma separated "
+        "list",
+    )
+    group.add_argument(
+        "--jsonl-keys",
+        nargs="+",
+        default=["text"],
+        help="space separate listed of keys to extract from jsonl. Defa",
+    )
+    group.add_argument(
+        "--num-docs",
+        default=None,
+        help="Optional: Number of documents in the input data (if known) for an accurate progress bar.",
+        type=int,
+    )
+    group = parser.add_argument_group(title="tokenizer")
+    group.add_argument(
+        "--tokenizer-type",
+        type=str,
+        required=True,
+        choices=[
+            "HFGPT2Tokenizer",
+            "HFTokenizer",
+            "GPT2BPETokenizer",
+            "CharLevelTokenizer",
+            "TiktokenTokenizer",
+            "RWKVTokenizer",
+        ],
+        help="What type of tokenizer to use.",
+    )
+    group.add_argument(
+        "--vocab-file", type=str, default=None, help="Path to the vocab file"
+    )
+    group.add_argument(
+        "--merge-file",
+        type=str,
+        default=None,
+        help="Path to the BPE merge file (if necessary).",
+    )
+    group.add_argument(
+        "--append-eod",
+        action="store_true",
+        help="Append an <eod> token to the end of a document.",
+    )
+    group.add_argument("--ftfy", action="store_true", help="Use ftfy to clean text")
+    group = parser.add_argument_group(title="output data")
+    group.add_argument(
+        "--output-prefix",
+        type=str,
+        required=True,
+        help="Path to binary output file without suffix",
+    )
+    group.add_argument(
+        "--dataset-impl",
+        type=str,
+        default="mmap",
+        choices=["lazy", "cached", "mmap"],
+        help="Dataset implementation to use. Default: mmap",
+    )
+
+    group = parser.add_argument_group(title="runtime")
+    group.add_argument(
+        "--workers", type=int, default=1, help="Number of worker processes to launch"
+    )
+    group.add_argument(
+        "--log-interval",
+        type=int,
+        default=100,
+        help="Interval between progress updates",
+    )
+    args = parser.parse_args()
+    args.keep_empty = False
+
+    # some default/dummy values for the tokenizer
+    args.rank = 0
+    args.make_vocab_size_divisible_by = 128
+    args.model_parallel_size = 1
+
+    return args
+
+
+def yield_from_files(fnames: list, semaphore):
+    """
+    Iterator over input documents using lm_dataformat. Should be able to handle jsons / texts /
+    other compressed formats. Also filters out empty documents.
+
+    :param fnames: list of filenames
+    """
+
+    def yielder(fname, semaphore):
+        for f in filter(lambda x: x, lmd.Reader(fname).stream_data()):
+            semaphore.acquire()
+            yield f
+
+    for fname in fnames:
+        semaphore.acquire()
+
+        yield from yielder(fname, semaphore)
+
+
+def main():
+    args = get_args()
+    encoder = Encoder(args)
+    tokenizer = build_tokenizer(args)
+    print(f"Vocab size: {tokenizer.vocab_size}")
+    print(f"Output prefix: {args.output_prefix}")
+
+    # build a semaphore object to stop `yield_from_files` from getting ahead of encoder.encode and
+    # hence building up memory
+    semaphore = Semaphore(10000 + args.workers)
+
+    # use multiprocessing to iterate over input documents
+    fin = yield_from_files(args.input.split(","), semaphore)
+
+    if args.workers > 1:
+        pool = multiprocessing.Pool(args.workers, initializer=encoder.initializer)
+        encoded_docs = pool.imap(encoder.encode, fin, chunksize=25)
+    else:
+        encoder.initializer()
+        encoded_docs = (encoder.encode(doc) for doc in fin)
+
+    # make a dataset builder for each key in args.jsonl_keys
+    # each key will output to a different file beginning with args.output_prefix
+    output_bin_files = {}
+    output_idx_files = {}
+    builders = {}
+    for key in args.jsonl_keys:
+        output_bin_files[key] = "{}_{}_{}.bin".format(
+            args.output_prefix, key, "document"
+        )
+        output_idx_files[key] = "{}_{}_{}.idx".format(
+            args.output_prefix, key, "document"
+        )
+        builders[key] = indexed_dataset.make_builder(
+            output_bin_files[key],
+            impl=args.dataset_impl,
+            vocab_size=tokenizer.vocab_size,
+        )
+
+    # actually do tokenization
+    proc_start = time.time()
+    total_bytes_processed = 0
+    pbar = tqdm.tqdm()
+    for i, (doc, bytes_processed) in enumerate(encoded_docs, start=1):
+        total_bytes_processed += bytes_processed
+
+        # release semaphore so `yield_from_files` can add another file to the buffer
+        semaphore.release()
+
+        # add each tokenized document / sentence
+        for key, sentences in doc.items():
+            for sentence in sentences:
+                builders[key].add_item(np.array(sentence, dtype=builders[key].dtype))
+            # separate with eos token
+            builders[key].end_document()
+
+        # log progress
+        if i % args.log_interval == 0:
+            current = time.time()
+            elapsed = current - proc_start
+            mbs = total_bytes_processed / elapsed / 1024 / 1024
+            pbar.set_description(
+                f"Processed {i}{'' if args.num_docs is None else '/' + str(args.num_docs)} documents ({i / elapsed:0.2f} docs/s, {mbs:0.2f} MB/s)."
+            )
+            if i != 0:
+                pbar.update(args.log_interval)
+
+    # save output file
+    for key in args.jsonl_keys:
+        builders[key].finalize(output_idx_files[key])
+
+
+if __name__ == "__main__":
+    try:
+        main()
+    except Exception as e:
+        with open("error.txt", "w") as f:
+            f.write(str(e))

finetune/json2binidx_tool/tools/rwkv_tokenizer.py

@@ -0,0 +1,232 @@
+########################################################################################################
+# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
+# Source: https://github.com/BlinkDL/ChatRWKV/blob/main/tokenizer/rwkv_tokenizer.py
+########################################################################################################
+
+import os, sys, time, random
+
+print('''
+#######################################################################################################################
+
+This tokenizer is not used in any RWKV models yet. I plan to use it for the future multilang RWKV models.
+
+Benefits:
+
+* Good support of most languages, from European to CJK to Arabic and Hindi and more.
+
+* Clean vocab. Good for code too. Vocab size = 65525 (use 0 for <|endoftext|>).
+
+* Good at numbers: the numerical tokens are '0'~'9', '10'~'99', ' 0'~' 9', ' 10'~' 99'.
+
+* Very easy tokenization:
+
+** The input text must be in UTF-8.
+
+** Greedy encoding: always pick the longest (in bytes) token (with the highest id) that matches your UTF-8 bytes.
+
+* The tokenization result is surprisingly good, because the vocab respects word boundaries and UTF-8 boundaries.
+
+For 10x faster speed:
+mypyc rwkv_tokenizer.py
+python3 -c "import rwkv_tokenizer"
+
+#######################################################################################################################
+''')
+
+########################################################################################################
+# Tokenizer #1 (reference, naive, slow)
+########################################################################################################
+
+class RWKV_TOKENIZER():
+    table = None  # : list[list[list[bytes]]] = None
+    good = None  # : list[set[int]]
+    wlen = None  # : list[int]
+    def __init__(self, file_name):
+        self.vocab_size = 65525
+        self.idx2token = {}
+        sorted = [] # must be already sorted
+        lines = open(file_name, "r", encoding="utf-8").readlines()
+        for l in lines:
+            idx = int(l[:l.index(' ')])
+            x = eval(l[l.index(' '):l.rindex(' ')])
+            x = x.encode("utf-8") if isinstance(x, str) else x
+            assert isinstance(x, bytes)
+            assert len(x) == int(l[l.rindex(' '):])
+            sorted += [x]
+            self.idx2token[idx] = x
+
+        self.token2idx = {}
+        for k, v in self.idx2token.items():
+            self.token2idx[v] = int(k)
+
+        # precompute some tables for fast matching
+        self.table = [[[] for j in range(256)] for i in range(256)]
+        self.good = [set() for i in range(256)]
+        self.wlen = [0 for i in range(256)]
+
+        for i in reversed(range(len(sorted))): # reverse order - match longer tokens first
+            s = sorted[i]
+            if len(s) >= 2:
+                s0 = int(s[0])
+                s1 = int(s[1])
+                self.table[s0][s1] += [s]
+                self.wlen[s0] = max(self.wlen[s0], len(s))
+                self.good[s0].add(s1)
+
+    def encodeBytes(self, src: bytes):
+        src_len: int = len(src)
+        tokens = []
+        i: int = 0
+        while i < src_len:
+            s: bytes = src[i : i + 1]
+
+            if i < src_len - 1:
+                s1: int = int(src[i + 1])
+                s0: int = int(src[i])
+                if s1 in self.good[s0]:
+                    sss: bytes = src[i : i + self.wlen[s0]]
+                    try:
+                        s = next(filter(sss.startswith, self.table[s0][s1]))
+                    except:
+                        pass
+            tokens.append(self.token2idx[s])
+            i += len(s)
+
+        return tokens
+
+    def decodeBytes(self, tokens):
+        return b''.join(map(lambda i: self.idx2token[i], tokens))
+
+    def encode(self, src: str):
+        return self.encodeBytes(src.encode("utf-8"))
+
+    def decode(self, tokens):
+        return self.decodeBytes(tokens).decode('utf-8')
+
+    def token_to_id(self, token):
+        return self.token2idx[token]
+
+    def get_vocab_size(self):
+        return self.vocab_size
+
+    def get_vocab(self):
+        return self.idx2token
+
+    def printTokens(self, tokens):
+        for i in tokens:
+            s = self.idx2token[i]
+            try:
+                s = s.decode('utf-8')
+            except:
+                pass
+            print(f'{repr(s)}{i}', end=' ')
+            # print(repr(s), i)
+        print()
+
+########################################################################################################
+# Tokenizer #2 (trie, faster) https://github.com/TkskKurumi/ChatRWKV-TRIE-Tokenizer
+########################################################################################################
+
+class TRIE:
+    __slots__ = tuple("ch,to,values,front".split(","))
+    to:list
+    values:set
+    def __init__(self, front=None, ch=None):
+        self.ch = ch
+        self.to = [None for ch in range(256)]
+        self.values = set()
+        self.front = front
+
+    def __repr__(self):
+        fr = self
+        ret = []
+        while(fr!=None):
+            if(fr.ch!=None):
+                ret.append(fr.ch)
+            fr = fr.front
+        return "<TRIE %s %s>"%(ret[::-1], self.values)
+    
+    def add(self, key:bytes, idx:int=0, val=None):
+        if(idx == len(key)):
+            if(val is None):
+                val = key
+            self.values.add(val)
+            return self
+        ch = key[idx]
+        if(self.to[ch] is None):
+            self.to[ch] = TRIE(front=self, ch=ch)
+        return self.to[ch].add(key, idx=idx+1, val=val)
+    
+    def find_longest(self, key:bytes, idx:int=0):
+        u:TRIE = self
+        ch:int = key[idx]
+        
+        while(u.to[ch] is not None):
+            u = u.to[ch]
+            idx += 1
+            if(u.values):
+                ret = idx, u, u.values
+            if(idx==len(key)):
+                break
+            ch = key[idx]
+        return ret
+
+class TRIE_TOKENIZER():
+    def __init__(self, file_name):
+        self.vocab_size = 65525
+        self.idx2token = {}
+        sorted = [] # must be already sorted
+        with open(file_name, "r", encoding="utf-8") as f:
+            lines = f.readlines()
+        for l in lines:
+            idx = int(l[:l.index(' ')])
+            x = eval(l[l.index(' '):l.rindex(' ')])
+            x = x.encode("utf-8") if isinstance(x, str) else x
+            assert isinstance(x, bytes)
+            assert len(x) == int(l[l.rindex(' '):])
+            sorted += [x]
+            self.idx2token[idx] = x
+
+        self.token2idx = {}
+        for k,v in self.idx2token.items():
+            self.token2idx[v] = int(k)
+
+        self.root = TRIE()
+        for t, i in self.token2idx.items():
+            _ = self.root.add(t, val=(t, i))
+
+    def encodeBytes(self, src:bytes):
+        idx:int = 0
+        tokens = []
+        while (idx < len(src)):
+            _idx:int = idx
+            idx, _, values = self.root.find_longest(src, idx)
+            assert(idx != _idx)
+            _, token = next(iter(values))            
+            tokens.append(token)
+        return tokens
+
+    def decodeBytes(self, tokens):
+        return b''.join(map(lambda i: self.idx2token[i], tokens))
+
+    def encode(self, src):
+        return self.encodeBytes(src.encode("utf-8"))
+
+    def decode(self, tokens):
+        return self.decodeBytes(tokens).decode('utf-8')
+
+    def get_vocab_size(self):
+        return self.vocab_size
+
+    def get_vocab(self):
+        return self.idx2token
+
+    def printTokens(self, tokens):
+        for i in tokens:
+            s = self.idx2token[i]
+            try:
+                s = s.decode('utf-8')
+            except:
+                pass
+            print(f'{repr(s)}{i}', end=' ')
+        print()

finetune/json2binidx_tool/tools/tokenizer.py

@@ -0,0 +1,205 @@
+# Copyright (c) 2021, EleutherAI
+# This file is based on code by the authors denoted below and has been modified from its original version.
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Megatron tokenizers."""
+
+from abc import ABC
+from abc import abstractmethod
+
+from tokenizers import Tokenizer
+from rwkv_tokenizer import RWKV_TOKENIZER, TRIE_TOKENIZER
+
+from typing import List, Union
+
+
+def build_tokenizer(args):
+    """Initialize tokenizer."""
+    if args.rank == 0:
+        print("> building {} tokenizer ...".format(args.tokenizer_type), flush=True)
+
+    # Select and instantiate the tokenizer.
+
+    if args.tokenizer_type.lower() == "HFTokenizer".lower():
+        assert args.vocab_file is not None
+        tokenizer = HFTokenizer(args.vocab_file)
+    elif args.tokenizer_type.lower() == "RWKVTokenizer".lower():
+        assert args.vocab_file is not None
+        tokenizer = RWKVTokenizer(args.vocab_file)
+    else:
+        raise NotImplementedError(
+            "{} tokenizer is not " "implemented.".format(args.tokenizer_type)
+        )
+
+    # Add vocab size.
+    args.padded_vocab_size = _vocab_size_with_padding(tokenizer.vocab_size, args)
+
+    return tokenizer
+
+
+def _vocab_size_with_padding(orig_vocab_size, args):
+    """Pad vocab size so it is divisible by model parallel size and
+    still having GPU friendly size."""
+
+    after = orig_vocab_size
+    multiple = args.make_vocab_size_divisible_by * args.model_parallel_size
+    while (after % multiple) != 0:
+        after += 1
+    if args.rank == 0:
+        print(
+            " > padded vocab (size: {}) with {} dummy tokens "
+            "(new size: {})".format(orig_vocab_size, after - orig_vocab_size, after),
+            flush=True,
+        )
+    return after
+
+
+class AbstractTokenizer(ABC):
+    """Abstract class for tokenizer."""
+
+    def __init__(self, name):
+        self.name = name
+        super().__init__()
+
+    @property
+    @abstractmethod
+    def vocab_size(self):
+        pass
+
+    @property
+    @abstractmethod
+    def vocab(self):
+        """Dictionary from vocab text token to id token."""
+        pass
+
+    @property
+    @abstractmethod
+    def inv_vocab(self):
+        """Dictionary from vocab id token to text token."""
+        pass
+
+    @abstractmethod
+    def tokenize(self, text):
+        pass
+
+    def detokenize(self, token_ids):
+        raise NotImplementedError(
+            "detokenizer is not implemented for {} " "tokenizer".format(self.name)
+        )
+
+    @property
+    def cls(self):
+        raise NotImplementedError(
+            "CLS is not provided for {} " "tokenizer".format(self.name)
+        )
+
+    @property
+    def sep(self):
+        raise NotImplementedError(
+            "SEP is not provided for {} " "tokenizer".format(self.name)
+        )
+
+    @property
+    def pad(self):
+        raise NotImplementedError(
+            "PAD is not provided for {} " "tokenizer".format(self.name)
+        )
+
+    @property
+    def eod(self):
+        raise NotImplementedError(
+            "EOD is not provided for {} " "tokenizer".format(self.name)
+        )
+
+    @property
+    def mask(self):
+        raise NotImplementedError(
+            "MASK is not provided for {} " "tokenizer".format(self.name)
+        )
+
+
+class HFTokenizer(AbstractTokenizer):
+    """Designed to Integrate HF's Tokenizer library."""
+
+    def __init__(self, vocab_file):
+        name = "HFTokenizer"
+        super().__init__(name)
+
+        self.tokenizer = Tokenizer.from_file(vocab_file)
+        self.eod_id = self.tokenizer.token_to_id("<|endoftext|>")
+        self.pad_id = self.tokenizer.token_to_id("<|padding|>")
+
+    @property
+    def vocab_size(self):
+        return self.tokenizer.get_vocab_size()
+
+    @property
+    def vocab(self):
+        return self.tokenizer.get_vocab()
+
+    @property
+    def inv_vocab(self):
+        return self.tokenizer.decoder
+
+    def tokenize(self, text: str):
+        return self.tokenizer.encode(text).ids
+
+    def tokenize_batch(self, text_batch: Union[List[str], str]):
+        return self.tokenizer.encode_batch(text_batch)
+
+    def detokenize(self, token_ids):
+        return self.tokenizer.decode(token_ids)
+
+    @property
+    def eod(self):
+        return self.eod_id
+
+
+class RWKVTokenizer(AbstractTokenizer):
+    """RWKV Worlds Tokenizer."""
+
+    def __init__(self, vocab_file='rwkv_vocab_v20230424.txt'):
+        name = "RWKVTokenizer"
+        super().__init__(name)
+
+        self.tokenizer = TRIE_TOKENIZER(vocab_file)
+        self.eod_id = 0  # self.tokenizer.token_to_id("<|endoftext|>")
+        # self.pad_id = self.tokenizer.token_to_id("<|padding|>")
+
+    @property
+    def vocab_size(self):
+        return self.tokenizer.get_vocab_size()
+
+    @property
+    def vocab(self):
+        return self.tokenizer.get_vocab()
+
+    @property
+    def inv_vocab(self):
+        return self.tokenizer.decode
+
+    def tokenize(self, text: str):
+        return self.tokenizer.encode(text)
+
+    def tokenize_batch(self, text_batch: Union[List[str], str]):
+        return self.tokenizer.encode_batch(text_batch)
+
+    def detokenize(self, token_ids):
+        return self.tokenizer.decode(token_ids)
+
+    @property
+    def eod(self):
+        return self.eod_id

finetune/lora/cuda/wkv_cuda.cu

@@ -0,0 +1,133 @@
+#include <stdio.h>
+#include <assert.h>
+
+#define MIN_VALUE (-1e38)
+
+template <typename F>
+__global__ void kernel_forward(const int B, const int T, const int C,
+                               const F *__restrict__ const _w, const F *__restrict__ const _u, const F *__restrict__ const _k, const F *__restrict__ const _v,
+                               F *__restrict__ const _y) {
+    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const int _b = idx / C;
+    const int _c = idx % C;
+    const int _offset = _b * T * C + _c;
+
+    F u = _u[_c];
+    F w = _w[_c];
+    const F *__restrict__ const k = _k + _offset;
+    const F *__restrict__ const v = _v + _offset;
+    F *__restrict__ const y = _y + _offset;
+
+    // aa and bb are running sums divided by exp(pp) (to avoid overflow)
+    F aa = 0, bb = 0, pp = MIN_VALUE;
+    for (int i = 0; i < T; i++) {
+        const int ii = i * C;
+        const F kk = k[ii];
+        const F vv = v[ii];
+
+        F ww = u + kk;
+        F p = max(pp, ww);
+        F e1 = exp(pp - p);
+        F e2 = exp(ww - p);
+        y[ii] = (e1 * aa + e2 * vv) / (e1 * bb + e2);
+        
+        ww = w + pp;
+        p = max(ww, kk);
+        e1 = exp(ww - p);
+        e2 = exp(kk - p);
+        aa = e1 * aa + e2 * vv;
+        bb = e1 * bb + e2;
+        pp = p;
+    }
+}
+
+template <typename F>
+__global__ void kernel_backward(const int B, const int T, const int C,
+                                const F *__restrict__ const _w, const F *__restrict__ const _u, const F *__restrict__ const _k, const F *__restrict__ const _v,
+                                const F *__restrict__ const _y, const F *__restrict__ const _gy,
+                                F *__restrict__ const _gw, F *__restrict__ const _gu, F *__restrict__ const _gk, F *__restrict__ const _gv) {
+    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const int _b = idx / C;
+    const int _c = idx % C;
+    const int _offset = _b * T * C + _c;
+
+    F u = _u[_c];
+    F w = _w[_c];
+    const F *__restrict__ const k = _k + _offset;
+    const F *__restrict__ const v = _v + _offset;
+    const F *__restrict__ const y = _y + _offset;
+    const F *__restrict__ const gy = _gy + _offset;
+    F *__restrict__ const gk = _gk + _offset;
+    F *__restrict__ const gv = _gv + _offset;
+
+    F q[Tmax], r[Tmax];
+
+    F gw = 0, gu = 0, aa = 0, bb = 0, ga = 0, gb = 0, pp = MIN_VALUE;
+    for (int i = 0; i < T; i++) {
+        const int ii = i * C;
+        const F kk = k[ii];
+        const F vv = v[ii];
+        const F yy = y[ii];
+
+        F ww = u + kk;
+        F p = max(pp, ww);
+        F e1 = exp(pp - p);
+        F e2 = exp(ww - p);
+        const F qq = gy[ii] / (e1 * bb + e2);
+        gw += (ga - gb * yy) * e1 * qq;
+        gu += (vv - yy) * e2 * qq;
+        q[i] = qq;
+        r[i] = ww - p;
+
+        ww = w + pp;
+        p = max(ww, kk);
+        e1 = exp(ww - p);
+        e2 = exp(kk - p);
+        ga = e1 * (aa + ga);
+        gb = e1 * (bb + gb);
+        aa = e1 * aa + e2 * vv;
+        bb = e1 * bb + e2;
+        pp = p;
+    }
+    const int _offsetBC = _b * C + _c;
+    _gw[_offsetBC] = gw * _w[_c]; // multiply by w because of w -> -exp(w) in python forward()
+    _gu[_offsetBC] = gu;
+
+    aa = 0, bb = 0, pp = MIN_VALUE;
+    for (int i = T - 1; i >= 0; i--) {
+        const int ii = i * C;
+        const F kk = k[ii];
+        const F vv = v[ii];
+        const F yy = y[ii];
+        const F qq = q[i];
+        const F rr = r[i];
+
+        F e1 = qq * exp(rr);
+        F e2 = exp(kk + pp);
+        gk[ii] = e1 * (vv - yy) + e2 * (aa * vv + bb);
+        gv[ii] = e1 + e2 * aa;
+
+        const F ww = w + pp;
+        const F www = rr - u - kk;
+        const F p = max(ww, www);
+        e1 = exp(ww - p);
+        e2 = qq * exp(www - p);
+        aa = e1 * aa + e2;
+        bb = e1 * bb - e2 * yy;
+        pp = p;
+    }
+}
+
+void cuda_forward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y) {
+    dim3 threadsPerBlock( min(C, 32) ); // requires --maxrregcount 60 for optimal performance
+    assert(B * C % threadsPerBlock.x == 0);
+    dim3 numBlocks(B * C / threadsPerBlock.x);
+    kernel_forward<<<numBlocks, threadsPerBlock>>>(B, T, C, w, u, k, v, y);
+}
+
+void cuda_backward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y, float *gy, float *gw, float *gu, float *gk, float *gv) {
+    dim3 threadsPerBlock( min(C, 32) ); // requires --maxrregcount 60 for optimal performance
+    assert(B * C % threadsPerBlock.x == 0);
+    dim3 numBlocks(B * C / threadsPerBlock.x);
+    kernel_backward<<<numBlocks, threadsPerBlock>>>(B, T, C, w, u, k, v, y, gy, gw, gu, gk, gv);
+}

finetune/lora/cuda/wkv_cuda_bf16.cu

@@ -0,0 +1,132 @@
+#include <stdio.h>
+#include <assert.h>
+#include "ATen/ATen.h"
+#define MIN_VALUE (-1e38)
+typedef at::BFloat16 bf16;
+
+__global__ void kernel_forward(const int B, const int T, const int C,
+                               const float *__restrict__ const _w, const bf16 *__restrict__ const _u, const bf16 *__restrict__ const _k, const bf16 *__restrict__ const _v,
+                               bf16 *__restrict__ const _y) {
+    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const int _b = idx / C;
+    const int _c = idx % C;
+    const int _offset = _b * T * C + _c;
+
+    float u = float(_u[_c]);
+    float w = _w[_c];
+    const bf16 *__restrict__ const k = _k + _offset;
+    const bf16 *__restrict__ const v = _v + _offset;
+    bf16 *__restrict__ const y = _y + _offset;
+
+    // aa and bb are running sums divided by exp(pp) (to avoid overflow)
+    float aa = 0, bb = 0, pp = MIN_VALUE;
+    for (int i = 0; i < T; i++) {
+        const int ii = i * C;
+        const float kk = float(k[ii]);
+        const float vv = float(v[ii]);
+
+        float ww = u + kk;
+        float p = max(pp, ww);
+        float e1 = exp(pp - p);
+        float e2 = exp(ww - p);
+        y[ii] = bf16((e1 * aa + e2 * vv) / (e1 * bb + e2));
+        
+        ww = w + pp;
+        p = max(ww, kk);
+        e1 = exp(ww - p);
+        e2 = exp(kk - p);
+        aa = e1 * aa + e2 * vv;
+        bb = e1 * bb + e2;
+        pp = p;
+    }
+}
+
+__global__ void kernel_backward(const int B, const int T, const int C,
+                                const float *__restrict__ const _w, const bf16 *__restrict__ const _u, const bf16 *__restrict__ const _k, const bf16 *__restrict__ const _v,
+                                const bf16 *__restrict__ const _y, const bf16 *__restrict__ const _gy,
+                                bf16 *__restrict__ const _gw, bf16 *__restrict__ const _gu, bf16 *__restrict__ const _gk, bf16 *__restrict__ const _gv) {
+    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const int _b = idx / C;
+    const int _c = idx % C;
+    const int _offset = _b * T * C + _c;
+
+    float u = float(_u[_c]);
+    float w = _w[_c];
+    const bf16 *__restrict__ const k = _k + _offset;
+    const bf16 *__restrict__ const v = _v + _offset;
+    const bf16 *__restrict__ const y = _y + _offset;
+    const bf16 *__restrict__ const gy = _gy + _offset;
+    bf16 *__restrict__ const gk = _gk + _offset;
+    bf16 *__restrict__ const gv = _gv + _offset;
+
+    float q[Tmax], r[Tmax];
+
+    float gw = 0, gu = 0, aa = 0, bb = 0, ga = 0, gb = 0, pp = MIN_VALUE;
+    for (int i = 0; i < T; i++) {
+        const int ii = i * C;
+        const float kk = float(k[ii]);
+        const float vv = float(v[ii]);
+        const float yy = float(y[ii]);
+
+        float ww = u + kk;
+        float p = max(pp, ww);
+        float e1 = exp(pp - p);
+        float e2 = exp(ww - p);
+        const float qq = float(gy[ii]) / (e1 * bb + e2);
+        gw += (ga - gb * yy) * e1 * qq;
+        gu += (vv - yy) * e2 * qq;
+        q[i] = qq;
+        r[i] = ww - p;
+
+        ww = w + pp;
+        p = max(ww, kk);
+        e1 = exp(ww - p);
+        e2 = exp(kk - p);
+        ga = e1 * (aa + ga);
+        gb = e1 * (bb + gb);
+        aa = e1 * aa + e2 * vv;
+        bb = e1 * bb + e2;
+        pp = p;
+    }
+    const int _offsetBC = _b * C + _c;
+    _gw[_offsetBC] = bf16(gw * _w[_c]); // multiply by w because of w -> -exp(w) in python forward()
+    _gu[_offsetBC] = bf16(gu);
+
+    aa = 0, bb = 0, pp = MIN_VALUE;
+    for (int i = T - 1; i >= 0; i--) {
+        const int ii = i * C;
+        const float kk = float(k[ii]);
+        const float vv = float(v[ii]);
+        const float yy = float(y[ii]);
+        const float qq = q[i];
+        const float rr = r[i];
+
+        float e1 = qq * exp(rr);
+        float e2 = exp(kk + pp);
+        gk[ii] = bf16(e1 * (vv - yy) + e2 * (aa * vv + bb));
+        gv[ii] = bf16(e1 + e2 * aa);
+
+        const float ww = w + pp;
+        const float www = rr - u - kk;
+        const float p = max(ww, www);
+        e1 = exp(ww - p);
+        e2 = qq * exp(www - p);
+        aa = e1 * aa + e2;
+        bb = e1 * bb - e2 * yy;
+        pp = p;
+    }
+}
+
+void cuda_forward(int B, int T, int C, float *w, bf16 *u, bf16 *k, bf16 *v, bf16 *y) {
+    dim3 threadsPerBlock( min(C, 32) ); // requires --maxrregcount 60 for optimal performance
+    assert(B * C % threadsPerBlock.x == 0);
+    dim3 numBlocks(B * C / threadsPerBlock.x);
+    kernel_forward<<<numBlocks, threadsPerBlock>>>(B, T, C, w, u, k, v, y);
+}
+
+void cuda_backward(int B, int T, int C, float *w, bf16 *u, bf16 *k, bf16 *v, bf16 *y, bf16 *gy, bf16 *gw, bf16 *gu, bf16 *gk, bf16 *gv) {
+    dim3 threadsPerBlock( min(C, 32) ); // requires --maxrregcount 60 for optimal performance
+    assert(B * C % threadsPerBlock.x == 0);
+    dim3 numBlocks(B * C / threadsPerBlock.x);
+    kernel_backward<<<numBlocks, threadsPerBlock>>>(B, T, C, w, u, k, v, y, gy, gw, gu, gk, gv);
+}

finetune/lora/cuda/wkv_op.cpp

@@ -0,0 +1,21 @@
+#include <torch/extension.h>
+
+void cuda_forward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y);
+void cuda_backward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y, float *gy, float *gw, float *gu, float *gk, float *gv);
+
+void forward(int64_t B, int64_t T, int64_t C, torch::Tensor &w, torch::Tensor &u, torch::Tensor &k, torch::Tensor &v, torch::Tensor &y) {
+    cuda_forward(B, T, C, w.data_ptr<float>(), u.data_ptr<float>(), k.data_ptr<float>(), v.data_ptr<float>(), y.data_ptr<float>());
+}
+void backward(int64_t B, int64_t T, int64_t C, torch::Tensor &w, torch::Tensor &u, torch::Tensor &k, torch::Tensor &v, torch::Tensor &y, torch::Tensor &gy, torch::Tensor &gw, torch::Tensor &gu, torch::Tensor &gk, torch::Tensor &gv) {
+    cuda_backward(B, T, C, w.data_ptr<float>(), u.data_ptr<float>(), k.data_ptr<float>(), v.data_ptr<float>(), y.data_ptr<float>(), gy.data_ptr<float>(), gw.data_ptr<float>(), gu.data_ptr<float>(), gk.data_ptr<float>(), gv.data_ptr<float>());
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("forward", &forward, "wkv forward");
+    m.def("backward", &backward, "wkv backward");
+}
+
+TORCH_LIBRARY(wkv, m) {
+    m.def("forward", forward);
+    m.def("backward", backward);
+}

finetune/lora/cuda/wkv_op_bf16.cpp

@@ -0,0 +1,25 @@
+#include <torch/extension.h>
+#include "ATen/ATen.h"
+typedef at::BFloat16 bf16;
+
+void cuda_forward(int B, int T, int C, float *w, bf16 *u, bf16 *k, bf16 *v, bf16 *y);
+void cuda_backward(int B, int T, int C, float *w, bf16 *u, bf16 *k, bf16 *v, bf16 *y, bf16 *gy, bf16 *gw, bf16 *gu, bf16 *gk, bf16 *gv);
+
+void forward(int64_t B, int64_t T, int64_t C, torch::Tensor &w, torch::Tensor &u, torch::Tensor &k, torch::Tensor &v, torch::Tensor &y) {
+    cuda_forward(B, T, C, w.data_ptr<float>(), u.data_ptr<bf16>(), k.data_ptr<bf16>(), v.data_ptr<bf16>(), y.data_ptr<bf16>());
+}
+void backward(int64_t B, int64_t T, int64_t C, torch::Tensor &w, torch::Tensor &u, torch::Tensor &k, torch::Tensor &v, torch::Tensor &y,
+    torch::Tensor &gy, torch::Tensor &gw, torch::Tensor &gu, torch::Tensor &gk, torch::Tensor &gv) {
+    cuda_backward(B, T, C, w.data_ptr<float>(), u.data_ptr<bf16>(), k.data_ptr<bf16>(), v.data_ptr<bf16>(), y.data_ptr<bf16>(),
+        gy.data_ptr<bf16>(), gw.data_ptr<bf16>(), gu.data_ptr<bf16>(), gk.data_ptr<bf16>(), gv.data_ptr<bf16>());
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("forward", &forward, "wkv forward");
+    m.def("backward", &backward, "wkv backward");
+}
+
+TORCH_LIBRARY(wkv, m) {
+    m.def("forward", forward);
+    m.def("backward", backward);
+}

finetune/lora/merge_lora.py

@@ -0,0 +1,68 @@
+from collections import OrderedDict
+import os
+import sys
+from typing import Dict
+import typing
+import torch
+
+try:
+    if "-h" in sys.argv or "--help" in sys.argv:
+        print(
+            f"Usage: python3 {sys.argv[0]} [--use-gpu] <lora_alpha> <base_model.pth> <lora_checkpoint.pth> <output.pth>"
+        )
+
+    if sys.argv[1] == "--use-gpu":
+        device = "cuda"
+        lora_alpha, base_model, lora, output = (
+            float(sys.argv[2]),
+            sys.argv[3],
+            sys.argv[4],
+            sys.argv[5],
+        )
+    else:
+        device = "cpu"
+        lora_alpha, base_model, lora, output = (
+            float(sys.argv[1]),
+            sys.argv[2],
+            sys.argv[3],
+            sys.argv[4],
+        )
+
+    with torch.no_grad():
+        w: Dict[str, torch.Tensor] = torch.load(base_model, map_location="cpu")
+        # merge LoRA-only slim checkpoint into the main weights
+        w_lora: Dict[str, torch.Tensor] = torch.load(lora, map_location="cpu")
+        for k in w_lora.keys():
+            w[k] = w_lora[k]
+        output_w: typing.OrderedDict[str, torch.Tensor] = OrderedDict()
+        # merge LoRA weights
+        keys = list(w.keys())
+        for k in keys:
+            if k.endswith(".weight"):
+                prefix = k[: -len(".weight")]
+                lora_A = prefix + ".lora_A"
+                lora_B = prefix + ".lora_B"
+                if lora_A in keys:
+                    assert lora_B in keys
+                    print(f"merging {lora_A} and {lora_B} into {k}")
+                    assert w[lora_B].shape[1] == w[lora_A].shape[0]
+                    lora_r = w[lora_B].shape[1]
+                    w[k] = w[k].to(device=device)
+                    w[lora_A] = w[lora_A].to(device=device)
+                    w[lora_B] = w[lora_B].to(device=device)
+                    w[k] += w[lora_B] @ w[lora_A] * (lora_alpha / lora_r)
+                    output_w[k] = w[k].to(device="cpu", copy=True)
+                    del w[k]
+                    del w[lora_A]
+                    del w[lora_B]
+                    continue
+
+            if "lora" not in k:
+                print(f"retaining {k}")
+                output_w[k] = w[k].clone()
+                del w[k]
+
+        torch.save(output_w, output)
+except Exception as e:
+    with open("error.txt", "w") as f:
+        f.write(str(e))

finetune/lora/src/binidx.py

@@ -0,0 +1,269 @@
+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
+
+    @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/src/dataset.py

@@ -0,0 +1,224 @@
+########################################################################################################
+# 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.data_file.endswith('/'):
+                d_all = []
+                for p in os.listdir(args.data_file):
+                    if p.endswith(".idx"):
+                        d_all += [p[:-4]]
+                d_all.sort()
+                rank_zero_info(d_all)
+                exit(0)
+            else:
+                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.")
+
+            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
+
+            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("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("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":
+            self.vocab_size = -1
+            self.data_size = -1
+            self.data = None
+            self.error_count = 0
+        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-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.")
+            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 == "wds_img":
+            def init_wds(self, bias=0):
+                def identity(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)
+                for pp in self.data_raw.pipeline:
+                    if 'Resampled' in str(pp):
+                        pp.deterministic = True
+                        def worker_seed():
+                            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
+                    break
+                except:
+                    print(f'[dataloader error - epoch {epoch} rank {rank} - trying a new shuffle]')
+                    self.error_count += 1
+                    init_wds(self, self.error_count)
+                    trial += 1
+                    pass
+            # print(f"epoch {epoch} idx {idx} rank {rank}/{world_size} {dd[2]}")
+            # with open(f"sample_{rank}.txt", "a", encoding="utf-8") as tmp:
+            #     tmp.write(f"epoch {epoch} idx {idx} rank {rank}/{world_size} {int(dd[1]['key'])}\n")
+            return dd[0], dd[2]
+        else:
+            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 and args.my_pile_stage != 4:
+                    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 = (ii // 2) * args.magic_prime
+                            if args.ctx_len == 1024:
+                                magic_prime = 324331313
+                            elif args.ctx_len == 2048:
+                                magic_prime = 162165671
+                            elif args.ctx_len == 4096:
+                                magic_prime = 81082817
+                            data = self.data_pile
+                        else:
+                            ii = ii // 2
+
+                    factor = (math.sqrt(5) - 1) / 2
+                    factor = int(magic_prime * factor)
+                    i = ((factor * ii * ii * ii) % magic_prime) * ctx_len
+                    if (args.my_qa_mask == 0) or (data == self.data_pile):
+                        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":
+                    dix = data.get(idx=0, offset=i, length=req_len).astype(int)
+                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/src/model.py

@@ -0,0 +1,678 @@
+########################################################################################################
+# 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 = {
+    "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
+########################################################################################################
+
+T_MAX = int(os.environ["RWKV_T_MAX"])  # TAKES LOTS OF VRAM!
+# it's possible to go beyond CUDA limitations if you slice the ctx and pass the hidden state in each slice
+
+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}"])
+    class WKV(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, B, T, C, w, u, k, v):
+            ctx.B = B
+            ctx.T = T
+            ctx.C = C
+            assert T <= T_MAX
+            assert B * C % min(C, 32) == 0
+            w = -torch.exp(w.float().contiguous())
+            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)
+            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
+            T = ctx.T
+            C = ctx.C
+            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)
+            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}"])
+    class WKV(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, B, T, C, w, u, k, v):
+            ctx.B = B
+            ctx.T = T
+            ctx.C = C
+            assert T <= T_MAX
+            assert B * C % min(C, 32) == 0
+            if "32" in os.environ["RWKV_FLOAT_MODE"]:
+                w = -torch.exp(w.contiguous())
+                u = u.contiguous()
+                k = k.contiguous()
+                v = v.contiguous()
+            else:
+                w = -torch.exp(w.float().contiguous())
+                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)
+            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"]:
+                return y
+            elif os.environ["RWKV_FLOAT_MODE"] == "fp16":
+                return y.half()
+            elif os.environ["RWKV_FLOAT_MODE"] == "bf16":
+                return y.bfloat16()
+        @staticmethod
+        def backward(ctx, gy):
+            B = ctx.B
+            T = ctx.T
+            C = ctx.C
+            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)
+            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)
+            else:
+                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"]:
+                return (None, None, None, gw, gu, gk, gv)
+            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())
+
+
+def RUN_CUDA(B, T, C, w, u, k, v):
+    return WKV.apply(B, T, C, w, u, k, v)
+
+
+########################################################################################################
+# LoRA
+########################################################################################################
+
+
+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)
+
+
+########################################################################################################
+# RWKV: RWKV Time-mix + RWKV Channel-mix
+########################################################################################################
+
+
+class RWKV_TimeMix(MyModule):
+    def __init__(self, args, layer_id):
+        super().__init__()
+        self.args = args
+        self.layer_id = layer_id
+        self.ctx_len = args.ctx_len
+        self.n_embd = args.n_embd
+
+        with torch.no_grad():  # fancy init
+            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_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)
+            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)
+
+            # 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_shift = nn.ZeroPad2d((0, 0, 1, -1))
+
+        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.receptance = make_linear_att(args.n_embd, args.dim_att, bias=False)
+
+        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)))
+            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)
+            self.vv = nn.Linear(args.n_embd, d_qkv, bias=False)
+            self.oo = nn.Linear(d_qkv, args.n_embd, bias=False)
+            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)
+
+    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
+            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)
+            k = self.key(xk)
+            v = self.value(xv)
+            r = self.receptance(xr)
+            sr = torch.sigmoid(r)
+            return sr, k, v
+
+        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)
+            return self.output(rwkv)
+
+    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)
+            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
+            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)
+            xqq = x * self.time_mix_qq + xx * (1 - self.time_mix_qq)
+            xkk = x * self.time_mix_kk + xx * (1 - self.time_mix_kk)
+            xvv = x * self.time_mix_vv + xx * (1 - self.time_mix_vv)
+            k = self.key(xk)
+            v = self.value(xv)
+            r = self.receptance(xr)
+            sr = torch.sigmoid(r)
+            qq = self.qq(xqq)
+            kk = self.kk(xkk)
+            vv = self.vv(xvv)
+            return sr, k, v, qq, kk, vv
+
+        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 = self.output(rwkv) + self.oo(self.QKV(qq, kk, vv))
+            return rwkv
+
+########################################################################################################
+
+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.square(torch.relu(k))
+        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(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)))
+
+    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.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))
+
+        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
+
+        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)))
+
+    def configure_optimizers(self):
+        args = self.args
+        if args.layerwise_lr > 0:
+            lr_1x = set()
+            lr_2x = set()
+            lr_3x = set()
+            for n, p in self.named_parameters():
+                if "time_mix" in n:
+                    if args.my_pile_stage == 2:
+                        lr_2x.add(n)
+                    else:
+                        lr_1x.add(n)
+                elif "time_decay" in n:
+                    if args.my_pile_stage == 2:
+                        lr_3x.add(n)
+                    else:
+                        lr_2x.add(n)
+                elif "time_first" in n:
+                    lr_3x.add(n)
+                else:
+                    lr_1x.add(n)
+            lr_1x = sorted(list(lr_1x))
+            lr_2x = sorted(list(lr_2x))
+            lr_3x = sorted(list(lr_3x))
+            # 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.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": [p for n, p in self.named_parameters()], "weight_decay": 0.0},
+            ]
+
+        for g in optim_groups:
+            g["params"] = [p for p in g["params"] if p.requires_grad]
+        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 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.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))
+        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):
+        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:
+                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])
+                    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":
+                        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/src/trainer.py

@@ -0,0 +1,203 @@
+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(dd, ff):
+    if '14b-run1' not in ff:
+        torch.save(dd, ff)
+    else:
+        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__()
+        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.global_step < w_step:
+                lr = lr * (0.2 + 0.8 * trainer.global_step / w_step)
+            # if trainer.is_global_zero:
+            #     print(trainer.global_step, decay_step, decay_total, w_step, progress, lr)
+
+        for param_group in trainer.optimizers[0].param_groups:
+            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
+        # 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
+        if trainer.is_global_zero:  # logging
+            t_now = time.time_ns()
+            token_per_step = args.ctx_len * args.real_bsz
+            real_step = trainer.global_step + args.epoch_begin * args.epoch_steps
+            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
+            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, "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:
+                    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
+        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
+        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':
+                    raw_dict = pl_module.state_dict()
+                    to_save_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.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 ('.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(
+                        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')
+            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
+
+
+@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:
+                assert k in mm
+                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/src/utils.py

@@ -0,0 +1,130 @@
+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/train.py

@@ -0,0 +1,479 @@
+########################################################################################################
+# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
+########################################################################################################
+
+if __name__ == "__main__":
+    from argparse import ArgumentParser
+    from pytorch_lightning import Trainer
+    from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
+
+    rank_zero_info("########## work in progress ##########")
+
+    ########################################################################################################
+    #
+    # example: train a simple L12-D768 RWKV on dummy data
+    #
+    # python train.py --load_model "" --wandb "" --proj_dir "out" \
+    # --data_file "" --data_type "dummy" --vocab_size 0 \
+    # --ctx_len 128 --epoch_steps 1000 --epoch_count 20 --epoch_begin 0 --epoch_save 10 \
+    # --micro_bsz 16 --n_layer 12 --n_embd 768 --pre_ffn 0 --head_qk 0 \
+    # --lr_init 6e-4 --lr_final 1e-5 --warmup_steps 0 --beta1 0.9 --beta2 0.99 --adam_eps 1e-8 \
+    # --accelerator gpu --devices 1 --precision bf16 --strategy ddp_find_unused_parameters_false --grad_cp 0
+
+    # example: train a simple L6-D512 RWKV from scratch on enwik8
+    #
+    # python train.py --load_model "" --wandb "" --proj_dir "out" \
+    # --data_file "../data/enwik8" --data_type "utf-8" --vocab_size 0 \
+    # --ctx_len 512 --epoch_steps 5000 --epoch_count 500 --epoch_begin 0 --epoch_save 5 \
+    # --micro_bsz 12 --n_layer 6 --n_embd 512 --pre_ffn 0 --head_qk 0 \
+    # --lr_init 8e-4 --lr_final 1e-5 --warmup_steps 0 --beta1 0.9 --beta2 0.99 --adam_eps 1e-8 \
+    # --accelerator gpu --devices 1 --precision bf16 --strategy ddp_find_unused_parameters_false --grad_cp 0
+
+    # example: fine-tune RWKV 1.5B using 8xA100 40G = 1.76it/s = 115k token/s, VRAM 37477M
+    #
+    # python train.py --load_model "/fsx/BlinkDL/CODE/FP16/out_1b2/all-8040.pth" --wandb "" --proj_dir "out" \
+    # --data_file "../data/train.npy" --data_type "numpy" --vocab_size 50277 \
+    # --ctx_len 1024 --epoch_steps 1000 --epoch_count 1000 --epoch_begin 0 --epoch_save 5 \
+    # --micro_bsz 8 --n_layer 24 --n_embd 2048 --pre_ffn 0 --head_qk 0 \
+    # --lr_init 1e-5 --lr_final 1e-5 --warmup_steps 0 --beta1 0.9 --beta2 0.999 --adam_eps 1e-8 \
+    # --accelerator gpu --devices 8 --precision bf16 --strategy deepspeed_stage_2 --grad_cp 0
+
+    # example: fine-tune RWKV 1.5B using 1 GPU fp16 (VRAM 16G) NOTE: fp16 might overflow
+    #
+    # python train.py --load_model "/fsx/BlinkDL/CODE/FP16/out_1b2/all-8040.pth" --wandb "" --proj_dir "out" \
+    # --data_file "../data/train.npy" --data_type "numpy" --vocab_size 50277 \
+    # --ctx_len 1024 --epoch_steps 200 --epoch_count 1000 --epoch_begin 0 --epoch_save 1 \
+    # --micro_bsz 11 --n_layer 24 --n_embd 2048 --pre_ffn 0 --head_qk 0 \
+    # --lr_init 1e-5 --lr_final 1e-5 --warmup_steps 0 --beta1 0.9 --beta2 0.999 --adam_eps 1e-8 \
+    # --accelerator gpu --devices 1 --precision fp16 --strategy deepspeed_stage_2_offload --grad_cp 1
+
+    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=0, 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("--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_img_version", default=0, type=str)
+    parser.add_argument("--my_img_size", default=0, type=int)
+    parser.add_argument("--my_img_bit", default=0, type=int)
+    parser.add_argument("--my_img_clip", default="x", type=str)
+    parser.add_argument("--my_img_clip_scale", default=1, type=float)
+    parser.add_argument("--my_img_l1_scale", default=0, type=float)
+    parser.add_argument("--my_img_encoder", default="x", type=str)
+    # parser.add_argument("--my_img_noise_scale", default=0, type=float)
+    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("--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_testing", default="", type=str)
+
+    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)
+
+    parser = Trainer.add_argparse_args(parser)
+    args = parser.parse_args()
+
+    ########################################################################################################
+
+    import os, warnings, math, datetime, sys, time, importlib
+    import numpy as np
+    import torch
+    from torch.utils.data import DataLoader
+
+    if "deepspeed" in args.strategy:
+        import deepspeed
+    import pytorch_lightning as pl
+    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 = -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)
+    os.environ["RWKV_MY_TESTING"] = args.my_testing
+    if args.dim_att <= 0:
+        args.dim_att = args.n_embd
+    if args.dim_ffn <= 0:
+        args.dim_ffn = args.n_embd * 4
+
+    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.ctx_len == 1024:
+            args.magic_prime = 324331313
+            args.epoch_count = 8043
+        elif args.ctx_len == 2048:
+            args.magic_prime = 162165671
+            args.epoch_count = 4021
+        elif args.ctx_len == 4096:
+            args.magic_prime = 81082817
+            args.epoch_count = 2010
+        if args.my_pile_shift < 0:
+            if args.ctx_len == 1024:
+                args.my_pile_shift = 0
+            elif args.ctx_len == 2048:
+                args.my_pile_shift = 512
+            elif args.ctx_len == 4096:
+                args.my_pile_shift = 768
+
+        if magic_prime_bak > 0:
+            args.magic_prime = magic_prime_bak
+
+        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 == "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.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
+    rank_zero_info(
+        f"""
+############################################################################
+#
+# RWKV-4 {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} (will continue afterwards), 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
+# LoRA = {f'enabled, {args.lora_r} r, {args.lora_alpha} alpha, {args.lora_dropout} dropout, on {args.lora_parts}' if args.lora else 'disabled'}
+#
+# 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__ if importlib.util.find_spec('deepspeed') else 'None'}, recommend 0.7.0 (faster than newer versions)
+# Found pytorch_lightning {pl.__version__}, recommend 1.9.1 or newer
+#
+############################################################################
+"""
+    )
+    rank_zero_info(str(vars(args)) + "\n")
+
+    assert args.data_type in [
+        "utf-8",
+        "utf-16le",
+        "numpy",
+        "binidx",
+        "dummy",
+        "wds_img",
+        "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"] = "1"
+    if "deepspeed_stage_3" in args.strategy:
+        os.environ["RWKV_JIT_ON"] = "0"
+    if args.lora and args.grad_cp == 1:
+        print(
+            "!!!!! LoRA Warning: Gradient Checkpointing requires JIT off, disabling it"
+        )
+        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
+
+    if args.data_type == "wds_img":
+        from src.model_img import RWKV_IMG
+
+        assert args.lora, "LoRA not yet supported for RWKV_IMG"
+        model = RWKV_IMG(args)
+    else:
+        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():
+                # have to check param name since it may have been wrapped by torchscript
+                if any(n.startswith("lora_") for n, _ in module.named_parameters()):
+                    print(f"  LoRA 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")
+        model.load_state_dict(load_dict, strict=(not args.lora))
+    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")
+            model.load_state_dict(load_dict, strict=(not args.lora))
+
+    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 using LoRA, the LoRA keys might be missing in the original model
+    # 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
+        )
+
+    trainer: Trainer = Trainer.from_argparse_args(
+        args,
+        callbacks=[train_callback(args)],
+    )
+
+    if (
+        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:
+                print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+                print(
+                    f"  WARNING: you are using too large LR ({args.lr_init} > 1e-4) or too small global batch size ({trainer.world_size} * {args.micro_bsz} * {trainer.accumulate_grad_batches} < 8)"
+                )
+                print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+        else:
+            if trainer.global_rank == 0:
+                print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+                print(
+                    f"  ERROR: you are using too large LR ({args.lr_init} > 1e-4) or too small global batch size ({trainer.world_size} * {args.micro_bsz} * {trainer.accumulate_grad_batches} < 8)"
+                )
+                print(
+                    f"  Unless you are sure this is what you want, adjust them accordingly"
+                )
+                print(
+                    f'  (to suppress this, set environment variable "I_KNOW_WHAT_IM_DOING")'
+                )
+                print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+            exit(0)
+
+    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

@@ -0,0 +1,3 @@
+torch==1.13.1
+pytorch_lightning==1.9.5
+deepspeed

frontend/package.json

@@ -11,14 +11,19 @@
   "dependencies": {
     "@fluentui/react-components": "^9.20.0",
     "@fluentui/react-icons": "^2.0.201",
+    "@magenta/music": "^1.23.1",
     "@microsoft/fetch-event-source": "^2.0.1",
     "@primer/octicons-react": "^19.1.0",
+    "chart.js": "^4.3.0",
     "classnames": "^2.3.2",
     "github-markdown-css": "^5.2.0",
+    "html-midi-player": "^1.5.0",
     "i18next": "^22.4.15",
     "mobx": "^6.9.0",
     "mobx-react-lite": "^3.4.3",
     "react": "^18.2.0",
+    "react-beautiful-dnd": "^13.1.1",
+    "react-chartjs-2": "^5.2.0",
     "react-dom": "^18.2.0",
     "react-i18next": "^12.2.2",
     "react-markdown": "^8.0.7",
@@ -34,6 +39,7 @@
   },
   "devDependencies": {
     "@types/react": "^18.2.6",
+    "@types/react-beautiful-dnd": "^13.1.4",
     "@types/react-dom": "^18.2.4",
     "@types/uuid": "^9.0.1",
     "@vitejs/plugin-react": "^4.0.0",

frontend/src/App.tsx

@@ -28,10 +28,10 @@ import { FC, useEffect, useState } from 'react';
 import { Route, Routes, useLocation, useNavigate } from 'react-router';
 import { pages } from './pages';
 import { useMediaQuery } from 'usehooks-ts';
-import { ToastContainer } from 'react-toastify';
 import commonStore from './stores/commonStore';
 import { observer } from 'mobx-react-lite';
 import { useTranslation } from 'react-i18next';
+import { CustomToastContainer } from './components/CustomToastContainer';
 
 const App: FC = observer(() => {
   const { t } = useTranslation();
@@ -87,21 +87,7 @@ const App: FC = observer(() => {
           </Routes>
         </div>
       </div>
-      <ToastContainer
-        style={{
-          width: '350px'
-        }}
-        position="top-center"
-        autoClose={4000}
-        pauseOnHover={true}
-        hideProgressBar={true}
-        newestOnTop={true}
-        closeOnClick={false}
-        rtl={false}
-        pauseOnFocusLoss={false}
-        draggable={false}
-        theme={commonStore.settings.darkMode ? 'dark' : 'light'}
-      />
+      <CustomToastContainer />
     </FluentProvider>
   );
 });

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

@@ -70,7 +70,7 @@
   "Type your message here": "在此输入消息",
   "Copy": "复制",
   "Read Aloud": "朗读",
-  "Hello! I'm RWKV, an open-source and commercially usable large language model.": "你好! 我是RWKV, 一个开源可商用的大语言模型.",
+  "Hello! I'm RWKV, an open-source and commercially usable large language model.": "你好！我是RWKV，一个开源可商用的大语言模型。",
   "This tool's API is compatible with OpenAI API. It can be used with any ChatGPT tool you like. Go to the settings of some ChatGPT tool, replace the 'https://api.openai.com' part in the API address with '": "本工具的API与OpenAI API兼容. 因此可以配合任意你喜欢的ChatGPT工具使用. 打开某个ChatGPT工具的设置, 将API地址中的'https://api.openai.com'部分替换为'",
   "New Version Available": "新版本可用",
   "Update": "更新",
@@ -104,7 +104,7 @@
   "Supported custom cuda file not found": "没有找到支持的自定义cuda文件",
   "Failed to copy custom cuda file": "自定义cuda文件复制失败",
   "Downloading update, please wait. If it is not completed, please manually download the program from GitHub and replace the original program.": "正在下载更新，请等待。如果一直未完成，请从Github手动下载并覆盖原程序",
-  "Completion": "补全",
+  "Completion": "续写",
   "Parameters": "参数",
   "Stop Sequences": "停止词",
   "When this content appears in the response result, the generation will end.": "响应结果出现该内容时就结束生成",
@@ -113,17 +113,17 @@
   "Writer": "写作",
   "Translator": "翻译",
   "Catgirl": "猫娘",
-  "Explain Code": "代码解释",
+  "Code Generation": "代码生成",
   "Werewolf": "狼人杀",
   "Instruction": "指令",
   "Blank": "空白",
-  "The following is an epic science fiction masterpiece that is immortalized, with delicate descriptions and grand depictions of interstellar civilization wars.\nChapter 1.\n": "以下是不朽的科幻史诗巨著，描写细腻，刻画了宏大的星际文明战争。\n第一章\n",
-  "The following is a conversation between a cat girl and her owner. The cat girl is a humanized creature that behaves like a cat but is humanoid. At the end of each sentence in the dialogue, she will add \"Meow~\". In the following content, Bob represents the owner and Alice represents the cat girl.\n\nBob: Hello.\n\nAlice: I'm here, meow~.\n\nBob: Can you tell jokes?": "以下是一位猫娘的主人和猫娘的对话内容，猫娘是一种拟人化的生物，其行为似猫但类人，在每一句对话末尾都会加上\"喵~\"。以下内容中，Bob代表主人，Alice代表猫娘。\n\nBob: 你好\n\nAlice: 主人我在哦，喵~\n\nBob: 你会讲笑话吗?",
+  "The following is an epic science fiction masterpiece that is immortalized, with delicate descriptions and grand depictions of interstellar civilization wars.\nChapter 1.\n": "《背影》\n我与父亲不相见已二年余了，我最不能忘记的是他的背影。\n那年冬天，祖母死了，父亲的差使也交卸了，正是祸不单行的日子。我从北京到徐州，打算",
+  "The following is a conversation between a cat girl and her owner. The cat girl is a humanized creature that behaves like a cat but is humanoid. At the end of each sentence in the dialogue, she will add \"Meow~\". In the following content, User represents the owner and Assistant represents the cat girl.\n\nUser: Hello.\n\nAssistant: I'm here, meow~.\n\nUser: Can you tell jokes?": "以下是一位猫娘的主人和猫娘的对话内容，猫娘是一种拟人化的生物，其行为似猫但类人，在每一句对话末尾都会加上\"喵~\"。以下内容中，User代表主人，Assistant代表猫娘。\n\nUser: 你好\n\nAssistant: 主人我在哦，喵~\n\nUser: 你会讲笑话吗?",
   "When response finished, inject this content.": "响应结束时，插入此内容到末尾",
   "Inject start text": "起始注入文本",
   "Inject end text": "结尾注入文本",
   "Before the response starts, inject this content.": "响应开始前，在开头插入此内容",
-  "There is currently a game of Werewolf with six players, including a Seer (who can check identities at night), two Werewolves (who can choose someone to kill at night), a Bodyguard (who can choose someone to protect at night), two Villagers (with no special abilities), and a game host. Bob will play as Player 1, Alice will play as Players 2-6 and the game host, and they will begin playing together. Every night, the host will ask Bob for his action and simulate the actions of the other players. During the day, the host will oversee the voting process and ask Bob for his vote. \n\nAlice: Next, I will act as the game host and assign everyone their roles, including randomly assigning yours. Then, I will simulate the actions of Players 2-6 and let you know what happens each day. Based on your assigned role, you can tell me your actions and I will let you know the corresponding results each day.\n\nBob: Okay, I understand. Let's begin. Please assign me a role. Am I the Seer, Werewolf, Villager, or Bodyguard?\n\nAlice: You are the Seer. Now that night has fallen, please choose a player to check his identity.\n\nBob: Tonight, I want to check Player 2 and find out his role.": "现在有一场六人狼人杀游戏，包括一名预言家(可以在夜晚查验身份)，两名狼人(可以在夜晚选择杀人)，一名守卫(可以在夜晚选择要守护的人)，两名平民（无技能)，一名主持人，以下内容中Bob将扮演其中的1号玩家，Alice来扮演2-6号玩家，以及主持人，并开始与Bob进行游戏，主持人每晚都会询问Bob的行动，并模拟其他人的行动，在白天则要主持投票，并同样询问Bob投票对象，公布投票结果。\n\nAlice: 接下来，我将首先作为主持人进行角色分配，并给你赋予随机的角色，之后我将模拟2-6号玩家进行行动，告知你每天的动态，根据你被分配的角色，你可以回复我你做的行动，我会告诉你每天对应的结果\n\nBob: 好的，我明白了，那么开始吧。请先给我一个角色身份。我是预言家，狼人，平民，守卫中的哪一个呢？\n\nAlice: 你的身份是预言家。现在夜晚降临，请选择你要查验的玩家。\n\nBob: 今晚我要验2号玩家，他是什么身份？",
+  "There is currently a game of Werewolf with six players, including a Seer (who can check identities at night), two Werewolves (who can choose someone to kill at night), a Bodyguard (who can choose someone to protect at night), two Villagers (with no special abilities), and a game host. User will play as Player 1, Assistant will play as Players 2-6 and the game host, and they will begin playing together. Every night, the host will ask User for his action and simulate the actions of the other players. During the day, the host will oversee the voting process and ask User for his vote. \n\nAssistant: Next, I will act as the game host and assign everyone their roles, including randomly assigning yours. Then, I will simulate the actions of Players 2-6 and let you know what happens each day. Based on your assigned role, you can tell me your actions and I will let you know the corresponding results each day.\n\nUser: Okay, I understand. Let's begin. Please assign me a role. Am I the Seer, Werewolf, Villager, or Bodyguard?\n\nAssistant: You are the Seer. Now that night has fallen, please choose a player to check his identity.\n\nUser: Tonight, I want to check Player 2 and find out his role.": "现在有一场六人狼人杀游戏，包括一名预言家(可以在夜晚查验身份)，两名狼人(可以在夜晚选择杀人)，一名守卫(可以在夜晚选择要守护的人)，两名平民（无技能)，一名主持人，以下内容中User将扮演其中的1号玩家，Assistant来扮演2-6号玩家，以及主持人，并开始与User进行游戏，主持人每晚都会询问User的行动，并模拟其他人的行动，在白天则要主持投票，并同样询问User投票对象，公布投票结果。\n\nAssistant: 接下来，我将首先作为主持人进行角色分配，并给你赋予随机的角色，之后我将模拟2-6号玩家进行行动，告知你每天的动态，根据你被分配的角色，你可以回复我你做的行动，我会告诉你每天对应的结果\n\nUser: 好的，我明白了，那么开始吧。请先给我一个角色身份。我是预言家，狼人，平民，守卫中的哪一个呢？\n\nAssistant: 你的身份是预言家。现在夜晚降临，请选择你要查验的玩家。\n\nUser: 今晚我要验2号玩家，他是什么身份？",
   "Writer, Translator, Role-playing": "写作，翻译，角色扮演",
   "Chinese Kongfu": "情境冒险",
   "Allow external access to the API (service must be restarted)": "允许外部访问API (必须重启服务)",
@@ -153,9 +153,92 @@
   "Restart the app to apply DPI Scaling.": "重启应用以使显示缩放生效",
   "Restart": "重启",
   "API Chat Model Name": "API聊天模型名",
-  "API Completion Model Name": "API补全模型名",
+  "API Completion Model Name": "API续写模型名",
   "Localhost": "本地",
   "Retry": "重试",
   "Delete": "删除",
-  "Edit": "编辑"
+  "Edit": "编辑",
+  "Memory is not enough, try to increase the virtual memory or use a smaller model.": "内存不足，尝试增加虚拟内存，或使用一个更小规模的模型",
+  "Bad PyTorch version, please reinstall PyTorch with cuda.": "错误的PyTorch版本，请重新安装CUDA版本的PyTorch",
+  "The model file is corrupted, please download again.": "模型文件损坏，请重新下载",
+  "Found no NVIDIA driver, please install the latest driver.": "没有找到NVIDIA驱动，请安装最新驱动",
+  "VRAM is not enough, please reduce stored layers or use a lower precision in Configs page.": "显存不足，请在配置页面减少载入显存层数，或使用更低的精度",
+  "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.": "自定义CUDA算子开启失败，需要安装Ninja来读取C++扩展。你可能正在使用CPU版本的PyTorch，请重新安装CUDA版本的PyTorch。如果你正在使用自定义Python解释器，你必须自己编译CUDA算子或禁用自定义CUDA算子加速",
+  "Presets": "预设",
+  "Online": "在线",
+  "english": "英文",
+  "chinese": "中文",
+  "default": "默认",
+  "japanese": "日文",
+  "New Preset": "新建预设",
+  "Import": "导入",
+  "Name": "名称",
+  "Imported successfully": "导入成功",
+  "Failed to import. Please copy a preset to the clipboard.": "导入失败。请复制一个预设到剪贴板",
+  "Clipboard is empty.": "剪贴板没有内容",
+  "Successfully copied to clipboard.": "成功复制到剪贴板",
+  "Edit Messages": "编辑对话",
+  "Go Back": "返回",
+  "Description": "描述",
+  "Avatar Url": "头像图片地址",
+  "Welcome Message": "欢迎语",
+  "Display Preset Messages": "显示预设中的对话",
+  "Tag": "标签",
+  "Activate": "激活",
+  "New": "新建",
+  "user": "用户",
+  "assistant": "AI",
+  "system": "系统",
+  "Regenerate": "重新生成",
+  "LoRA Finetune": "LoRA微调",
+  "Command Stopped": "命令已终止",
+  "Please convert data first.": "请先转换数据",
+  "Ubuntu is not installed, do you want to install it?": "Ubuntu未安装，是否安装？",
+  "Install Ubuntu": "安装Ubuntu",
+  "Please install Ubuntu using Microsoft Store, after installation click the Open button in Microsoft Store and then click the Train button": "请用Microsoft Store安装Ubuntu，安装完成后，点击Microsoft Store界面的“打开”按钮，然后点击“训练”按钮",
+  "WSL is not enabled, do you want to enable it?": "WSL未启用，是否启用？",
+  "Enable WSL": "启用WSL",
+  "After installation, please restart your computer to enable WSL": "安装完成后，请重启电脑以启用WSL",
+  "Data Process": "数据处理",
+  "Data Path": "数据路径",
+  "Vocab Path": "词表路径",
+  "Train Parameters": "训练参数",
+  "Base Model": "基底模型",
+  "LoRA Model": "LoRA模型",
+  "Merge Model": "合并模型",
+  "Devices": "显卡数量",
+  "Gradient Checkpoint": "梯度检查点标志",
+  "Context Length": "上下文长度",
+  "Epoch Steps": "每轮训练步数",
+  "Epoch Count": "训练轮次",
+  "Epoch Begin": "起始轮次",
+  "Epoch Save": "保存间隔轮次",
+  "Learning Rate Init": "初始学习率",
+  "Learning Rate Final": "最终学习率",
+  "Micro Batch Size": "微批次大小",
+  "Accumulate Gradient Batches": "梯度累积批次",
+  "Warmup Steps": "学习率预热步数",
+  "Pre-FFN": "前馈网络预处理",
+  "None": "空",
+  "Merge model successfully": "合并模型成功",
+  "Convert Data successfully": "数据转换成功",
+  "Please select a LoRA model": "请选择一个LoRA模型",
+  "You are using sample data for training. For formal training, please make sure to create your own jsonl file.": "你正在使用示例数据训练，对于正式训练场合，请务必创建你自己的jsonl训练数据",
+  "WSL is not running, please retry. If it keeps happening, it means you may be using an outdated version of WSL, run \"wsl --update\" to update.": "WSL没有运行，请重试。如果一直出现此错误，意味着你可能正在使用旧版本的WSL，请在cmd执行\"wsl --update\"以更新",
+  "Memory is not enough, try to increase the virtual memory or use a smaller base model.": "内存不足，尝试增加虚拟内存，或使用一个更小规模的基底模型",
+  "VRAM is not enough": "显存不足",
+  "Training data is not enough, reduce context length or add more data for training": "训练数据不足，请减小上下文长度或增加训练数据",
+  "You are using WSL 1 for training, please upgrade to WSL 2. e.g. Run \"wsl --set-version Ubuntu-22.04 2\"": "你正在使用WSL 1进行训练，请升级到WSL 2。例如，运行\"wsl --set-version Ubuntu-22.04 2\"",
+  "Matched CUDA is not installed": "未安装匹配的CUDA",
+  "Failed to convert data": "数据转换失败",
+  "Failed to merge model": "合并模型失败",
+  "The data path should be a directory or a file in jsonl format (more formats will be supported in the future).\n\nWhen you provide a directory path, all the txt files within that directory will be automatically converted into training data. This is commonly used for large-scale training in writing, code generation, or knowledge bases.\n\nThe jsonl format file can be referenced at https://github.com/Abel2076/json2binidx_tool/blob/main/sample.jsonl.\nYou can also write it similar to OpenAI's playground format, as shown in https://platform.openai.com/playground/p/default-chat.\nEven for multi-turn conversations, they must be written in a single line using `\\n` to indicate line breaks. If they are different dialogues or topics, they should be written in separate lines.": "数据路径必须是一个文件夹，或者jsonl格式文件 (未来会支持更多格式)\n\n当你填写的路径是一个文件夹时，该文件夹内的所有txt文件会被自动转换为训练数据，通常这用于大批量训练写作，代码生成或知识库\n\njsonl文件的格式参考 https://github.com/Abel2076/json2binidx_tool/blob/main/sample.jsonl\n你也可以仿照openai的playground编写，参考 https://platform.openai.com/playground/p/default-chat\n即使是多轮对话也必须写在一行，用`\\n`表示换行，如果是不同对话或主题，则另起一行",
+  "Size mismatch for blocks. You are attempting to continue training from the LoRA model, but it does not match the base model. Please set LoRA model to None.": "尺寸不匹配块。你正在尝试从LoRA模型继续训练，但该LoRA模型与基底模型不匹配，请将LoRA模型设为空",
+  "Instruction: Write a story using the following information\n\nInput: A man named Alex chops a tree down\n\nResponse:": "Instruction: Write a story using the following information\n\nInput: 艾利克斯砍倒了一棵树\n\nResponse:",
+  "Composition": "作曲",
+  "Use Local Sound Font": "使用本地音色资源",
+  "Auto Play At The End": "结束时自动播放",
+  "No File to save": "无文件可保存",
+  "File Saved": "文件已保存",
+  "Failed to load local sound font, please check if the files exist - assets/sound-font": "加载本地音色资源失败，请检查文件是否存在 - assets/sound-font"
 }

frontend/src/components/CustomToastContainer.tsx

@@ -0,0 +1,17 @@
+import commonStore from '../stores/commonStore';
+import { ToastContainer } from 'react-toastify';
+
+export const CustomToastContainer = () =>
+  <ToastContainer
+    style={{ width: '350px' }}
+    position="top-center"
+    autoClose={4000}
+    pauseOnHover={true}
+    hideProgressBar={true}
+    newestOnTop={true}
+    closeOnClick={false}
+    rtl={false}
+    pauseOnFocusLoss={false}
+    draggable={false}
+    theme={commonStore.settings.darkMode ? 'dark' : 'light'}
+  />;

frontend/src/components/DialogButton.tsx

@@ -11,6 +11,7 @@ import {
 } from '@fluentui/react-components';
 import { ToolTipButton } from './ToolTipButton';
 import { useTranslation } from 'react-i18next';
+import MarkdownRender from './MarkdownRender';
 
 export const DialogButton: FC<{
   text?: string | null
@@ -19,12 +20,13 @@ export const DialogButton: FC<{
   className?: string,
   title: string,
   contentText: string,
-  onConfirm: () => void,
+  markdown?: boolean,
+  onConfirm?: () => void,
   size?: 'small' | 'medium' | 'large',
   shape?: 'rounded' | 'circular' | 'square',
   appearance?: 'secondary' | 'primary' | 'outline' | 'subtle' | 'transparent',
 }> = ({
-  text, icon, tooltip, className, title, contentText,
+  text, icon, tooltip, className, title, contentText, markdown,
   onConfirm, size, shape, appearance
 }) => {
   const { t } = useTranslation();
@@ -41,7 +43,11 @@ export const DialogButton: FC<{
       <DialogBody>
         <DialogTitle>{title}</DialogTitle>
         <DialogContent>
-          {contentText}
+          {
+            markdown ?
+              <MarkdownRender>{contentText}</MarkdownRender> :
+              contentText
+          }
         </DialogContent>
         <DialogActions>
           <DialogTrigger disableButtonEnhancement>

frontend/src/components/ResetConfigsButton.tsx

@@ -4,7 +4,7 @@ import { useTranslation } from 'react-i18next';
 import { ArrowReset20Regular } from '@fluentui/react-icons';
 import commonStore from '../stores/commonStore';
 
-import { defaultModelConfigs, defaultModelConfigsMac } from '../pages/defaultModelConfigs';
+import { defaultModelConfigs, defaultModelConfigsMac } from '../pages/defaultConfigs';
 
 export const ResetConfigsButton: FC<{ afterConfirm?: () => void }> = ({ afterConfirm }) => {
   const { t } = useTranslation();

frontend/src/components/RunButton.tsx

@@ -1,23 +1,16 @@
 import React, { FC, MouseEventHandler, ReactElement } from 'react';
 import commonStore, { ModelStatus } from '../stores/commonStore';
-import {
-  AddToDownloadList,
-  CopyFile,
-  DepCheck,
-  FileExists,
-  InstallPyDep,
-  StartServer
-} from '../../wailsjs/go/backend_golang/App';
+import { AddToDownloadList, CopyFile, FileExists, StartServer } from '../../wailsjs/go/backend_golang/App';
 import { Button } from '@fluentui/react-components';
 import { observer } from 'mobx-react-lite';
 import { exit, getStatus, readRoot, switchModel, updateConfig } from '../apis';
 import { toast } from 'react-toastify';
-import { getStrategy, getSupportedCustomCudaFile, saveCache, toastWithButton } from '../utils';
+import { checkDependencies, getStrategy, getSupportedCustomCudaFile, toastWithButton } from '../utils';
 import { useTranslation } from 'react-i18next';
 import { ToolTipButton } from './ToolTipButton';
 import { Play16Regular, Stop16Regular } from '@fluentui/react-icons';
 import { useNavigate } from 'react-router';
-import { BrowserOpenURL, WindowShow } from '../../wailsjs/runtime/runtime';
+import { WindowShow } from '../../wailsjs/runtime/runtime';
 
 const mainButtonText = {
   [ModelStatus.Offline]: 'Run',
@@ -57,53 +50,9 @@ export const RunButton: FC<{ onClickRun?: MouseEventHandler, iconMode?: boolean
         return;
       }
 
-      if (!commonStore.depComplete) {
-        let depErrorMsg = '';
-        await DepCheck(commonStore.settings.customPythonPath).catch((e) => {
-          depErrorMsg = e.message || e;
-          WindowShow();
-          if (depErrorMsg === 'python zip not found') {
-            toastWithButton(t('Python target not found, would you like to download it?'), t('Download'), () => {
-              toastWithButton(`${t('Downloading')} Python`, t('Check'), () => {
-                navigate({ pathname: '/downloads' });
-              }, { autoClose: 3000 });
-              AddToDownloadList('python-3.10.11-embed-amd64.zip', 'https://www.python.org/ftp/python/3.10.11/python-3.10.11-embed-amd64.zip');
-            });
-          } else if (depErrorMsg.includes('DepCheck Error')) {
-            if (depErrorMsg.includes('vc_redist')) {
-              toastWithButton(t('Microsoft Visual C++ Redistributable is not installed, would you like to download it?'), t('Download'), () => {
-                BrowserOpenURL('https://aka.ms/vs/16/release/vc_redist.x64.exe');
-              });
-            } else {
-              toast(depErrorMsg, { type: 'info', position: 'bottom-left' });
-              if (commonStore.platform != 'linux')
-                toastWithButton(t('Python dependencies are incomplete, would you like to install them?'), t('Install'), () => {
-                  InstallPyDep(commonStore.settings.customPythonPath, commonStore.settings.cnMirror).catch((e) => {
-                    const errMsg = e.message || e;
-                    toast(t('Error') + ' - ' + errMsg, { type: 'error' });
-                  });
-                  setTimeout(WindowShow, 1000);
-                }, {
-                  autoClose: 8000
-                });
-              else
-                toastWithButton(t('On Linux system, you must manually install python dependencies.'), t('Check'), () => {
-                  BrowserOpenURL('https://github.com/josStorer/RWKV-Runner/blob/master/build/linux/Readme_Install.txt');
-                });
-            }
-          } else {
-            toast(depErrorMsg, { type: 'error' });
-          }
-        });
-        if (depErrorMsg) {
-          commonStore.setStatus({ status: ModelStatus.Offline });
-          return;
-        }
-        commonStore.setDepComplete(true);
-        if (commonStore.platform === 'windows')
-          CopyFile('./backend-python/wkv_cuda_utils/wkv_cuda_model.py', './py310/Lib/site-packages/rwkv/model.py');
-        saveCache();
-      }
+      const ok = await checkDependencies(navigate);
+      if (!ok)
+        return;
 
       const currentModelSource = commonStore.modelSourceList.find(item => item.name === modelName);
 
@@ -126,7 +75,7 @@ export const RunButton: FC<{ onClickRun?: MouseEventHandler, iconMode?: boolean
         showDownloadPrompt(t('Model file not found'), modelName);
         commonStore.setStatus({ status: ModelStatus.Offline });
         return;
-      } else if (!currentModelSource?.isLocal) {
+      } else if (!currentModelSource?.isComplete) {
         showDownloadPrompt(t('Model file download is not complete'), modelName);
         commonStore.setStatus({ status: ModelStatus.Offline });
         return;
@@ -200,18 +149,36 @@ export const RunButton: FC<{ onClickRun?: MouseEventHandler, iconMode?: boolean
             }).then(async (r) => {
               if (r.ok) {
                 commonStore.setStatus({ status: ModelStatus.Working });
-                toastWithButton(t('Startup Completed'), t('Chat'), () => {
-                  navigate({ pathname: '/chat' });
-                }, { type: 'success', autoClose: 3000 });
+                let buttonNameMap = {
+                  'novel': 'Completion',
+                  'midi': 'Composition'
+                };
+                let buttonName = 'Chat';
+                buttonName = Object.entries(buttonNameMap).find(([key, value]) => modelName.toLowerCase().includes(key))?.[1] || buttonName;
+                const buttonFn = () => {
+                  navigate({ pathname: '/' + buttonName.toLowerCase() });
+                };
+                toastWithButton(t('Startup Completed'), t(buttonName), buttonFn, { type: 'success', autoClose: 3000 });
               } else if (r.status === 304) {
                 toast(t('Loading Model'), { type: 'info' });
               } else {
                 commonStore.setStatus({ status: ModelStatus.Offline });
-                toast(t('Failed to switch model') + ' - ' + await r.text(), { type: 'error' });
+                const error = await r.text();
+                const errorsMap = {
+                  'not enough memory': 'Memory is not enough, try to increase the virtual memory or use a smaller model.',
+                  'not compiled with CUDA': 'Bad PyTorch version, please reinstall PyTorch with cuda.',
+                  '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.',
+                  '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.'
+                };
+                const matchedError = Object.entries(errorsMap).find(([key, _]) => error.includes(key));
+                const message = matchedError ? t(matchedError[1]) : error;
+                toast(t('Failed to switch model') + ' - ' + message, { autoClose: 5000, type: 'error' });
               }
             }).catch((e) => {
               commonStore.setStatus({ status: ModelStatus.Offline });
-              toast(t('Failed to switch model') + ' - ' + e.message || e, { type: 'error' });
+              toast(t('Failed to switch model') + ' - ' + (e.message || e), { type: 'error' });
             });
           }
         }).catch(() => {

frontend/src/components/ToolTipButton.tsx

@@ -1,4 +1,4 @@
-import React, { FC, MouseEventHandler, ReactElement } from 'react';
+import React, { CSSProperties, FC, MouseEventHandler, ReactElement } from 'react';
 import { Button, Tooltip } from '@fluentui/react-components';
 
 export const ToolTipButton: FC<{
@@ -6,6 +6,7 @@ export const ToolTipButton: FC<{
   desc: string,
   icon?: ReactElement,
   className?: string,
+  style?: CSSProperties,
   size?: 'small' | 'medium' | 'large',
   shape?: 'rounded' | 'circular' | 'square';
   appearance?: 'secondary' | 'primary' | 'outline' | 'subtle' | 'transparent';
@@ -17,6 +18,7 @@ export const ToolTipButton: FC<{
   desc,
   icon,
   className,
+  style,
   size,
   shape,
   appearance,
@@ -26,8 +28,8 @@ export const ToolTipButton: FC<{
 }) => {
   return (
     <Tooltip content={desc} showDelay={showDelay} hideDelay={0} relationship="label">
-      <Button className={className} disabled={disabled} icon={icon} onClick={onClick} size={size} shape={shape}
-        appearance={appearance}>{text}</Button>
+      <Button style={style} className={className} disabled={disabled} icon={icon} onClick={onClick} size={size}
+        shape={shape} appearance={appearance}>{text}</Button>
     </Tooltip>
   );
 };

frontend/src/main.tsx

@@ -6,6 +6,7 @@ import App from './App';
 import { HashRouter } from 'react-router-dom';
 import { startup } from './startup';
 import './_locales/i18n-react';
+import 'html-midi-player';
 import { WindowShow } from '../wailsjs/runtime';
 
 startup().then(() => {

frontend/src/pages/Chat.tsx

@@ -7,8 +7,7 @@ import { v4 as uuid } from 'uuid';
 import classnames from 'classnames';
 import { fetchEventSource } from '@microsoft/fetch-event-source';
 import { KebabHorizontalIcon, PencilIcon, SyncIcon, TrashIcon } from '@primer/octicons-react';
-import { ConversationPair } from '../utils/get-conversation-pairs';
-import logo from '../assets/images/logo.jpg';
+import logo from '../assets/images/logo.png';
 import MarkdownRender from '../components/MarkdownRender';
 import { ToolTipButton } from '../components/ToolTipButton';
 import { ArrowCircleUp28Regular, Delete28Regular, RecordStop28Regular, Save28Regular } from '@fluentui/react-icons';
@@ -19,6 +18,8 @@ import { WorkHeader } from '../components/WorkHeader';
 import { DialogButton } from '../components/DialogButton';
 import { OpenFileFolder, OpenSaveFileDialog } from '../../wailsjs/go/backend_golang/App';
 import { toastWithButton } from '../utils';
+import { PresetsButton } from './PresetsManager/PresetsButton';
+import { useMediaQuery } from 'usehooks-ts';
 
 export const userName = 'M E';
 export const botName = 'A I';
@@ -49,6 +50,13 @@ export type Conversation = {
   [uuid: string]: MessageItem
 }
 
+export type Role = 'assistant' | 'user' | 'system';
+
+export type ConversationMessage = {
+  role: Role;
+  content: string;
+}
+
 let chatSseController: AbortController | null = null;
 
 const MoreUtilsButton: FC<{ uuid: string, setEditing: (editing: boolean) => void }> = observer(({
@@ -123,7 +131,7 @@ const ChatMessageItem: FC<{
     <Avatar
       color={messageItem.color}
       name={messageItem.sender}
-      image={messageItem.avatarImg ? { src: messageItem.avatarImg } : undefined}
+      image={(commonStore.activePreset && messageItem.sender === botName) ? { src: commonStore.activePreset.avatarImg } : messageItem.avatarImg ? { src: messageItem.avatarImg } : undefined}
     />
     <div
       className={classnames(
@@ -175,7 +183,10 @@ const ChatPanel: FC = observer(() => {
   const { t } = useTranslation();
   const bodyRef = useRef<HTMLDivElement>(null);
   const inputRef = useRef<HTMLTextAreaElement>(null);
-  const port = commonStore.getCurrentModelConfig().apiParameters.apiPort;
+  const mq = useMediaQuery('(min-width: 640px)');
+  const currentConfig = commonStore.getCurrentModelConfig();
+  const apiParams = currentConfig.apiParameters;
+  const port = apiParams.apiPort;
 
   let lastMessageId: string;
   let generating: boolean = false;
@@ -255,7 +266,7 @@ const ChatPanel: FC = observer(() => {
     let endIndex = endUuid ? (commonStore.conversationOrder.indexOf(endUuid) + (includeEndUuid ? 1 : 0)) : commonStore.conversationOrder.length;
     let targetRange = commonStore.conversationOrder.slice(startIndex, endIndex);
 
-    const messages: ConversationPair[] = [];
+    const messages: ConversationMessage[] = [];
     targetRange.forEach((uuid, index) => {
       if (uuid === welcomeUuid)
         return;
@@ -299,12 +310,14 @@ const ChatPanel: FC = observer(() => {
         body: JSON.stringify({
           messages,
           stream: true,
-          model: commonStore.settings.apiChatModelName // 'gpt-3.5-turbo'
+          model: commonStore.settings.apiChatModelName, // 'gpt-3.5-turbo'
+          temperature: apiParams.temperature,
+          top_p: apiParams.topP
         }),
         signal: chatSseController?.signal,
         onmessage(e) {
           scrollToBottom();
-          if (e.data === '[DONE]') {
+          if (e.data.trim() === '[DONE]') {
             commonStore.conversation[answerId!].done = true;
             commonStore.conversation[answerId!].content = commonStore.conversation[answerId!].content.trim();
             commonStore.setConversation(commonStore.conversation);
@@ -357,10 +370,11 @@ const ChatPanel: FC = observer(() => {
           <ChatMessageItem key={uuid} uuid={uuid} onSubmit={onSubmit} />
         )}
       </div>
-      <div className="flex items-end gap-2">
+      <div className={classnames('flex items-end', mq ? 'gap-2' : '')}>
+        <PresetsButton tab="Chat" size={mq ? 'large' : 'small'} shape="circular" appearance="subtle" />
         <DialogButton tooltip={t('Clear')}
           icon={<Delete28Regular />}
-          size="large" shape="circular" appearance="subtle" title={t('Clear')}
+          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.')}
           onConfirm={() => {
             if (generating)
@@ -370,6 +384,7 @@ const ChatPanel: FC = observer(() => {
           }} />
         <Textarea
           ref={inputRef}
+          style={{ minWidth: 0 }}
           className="grow"
           resize="vertical"
           placeholder={t('Type your message here')!}
@@ -379,7 +394,7 @@ const ChatPanel: FC = observer(() => {
         />
         <ToolTipButton desc={generating ? t('Stop') : t('Send')}
           icon={generating ? <RecordStop28Regular /> : <ArrowCircleUp28Regular />}
-          size="large" shape="circular" appearance="subtle"
+          size={mq ? 'large' : 'small'} shape="circular" appearance="subtle"
           onClick={(e) => {
             if (generating) {
               chatSseController?.abort();
@@ -395,21 +410,28 @@ const ChatPanel: FC = observer(() => {
           }} />
         <ToolTipButton desc={t('Save')}
           icon={<Save28Regular />}
-          size="large" shape="circular" appearance="subtle"
+          size={mq ? 'large' : 'small'} shape="circular" appearance="subtle"
           onClick={() => {
             let savedContent: string = '';
+            const isWorldModel = commonStore.getCurrentModelConfig().modelParameters.modelName.toLowerCase().includes('world');
+            const user = isWorldModel ? 'Question' : 'Bob';
+            const bot = isWorldModel ? 'Answer' : 'Alice';
             commonStore.conversationOrder.forEach((uuid) => {
+              if (uuid === welcomeUuid)
+                return;
               const messageItem = commonStore.conversation[uuid];
-              savedContent += `**${messageItem.sender}**\n - ${new Date(messageItem.time).toLocaleString()}\n\n${messageItem.content}\n\n`;
+              if (messageItem.type !== MessageType.Error) {
+                savedContent += `${messageItem.sender === userName ? user : bot}: ${messageItem.content}\n\n`;
+              }
             });
 
-            OpenSaveFileDialog('*.md', 'conversation.md', savedContent).then((path) => {
+            OpenSaveFileDialog('*.txt', 'conversation.txt', savedContent).then((path) => {
               if (path)
                 toastWithButton(t('Conversation Saved'), t('Open'), () => {
                   OpenFileFolder(path, false);
                 });
             }).catch(e => {
-              toast(t('Error') + ' - ' + e.message || e, { type: 'error', autoClose: 2500 });
+              toast(t('Error') + ' - ' + (e.message || e), { type: 'error', autoClose: 2500 });
             });
           }} />
       </div>

frontend/src/pages/Completion.tsx

@@ -10,6 +10,10 @@ import commonStore, { ModelStatus } from '../stores/commonStore';
 import { fetchEventSource } from '@microsoft/fetch-event-source';
 import { toast } from 'react-toastify';
 import { DialogButton } from '../components/DialogButton';
+import { PresetsButton } from './PresetsManager/PresetsButton';
+import { ToolTipButton } from '../components/ToolTipButton';
+import { ArrowSync20Regular } from '@fluentui/react-icons';
+import { defaultPresets } from './defaultConfigs';
 
 export type CompletionParams = Omit<ApiParameters, 'apiPort'> & {
   stop: string,
@@ -23,113 +27,6 @@ export type CompletionPreset = {
   params: CompletionParams
 }
 
-export const defaultPresets: CompletionPreset[] = [{
-  name: 'Writer',
-  prompt: 'The following is an epic science fiction masterpiece that is immortalized, with delicate descriptions and grand depictions of interstellar civilization wars.\nChapter 1.\n',
-  params: {
-    maxResponseToken: 500,
-    temperature: 1.2,
-    topP: 0.5,
-    presencePenalty: 0.4,
-    frequencyPenalty: 0.4,
-    stop: '\\n\\nBob',
-    injectStart: '',
-    injectEnd: ''
-  }
-}, {
-  name: 'Translator',
-  prompt: 'Translate this into Chinese.\n\nEnglish: What rooms do you have available?',
-  params: {
-    maxResponseToken: 500,
-    temperature: 1,
-    topP: 0.3,
-    presencePenalty: 0.4,
-    frequencyPenalty: 0.4,
-    stop: '\\nEnglish',
-    injectStart: '\\nChinese: ',
-    injectEnd: '\\nEnglish: '
-  }
-}, {
-  name: 'Catgirl',
-  prompt: 'The following is a conversation between a cat girl and her owner. The cat girl is a humanized creature that behaves like a cat but is humanoid. At the end of each sentence in the dialogue, she will add \"Meow~\". In the following content, Bob represents the owner and Alice represents the cat girl.\n\nBob: Hello.\n\nAlice: I\'m here, meow~.\n\nBob: Can you tell jokes?',
-  params: {
-    maxResponseToken: 500,
-    temperature: 1.2,
-    topP: 0.5,
-    presencePenalty: 0.4,
-    frequencyPenalty: 0.4,
-    stop: '\\n\\nBob',
-    injectStart: '\\n\\nAlice: ',
-    injectEnd: '\\n\\nBob: '
-  }
-}, {
-  name: 'Chinese Kongfu',
-  prompt: 'Bob: 请你扮演一个文本冒险游戏，我是游戏主角。这是一个玄幻修真世界，有四大门派。我输入我的行动，请你显示行动结果，并具体描述环境。我的第一个行动是“醒来”，请开始故事。',
-  params: {
-    maxResponseToken: 500,
-    temperature: 1.1,
-    topP: 0.7,
-    presencePenalty: 0.3,
-    frequencyPenalty: 0.3,
-    stop: '\\n\\nBob',
-    injectStart: '\\n\\nAlice: ',
-    injectEnd: '\\n\\nBob: '
-  }
-}, {
-// }, {
-//   name: 'Explain Code',
-//   prompt: 'export async function startup() {\n  FileExists(\'cache.json\').then((exists) => {\n    if (exists)\n      downloadProgramFiles();\n    else {\n      deleteDynamicProgramFiles().then(downloadProgramFiles);\n    }\n  });\n  EventsOn(\'downloadList\', (data) => {\n    if (data)\n      commonStore.setDownloadList(data);\n  });\n\n  initCache().then(initRemoteText);\n\n  await initConfig();\n\n  if (commonStore.settings.autoUpdatesCheck) // depends on config settings\n    checkUpdate();\n\n  getStatus(1000).then(status => { // depends on config api port\n    if (status)\n      commonStore.setStatus(status);\n  });\n}\n\n\"\"\"\nHere\'s what the above code is doing, explained in a concise way:\n',
-//   params: {
-//     maxResponseToken: 500,
-//     temperature: 0.8,
-//     topP: 0.7,
-//     presencePenalty: 0.4,
-//     frequencyPenalty: 0.4,
-//     stop: '\\n\\n',
-//     injectStart: '',
-//     injectEnd: ''
-//   }
-// }, {
-  name: 'Werewolf',
-  prompt: 'There is currently a game of Werewolf with six players, including a Seer (who can check identities at night), two Werewolves (who can choose someone to kill at night), a Bodyguard (who can choose someone to protect at night), two Villagers (with no special abilities), and a game host. Bob will play as Player 1, Alice will play as Players 2-6 and the game host, and they will begin playing together. Every night, the host will ask Bob for his action and simulate the actions of the other players. During the day, the host will oversee the voting process and ask Bob for his vote. \n\nAlice: Next, I will act as the game host and assign everyone their roles, including randomly assigning yours. Then, I will simulate the actions of Players 2-6 and let you know what happens each day. Based on your assigned role, you can tell me your actions and I will let you know the corresponding results each day.\n\nBob: Okay, I understand. Let\'s begin. Please assign me a role. Am I the Seer, Werewolf, Villager, or Bodyguard?\n\nAlice: You are the Seer. Now that night has fallen, please choose a player to check his identity.\n\nBob: Tonight, I want to check Player 2 and find out his role.',
-  params: {
-    maxResponseToken: 500,
-    temperature: 1.2,
-    topP: 0.4,
-    presencePenalty: 0.5,
-    frequencyPenalty: 0.5,
-    stop: '\\n\\nBob',
-    injectStart: '\\n\\nAlice: ',
-    injectEnd: '\\n\\nBob: '
-  }
-}, {
-  name: 'Instruction',
-  prompt: 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n# Instruction:\nWrite a story using the following information\n\n# Input:\nA man named Alex chops a tree down\n\n# Response:\n',
-  params: {
-    maxResponseToken: 500,
-    temperature: 1.2,
-    topP: 0.5,
-    presencePenalty: 0.4,
-    frequencyPenalty: 0.4,
-    stop: '',
-    injectStart: '',
-    injectEnd: ''
-  }
-}, {
-  name: 'Blank',
-  prompt: '',
-  params: {
-    maxResponseToken: 500,
-    temperature: 1,
-    topP: 0.5,
-    presencePenalty: 0.4,
-    frequencyPenalty: 0.4,
-    stop: '',
-    injectStart: '',
-    injectEnd: ''
-  }
-}];
-
 let completionSseController: AbortController | null = null;
 
 const CompletionPanel: FC = observer(() => {
@@ -149,6 +46,7 @@ const CompletionPanel: FC = observer(() => {
   }, []);
 
   const setPreset = (preset: CompletionPreset) => {
+    commonStore.setCompletionSubmittedPrompt(t(preset.prompt));
     commonStore.setCompletionPreset({
       ...preset,
       prompt: t(preset.prompt)
@@ -180,6 +78,8 @@ const CompletionPanel: FC = observer(() => {
   };
 
   const onSubmit = (prompt: string) => {
+    commonStore.setCompletionSubmittedPrompt(prompt);
+
     if (commonStore.status.status === ModelStatus.Offline && !commonStore.settings.apiUrl) {
       toast(t('Please click the button in the top right corner to start the model'), { type: 'warning' });
       commonStore.setCompletionGenerating(false);
@@ -214,7 +114,7 @@ const CompletionPanel: FC = observer(() => {
         signal: completionSseController?.signal,
         onmessage(e) {
           scrollToBottom();
-          if (e.data === '[DONE]') {
+          if (e.data.trim() === '[DONE]') {
             commonStore.setCompletionGenerating(false);
             return;
           }
@@ -226,8 +126,8 @@ const CompletionPanel: FC = observer(() => {
             return;
           }
           if (data.choices && Array.isArray(data.choices) && data.choices.length > 0) {
-            answer += data.choices[0].text;
-            setPrompt(prompt + answer.trim() + params.injectEnd.replaceAll('\\n', '\n'));
+            answer += data.choices[0]?.text || data.choices[0]?.delta?.content || '';
+            setPrompt(prompt + answer.replace(/\s+$/, '') + params.injectEnd.replaceAll('\\n', '\n'));
           }
         },
         async onopen(response) {
@@ -258,22 +158,29 @@ const CompletionPanel: FC = observer(() => {
         ref={inputRef}
         className="grow"
         value={prompt}
-        onChange={(e) => setPrompt(e.target.value)}
+        onChange={(e) => {
+          commonStore.setCompletionSubmittedPrompt(e.target.value);
+          setPrompt(e.target.value);
+        }}
       />
       <div className="flex flex-col gap-1 max-h-48 sm:max-w-sm sm:max-h-full">
-        <Dropdown style={{ minWidth: 0 }}
-          value={t(commonStore.completionPreset!.name)!}
-          selectedOptions={[commonStore.completionPreset!.name]}
-          onOptionSelect={(_, data) => {
-            if (data.optionValue) {
-              setPreset(defaultPresets.find((preset) => preset.name === data.optionValue)!);
+        <div className="flex gap-2">
+          <Dropdown style={{ minWidth: 0 }}
+            className="grow"
+            value={t(commonStore.completionPreset!.name)!}
+            selectedOptions={[commonStore.completionPreset!.name]}
+            onOptionSelect={(_, data) => {
+              if (data.optionValue) {
+                setPreset(defaultPresets.find((preset) => preset.name === data.optionValue)!);
+              }
+            }}>
+            {
+              defaultPresets.map((preset) =>
+                <Option key={preset.name} value={preset.name}>{t(preset.name)!}</Option>)
             }
-          }}>
-          {
-            defaultPresets.map((preset) =>
-              <Option key={preset.name} value={preset.name}>{t(preset.name)!}</Option>)
-          }
-        </Dropdown>
+          </Dropdown>
+          <PresetsButton tab="Completion" />
+        </div>
         <div className="flex flex-col gap-1 overflow-x-hidden overflow-y-auto p-1">
           <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.')}
@@ -363,6 +270,12 @@ const CompletionPanel: FC = observer(() => {
         </div>
         <div className="grow" />
         <div className="flex justify-between gap-2">
+          <ToolTipButton desc={t('Regenerate')} icon={<ArrowSync20Regular />} onClick={() => {
+            completionSseController?.abort();
+            commonStore.setCompletionGenerating(true);
+            setPrompt(commonStore.completionSubmittedPrompt);
+            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.')}
             onConfirm={() => {

frontend/src/pages/Composition.tsx

@@ -0,0 +1,345 @@
+import React, { FC, useEffect, useRef } from 'react';
+import { observer } from 'mobx-react-lite';
+import { WorkHeader } from '../components/WorkHeader';
+import { Button, Checkbox, Textarea } from '@fluentui/react-components';
+import { Labeled } from '../components/Labeled';
+import { ValuedSlider } from '../components/ValuedSlider';
+import { useTranslation } from 'react-i18next';
+import commonStore, { ModelStatus } from '../stores/commonStore';
+import { fetchEventSource } from '@microsoft/fetch-event-source';
+import { toast } from 'react-toastify';
+import { DialogButton } from '../components/DialogButton';
+import { ToolTipButton } from '../components/ToolTipButton';
+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 { FileExists, OpenFileFolder, OpenSaveFileDialogBytes } from '../../wailsjs/go/backend_golang/App';
+import { toastWithButton } from '../utils';
+
+export type CompositionParams = {
+  prompt: string,
+  maxResponseToken: number,
+  temperature: number,
+  topP: number,
+  autoPlay: boolean,
+  useLocalSoundFont: boolean,
+  midi: ArrayBuffer | null,
+  ns: NoteSequence | null
+}
+
+let compositionSseController: AbortController | null = null;
+
+const CompositionPanel: FC = observer(() => {
+  const { t } = useTranslation();
+  const inputRef = useRef<HTMLTextAreaElement>(null);
+  const port = commonStore.getCurrentModelConfig().apiParameters.apiPort;
+  const visualizerRef = useRef<VisualizerElement>(null);
+  const playerRef = useRef<PlayerElement>(null);
+
+  const scrollToBottom = () => {
+    if (inputRef.current)
+      inputRef.current.scrollTop = inputRef.current.scrollHeight;
+  };
+
+  const params = commonStore.compositionParams;
+  const setParams = (newParams: Partial<CompositionParams>) => {
+    commonStore.setCompositionParams({
+      ...commonStore.compositionParams,
+      ...newParams
+    });
+  };
+
+  const setPrompt = (prompt: string) => {
+    setParams({
+      prompt
+    });
+    if (!commonStore.compositionGenerating)
+      generateNs(false);
+  };
+
+  const updateNs = (ns: NoteSequence | null) => {
+    if (playerRef.current) {
+      playerRef.current.noteSequence = ns;
+      playerRef.current.reload();
+    }
+    if (visualizerRef.current) {
+      visualizerRef.current.noteSequence = ns;
+      visualizerRef.current.reload();
+    }
+  };
+
+  const setSoundFont = async () => {
+    let soundUrl: string;
+    if (params.useLocalSoundFont)
+      soundUrl = 'assets/sound-font';
+    else
+      soundUrl = !commonStore.settings.giteeUpdatesSource ?
+        `https://raw.githubusercontent.com/josStorer/sgm_plus/master` :
+        `https://gitee.com/josc146/sgm_plus/raw/master`;
+    const fallbackUrl = 'https://cdn.jsdelivr.net/gh/josstorer/sgm_plus';
+    await fetch(soundUrl + '/soundfont.json').then(r => {
+      if (!r.ok)
+        soundUrl = fallbackUrl;
+    }).catch(() => soundUrl = fallbackUrl);
+    if (playerRef.current) {
+      playerRef.current.soundFont = soundUrl;
+    }
+  };
+
+  useEffect(() => {
+    if (inputRef.current)
+      inputRef.current.style.height = '100%';
+    scrollToBottom();
+
+    if (playerRef.current && visualizerRef.current) {
+      playerRef.current.addVisualizer(visualizerRef.current);
+      playerRef.current.addEventListener('start', () => {
+        visualizerRef.current?.reload();
+      });
+      setSoundFont().then(() => {
+        updateNs(params.ns);
+      });
+
+      const button = playerRef.current.shadowRoot?.querySelector('.controls .play') as HTMLElement | null;
+      if (button)
+        button.style.background = '#f2f5f6';
+    }
+  }, []);
+
+  const generateNs = (autoPlay: boolean) => {
+    fetch(commonStore.settings.apiUrl ?
+      commonStore.settings.apiUrl + '/text-to-midi' :
+      `http://127.0.0.1:${port}/text-to-midi`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify({
+        'text': params.prompt.replaceAll(/<pad>|<start>|<end>/g, '').replaceAll('  ', '').trim()
+      })
+    }).then(r => {
+      r.arrayBuffer().then(midi => {
+        const ns = mm.midiToSequenceProto(midi);
+        setParams({
+          midi,
+          ns
+        });
+        updateNs(ns);
+        if (autoPlay) {
+          playerRef.current?.start();
+        }
+      });
+    });
+  };
+
+  const onSubmit = (prompt: string) => {
+    commonStore.setCompositionSubmittedPrompt(prompt);
+
+    if (commonStore.status.status === ModelStatus.Offline && !commonStore.settings.apiUrl) {
+      toast(t('Please click the button in the top right corner to start the model'), { type: 'warning' });
+      commonStore.setCompositionGenerating(false);
+      return;
+    }
+
+    let answer = '';
+    compositionSseController = new AbortController();
+    fetchEventSource( // https://api.openai.com/v1/completions || http://127.0.0.1:${port}/completions
+      commonStore.settings.apiUrl ?
+        commonStore.settings.apiUrl + '/v1/completions' :
+        `http://127.0.0.1:${port}/completions`,
+      {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+          Authorization: `Bearer ${commonStore.settings.apiKey}`
+        },
+        body: JSON.stringify({
+          prompt,
+          stream: true,
+          model: commonStore.settings.apiCompletionModelName, // 'text-davinci-003'
+          max_tokens: params.maxResponseToken,
+          temperature: params.temperature,
+          top_p: params.topP
+        }),
+        signal: compositionSseController?.signal,
+        onmessage(e) {
+          scrollToBottom();
+          if (e.data.trim() === '[DONE]') {
+            commonStore.setCompositionGenerating(false);
+            generateNs(params.autoPlay);
+            return;
+          }
+          let data;
+          try {
+            data = JSON.parse(e.data);
+          } catch (error) {
+            console.debug('json error', error);
+            return;
+          }
+          if (data.choices && Array.isArray(data.choices) && data.choices.length > 0) {
+            answer += data.choices[0]?.text || data.choices[0]?.delta?.content || '';
+            setPrompt(prompt + answer.replace(/\s+$/, ''));
+          }
+        },
+        async onopen(response) {
+          if (response.status !== 200) {
+            toast(response.statusText + '\n' + (await response.text()), {
+              type: 'error'
+            });
+          }
+        },
+        onclose() {
+          console.log('Connection closed');
+        },
+        onerror(err) {
+          err = err.message || err;
+          if (err && !err.includes('ReadableStreamDefaultReader'))
+            toast(err, {
+              type: 'error'
+            });
+          commonStore.setCompositionGenerating(false);
+          throw err;
+        }
+      });
+  };
+
+  return (
+    <div className="flex flex-col gap-2 overflow-hidden grow">
+      <div className="flex flex-col sm:flex-row gap-2 overflow-hidden grow">
+        <Textarea
+          ref={inputRef}
+          className="grow"
+          value={params.prompt}
+          onChange={(e) => {
+            commonStore.setCompositionSubmittedPrompt(e.target.value);
+            setPrompt(e.target.value);
+          }}
+        />
+        <div className="flex flex-col gap-1 max-h-48 sm:max-w-sm sm:max-h-full overflow-x-hidden overflow-y-auto p-1">
+          <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={
+              <ValuedSlider value={params.maxResponseToken} min={100} max={4100}
+                step={100}
+                input
+                onChange={(e, data) => {
+                  setParams({
+                    maxResponseToken: data.value
+                  });
+                }} />
+            } />
+          <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}
+                input
+                onChange={(e, data) => {
+                  setParams({
+                    temperature: data.value
+                  });
+                }} />
+            } />
+          <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
+                onChange={(e, data) => {
+                  setParams({
+                    topP: data.value
+                  });
+                }} />
+            } />
+          <div className="grow" />
+          <Checkbox className="select-none"
+            size="large" label={t('Use Local Sound Font')} checked={params.useLocalSoundFont}
+            onChange={async (_, data) => {
+              if (data.checked) {
+                if (!await FileExists('assets/sound-font/accordion/instrument.json')) {
+                  toast(t('Failed to load local sound font, please check if the files exist - assets/sound-font'),
+                    { type: 'warning' });
+                  return;
+                }
+              }
+              setParams({
+                useLocalSoundFont: data.checked as boolean
+              });
+              setSoundFont();
+            }} />
+          <Checkbox className="select-none"
+            size="large" label={t('Auto Play At The End')} checked={params.autoPlay} onChange={(_, data) => {
+            setParams({
+              autoPlay: data.checked as boolean
+            });
+          }} />
+          <div className="flex justify-between gap-2">
+            <ToolTipButton desc={t('Regenerate')} icon={<ArrowSync20Regular />} onClick={() => {
+              compositionSseController?.abort();
+              commonStore.setCompositionGenerating(true);
+              setPrompt(commonStore.compositionSubmittedPrompt);
+              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.')}
+              onConfirm={() => {
+                commonStore.setCompositionSubmittedPrompt(defaultCompositionPrompt);
+                setPrompt(defaultCompositionPrompt);
+              }} />
+            <Button className="grow" appearance="primary" onClick={() => {
+              if (commonStore.compositionGenerating) {
+                compositionSseController?.abort();
+                commonStore.setCompositionGenerating(false);
+                generateNs(params.autoPlay);
+              } else {
+                commonStore.setCompositionGenerating(true);
+                onSubmit(params.prompt);
+              }
+            }}>{!commonStore.compositionGenerating ? t('Generate') : t('Stop')}</Button>
+          </div>
+        </div>
+      </div>
+      <div className="flex flex-col">
+        <div className="ml-auto mr-auto">
+          <midi-visualizer
+            ref={visualizerRef}
+            type="waterfall"
+          />
+        </div>
+        <div className="flex">
+          <midi-player
+            ref={playerRef}
+            style={{ width: '100%' }}
+          />
+          <Button icon={<Save28Regular />}
+            onClick={() => {
+              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);
+                    });
+                }).catch((e: any) => {
+                  toast(t('Error') + ' - ' + (e.message || e), { type: 'error', autoClose: 2500 });
+                });
+              } else {
+                toast(t('No File to save'), { type: 'warning', autoClose: 1500 });
+              }
+            }}
+          >
+            {t('Save')}
+          </Button>
+        </div>
+      </div>
+    </div>
+  );
+});
+
+export const Composition: FC = observer(() => {
+  return (
+    <div className="flex flex-col gap-1 p-2 h-full overflow-hidden">
+      <WorkHeader />
+      <CompositionPanel />
+    </div>
+  );
+});

frontend/src/pages/Configs.tsx

@@ -13,8 +13,8 @@ import { Page } from '../components/Page';
 import { useNavigate } from 'react-router';
 import { RunButton } from '../components/RunButton';
 import { updateConfig } from '../apis';
-import { ConvertModel, FileExists } from '../../wailsjs/go/backend_golang/App';
-import { getStrategy, refreshLocalModels } from '../utils';
+import { ConvertModel, FileExists, GetPyError } from '../../wailsjs/go/backend_golang/App';
+import { getStrategy } from '../utils';
 import { useTranslation } from 'react-i18next';
 import { WindowShow } from '../../wailsjs/runtime/runtime';
 import strategyImg from '../assets/images/strategy.jpg';
@@ -224,12 +224,12 @@ export const Configs: FC = observer(() => {
                           modelName: data.value
                         });
                       }}>
-                      {!commonStore.modelSourceList.find(item => item.name === selectedConfig.modelParameters.modelName)?.isLocal
+                      {!commonStore.modelSourceList.find(item => item.name === selectedConfig.modelParameters.modelName)?.isComplete
                         && <option key={-1}
                           value={selectedConfig.modelParameters.modelName}>{selectedConfig.modelParameters.modelName}
                         </option>}
                       {commonStore.modelSourceList.map((modelItem, index) =>
-                        modelItem.isLocal && <option key={index} value={modelItem.name}>{modelItem.name}</option>
+                        modelItem.isComplete && <option key={index} value={modelItem.name}>{modelItem.name}</option>
                       )}
                     </Select>
                     <ToolTipButton desc={t('Manage Models')} icon={<DataUsageSettings20Regular />} onClick={() => {
@@ -253,9 +253,12 @@ export const Configs: FC = observer(() => {
                       const strategy = getStrategy(selectedConfig);
                       const newModelPath = modelPath + '-' + strategy.replace(/[:> *+]/g, '-');
                       toast(t('Start Converting'), { autoClose: 1000, type: 'info' });
-                      ConvertModel(commonStore.settings.customPythonPath, modelPath, strategy, newModelPath).then(() => {
-                        toast(`${t('Convert Success')} - ${newModelPath}`, { type: 'success' });
-                        refreshLocalModels({ models: commonStore.modelSourceList }, false);
+                      ConvertModel(commonStore.settings.customPythonPath, modelPath, strategy, newModelPath).then(async () => {
+                        if (!await FileExists(newModelPath + '.pth')) {
+                          toast(t('Convert Failed') + ' - ' + await GetPyError(), { type: 'error' });
+                        } else {
+                          toast(`${t('Convert Success')} - ${newModelPath}`, { type: 'success' });
+                        }
                       }).catch(e => {
                         const errMsg = e.message || e;
                         if (errMsg.includes('path contains space'))
@@ -328,7 +331,8 @@ export const Configs: FC = observer(() => {
                 }
                 {
                   displayStrategyImg &&
-                  <img style={{ width: '80vh', height: 'auto', zIndex: 100 }} className="fixed left-0 top-0"
+                  <img style={{ width: '80vh', height: 'auto', zIndex: 100 }}
+                    className="fixed left-0 top-0 rounded-xl select-none"
                     src={commonStore.settings.language === 'zh' ? strategyZhImg : strategyImg} />
                 }
                 {

frontend/src/pages/Downloads.tsx

@@ -1,13 +1,13 @@
-import React, { FC, useEffect } from 'react';
+import React, { FC } from 'react';
 import { useTranslation } from 'react-i18next';
 import { Page } from '../components/Page';
 import { observer } from 'mobx-react-lite';
 import commonStore from '../stores/commonStore';
 import { Divider, Field, ProgressBar } from '@fluentui/react-components';
-import { bytesToGb, bytesToKb, bytesToMb, refreshLocalModels } from '../utils';
+import { bytesToGb, bytesToKb, bytesToMb } from '../utils';
 import { ToolTipButton } from '../components/ToolTipButton';
 import { Folder20Regular, Pause20Regular, Play20Regular } from '@fluentui/react-icons';
-import { ContinueDownload, OpenFileFolder, PauseDownload } from '../../wailsjs/go/backend_golang/App';
+import { AddToDownloadList, OpenFileFolder, PauseDownload } from '../../wailsjs/go/backend_golang/App';
 
 export type DownloadStatus = {
   name: string;
@@ -23,17 +23,28 @@ export type DownloadStatus = {
 
 export const Downloads: FC = observer(() => {
   const { t } = useTranslation();
-  const finishedModelsLen = commonStore.downloadList.filter((status) => status.done && status.name.endsWith('.pth')).length;
-  useEffect(() => {
-    if (finishedModelsLen > 0)
-      refreshLocalModels({ models: commonStore.modelSourceList }, false);
-    console.log('finishedModelsLen:', finishedModelsLen);
-  }, [finishedModelsLen]);
+
+  let displayList = commonStore.downloadList.slice();
+  const downloadListNames = displayList.map(s => s.name);
+  commonStore.lastUnfinishedModelDownloads.forEach((status) => {
+    const unfinishedIndex = downloadListNames.indexOf(status.name);
+    if (unfinishedIndex === -1) {
+      displayList.push(status);
+    } else {
+      const unfinishedStatus = displayList[unfinishedIndex];
+      if (unfinishedStatus.transferred < status.transferred) {
+        status.downloading = unfinishedStatus.downloading;
+        delete displayList[unfinishedIndex];
+        displayList.push(status);
+      }
+    }
+  });
+  displayList = displayList.reverse();
 
   return (
     <Page title={t('Downloads')} content={
       <div className="flex flex-col gap-2 overflow-y-auto overflow-x-hidden p-1">
-        {commonStore.downloadList.slice().reverse().map((status, index) => {
+        {displayList.map((status, index) => {
           const downloadProgress = `${status.progress.toFixed(2)}%`;
           const downloadSpeed = `${status.downloading ? bytesToMb(status.speed) : '0'}MB/s`;
           let downloadDetails: string;
@@ -59,7 +70,7 @@ export const Downloads: FC = observer(() => {
                       if (status.downloading)
                         PauseDownload(status.url);
                       else
-                        ContinueDownload(status.url);
+                        AddToDownloadList(status.path, status.url);
                     }} />}
                 <ToolTipButton desc={t('Open Folder')} icon={<Folder20Regular />} onClick={() => {
                   OpenFileFolder(status.path, false);

frontend/src/pages/Models.tsx

@@ -31,7 +31,9 @@ export type ModelSourceItem = {
   SHA256?: string;
   url?: string;
   downloadUrl?: string;
+  isComplete?: boolean;
   isLocal?: boolean;
+  localSize?: number;
   lastUpdatedMs?: number;
   hide?: boolean;
 };
@@ -125,7 +127,7 @@ const columns: TableColumnDefinition<ModelSourceItem>[] = [
   createTableColumn<ModelSourceItem>({
     columnId: 'actions',
     compare: (a, b) => {
-      return a.isLocal ? -1 : 1;
+      return a.isComplete ? -1 : 1;
     },
     renderHeaderCell: () => {
       const { t } = useTranslation();
@@ -140,12 +142,12 @@ const columns: TableColumnDefinition<ModelSourceItem>[] = [
         <TableCellLayout>
           <div className="flex gap-1">
             {
-              item.isLocal &&
+              item.isComplete &&
               <ToolTipButton desc={t('Open Folder')} icon={<Folder20Regular />} onClick={() => {
                 OpenFileFolder(`${commonStore.settings.customModelsPath}/${item.name}`, true);
               }} />
             }
-            {item.downloadUrl && !item.isLocal &&
+            {item.downloadUrl && !item.isComplete &&
               <ToolTipButton desc={t('Download')} icon={<ArrowDownload20Regular />} onClick={() => {
                 toastWithButton(`${t('Downloading')} ${item.name}`, t('Check'), () => {
                     navigate({ pathname: '/downloads' });
@@ -203,7 +205,7 @@ export const Models: FC = observer(() => {
             <div className="overflow-y-auto overflow-x-hidden">
               <DataGridBody<ModelSourceItem>>
                 {({ item, rowId }) => (
-                  (!item.hide || item.isLocal) &&
+                  (!item.hide || item.isComplete) &&
                   <DataGridRow<ModelSourceItem> key={rowId}>
                     {({ renderCell }) => (
                       <DataGridCell>{renderCell(item)}</DataGridCell>

frontend/src/pages/PresetsManager/MessagesEditor.tsx

@@ -0,0 +1,154 @@
+import React, { FC, useState } from 'react';
+import { DragDropContext, Draggable, Droppable, DropResult } from 'react-beautiful-dnd';
+import commonStore from '../../stores/commonStore';
+import { Preset } from './PresetsButton';
+import { observer } from 'mobx-react-lite';
+import { v4 as uuid } from 'uuid';
+import { Button, 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';
+import { ConversationMessage, Role } from '../Chat';
+
+type Item = {
+  id: string;
+  role: Role;
+  content: string;
+}
+
+const getItems = (messages: ConversationMessage[]) =>
+  messages.map((message, index) => ({
+    id: uuid(),
+    role: message.role,
+    content: message.content
+  })) as Item[];
+
+const reorder = (list: Item[], startIndex: number, endIndex: number) => {
+  const result = Array.from(list);
+  const [removed] = result.splice(startIndex, 1);
+  result.splice(endIndex, 0, removed);
+
+  return result;
+};
+
+export const MessagesEditor: FC = observer(() => {
+  const { t } = useTranslation();
+
+  const editingPreset = commonStore.editingPreset!;
+  const setEditingPreset = (newParams: Partial<Preset>) => {
+    commonStore.setEditingPreset({
+      ...editingPreset,
+      ...newParams
+    });
+  };
+
+  const [items, setItems] = useState(getItems(editingPreset.messages));
+
+  const updateItems = (items: Item[]) => {
+    setEditingPreset({
+      messages: items.map(item => ({
+        role: item.role,
+        content: item.content
+      }))
+    });
+    setItems(items);
+  };
+
+  const onDragEnd = (result: DropResult) => {
+    if (!result.destination) {
+      return;
+    }
+
+    const newItems = reorder(
+      items,
+      result.source.index,
+      result.destination.index
+    );
+
+    updateItems(newItems);
+  };
+
+  const createNewItem = () => {
+    const newItems: Item[] = [...items, {
+      id: uuid(),
+      role: 'assistant',
+      content: ''
+    }];
+    updateItems(newItems);
+  };
+
+  const deleteItem = (id: string) => {
+    const newItems: Item[] = items.filter(item => item.id !== id);
+    updateItems(newItems);
+  };
+
+  return (
+    <div className="grid grid-cols-1 gap-2 overflow-hidden">
+      <Button style={{ width: '100%' }} onClick={createNewItem}>{t('New')}</Button>
+      <div className="overflow-x-hidden overflow-y-auto p-2">
+        <DragDropContext onDragEnd={onDragEnd}>
+          <Droppable droppableId="droppable">
+            {(provided, snapshot) => (
+              <div
+                {...provided.droppableProps}
+                ref={provided.innerRef}
+              >
+                {items.map((item, index) => (
+                  <Draggable key={item.id} draggableId={item.id} index={index}>
+                    {(provided, snapshot) => (
+                      <div
+                        ref={provided.innerRef}
+                        {...provided.draggableProps}
+                        {...provided.dragHandleProps}
+                        style={provided.draggableProps.style}
+                        className="select-none mb-2"
+                      >
+                        <div className="flex">
+                          <Card appearance="outline"
+                            style={{ borderTopRightRadius: 0, borderBottomRightRadius: 0 }}>
+                            <ReOrderDotsVertical20Regular />
+                          </Card>
+                          <Dropdown style={{ minWidth: 0, borderRadius: 0 }} listbox={{ style: { minWidth: 0 } }}
+                            value={t(item.role)!}
+                            selectedOptions={[item.role]}
+                            onOptionSelect={(_, data) => {
+                              if (data.optionValue) {
+                                items[index] = {
+                                  ...item,
+                                  role: data.optionValue as Role
+                                };
+                                updateItems([...items]);
+                              }
+                            }}>
+                            <Option value="user">{t('user')!}</Option>
+                            <Option value="assistant">{t('assistant')!}</Option>
+                            {/* TODO <Option value="system">{t('system')!}</Option>*/}
+                          </Dropdown>
+                          <Textarea resize="vertical" className="grow" value={item.content}
+                            style={{ minWidth: 0, borderRadius: 0 }}
+                            onChange={(e, data) => {
+                              items[index] = {
+                                ...item,
+                                content: data.value
+                              };
+                              updateItems([...items]);
+                            }}></Textarea>
+                          <ToolTipButton
+                            style={{ borderTopLeftRadius: 0, borderBottomLeftRadius: 0 }} desc={t('Delete')}
+                            icon={<Delete20Regular />} onClick={() => {
+                            deleteItem(item.id);
+                          }} />
+                        </div>
+                      </div>
+                    )}
+                  </Draggable>
+                ))}
+                {provided.placeholder}
+              </div>
+            )}
+          </Droppable>
+        </DragDropContext>
+      </div>
+    </div>
+  );
+});

frontend/src/pages/PresetsManager/PresetsButton.tsx

@@ -0,0 +1,431 @@
+// TODO refactor
+
+import React, { FC, PropsWithChildren, ReactElement, useState } from 'react';
+import {
+  Button,
+  Dialog,
+  DialogBody,
+  DialogContent,
+  DialogSurface,
+  DialogTrigger,
+  Input,
+  Switch,
+  Tab,
+  TabList,
+  Text
+} from '@fluentui/react-components';
+import {
+  Accessibility28Regular,
+  Chat20Regular,
+  ClipboardEdit20Regular,
+  Delete20Regular,
+  Dismiss20Regular,
+  Edit20Regular,
+  Globe20Regular
+} from '@fluentui/react-icons';
+import { ToolTipButton } from '../../components/ToolTipButton';
+import { useTranslation } from 'react-i18next';
+import { botName, Conversation, ConversationMessage, MessageType, userName } from '../Chat';
+import { SelectTabEventHandler } from '@fluentui/react-tabs';
+import { Labeled } from '../../components/Labeled';
+import commonStore from '../../stores/commonStore';
+import logo from '../../assets/images/logo.png';
+import { observer } from 'mobx-react-lite';
+import { MessagesEditor } from './MessagesEditor';
+import { ClipboardGetText, ClipboardSetText } from '../../../wailsjs/runtime';
+import { toast } from 'react-toastify';
+import { CustomToastContainer } from '../../components/CustomToastContainer';
+import { v4 as uuid } from 'uuid';
+
+export type PresetType = 'chat' | 'completion' | 'chatInCompletion'
+
+export type Preset = {
+  name: string,
+  tag: string,
+  // if name and sourceUrl are same, it will be overridden when importing
+  sourceUrl: string,
+  desc: string,
+  avatarImg: string,
+  type: PresetType,
+  // chat
+  welcomeMessage: string,
+  messages: ConversationMessage[],
+  displayPresetMessages: boolean,
+  // completion
+  prompt: string,
+  stop: string,
+  injectStart: string,
+  injectEnd: string,
+}
+
+export const defaultPreset: Preset = {
+  name: 'RWKV',
+  tag: 'default',
+  sourceUrl: '',
+  desc: '',
+  avatarImg: logo,
+  type: 'chat',
+  welcomeMessage: '',
+  displayPresetMessages: true,
+  messages: [],
+  prompt: '',
+  stop: '',
+  injectStart: '',
+  injectEnd: ''
+};
+
+const setActivePreset = (preset: Preset) => {
+  commonStore.setActivePreset(preset);
+  //TODO if (preset.displayPresetMessages) {
+  const conversation: Conversation = {};
+  const conversationOrder: string[] = [];
+  for (const message of preset.messages) {
+    const newUuid = uuid();
+    conversationOrder.push(newUuid);
+    conversation[newUuid] = {
+      sender: message.role === 'user' ? userName : botName,
+      type: MessageType.Normal,
+      color: message.role === 'user' ? 'brand' : 'colorful',
+      time: new Date().toISOString(),
+      content: message.content,
+      side: message.role === 'user' ? 'right' : 'left',
+      done: true
+    };
+  }
+  commonStore.setConversation(conversation);
+  commonStore.setConversationOrder(conversationOrder);
+  //}
+};
+
+export const PresetCardFrame: FC<PropsWithChildren & { onClick?: () => void }> = (props) => {
+  return <Button
+    className="flex flex-col gap-1 w-32 h-56 break-all"
+    style={{ minWidth: 0, borderRadius: '0.75rem', justifyContent: 'unset' }}
+    onClick={props.onClick}
+  >
+    {props.children}
+  </Button>;
+};
+
+export const PresetCard: FC<{
+  avatarImg: string,
+  name: string,
+  desc: string,
+  tag: string,
+  editable: boolean,
+  presetIndex: number,
+  onClick?: () => void
+}> = observer(({
+  avatarImg, name, desc, tag, editable, presetIndex, onClick
+}) => {
+  const { t } = useTranslation();
+
+  return <PresetCardFrame onClick={onClick}>
+    <img src={avatarImg} className="rounded-xl select-none ml-auto mr-auto h-28" />
+    <Text size={400}>{name}</Text>
+    <Text size={200} style={{
+      overflow: 'hidden', textOverflow: 'ellipsis',
+      display: '-webkit-box', WebkitLineClamp: 3, WebkitBoxOrient: 'vertical'
+    }}>{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>
+      {editable ?
+        <ChatPresetEditor presetIndex={presetIndex} triggerButton={
+          <ToolTipButton size="small" appearance="transparent" desc={t('Edit')} icon={<Edit20Regular />}
+            onClick={() => {
+              commonStore.setEditingPreset({ ...commonStore.presets[presetIndex] });
+            }} />
+        } />
+        : <div />
+      }
+    </div>
+  </PresetCardFrame>;
+});
+
+export const ChatPresetEditor: FC<{
+  triggerButton: ReactElement,
+  presetIndex: number
+}> = observer(({ triggerButton, presetIndex }) => {
+  const { t } = useTranslation();
+  const [editingMessages, setEditingMessages] = useState(false);
+
+  if (!commonStore.editingPreset)
+    commonStore.setEditingPreset({ ...defaultPreset });
+  const editingPreset = commonStore.editingPreset!;
+
+  const setEditingPreset = (newParams: Partial<Preset>) => {
+    commonStore.setEditingPreset({
+      ...editingPreset,
+      ...newParams
+    });
+  };
+
+  const importPreset = () => {
+    ClipboardGetText().then((text) => {
+      try {
+        const preset = JSON.parse(text);
+        setEditingPreset(preset);
+        toast(t('Imported successfully'), {
+          type: 'success',
+          autoClose: 1000
+        });
+      } catch (e) {
+        toast(t('Failed to import. Please copy a preset to the clipboard.'), {
+          type: 'error',
+          autoClose: 2500
+        });
+      }
+    }).catch(() => {
+      toast(t('Clipboard is empty.'), {
+        type: 'info',
+        autoClose: 1000
+      });
+    });
+  };
+
+  const copyPreset = () => {
+    ClipboardSetText(JSON.stringify(editingPreset)).then((success) => {
+      if (success)
+        toast(t('Successfully copied to clipboard.'), {
+          type: 'success',
+          autoClose: 1000
+        });
+    });
+  };
+
+  const savePreset = () => {
+    if (presetIndex === -1) {
+      commonStore.setPresets([...commonStore.presets, { ...editingPreset }]);
+      setEditingPreset(defaultPreset);
+    } else {
+      commonStore.presets[presetIndex] = editingPreset;
+      commonStore.setPresets(commonStore.presets);
+    }
+  };
+
+  const activatePreset = () => {
+    savePreset();
+    setActivePreset(editingPreset);
+  };
+
+  const deletePreset = () => {
+    commonStore.presets.splice(presetIndex, 1);
+    commonStore.setPresets(commonStore.presets);
+  };
+
+  return <Dialog>
+    <DialogTrigger disableButtonEnhancement>
+      {triggerButton}
+    </DialogTrigger>
+    <DialogSurface style={{
+      paddingTop: 0,
+      maxWidth: '80vw',
+      maxHeight: '80vh',
+      width: '500px',
+      height: '100%'
+    }}>
+      <DialogBody style={{ height: '100%', overflow: 'hidden' }}>
+        <DialogContent className="flex flex-col gap-1 overflow-hidden">
+          <CustomToastContainer />
+          <div className="flex justify-between">{
+            presetIndex === -1
+              ? <div />
+              : <DialogTrigger disableButtonEnhancement>
+                <Button appearance="subtle" icon={<Delete20Regular />} onClick={deletePreset} />
+              </DialogTrigger>
+          }
+            <DialogTrigger disableButtonEnhancement>
+              <Button appearance="subtle" icon={<Dismiss20Regular />} />
+            </DialogTrigger>
+          </div>
+          <img src={editingPreset.avatarImg} className="rounded-xl select-none ml-auto mr-auto h-28" />
+          <Labeled flex breakline label={t('Name')}
+            content={
+              <div className="flex gap-2">
+                <Input className="grow" value={editingPreset.name} onChange={(e, data) => {
+                  setEditingPreset({
+                    name: data.value
+                  });
+                }} />
+                <Button onClick={() => {
+                  setEditingMessages(!editingMessages);
+                }}>{!editingMessages ? t('Edit Messages') : t('Go Back')}</Button>
+              </div>
+            } />
+          {
+            editingMessages ?
+              <MessagesEditor /> :
+              <div className="flex flex-col gap-1 p-2 overflow-x-hidden overflow-y-auto">
+                <Labeled flex breakline label={t('Description')}
+                  content={
+                    <Input value={editingPreset.desc} onChange={(e, data) => {
+                      setEditingPreset({
+                        desc: data.value
+                      });
+                    }} />
+                  } />
+                <Labeled flex breakline label={t('Avatar Url')}
+                  content={
+                    <Input value={editingPreset.avatarImg} onChange={(e, data) => {
+                      setEditingPreset({
+                        avatarImg: data.value
+                      });
+                    }} />
+                  } />
+                <Labeled flex breakline label={t('Welcome Message')}
+                  content={
+                    <Input disabled value={editingPreset.welcomeMessage} onChange={(e, data) => {
+                      setEditingPreset({
+                        welcomeMessage: data.value
+                      });
+                    }} />
+                  } />
+                <Labeled flex spaceBetween label={t('Display Preset Messages')}
+                  content={
+                    <Switch disabled checked={editingPreset.displayPresetMessages}
+                      onChange={(e, data) => {
+                        setEditingPreset({
+                          displayPresetMessages: data.checked
+                        });
+                      }} />
+                  } />
+                <Labeled flex breakline label={t('Tag')}
+                  content={
+                    <Input value={editingPreset.tag} onChange={(e, data) => {
+                      setEditingPreset({
+                        tag: data.value
+                      });
+                    }} />
+                  } />
+              </div>
+          }
+          <div className="grow" />
+          <div className="flex justify-between">
+            <Button onClick={importPreset}>{t('Import')}</Button>
+            <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>
+          </div>
+        </DialogContent>
+      </DialogBody>
+    </DialogSurface>
+  </Dialog>;
+});
+
+export const ChatPresets: FC = observer(() => {
+  const { t } = useTranslation();
+
+  return <div className="flex flex-wrap gap-2">
+    <ChatPresetEditor presetIndex={-1} triggerButton={
+      <PresetCardFrame>
+        <div className="h-full flex items-center">
+          {t('New Preset')}
+        </div>
+      </PresetCardFrame>}
+    />
+    {/*TODO <PresetCardFrame>*/}
+    {/*  <div className="h-full flex items-center">*/}
+    {/*    {t('Import')}*/}
+    {/*  </div>*/}
+    {/*</PresetCardFrame>*/}
+    <PresetCard
+      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
+        presetIndex={index}
+        editable={true}
+        onClick={() => {
+          setActivePreset(preset);
+        }}
+        key={index} avatarImg={preset.avatarImg} name={preset.name} desc={preset.desc} tag={preset.tag}
+      />;
+    })}
+  </div>;
+});
+
+type PresetsNavigationItem = {
+  icon: ReactElement;
+  element: ReactElement;
+};
+
+const pages: { [label: string]: PresetsNavigationItem } = {
+  Chat: {
+    icon: <Chat20Regular />,
+    element: <ChatPresets />
+  },
+  Completion: {
+    icon: <ClipboardEdit20Regular />,
+    element: <div>In Development</div>
+  },
+  Online: {
+    icon: <Globe20Regular />,
+    element: <div>In Development</div>
+  }
+};
+
+export const PresetsManager: FC<{ initTab: string }> = ({ initTab }) => {
+  const { t } = useTranslation();
+  const [tab, setTab] = useState(initTab);
+
+  const selectTab: SelectTabEventHandler = (e, data) =>
+    typeof data.value === 'string' ? setTab(data.value) : null;
+
+  return <div className="flex flex-col gap-2 w-full h-full">
+    <div className="flex justify-between">
+      <TabList
+        size="small"
+        appearance="subtle"
+        selectedValue={tab}
+        onTabSelect={selectTab}
+      >
+        {Object.entries(pages).map(([label, { icon }]) => (
+          <Tab icon={icon} key={label} value={label}>
+            {t(label)}
+          </Tab>
+        ))}
+      </TabList>
+      <DialogTrigger disableButtonEnhancement>
+        <Button appearance="subtle" icon={<Dismiss20Regular />} />
+      </DialogTrigger>
+    </div>
+    <div className="grow overflow-x-hidden overflow-y-auto">
+      {pages[tab].element}
+    </div>
+  </div>;
+};
+
+export const PresetsButton: FC<{
+  tab: string,
+  size?: 'small' | 'medium' | 'large',
+  shape?: 'rounded' | 'circular' | 'square';
+  appearance?: 'secondary' | 'primary' | 'outline' | 'subtle' | 'transparent';
+}> = ({ tab, size, shape, appearance }) => {
+  const { t } = useTranslation();
+
+  return <Dialog>
+    <DialogTrigger disableButtonEnhancement>
+      <ToolTipButton desc={t('Presets')} size={size} shape={shape} appearance={appearance}
+        icon={<Accessibility28Regular />} />
+    </DialogTrigger>
+    <DialogSurface style={{ paddingTop: 0, maxWidth: '90vw', width: 'fit-content' }}>
+      <DialogBody>
+        <DialogContent>
+          <CustomToastContainer />
+          <PresetsManager initTab={tab} />
+        </DialogContent>
+      </DialogBody>
+    </DialogSurface>
+  </Dialog>;
+};

frontend/src/pages/Settings.tsx

@@ -166,6 +166,7 @@ export const Settings: FC = observer(() => {
                   content={
                     <Input className="grow" placeholder="./py310/python" value={commonStore.settings.customPythonPath}
                       onChange={(e, data) => {
+                        commonStore.setDepComplete(false);
                         commonStore.setSettings({
                           customPythonPath: data.value
                         });

frontend/src/pages/Train.tsx

@@ -1,13 +1,609 @@
-import React, { FC } from 'react';
-import { Text } from '@fluentui/react-components';
+import React, { FC, ReactElement, useEffect, useRef, useState } from 'react';
 import { useTranslation } from 'react-i18next';
+import { Button, Dropdown, Input, Option, Select, Switch, Tab, TabList } from '@fluentui/react-components';
+import {
+  ConvertData,
+  FileExists,
+  GetPyError,
+  MergeLora,
+  OpenFileFolder,
+  WslCommand,
+  WslEnable,
+  WslInstallUbuntu,
+  WslIsEnabled,
+  WslStart,
+  WslStop
+} from '../../wailsjs/go/backend_golang/App';
+import { toast } from 'react-toastify';
+import commonStore from '../stores/commonStore';
+import { observer } from 'mobx-react-lite';
+import { SelectTabEventHandler } from '@fluentui/react-tabs';
+import { checkDependencies, toastWithButton } from '../utils';
+import { Section } from '../components/Section';
+import { Labeled } from '../components/Labeled';
+import { ToolTipButton } from '../components/ToolTipButton';
+import { DataUsageSettings20Regular, Folder20Regular } from '@fluentui/react-icons';
+import { useNavigate } from 'react-router';
+import { Precision } from './Configs';
+import {
+  CategoryScale,
+  Chart as ChartJS,
+  Legend,
+  LinearScale,
+  LineElement,
+  PointElement,
+  Title,
+  Tooltip
+} from 'chart.js';
+import { Line } from 'react-chartjs-2';
+import { ChartJSOrUndefined } from 'react-chartjs-2/dist/types';
+import { WindowShow } from '../../wailsjs/runtime';
+import { t } from 'i18next';
+import { DialogButton } from '../components/DialogButton';
+
+ChartJS.register(
+  CategoryScale,
+  LinearScale,
+  PointElement,
+  LineElement,
+  Tooltip,
+  Title,
+  Legend
+);
+
+const parseLossData = (data: string) => {
+  const regex = /Epoch (\d+):\s+(\d+%)\|[\s\S]*\| (\d+)\/(\d+) \[(\S+)<(\S+),\s+(\S+), loss=(\S+),[\s\S]*\]/g;
+  const matches = Array.from(data.matchAll(regex));
+  if (matches.length === 0)
+    return false;
+  const lastMatch = matches[matches.length - 1];
+  const epoch = parseInt(lastMatch[1]);
+  const loss = parseFloat(lastMatch[8]);
+  commonStore.setChartTitle(`Epoch ${epoch}: ${lastMatch[2]} - ${lastMatch[3]}/${lastMatch[4]} - ${lastMatch[5]}/${lastMatch[6]} - ${lastMatch[7]} Loss=${loss}`);
+  addLossDataToChart(epoch, loss);
+  return true;
+};
+
+let chartLine: ChartJSOrUndefined<'line', (number | null)[], string>;
+
+const addLossDataToChart = (epoch: number, loss: number) => {
+  const epochIndex = commonStore.chartData.labels!.findIndex(l => l.includes(epoch.toString()));
+  if (epochIndex === -1) {
+    if (epoch === 0) {
+      commonStore.chartData.labels!.push('Init');
+      commonStore.chartData.datasets[0].data = [...commonStore.chartData.datasets[0].data, loss];
+    }
+    commonStore.chartData.labels!.push('Epoch ' + epoch.toString());
+    commonStore.chartData.datasets[0].data = [...commonStore.chartData.datasets[0].data, loss];
+  } else {
+    if (chartLine) {
+      const newData = [...commonStore.chartData.datasets[0].data];
+      newData[epochIndex] = loss;
+      chartLine.data.datasets[0].data = newData;
+      chartLine.update();
+    }
+  }
+  commonStore.setChartData(commonStore.chartData);
+};
+
+export type DataProcessParameters = {
+  dataPath: string;
+  vocabPath: string;
+}
+
+export type LoraFinetunePrecision = 'bf16' | 'fp16' | 'tf32';
+
+export type LoraFinetuneParameters = {
+  baseModel: string;
+  ctxLen: number;
+  epochSteps: number;
+  epochCount: number;
+  epochBegin: number;
+  epochSave: number;
+  microBsz: number;
+  accumGradBatches: number;
+  preFfn: boolean;
+  headQk: boolean;
+  lrInit: string;
+  lrFinal: string;
+  warmupSteps: number;
+  beta1: number;
+  beta2: number;
+  adamEps: string;
+  devices: number;
+  precision: LoraFinetunePrecision;
+  gradCp: boolean;
+  loraR: number;
+  loraAlpha: number;
+  loraDropout: number;
+  loraLoad: string
+}
+
+const loraFinetuneParametersOptions: Array<[key: keyof LoraFinetuneParameters, type: string, name: string]> = [
+  ['devices', 'number', 'Devices'],
+  ['precision', 'LoraFinetunePrecision', 'Precision'],
+  ['gradCp', 'boolean', 'Gradient Checkpoint'],
+  ['ctxLen', 'number', 'Context Length'],
+  ['epochSteps', 'number', 'Epoch Steps'],
+  ['epochCount', 'number', 'Epoch Count'],
+  ['epochBegin', 'number', 'Epoch Begin'],
+  ['epochSave', 'number', 'Epoch Save'],
+  ['lrInit', 'string', 'Learning Rate Init'],
+  ['lrFinal', 'string', 'Learning Rate Final'],
+  ['microBsz', 'number', 'Micro Batch Size'],
+  ['accumGradBatches', 'number', 'Accumulate Gradient Batches'],
+  ['warmupSteps', 'number', 'Warmup Steps'],
+  ['adamEps', 'string', 'Adam Epsilon'],
+  ['beta1', 'number', 'Beta 1'],
+  ['beta2', 'number', 'Beta 2'],
+  ['loraR', 'number', 'LoRA R'],
+  ['loraAlpha', 'number', 'LoRA Alpha'],
+  ['loraDropout', 'number', 'LoRA Dropout'],
+  ['beta1', 'any', ''],
+  ['preFfn', 'boolean', 'Pre-FFN'],
+  ['headQk', 'boolean', 'Head QK']
+];
+
+const showError = (e: any) => {
+  const msg = e.message || e;
+  if (msg === 'wsl not running') {
+    toast(t('WSL is not running, please retry. If it keeps happening, it means you may be using an outdated version of WSL, run "wsl --update" to update.'), { type: 'error' });
+  } else {
+    toast(t(msg), { type: 'error', toastId: 'train_error' });
+  }
+};
+
+const errorsMap = Object.entries({
+  'python3 ./finetune/lora/train.py': 'Memory is not enough, try to increase the virtual memory 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\'': 'You are using WSL 1 for training, please upgrade to WSL 2. e.g. Run "wsl --set-version Ubuntu-22.04 2"',
+  'size mismatch for blocks': 'Size mismatch for blocks. You are attempting to continue training from the LoRA model, but it does not match the base model. Please set LoRA model to None.',
+  'cuda_home environment variable is not set': 'Matched CUDA is not installed',
+  'unsupported gpu architecture': 'Matched CUDA is not installed',
+  'error building extension \'fused_adam\'': 'Matched CUDA is not installed'
+});
+
+export const wslHandler = (data: string) => {
+  if (data) {
+    addWslMessage(data);
+    const ok = parseLossData(data);
+    if (!ok)
+      for (const [key, value] of errorsMap) {
+        if (data.toLowerCase().includes(key)) {
+          showError(value);
+          return;
+        }
+      }
+  }
+};
+
+const addWslMessage = (message: string) => {
+  const newData = commonStore.wslStdout + '\n' + message;
+  let lines = newData.split('\n');
+  const result = lines.slice(-100).join('\n');
+  commonStore.setWslStdout(result);
+};
+
+const TerminalDisplay: FC = observer(() => {
+  const bodyRef = useRef<HTMLDivElement>(null);
+
+  const scrollToBottom = () => {
+    if (bodyRef.current)
+      bodyRef.current.scrollTop = bodyRef.current.scrollHeight;
+  };
+
+  useEffect(() => {
+    scrollToBottom();
+  });
+
+  return (
+    <div ref={bodyRef} className="grow overflow-x-hidden overflow-y-auto border-gray-500 border-2 rounded-md">
+      <div className="whitespace-pre-line">
+        {commonStore.wslStdout}
+      </div>
+    </div>
+  );
+});
+
+const Terminal: FC = observer(() => {
+  const { t } = useTranslation();
+  const [input, setInput] = useState('');
+
+  const handleKeyDown = (e: any) => {
+    e.stopPropagation();
+    if (e.keyCode === 13) {
+      e.preventDefault();
+      if (!input) return;
+
+      WslStart().then(() => {
+        addWslMessage('WSL> ' + input);
+        setInput('');
+        WslCommand(input).catch(showError);
+      }).catch(showError);
+    }
+  };
+
+  return (
+    <div className="flex flex-col h-full gap-4">
+      <TerminalDisplay />
+      <div className="flex gap-2 items-center">
+        WSL:
+        <Input className="grow" value={input} onChange={(e) => {
+          setInput(e.target.value);
+        }} onKeyDown={handleKeyDown}></Input>
+        <Button onClick={() => {
+          WslStop().then(() => {
+            toast(t('Command Stopped'), { type: 'success' });
+          }).catch(showError);
+        }}>
+          {t('Stop')}
+        </Button>
+      </div>
+    </div>
+  );
+});
+
+const LoraFinetune: FC = observer(() => {
+  const { t } = useTranslation();
+  const navigate = useNavigate();
+  const chartRef = useRef<ChartJSOrUndefined<'line', (number | null)[], string>>(null);
+
+  const dataParams = commonStore.dataProcessParams;
+  const loraParams = commonStore.loraFinetuneParams;
+
+  if (chartRef.current)
+    chartLine = chartRef.current;
+
+  const setDataParams = (newParams: Partial<DataProcessParameters>) => {
+    commonStore.setDataProcessParams({
+      ...dataParams,
+      ...newParams
+    });
+  };
+
+  const setLoraParams = (newParams: Partial<LoraFinetuneParameters>) => {
+    commonStore.setLoraFinetuneParameters({
+      ...loraParams,
+      ...newParams
+    });
+  };
+
+  useEffect(() => {
+    if (loraParams.baseModel === '')
+      setLoraParams({
+        baseModel: commonStore.modelSourceList.find(m => m.isComplete)?.name || ''
+      });
+  }, []);
+
+  const StartLoraFinetune = async () => {
+    const ok = await checkDependencies(navigate);
+    if (!ok)
+      return;
+
+    const convertedDataPath = './finetune/json2binidx_tool/data/' +
+      dataParams.dataPath.replace(/[\/\\]$/, '').split(/[\/\\]/).pop()!.split('.')[0] +
+      '_text_document';
+    if (!await FileExists(convertedDataPath + '.idx')) {
+      toast(t('Please convert data first.'), { type: 'error' });
+      return;
+    }
+
+    WslIsEnabled().then(() => {
+      WslStart().then(() => {
+        setTimeout(WindowShow, 1000);
+
+        let ctxLen = loraParams.ctxLen;
+        if (dataParams.dataPath === 'finetune/data/sample.jsonl') {
+          ctxLen = 150;
+          toast(t('You are using sample data for training. For formal training, please make sure to create your own jsonl file.'), {
+            type: 'info',
+            autoClose: 6000
+          });
+        }
+
+        commonStore.setChartData({
+          labels: [],
+          datasets: [
+            {
+              label: 'Loss',
+              data: [],
+              borderColor: 'rgb(53, 162, 235)',
+              backgroundColor: 'rgba(53, 162, 235, 0.5)'
+            }
+          ]
+        });
+        WslCommand(`export cnMirror=${commonStore.settings.cnMirror ? '1' : '0'} ` +
+          `&& export loadModel=models/${loraParams.baseModel} ` +
+          `&& sed -i 's/\\r$//' finetune/install-wsl-dep-and-train.sh ` +
+          `&& chmod +x finetune/install-wsl-dep-and-train.sh && ./finetune/install-wsl-dep-and-train.sh ` +
+          (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} ` +
+          `--pre_ffn ${loraParams.preFfn ? '1' : '0'} --head_qk ${loraParams.headQk ? '1' : '0'} --lr_init ${loraParams.lrInit} --lr_final ${loraParams.lrFinal} ` +
+          `--warmup_steps ${loraParams.warmupSteps} ` +
+          `--beta1 ${loraParams.beta1} --beta2 ${loraParams.beta2} --adam_eps ${loraParams.adamEps} ` +
+          `--devices ${loraParams.devices} --precision ${loraParams.precision} ` +
+          `--grad_cp ${loraParams.gradCp ? '1' : '0'} ` +
+          `--lora_r ${loraParams.loraR} --lora_alpha ${loraParams.loraAlpha} --lora_dropout ${loraParams.loraDropout}`).catch(showError);
+      }).catch(e => {
+        const msg = e.message || e;
+        if (msg === 'ubuntu not found') {
+          WindowShow();
+          toastWithButton(t('Ubuntu is not installed, do you want to install it?'), t('Install Ubuntu'), () => {
+            WslInstallUbuntu().then(() => {
+              WindowShow();
+              toast(t('Please install Ubuntu using Microsoft Store, after installation click the Open button in Microsoft Store and then click the Train button'), {
+                type: 'info',
+                autoClose: 10000
+              });
+            });
+          });
+        }
+      });
+    }).catch(e => {
+      const msg = e.message || e;
+
+      const enableWsl = (forceMode: boolean) => {
+        WindowShow();
+        toastWithButton(t('WSL is not enabled, do you want to enable it?'), t('Enable WSL'), () => {
+          WslEnable(forceMode).then(() => {
+            WindowShow();
+            toast(t('After installation, please restart your computer to enable WSL'), {
+              type: 'info',
+              autoClose: false
+            });
+          }).catch(showError);
+        });
+      };
+
+      if (msg === 'wsl is not enabled') {
+        enableWsl(false);
+      } else if (msg.includes('wsl.state: The system cannot find the file')) {
+        enableWsl(true);
+      } else {
+        showError(msg);
+      }
+    });
+  };
+
+  return (
+    <div className="flex flex-col h-full w-full gap-2">
+      {(commonStore.wslStdout.length > 0 || commonStore.chartData.labels!.length !== 0) &&
+        <div className="flex" style={{ height: '35%' }}>
+          {commonStore.wslStdout.length > 0 && commonStore.chartData.labels!.length === 0 && <TerminalDisplay />}
+          {commonStore.chartData.labels!.length !== 0 &&
+            <Line ref={chartRef} data={commonStore.chartData} options={{
+              responsive: true,
+              showLine: true,
+              plugins: {
+                legend: {
+                  position: 'right',
+                  align: 'start'
+                },
+                title: {
+                  display: true,
+                  text: commonStore.chartTitle
+                }
+              },
+              scales: {
+                y: {
+                  beginAtZero: true
+                }
+              },
+              maintainAspectRatio: false
+            }} style={{ width: '100%' }} />}
+        </div>
+      }
+      <div>
+        <Section
+          title={t('Data Process')}
+          content={
+            <div className="flex flex-col gap-2">
+              <div className="flex gap-2 items-center">
+                {t('Data Path')}
+                <Input className="grow" style={{ minWidth: 0 }} value={dataParams.dataPath}
+                  onChange={(e, data) => {
+                    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' +
+                    '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/Abel2076/json2binidx_tool/blob/main/sample.jsonl.\n' +
+                    'You can also write it similar to OpenAI\'s playground format, as shown in https://platform.openai.com/playground/p/default-chat.\n' +
+                    '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);
+                }} />
+              </div>
+              <div className="flex gap-2 items-center">
+                {t('Vocab Path')}
+                <Input className="grow" style={{ minWidth: 0 }} value={dataParams.vocabPath}
+                  onChange={(e, data) => {
+                    setDataParams({ vocabPath: data.value });
+                  }} />
+                <Button appearance="secondary" onClick={async () => {
+                  const ok = await checkDependencies(navigate);
+                  if (!ok)
+                    return;
+                  const outputPrefix = './finetune/json2binidx_tool/data/' +
+                    dataParams.dataPath.replace(/[\/\\]$/, '').split(/[\/\\]/).pop()!.split('.')[0];
+                  ConvertData(commonStore.settings.customPythonPath,
+                    dataParams.dataPath.replaceAll('\\', '/'),
+                    outputPrefix,
+                    dataParams.vocabPath).then(async () => {
+                    if (!await FileExists(outputPrefix + '_text_document.idx')) {
+                      toast(t('Failed to convert data') + ' - ' + await GetPyError(), { type: 'error' });
+                    } else {
+                      toast(t('Convert Data successfully'), { type: 'success' });
+                    }
+                  }).catch(showError);
+                }}>{t('Convert')}</Button>
+              </div>
+            </div>
+          }
+        />
+      </div>
+      <Section
+        title={t('Train Parameters')}
+        content={
+          <div className="grid grid-cols-1 sm:grid-cols-2 gap-2">
+            <div className="flex gap-2 items-center">
+              {t('Base Model')}
+              <Select style={{ minWidth: 0 }} className="grow"
+                value={loraParams.baseModel}
+                onChange={(e, data) => {
+                  setLoraParams({
+                    baseModel: data.value
+                  });
+                }}>
+                {commonStore.modelSourceList.map((modelItem, index) =>
+                  modelItem.isComplete && <option key={index} value={modelItem.name}>{modelItem.name}</option>
+                )}
+              </Select>
+              <ToolTipButton desc={t('Manage Models')} icon={<DataUsageSettings20Regular />} onClick={() => {
+                navigate({ pathname: '/models' });
+              }} />
+            </div>
+            <div className="flex gap-2 items-center">
+              {t('LoRA Model')}
+              <Select style={{ minWidth: 0 }} className="grow"
+                value={loraParams.loraLoad}
+                onChange={(e, data) => {
+                  setLoraParams({
+                    loraLoad: data.value
+                  });
+                }}>
+                <option value="">{t('None')}</option>
+                {commonStore.loraModels.map((name, index) =>
+                  <option key={index} value={name}>{name}</option>
+                )}
+              </Select>
+              <Button onClick={async () => {
+                const ok = await checkDependencies(navigate);
+                if (!ok)
+                  return;
+                if (loraParams.loraLoad) {
+                  const outputPath = `models/${loraParams.baseModel}-LoRA-${loraParams.loraLoad}`;
+                  MergeLora(commonStore.settings.customPythonPath, true, loraParams.loraAlpha,
+                    'models/' + loraParams.baseModel, 'lora-models/' + loraParams.loraLoad,
+                    outputPath).then(async () => {
+                    if (!await FileExists(outputPath)) {
+                      toast(t('Failed to merge model') + ' - ' + await GetPyError(), { type: 'error' });
+                    } else {
+                      toast(t('Merge model successfully'), { type: 'success' });
+                    }
+                  }).catch(showError);
+                } else {
+                  toast(t('Please select a LoRA model'), { type: 'info' });
+                }
+              }}>{t('Merge Model')}</Button>
+            </div>
+            {
+              loraFinetuneParametersOptions.map(([key, type, name], index) => {
+                return (
+                  <Labeled key={index} label={t(name)} content={
+                    type === 'number' ?
+                      <Input type="number" className="grow" value={loraParams[key].toString()}
+                        onChange={(e, data) => {
+                          setLoraParams({
+                            [key]: Number(data.value)
+                          });
+                        }} /> :
+                      type === 'boolean' ?
+                        <Switch className="grow" checked={loraParams[key] as boolean}
+                          onChange={(e, data) => {
+                            setLoraParams({
+                              [key]: data.checked
+                            });
+                          }} /> :
+                        type === 'string' ?
+                          <Input className="grow" value={loraParams[key].toString()}
+                            onChange={(e, data) => {
+                              setLoraParams({
+                                [key]: data.value
+                              });
+                            }} /> :
+                          type === 'LoraFinetunePrecision' ?
+                            <Dropdown style={{ minWidth: 0 }} className="grow"
+                              value={loraParams[key].toString()}
+                              selectedOptions={[loraParams[key].toString()]}
+                              onOptionSelect={(_, data) => {
+                                if (data.optionText) {
+                                  setLoraParams({
+                                    precision: data.optionText as LoraFinetunePrecision
+                                  });
+                                }
+                              }}
+                            >
+                              <Option>bf16</Option>
+                              <Option>fp16</Option>
+                              <Option>tf32</Option>
+                            </Dropdown>
+                            : <div />
+                  } />
+                );
+              })
+            }
+          </div>
+        }
+      />
+      <div className="grow" />
+      <div className="flex gap-2">
+        <div className="grow" />
+        <Button appearance="secondary" size="large" onClick={() => {
+          WslStop().then(() => {
+            toast(t('Command Stopped'), { type: 'success' });
+          }).catch(showError);
+        }}>{t('Stop')}</Button>
+        <Button appearance="primary" size="large" onClick={StartLoraFinetune}>{t('Train')}</Button>
+      </div>
+    </div>
+  );
+});
+
+type TrainNavigationItem = {
+  element: ReactElement;
+};
+
+const pages: { [label: string]: TrainNavigationItem } = {
+  'LoRA Finetune': {
+    element: <LoraFinetune />
+  },
+  WSL: {
+    element: <Terminal />
+  }
+};
+
 
 export const Train: FC = () => {
   const { t } = useTranslation();
+  const [tab, setTab] = useState('LoRA Finetune');
 
-  return (
-    <div className="flex flex-col box-border gap-5 p-2">
-      <Text size={600}>{t('In Development')}</Text>
+  const selectTab: SelectTabEventHandler = (e, data) =>
+    typeof data.value === 'string' ? setTab(data.value) : null;
+
+  return <div className="flex flex-col gap-2 w-full h-full">
+    <TabList
+      size="small"
+      appearance="subtle"
+      selectedValue={tab}
+      onTabSelect={selectTab}
+    >
+      {Object.entries(pages).map(([label]) => (
+        <Tab key={label} value={label}>
+          {t(label)}
+        </Tab>
+      ))}
+    </TabList>
+    <div className="grow overflow-hidden">
+      {pages[tab].element}
     </div>
-  );
+  </div>;
 };

frontend/src/pages/index.tsx

@@ -8,6 +8,7 @@ import {
   DocumentSettings20Regular,
   Home20Regular,
   Info20Regular,
+  MusicNote220Regular,
   Settings20Regular,
   Storage20Regular
 } from '@fluentui/react-icons';
@@ -19,6 +20,7 @@ import { Settings } from './Settings';
 import { About } from './About';
 import { Downloads } from './Downloads';
 import { Completion } from './Completion';
+import { Composition } from './Composition';
 
 type NavigationItem = {
   label: string;
@@ -50,6 +52,13 @@ export const pages: NavigationItem[] = [
     element: <Completion />,
     top: true
   },
+  {
+    label: 'Composition',
+    path: '/composition',
+    icon: <MusicNote220Regular />,
+    element: <Composition />,
+    top: true
+  },
   {
     label: 'Configs',
     path: '/configs',

frontend/src/startup.ts

@@ -1,10 +1,12 @@
 import commonStore, { Platform } from './stores/commonStore';
-import { GetPlatform, ReadJson } from '../wailsjs/go/backend_golang/App';
-import { Cache, checkUpdate, downloadProgramFiles, LocalConfig, refreshModels } from './utils';
+import { GetPlatform, ListDirFiles, ReadJson } from '../wailsjs/go/backend_golang/App';
+import { Cache, checkUpdate, downloadProgramFiles, LocalConfig, refreshLocalModels, refreshModels } from './utils';
 import { getStatus } from './apis';
 import { EventsOn } from '../wailsjs/runtime';
 import manifest from '../../manifest.json';
-import { defaultModelConfigs, defaultModelConfigsMac } from './pages/defaultModelConfigs';
+import { defaultModelConfigs, defaultModelConfigsMac } from './pages/defaultConfigs';
+import { Preset } from './pages/PresetsManager/PresetsButton';
+import { wslHandler } from './pages/Train';
 
 export async function startup() {
   downloadProgramFiles();
@@ -12,11 +14,19 @@ export async function startup() {
     if (data)
       commonStore.setDownloadList(data);
   });
+  EventsOn('wsl', wslHandler);
+  EventsOn('wslerr', (e) => {
+    console.log(e);
+  });
+  initLocalModelsNotify();
+  initLoraModels();
+
+  initPresets();
 
   await GetPlatform().then(p => commonStore.setPlatform(p as Platform));
   await initConfig();
 
-  initCache().then(initRemoteText); // depends on config customModelsPath
+  initCache(true).then(initRemoteText); // depends on config customModelsPath
 
   if (commonStore.settings.autoUpdatesCheck) // depends on config settings
     checkUpdate();
@@ -47,6 +57,12 @@ async function initConfig() {
     if (configData.settings)
       commonStore.setSettings(configData.settings, false);
 
+    if (configData.dataProcessParams)
+      commonStore.setDataProcessParams(configData.dataProcessParams, false);
+
+    if (configData.loraFinetuneParams)
+      commonStore.setLoraFinetuneParameters(configData.loraFinetuneParams, false);
+
     if (configData.modelConfigs && Array.isArray(configData.modelConfigs))
       commonStore.setModelConfigs(configData.modelConfigs, false);
     else throw new Error('Invalid config.json');
@@ -58,11 +74,46 @@ async function initConfig() {
   });
 }
 
-async function initCache() {
+async function initCache(initUnfinishedModels: boolean) {
   await ReadJson('cache.json').then((cacheData: Cache) => {
-    if (cacheData.depComplete)
-      commonStore.setDepComplete(cacheData.depComplete);
+    if (cacheData.version === manifest.version && cacheData.depComplete)
+      commonStore.setDepComplete(cacheData.depComplete, false);
   }).catch(() => {
   });
-  await refreshModels(false);
-}
+  await refreshModels(false, initUnfinishedModels);
+}
+
+async function initPresets() {
+  await ReadJson('presets.json').then((presets: Preset[]) => {
+    commonStore.setPresets(presets, false);
+  }).catch(() => {
+  });
+}
+
+async function initLoraModels() {
+  const refreshLoraModels = () => {
+    ListDirFiles('lora-models').then((data) => {
+      if (!data) return;
+      const loraModels = [];
+      for (const f of data) {
+        if (!f.isDir && f.name.endsWith('.pth')) {
+          loraModels.push(f.name);
+        }
+      }
+      commonStore.setLoraModels(loraModels);
+    });
+  };
+
+  refreshLoraModels();
+  EventsOn('fsnotify', (data: string) => {
+    if (data.includes('lora-models'))
+      refreshLoraModels();
+  });
+}
+
+async function initLocalModelsNotify() {
+  EventsOn('fsnotify', (data: string) => {
+    if (data.includes('models') && !data.includes('lora-models'))
+      refreshLocalModels({ models: commonStore.modelSourceList }, false); //TODO fix bug that only add models
+  });
+}

frontend/src/stores/commonStore.ts

@@ -1,5 +1,5 @@
 import { makeAutoObservable } from 'mobx';
-import { getUserLanguage, isSystemLightMode, saveConfigs } from '../utils';
+import { getUserLanguage, isSystemLightMode, saveCache, saveConfigs, savePresets } from '../utils';
 import { WindowSetDarkTheme, WindowSetLightTheme } from '../../wailsjs/runtime';
 import manifest from '../../../manifest.json';
 import { ModelConfig } from '../pages/Configs';
@@ -11,8 +11,12 @@ import { IntroductionContent } from '../pages/Home';
 import { AboutContent } from '../pages/About';
 import i18n from 'i18next';
 import { CompletionPreset } from '../pages/Completion';
-import { defaultModelConfigs, defaultModelConfigsMac } from '../pages/defaultModelConfigs';
+import { defaultCompositionPrompt, defaultModelConfigs, defaultModelConfigsMac } from '../pages/defaultConfigs';
 import commonStore from './commonStore';
+import { Preset } from '../pages/PresetsManager/PresetsButton';
+import { DataProcessParameters, LoraFinetuneParameters } from '../pages/Train';
+import { ChartData } from 'chart.js';
+import { CompositionParams } from '../pages/Composition';
 
 export enum ModelStatus {
   Offline,
@@ -29,6 +33,8 @@ export type Status = {
 
 export type Platform = 'windows' | 'darwin' | 'linux';
 
+const labels = ['January', 'February', 'March', 'April', 'May', 'June', 'July'];
+
 class CommonStore {
   // global
   status: Status = {
@@ -38,15 +44,33 @@ class CommonStore {
   };
   depComplete: boolean = false;
   platform: Platform = 'windows';
+  // presets manager
+  editingPreset: Preset | null = null;
+  presets: Preset[] = [];
   // home
   introduction: IntroductionContent = manifest.introduction;
   // chat
   currentInput: string = '';
   conversation: Conversation = {};
   conversationOrder: string[] = [];
+  activePreset: Preset | null = null;
   // completion
   completionPreset: CompletionPreset | null = null;
   completionGenerating: boolean = false;
+  completionSubmittedPrompt: string = '';
+  // composition
+  compositionParams: CompositionParams = {
+    prompt: defaultCompositionPrompt,
+    maxResponseToken: 200,
+    temperature: 1,
+    topP: 0.8,
+    autoPlay: true,
+    useLocalSoundFont: false,
+    midi: null,
+    ns: null
+  };
+  compositionGenerating: boolean = false;
+  compositionSubmittedPrompt: string = defaultCompositionPrompt;
   // configs
   currentModelConfigIndex: number = 0;
   modelConfigs: ModelConfig[] = [];
@@ -55,6 +79,41 @@ class CommonStore {
   modelSourceList: ModelSourceItem[] = [];
   // downloads
   downloadList: DownloadStatus[] = [];
+  lastUnfinishedModelDownloads: DownloadStatus[] = [];
+  // train
+  wslStdout: string = '';
+  chartTitle: string = '';
+  chartData: ChartData<'line', (number | null)[], string> = { labels: [], datasets: [] };
+  loraModels: string[] = [];
+  dataProcessParams: DataProcessParameters = {
+    dataPath: 'finetune/data/sample.jsonl',
+    vocabPath: 'backend-python/rwkv_pip/rwkv_vocab_v20230424.txt'
+  };
+  loraFinetuneParams: LoraFinetuneParameters = {
+    baseModel: '',
+    ctxLen: 1024,
+    epochSteps: 200,
+    epochCount: 20,
+    epochBegin: 0,
+    epochSave: 2,
+    microBsz: 1,
+    accumGradBatches: 8,
+    preFfn: false,
+    headQk: false,
+    lrInit: '5e-5',
+    lrFinal: '5e-5',
+    warmupSteps: 0,
+    beta1: 0.9,
+    beta2: 0.999,
+    adamEps: '1e-8',
+    devices: 1,
+    precision: 'bf16',
+    gradCp: false,
+    loraR: 8,
+    loraAlpha: 32,
+    loraDropout: 0.01,
+    loraLoad: ''
+  };
   // settings
   advancedCollapsed: boolean = true;
   settings: SettingsType = {
@@ -162,8 +221,10 @@ class CommonStore {
     this.about = value;
   };
 
-  setDepComplete = (value: boolean) => {
+  setDepComplete = (value: boolean, inSaveCache: boolean = true) => {
     this.depComplete = value;
+    if (inSaveCache)
+      saveCache();
   };
 
   setDownloadList = (value: DownloadStatus[]) => {
@@ -197,6 +258,68 @@ class CommonStore {
   setAdvancedCollapsed(value: boolean) {
     this.advancedCollapsed = value;
   }
+
+  setLastUnfinishedModelDownloads(value: DownloadStatus[]) {
+    this.lastUnfinishedModelDownloads = value;
+  }
+
+  setEditingPreset(value: Preset) {
+    this.editingPreset = value;
+  }
+
+  setPresets(value: Preset[], savePreset: boolean = true) {
+    this.presets = value;
+    if (savePreset)
+      savePresets();
+  }
+
+  setActivePreset(value: Preset) {
+    this.activePreset = value;
+  }
+
+  setCompletionSubmittedPrompt(value: string) {
+    this.completionSubmittedPrompt = value;
+  }
+
+  setCompositionParams(value: CompositionParams) {
+    this.compositionParams = value;
+  }
+
+  setCompositionGenerating(value: boolean) {
+    this.compositionGenerating = value;
+  }
+
+  setCompositionSubmittedPrompt(value: string) {
+    this.compositionSubmittedPrompt = value;
+  }
+
+  setWslStdout(value: string) {
+    this.wslStdout = value;
+  }
+
+  setDataProcessParams(value: DataProcessParameters, saveConfig: boolean = true) {
+    this.dataProcessParams = value;
+    if (saveConfig)
+      saveConfigs();
+  }
+
+  setLoraFinetuneParameters(value: LoraFinetuneParameters, saveConfig: boolean = true) {
+    this.loraFinetuneParams = value;
+    if (saveConfig)
+      saveConfigs();
+  }
+
+  setChartTitle(value: string) {
+    this.chartTitle = value;
+  }
+
+  setChartData(value: ChartData<'line', (number | null)[], string>) {
+    this.chartData = value;
+  }
+
+  setLoraModels(value: string[]) {
+    this.loraModels = value;
+  }
 }
 
 export default new CommonStore();

frontend/src/style.scss

@@ -28,6 +28,7 @@ body {
 /* Works on Chrome, Edge, and Safari */
 *::-webkit-scrollbar {
   width: 9px;
+  height: 9px;
 }
 
 *::-webkit-scrollbar-thumb {
@@ -92,3 +93,22 @@ body {
     }
   }
 }
+
+midi-player {
+  &::part(control-panel) {
+    background: none;
+  }
+}
+
+midi-visualizer {
+  $instrument-colors: #007bff, #20c997, #dc3545, #6610f2, #ffc107, #e83e8c, #17a2b8, #fd7e14, #28a745;
+
+  svg {
+    @for $i from 0 to 200 {
+      $color: nth($instrument-colors, ($i % length($instrument-colors)) + 1);
+      rect.note[data-instrument="#{$i}"] {
+        fill: $color;
+      }
+    }
+  }
+}

frontend/src/types/html-midi-player.d.ts

@@ -0,0 +1,9 @@
+declare module JSX {
+  import { PlayerElement } from 'html-midi-player';
+  import { VisualizerElement } from 'html-midi-player';
+
+  interface IntrinsicElements {
+    'midi-player': PlayerElement;
+    'midi-visualizer': VisualizerElement;
+  }
+}

frontend/src/utils/get-conversation-pairs.ts

@@ -1,27 +0,0 @@
-export type Record = {
-  question: string;
-  answer: string;
-}
-
-export type ConversationPair = {
-  role: string;
-  content: string;
-}
-
-export function getConversationPairs(records: Record[], isCompletion: boolean): string | ConversationPair[] {
-  let pairs;
-  if (isCompletion) {
-    pairs = '';
-    for (const record of records) {
-      pairs += 'Human: ' + record.question + '\nAI: ' + record.answer + '\n';
-    }
-  } else {
-    pairs = [];
-    for (const record of records) {
-      pairs.push({ role: 'user', content: record.question });
-      pairs.push({ role: 'assistant', content: record.answer });
-    }
-  }
-
-  return pairs;
-}

frontend/src/utils/index.tsx

@@ -1,6 +1,9 @@
 import {
   AddToDownloadList,
+  CopyFile,
   DeleteFile,
+  DepCheck,
+  InstallPyDep,
   ListDirFiles,
   ReadFileInfo,
   ReadJson,
@@ -8,7 +11,7 @@ import {
   UpdateApp
 } from '../../wailsjs/go/backend_golang/App';
 import manifest from '../../../manifest.json';
-import commonStore from '../stores/commonStore';
+import commonStore, { ModelStatus } from '../stores/commonStore';
 import { toast } from 'react-toastify';
 import { t } from 'i18next';
 import { ToastOptions } from 'react-toastify/dist/types';
@@ -16,8 +19,13 @@ import { Button } from '@fluentui/react-components';
 import { Language, Languages, SettingsType } from '../pages/Settings';
 import { ModelSourceItem } from '../pages/Models';
 import { ModelConfig, ModelParameters } from '../pages/Configs';
+import { DownloadStatus } from '../pages/Downloads';
+import { DataProcessParameters, LoraFinetuneParameters } from '../pages/Train';
+import { BrowserOpenURL, WindowShow } from '../../wailsjs/runtime';
+import { NavigateFunction } from 'react-router';
 
 export type Cache = {
+  version: string
   models: ModelSourceItem[]
   depComplete: boolean
 }
@@ -26,7 +34,9 @@ export type LocalConfig = {
   modelSourceManifestList: string
   currentModelConfigIndex: number
   modelConfigs: ModelConfig[]
-  settings: SettingsType
+  settings: SettingsType,
+  dataProcessParams: DataProcessParameters,
+  loraFinetuneParams: LoraFinetuneParameters
 }
 
 export async function refreshBuiltInModels(readCache: boolean = false) {
@@ -47,9 +57,11 @@ export async function refreshBuiltInModels(readCache: boolean = false) {
   return cache;
 }
 
-export async function refreshLocalModels(cache: { models: ModelSourceItem[] }, filter: boolean = true) {
+export async function refreshLocalModels(cache: {
+  models: ModelSourceItem[]
+}, filter: boolean = true, initUnfinishedModels: boolean = false) {
   if (filter)
-    cache.models = cache.models.filter(m => !m.isLocal); //TODO BUG cause local but in manifest files to be removed, so currently cache is disabled
+    cache.models = cache.models.filter(m => !m.isComplete); //TODO BUG cause local but in manifest files to be removed, so currently cache is disabled
 
   await ListDirFiles(commonStore.settings.customModelsPath).then((data) => {
     cache.models.push(...data.flatMap(d => {
@@ -58,8 +70,9 @@ export async function refreshLocalModels(cache: { models: ModelSourceItem[] }, f
           name: d.name,
           size: d.size,
           lastUpdated: d.modTime,
+          isComplete: true,
           isLocal: true
-        }];
+        }] as ModelSourceItem[];
       return [];
     }));
   }).catch(() => {
@@ -80,17 +93,43 @@ export async function refreshLocalModels(cache: { models: ModelSourceItem[] }, f
           } else {
             cache.models[i] = Object.assign({}, cache.models[j], cache.models[i]);
           }
-        } // else is bad local file
+        } // else is not complete local file
+        cache.models[i].isLocal = true;
+        cache.models[i].localSize = cache.models[j].size;
         cache.models.splice(j, 1);
         j--;
       }
     }
   }
   commonStore.setModelSourceList(cache.models);
+  if (initUnfinishedModels)
+    initLastUnfinishedModelDownloads();
   await saveCache().catch(() => {
   });
 }
 
+function initLastUnfinishedModelDownloads() {
+  const list: DownloadStatus[] = [];
+  commonStore.modelSourceList.forEach((item) => {
+    if (item.isLocal && !item.isComplete) {
+      list.push(
+        {
+          name: item.name,
+          path: `${commonStore.settings.customModelsPath}/${item.name}`,
+          url: item.downloadUrl!,
+          transferred: item.localSize!,
+          size: item.size,
+          speed: 0,
+          progress: item.localSize! / item.size * 100,
+          downloading: false,
+          done: false
+        }
+      );
+    }
+  });
+  commonStore.setLastUnfinishedModelDownloads(list);
+}
+
 export async function refreshRemoteModels(cache: { models: ModelSourceItem[] }) {
   const manifestUrls = commonStore.modelSourceManifestList.split(/[,，;；\n]/);
   const requests = manifestUrls.filter(url => url.endsWith('.json')).map(
@@ -116,9 +155,9 @@ export async function refreshRemoteModels(cache: { models: ModelSourceItem[] })
   });
 }
 
-export const refreshModels = async (readCache: boolean = false) => {
+export const refreshModels = async (readCache: boolean = false, initUnfinishedModels: boolean = false) => {
   const cache = await refreshBuiltInModels(readCache);
-  await refreshLocalModels(cache);
+  await refreshLocalModels(cache, false, initUnfinishedModels);
   await refreshRemoteModels(cache);
 };
 
@@ -163,19 +202,26 @@ export const saveConfigs = async () => {
     modelSourceManifestList: commonStore.modelSourceManifestList,
     currentModelConfigIndex: commonStore.currentModelConfigIndex,
     modelConfigs: commonStore.modelConfigs,
-    settings: commonStore.settings
+    settings: commonStore.settings,
+    dataProcessParams: commonStore.dataProcessParams,
+    loraFinetuneParams: commonStore.loraFinetuneParams
   };
   return SaveJson('config.json', data);
 };
 
 export const saveCache = async () => {
   const data: Cache = {
+    version: manifest.version,
     models: commonStore.modelSourceList,
     depComplete: commonStore.depComplete
   };
   return SaveJson('cache.json', data);
 };
 
+export const savePresets = async () => {
+  return SaveJson('presets.json', commonStore.presets);
+};
+
 export function getUserLanguage(): Language {
   // const l = navigator.language.toLowerCase();
   // if (['zh-hk', 'zh-mo', 'zh-tw', 'zh-cht', 'zh-hant'].includes(l)) return 'zhHant'
@@ -270,7 +316,7 @@ export async function checkUpdate(notifyEvenLatest: boolean = false) {
                                 }
                               );
                             }).catch((e) => {
-                              toast(t('Update Error') + ' - ' + e.message || e, {
+                              toast(t('Update Error') + ' - ' + (e.message || e), {
                                 type: 'error',
                                 position: 'bottom-left',
                                 autoClose: false
@@ -302,10 +348,60 @@ export async function checkUpdate(notifyEvenLatest: boolean = false) {
       }
     }
   ).catch((e) => {
-    toast(t('Updates Check Error') + ' - ' + e.message || e, { type: 'error', position: 'bottom-left' });
+    toast(t('Updates Check Error') + ' - ' + (e.message || e), { type: 'error', position: 'bottom-left' });
   });
 }
 
+export const checkDependencies = async (navigate: NavigateFunction) => {
+  if (!commonStore.depComplete) {
+    let depErrorMsg = '';
+    await DepCheck(commonStore.settings.customPythonPath).catch((e) => {
+      depErrorMsg = e.message || e;
+      WindowShow();
+      if (depErrorMsg === 'python zip not found') {
+        toastWithButton(t('Python target not found, would you like to download it?'), t('Download'), () => {
+          toastWithButton(`${t('Downloading')} Python`, t('Check'), () => {
+            navigate({ pathname: '/downloads' });
+          }, { autoClose: 3000 });
+          AddToDownloadList('python-3.10.11-embed-amd64.zip', 'https://www.python.org/ftp/python/3.10.11/python-3.10.11-embed-amd64.zip');
+        });
+      } else if (depErrorMsg.includes('DepCheck Error')) {
+        if (depErrorMsg.includes('vc_redist') || depErrorMsg.includes('DLL load failed while importing')) {
+          toastWithButton(t('Microsoft Visual C++ Redistributable is not installed, would you like to download it?'), t('Download'), () => {
+            BrowserOpenURL('https://aka.ms/vs/16/release/vc_redist.x64.exe');
+          });
+        } else {
+          toast(depErrorMsg, { type: 'info', position: 'bottom-left' });
+          if (commonStore.platform != 'linux')
+            toastWithButton(t('Python dependencies are incomplete, would you like to install them?'), t('Install'), () => {
+              InstallPyDep(commonStore.settings.customPythonPath, commonStore.settings.cnMirror).catch((e) => {
+                const errMsg = e.message || e;
+                toast(t('Error') + ' - ' + errMsg, { type: 'error' });
+              });
+              setTimeout(WindowShow, 1000);
+            }, {
+              autoClose: 8000
+            });
+          else
+            toastWithButton(t('On Linux system, you must manually install python dependencies.'), t('Check'), () => {
+              BrowserOpenURL('https://github.com/josStorer/RWKV-Runner/blob/master/build/linux/Readme_Install.txt');
+            });
+        }
+      } else {
+        toast(depErrorMsg, { type: 'error' });
+      }
+    });
+    if (depErrorMsg) {
+      commonStore.setStatus({ status: ModelStatus.Offline });
+      return false;
+    }
+    commonStore.setDepComplete(true);
+    if (commonStore.platform === 'windows')
+      CopyFile('./backend-python/wkv_cuda_utils/wkv_cuda_model.py', './py310/Lib/site-packages/rwkv/model.py');
+  }
+  return true;
+};
+
 export function toastWithButton(text: string, buttonText: string, onClickButton: () => void, options?: ToastOptions) {
   let triggered = false;
   const id = toast(

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

@@ -6,6 +6,8 @@ export function AddToDownloadList(arg1:string,arg2:string):Promise<void>;
 
 export function ContinueDownload(arg1:string):Promise<void>;
 
+export function ConvertData(arg1:string,arg2:string,arg3:string,arg4:string):Promise<string>;
+
 export function ConvertModel(arg1:string,arg2:string,arg3:string,arg4:string):Promise<string>;
 
 export function CopyFile(arg1:string,arg2:string):Promise<void>;
@@ -20,14 +22,20 @@ export function FileExists(arg1:string):Promise<boolean>;
 
 export function GetPlatform():Promise<string>;
 
+export function GetPyError():Promise<string>;
+
 export function InstallPyDep(arg1:string,arg2:boolean):Promise<string>;
 
 export function ListDirFiles(arg1:string):Promise<Array<backend_golang.FileInfo>>;
 
+export function MergeLora(arg1:string,arg2:boolean,arg3:number,arg4:string,arg5:string,arg6:string):Promise<string>;
+
 export function OpenFileFolder(arg1:string,arg2:boolean):Promise<void>;
 
 export function OpenSaveFileDialog(arg1:string,arg2:string,arg3:string):Promise<string>;
 
+export function OpenSaveFileDialogBytes(arg1:string,arg2:string,arg3:Array<number>):Promise<string>;
+
 export function PauseDownload(arg1:string):Promise<void>;
 
 export function ReadFileInfo(arg1:string):Promise<backend_golang.FileInfo>;
@@ -41,3 +49,15 @@ export function SaveJson(arg1:string,arg2:any):Promise<void>;
 export function StartServer(arg1:string,arg2:number,arg3:string):Promise<string>;
 
 export function UpdateApp(arg1:string):Promise<boolean>;
+
+export function WslCommand(arg1:string):Promise<void>;
+
+export function WslEnable(arg1:boolean):Promise<void>;
+
+export function WslInstallUbuntu():Promise<void>;
+
+export function WslIsEnabled():Promise<void>;
+
+export function WslStart():Promise<void>;
+
+export function WslStop():Promise<void>;

frontend/wailsjs/go/backend_golang/App.js

@@ -10,6 +10,10 @@ export function ContinueDownload(arg1) {
   return window['go']['backend_golang']['App']['ContinueDownload'](arg1);
 }
 
+export function ConvertData(arg1, arg2, arg3, arg4) {
+  return window['go']['backend_golang']['App']['ConvertData'](arg1, arg2, arg3, arg4);
+}
+
 export function ConvertModel(arg1, arg2, arg3, arg4) {
   return window['go']['backend_golang']['App']['ConvertModel'](arg1, arg2, arg3, arg4);
 }
@@ -38,6 +42,10 @@ export function GetPlatform() {
   return window['go']['backend_golang']['App']['GetPlatform']();
 }
 
+export function GetPyError() {
+  return window['go']['backend_golang']['App']['GetPyError']();
+}
+
 export function InstallPyDep(arg1, arg2) {
   return window['go']['backend_golang']['App']['InstallPyDep'](arg1, arg2);
 }
@@ -46,6 +54,10 @@ export function ListDirFiles(arg1) {
   return window['go']['backend_golang']['App']['ListDirFiles'](arg1);
 }
 
+export function MergeLora(arg1, arg2, arg3, arg4, arg5, arg6) {
+  return window['go']['backend_golang']['App']['MergeLora'](arg1, arg2, arg3, arg4, arg5, arg6);
+}
+
 export function OpenFileFolder(arg1, arg2) {
   return window['go']['backend_golang']['App']['OpenFileFolder'](arg1, arg2);
 }
@@ -54,6 +66,10 @@ export function OpenSaveFileDialog(arg1, arg2, arg3) {
   return window['go']['backend_golang']['App']['OpenSaveFileDialog'](arg1, arg2, arg3);
 }
 
+export function OpenSaveFileDialogBytes(arg1, arg2, arg3) {
+  return window['go']['backend_golang']['App']['OpenSaveFileDialogBytes'](arg1, arg2, arg3);
+}
+
 export function PauseDownload(arg1) {
   return window['go']['backend_golang']['App']['PauseDownload'](arg1);
 }
@@ -81,3 +97,27 @@ export function StartServer(arg1, arg2, arg3) {
 export function UpdateApp(arg1) {
   return window['go']['backend_golang']['App']['UpdateApp'](arg1);
 }
+
+export function WslCommand(arg1) {
+  return window['go']['backend_golang']['App']['WslCommand'](arg1);
+}
+
+export function WslEnable(arg1) {
+  return window['go']['backend_golang']['App']['WslEnable'](arg1);
+}
+
+export function WslInstallUbuntu() {
+  return window['go']['backend_golang']['App']['WslInstallUbuntu']();
+}
+
+export function WslIsEnabled() {
+  return window['go']['backend_golang']['App']['WslIsEnabled']();
+}
+
+export function WslStart() {
+  return window['go']['backend_golang']['App']['WslStart']();
+}
+
+export function WslStop() {
+  return window['go']['backend_golang']['App']['WslStop']();
+}

go.mod

@@ -5,12 +5,14 @@ go 1.20
 require (
 	github.com/cavaliergopher/grab/v3 v3.0.1
 	github.com/minio/selfupdate v0.6.0
+	github.com/ubuntu/gowsl v0.0.0-20230615094051-94945650cc1e
 	github.com/wailsapp/wails/v2 v2.5.1
 )
 
 require (
 	aead.dev/minisign v0.2.0 // indirect
 	github.com/bep/debounce v1.2.1 // indirect
+	github.com/fsnotify/fsnotify v1.6.0
 	github.com/go-ole/go-ole v1.2.6 // indirect
 	github.com/google/uuid v1.3.0 // indirect
 	github.com/jchv/go-winloader v0.0.0-20210711035445-715c2860da7e // indirect
@@ -21,17 +23,20 @@ require (
 	github.com/leaanthony/slicer v1.6.0 // indirect
 	github.com/mattn/go-colorable v0.1.13 // indirect
 	github.com/mattn/go-isatty v0.0.18 // indirect
+	github.com/nyaosorg/go-windows-su v0.2.1
 	github.com/pkg/browser v0.0.0-20210911075715-681adbf594b8 // indirect
 	github.com/pkg/errors v0.9.1 // indirect
 	github.com/rivo/uniseg v0.4.4 // indirect
 	github.com/samber/lo v1.38.1 // indirect
+	github.com/sirupsen/logrus v1.9.0 // indirect
 	github.com/tkrajina/go-reflector v0.5.6 // indirect
+	github.com/ubuntu/decorate v0.0.0-20230125165522-2d5b0a9bb117 // indirect
 	github.com/valyala/bytebufferpool v1.0.0 // indirect
 	github.com/valyala/fasttemplate v1.2.2 // indirect
 	github.com/wailsapp/mimetype v1.4.1 // indirect
 	golang.org/x/crypto v0.9.0 // indirect
 	golang.org/x/exp v0.0.0-20230515195305-f3d0a9c9a5cc // indirect
 	golang.org/x/net v0.10.0 // indirect
-	golang.org/x/sys v0.8.0 // indirect
+	golang.org/x/sys v0.9.0 // indirect
 	golang.org/x/text v0.9.0 // indirect
 )

go.sum

@@ -1,5 +1,6 @@
 aead.dev/minisign v0.2.0 h1:kAWrq/hBRu4AARY6AlciO83xhNnW9UaC8YipS2uhLPk=
 aead.dev/minisign v0.2.0/go.mod h1:zdq6LdSd9TbuSxchxwhpA9zEb9YXcVGoE8JakuiGaIQ=
+github.com/0xrawsec/golang-utils v1.3.2 h1:ww4jrtHRSnX9xrGzJYbalx5nXoZewy4zPxiY+ubJgtg=
 github.com/bep/debounce v1.2.1 h1:v67fRdBA9UQu2NhLFXrSg0Brw7CexQekrBwDMM8bzeY=
 github.com/bep/debounce v1.2.1/go.mod h1:H8yggRPQKLUhUoqrJC1bO2xNya7vanpDl7xR3ISbCJ0=
 github.com/cavaliergopher/grab/v3 v3.0.1 h1:4z7TkBfmPjmLAAmkkAZNX/6QJ1nNFdv3SdIHXju0Fr4=
@@ -7,6 +8,8 @@ github.com/cavaliergopher/grab/v3 v3.0.1/go.mod h1:1U/KNnD+Ft6JJiYoYBAimKH2XrYpt
 github.com/davecgh/go-spew v1.1.0/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
 github.com/davecgh/go-spew v1.1.1 h1:vj9j/u1bqnvCEfJOwUhtlOARqs3+rkHYY13jYWTU97c=
 github.com/davecgh/go-spew v1.1.1/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
+github.com/fsnotify/fsnotify v1.6.0 h1:n+5WquG0fcWoWp6xPWfHdbskMCQaFnG6PfBrh1Ky4HY=
+github.com/fsnotify/fsnotify v1.6.0/go.mod h1:sl3t1tCWJFWoRz9R8WJCbQihKKwmorjAbSClcnxKAGw=
 github.com/go-ole/go-ole v1.2.6 h1:/Fpf6oFPoeFik9ty7siob0G6Ke8QvQEuVcuChpwXzpY=
 github.com/go-ole/go-ole v1.2.6/go.mod h1:pprOEPIfldk/42T2oK7lQ4v4JSDwmV0As9GaiUsvbm0=
 github.com/google/uuid v1.3.0 h1:t6JiXgmwXMjEs8VusXIJk2BXHsn+wx8BZdTaoZ5fu7I=
@@ -37,6 +40,8 @@ github.com/mattn/go-isatty v0.0.18 h1:DOKFKCQ7FNG2L1rbrmstDN4QVRdS89Nkh85u68Uwp9
 github.com/mattn/go-isatty v0.0.18/go.mod h1:W+V8PltTTMOvKvAeJH7IuucS94S2C6jfK/D7dTCTo3Y=
 github.com/minio/selfupdate v0.6.0 h1:i76PgT0K5xO9+hjzKcacQtO7+MjJ4JKA8Ak8XQ9DDwU=
 github.com/minio/selfupdate v0.6.0/go.mod h1:bO02GTIPCMQFTEvE5h4DjYB58bCoZ35XLeBf0buTDdM=
+github.com/nyaosorg/go-windows-su v0.2.1 h1:5V0XavLyjOqPUp7psxxCvBISaneU4XmFPSMlejSl5sc=
+github.com/nyaosorg/go-windows-su v0.2.1/go.mod h1:fWKxSCXwGuDuW6ne0kLp/Cj0joXNDDw01G3LseQJYS0=
 github.com/pkg/browser v0.0.0-20210911075715-681adbf594b8 h1:KoWmjvw+nsYOo29YJK9vDA65RGE3NrOnUtO7a+RF9HU=
 github.com/pkg/browser v0.0.0-20210911075715-681adbf594b8/go.mod h1:HKlIX3XHQyzLZPlr7++PzdhaXEj94dEiJgZDTsxEqUI=
 github.com/pkg/errors v0.9.1 h1:FEBLx1zS214owpjy7qsBeixbURkuhQAwrK5UwLGTwt4=
@@ -48,11 +53,17 @@ github.com/rivo/uniseg v0.4.4 h1:8TfxU8dW6PdqD27gjM8MVNuicgxIjxpm4K7x4jp8sis=
 github.com/rivo/uniseg v0.4.4/go.mod h1:FN3SvrM+Zdj16jyLfmOkMNblXMcoc8DfTHruCPUcx88=
 github.com/samber/lo v1.38.1 h1:j2XEAqXKb09Am4ebOg31SpvzUTTs6EN3VfgeLUhPdXM=
 github.com/samber/lo v1.38.1/go.mod h1:+m/ZKRl6ClXCE2Lgf3MsQlWfh4bn1bz6CXEOxnEXnEA=
+github.com/sirupsen/logrus v1.9.0 h1:trlNQbNUG3OdDrDil03MCb1H2o9nJ1x4/5LYw7byDE0=
+github.com/sirupsen/logrus v1.9.0/go.mod h1:naHLuLoDiP4jHNo9R0sCBMtWGeIprob74mVsIT4qYEQ=
 github.com/stretchr/objx v0.1.0/go.mod h1:HFkY916IF+rwdDfMAkV7OtwuqBVzrE8GR6GFx+wExME=
 github.com/stretchr/testify v1.7.0/go.mod h1:6Fq8oRcR53rry900zMqJjRRixrwX3KX962/h/Wwjteg=
-github.com/stretchr/testify v1.8.1 h1:w7B6lhMri9wdJUVmEZPGGhZzrYTPvgJArz7wNPgYKsk=
+github.com/stretchr/testify v1.8.4 h1:CcVxjf3Q8PM0mHUKJCdn+eZZtm5yQwehR5yeSVQQcUk=
 github.com/tkrajina/go-reflector v0.5.6 h1:hKQ0gyocG7vgMD2M3dRlYN6WBBOmdoOzJ6njQSepKdE=
 github.com/tkrajina/go-reflector v0.5.6/go.mod h1:ECbqLgccecY5kPmPmXg1MrHW585yMcDkVl6IvJe64T4=
+github.com/ubuntu/decorate v0.0.0-20230125165522-2d5b0a9bb117 h1:XQpsQG5lqRJlx4mUVHcJvyyc1rdTI9nHvwrdfcuy8aM=
+github.com/ubuntu/decorate v0.0.0-20230125165522-2d5b0a9bb117/go.mod h1:mx0TjbqsaDD9DUT5gA1s3hw47U6RIbbIBfvGzR85K0g=
+github.com/ubuntu/gowsl v0.0.0-20230615094051-94945650cc1e h1:5hJ4Z9ISvbDUWL7TDvfoYp0bXsaX42WjAUJzyZ8NMCI=
+github.com/ubuntu/gowsl v0.0.0-20230615094051-94945650cc1e/go.mod h1:tu2rOgQGt6bZce1OE8G75Ca8+NvNmTNOvplLolr326I=
 github.com/valyala/bytebufferpool v1.0.0 h1:GqA5TC/0021Y/b9FG4Oi9Mr3q7XYx6KllzawFIhcdPw=
 github.com/valyala/bytebufferpool v1.0.0/go.mod h1:6bBcMArwyJ5K/AmCkWv1jt77kVWyCJ6HpOuEn7z0Csc=
 github.com/valyala/fasttemplate v1.2.1/go.mod h1:KHLXt3tVN2HBp8eijSv/kGJopbvo7S+qRAEEKiv+SiQ=
@@ -86,10 +97,12 @@ golang.org/x/sys v0.0.0-20210616045830-e2b7044e8c71/go.mod h1:oPkhp1MJrh7nUepCBc
 golang.org/x/sys v0.0.0-20210630005230-0f9fa26af87c/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.0.0-20210927094055-39ccf1dd6fa6/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.0.0-20211103235746-7861aae1554b/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
+golang.org/x/sys v0.0.0-20220715151400-c0bba94af5f8/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.0.0-20220811171246-fbc7d0a398ab/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
+golang.org/x/sys v0.0.0-20220908164124-27713097b956/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.6.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
-golang.org/x/sys v0.8.0 h1:EBmGv8NaZBZTWvrbjNoL6HVt+IVy3QDQpJs7VRIw3tU=
-golang.org/x/sys v0.8.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
+golang.org/x/sys v0.9.0 h1:KS/R3tvhPqvJvwcKfnBHJwwthS11LRhmM5D59eEXa0s=
+golang.org/x/sys v0.9.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/term v0.0.0-20201117132131-f5c789dd3221/go.mod h1:Nr5EML6q2oocZ2LXRh80K7BxOlk5/8JxuGnuhpl+muw=
 golang.org/x/term v0.0.0-20201126162022-7de9c90e9dd1/go.mod h1:bj7SfCRtBDWHUb9snDiAeCFNEtKQo2Wmx5Cou7ajbmo=
 golang.org/x/text v0.3.0/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ=

main.go

@@ -2,6 +2,8 @@ package main
 
 import (
 	"embed"
+	"fmt"
+	"net/http"
 	"os"
 	"runtime/debug"
 	"strings"
@@ -14,6 +16,27 @@ import (
 	"github.com/wailsapp/wails/v2/pkg/options/windows"
 )
 
+type FileLoader struct {
+	http.Handler
+}
+
+func NewFileLoader() *FileLoader {
+	return &FileLoader{}
+}
+
+func (h *FileLoader) ServeHTTP(res http.ResponseWriter, req *http.Request) {
+	var err error
+	requestedFilename := strings.TrimPrefix(req.URL.Path, "/")
+	println("Requesting file:", requestedFilename)
+	fileData, err := os.ReadFile(requestedFilename)
+	if err != nil {
+		res.WriteHeader(http.StatusBadRequest)
+		res.Write([]byte(fmt.Sprintf("Could not load file %s", requestedFilename)))
+	}
+
+	res.Write(fileData)
+}
+
 //go:embed all:frontend/dist
 var assets embed.FS
 
@@ -26,12 +49,23 @@ var cyacInfo embed.FS
 //go:embed backend-python
 var py embed.FS
 
+//go:embed finetune
+var finetune embed.FS
+
+//go:embed midi
+var midi embed.FS
+
+//go:embed assets/sound-font
+var midiAssets embed.FS
+
 func main() {
 	if buildInfo, ok := debug.ReadBuildInfo(); !ok || strings.Contains(buildInfo.String(), "-ldflags") {
 		backend.CopyEmbed(cyac)
 		backend.CopyEmbed(cyacInfo)
 		backend.CopyEmbed(py)
-		os.Mkdir("models", os.ModePerm)
+		backend.CopyEmbed(finetune)
+		backend.CopyEmbed(midi)
+		backend.CopyEmbed(midiAssets)
 	}
 
 	// Create an instance of the app structure
@@ -61,7 +95,8 @@ func main() {
 			IsZoomControlEnabled: true,
 		},
 		AssetServer: &assetserver.Options{
-			Assets: assets,
+			Assets:  assets,
+			Handler: NewFileLoader(),
 		},
 		OnStartup: app.OnStartup,
 		Bind: []any{

manifest.json

@@ -1,12 +1,12 @@
 {
-  "version": "1.2.7",
+  "version": "1.3.9",
   "introduction": {
     "en": "RWKV is an open-source, commercially usable large language model with high flexibility and great potential for development.\n### About This Tool\nThis tool aims to lower the barrier of entry for using large language models, making it accessible to everyone. It provides fully automated dependency and model management. You simply need to click and run, following the instructions, to deploy a local large language model. The tool itself is very compact and only requires a single executable file for one-click deployment.\nAdditionally, this tool offers an interface that is fully compatible with the OpenAI API. This means you can use any ChatGPT client as a client for RWKV, enabling capability expansion beyond just chat functionality.\n### Preset Configuration Rules at the Bottom\nThis tool comes with a series of preset configurations to reduce complexity. The naming rules for each configuration represent the following in order: device - required VRAM/memory - model size - model language.\nFor example, \"GPU-8G-3B-EN\" indicates that this configuration is for a graphics card with 8GB of VRAM, a model size of 3 billion parameters, and it uses an English language model.\nLarger model sizes have higher performance and VRAM requirements. Among configurations with the same model size, those with higher VRAM usage will have faster runtime.\nFor example, if you have 12GB of VRAM but running the \"GPU-12G-7B-EN\" configuration is slow, you can downgrade to \"GPU-8G-3B-EN\" for a significant speed improvement.\n### About RWKV\nRWKV is an RNN with Transformer-level LLM performance, which can also be directly trained like a GPT transformer (parallelizable). And it's 100% attention-free. You only need the hidden state at position t to compute the state at position t+1. You can use the \"GPT\" mode to quickly compute the hidden state for the \"RNN\" mode.<br/>So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, \"infinite\" ctx_len, and free sentence embedding (using the final hidden state).",
     "zh": "RWKV是一个开源且允许商用的大语言模型，灵活性很高且极具发展潜力。\n### 关于本工具\n本工具旨在降低大语言模型的使用门槛，做到人人可用，本工具提供了全自动化的依赖和模型管理，你只需要直接点击运行，跟随引导，即可完成本地大语言模型的部署，工具本身体积极小，只需要一个exe即可完成一键部署。\n此外，本工具提供了与OpenAI API完全兼容的接口，这意味着你可以把任意ChatGPT客户端用作RWKV的客户端，实现能力拓展，而不局限于聊天。\n### 底部的预设配置规则\n本工具内置了一系列预设配置，以降低使用难度，每个配置名的规则，依次代表着：设备-所需显存/内存-模型规模-模型语言。\n例如，GPU-8G-3B-CN，表示该配置用于显卡，需要8G显存，模型规模为30亿参数，使用的是中文模型。\n模型规模越大，性能要求越高，显存要求也越高，而同样模型规模的配置中，显存占用越高的，运行速度越快。\n例如当你有12G显存，但运行GPU-12G-7B-CN配置速度比较慢，可降级成GPU-8G-3B-CN，将会大幅提速。\n### 关于RWKV\nRWKV是具有Transformer级别LLM性能的RNN，也可以像GPT Transformer一样直接进行训练（可并行化）。而且它是100% attention-free的。你只需在位置t处获得隐藏状态即可计算位置t + 1处的状态。你可以使用“GPT”模式快速计算用于“RNN”模式的隐藏状态。\n因此，它将RNN和Transformer的优点结合起来 - 高性能、快速推理、节省显存、快速训练、“无限”上下文长度以及免费的语句嵌入（使用最终隐藏状态）。"
   },
   "about": {
-    "en": "<div align=\"center\">\n\nProject Source Code:\nhttps://github.com/josStorer/RWKV-Runner\nAuthor: [@josStorer](https://github.com/josStorer)\nFAQs: https://github.com/josStorer/RWKV-Runner/wiki/FAQs\n\nRelated Repositories:\nRWKV-4-Raven: https://huggingface.co/BlinkDL/rwkv-4-raven/tree/main\nChatRWKV: https://github.com/BlinkDL/ChatRWKV\nRWKV-LM: https://github.com/BlinkDL/RWKV-LM\n\n</div>",
-    "zh": "<div align=\"center\">\n\n本项目源码:\nhttps://github.com/josStorer/RWKV-Runner\n作者: [@josStorer](https://github.com/josStorer)\n演示与常见问题说明视频: https://www.bilibili.com/video/BV1hM4y1v76R\n疑难解答: https://www.bilibili.com/read/cv23921171\n\n相关仓库:\nRWKV-4-Raven: https://huggingface.co/BlinkDL/rwkv-4-raven/tree/main\nChatRWKV: https://github.com/BlinkDL/ChatRWKV\nRWKV-LM: https://github.com/BlinkDL/RWKV-LM\n\n</div>"
+    "en": "<div align=\"center\">\n\nProject Source Code:\nhttps://github.com/josStorer/RWKV-Runner\nAuthor: [@josStorer](https://github.com/josStorer)\nFAQs: https://github.com/josStorer/RWKV-Runner/wiki/FAQs\n\nRelated Repositories:\nRWKV-4-World: https://huggingface.co/BlinkDL/rwkv-4-world/tree/main\nRWKV-4-Raven: https://huggingface.co/BlinkDL/rwkv-4-raven/tree/main\nChatRWKV: https://github.com/BlinkDL/ChatRWKV\nRWKV-LM: https://github.com/BlinkDL/RWKV-LM\nRWKV-LM-LoRA: https://github.com/Blealtan/RWKV-LM-LoRA\nMIDI-LLM-tokenizer: https://github.com/briansemrau/MIDI-LLM-tokenizer\n\n</div>",
+    "zh": "<div align=\"center\">\n\n本项目源码:\nhttps://github.com/josStorer/RWKV-Runner\n作者: [@josStorer](https://github.com/josStorer)\n演示与常见问题说明视频: https://www.bilibili.com/video/BV1hM4y1v76R\n疑难解答: https://www.bilibili.com/read/cv23921171\n\n相关仓库:\nRWKV-4-World: https://huggingface.co/BlinkDL/rwkv-4-world/tree/main\nRWKV-4-Raven: https://huggingface.co/BlinkDL/rwkv-4-raven/tree/main\nChatRWKV: https://github.com/BlinkDL/ChatRWKV\nRWKV-LM: https://github.com/BlinkDL/RWKV-LM\nRWKV-LM-LoRA: https://github.com/Blealtan/RWKV-LM-LoRA\nMIDI-LLM-tokenizer: https://github.com/briansemrau/MIDI-LLM-tokenizer\n\n</div>"
   },
   "programFiles": [
     {
@@ -190,6 +190,18 @@
       "url": "https://huggingface.co/BlinkDL/rwkv-4-world/blob/main/RWKV-4-World-3B-v1-20230619-ctx4096.pth",
       "downloadUrl": "https://huggingface.co/BlinkDL/rwkv-4-world/resolve/main/RWKV-4-World-3B-v1-20230619-ctx4096.pth"
     },
+    {
+      "name": "RWKV-4-World-CHNtuned-3B-v1-20230625-ctx4096.pth",
+      "desc": {
+        "en": "Global Languages 3B v1 Enhanced Chinese",
+        "zh": "全球语言 3B v1 中文增强"
+      },
+      "size": 6125597618,
+      "SHA256": "7d3b5a4d0e9780a3e3d9ae7c2defbe8564d240bc9a238db4ba70cfb66dc33888",
+      "lastUpdated": "2023-06-25T14:53:27",
+      "url": "https://huggingface.co/BlinkDL/rwkv-4-world/blob/main/RWKV-4-World-CHNtuned-3B-v1-20230625-ctx4096.pth",
+      "downloadUrl": "https://huggingface.co/BlinkDL/rwkv-4-world/resolve/main/RWKV-4-World-CHNtuned-3B-v1-20230625-ctx4096.pth"
+    },
     {
       "name": "RWKV-4-World-7B-v1-OnlyForTest_30%_trained-20230529-ctx4096.pth",
       "desc": {
@@ -265,7 +277,56 @@
       "SHA256": "dfb56e8ba32907cb47df83c8d702e7f350d9ad50a59b71b031da4681637588b3",
       "lastUpdated": "2023-06-19T01:28:17",
       "url": "https://huggingface.co/BlinkDL/rwkv-4-world/blob/main/RWKV-4-World-7B-v1-OnlyForTest_84%25_trained-20230618-ctx4096.pth",
-      "downloadUrl": "https://huggingface.co/BlinkDL/rwkv-4-world/resolve/main/RWKV-4-World-7B-v1-OnlyForTest_84%25_trained-20230618-ctx4096.pth"
+      "downloadUrl": "https://huggingface.co/BlinkDL/rwkv-4-world/resolve/main/RWKV-4-World-7B-v1-OnlyForTest_84%25_trained-20230618-ctx4096.pth",
+      "hide": true
+    },
+    {
+      "name": "RWKV-4-World-7B-v1-20230626-ctx4096.pth",
+      "desc": {
+        "en": "Global Languages 7B v1",
+        "zh": "全球语言 7B v1"
+      },
+      "size": 15035393586,
+      "SHA256": "db7b011247a0fe4389e1d76e3d6a904185f85d509c8a44ad18bf401094efc293",
+      "lastUpdated": "2023-06-26T16:40:04",
+      "url": "https://huggingface.co/BlinkDL/rwkv-4-world/blob/main/RWKV-4-World-7B-v1-20230626-ctx4096.pth",
+      "downloadUrl": "https://huggingface.co/BlinkDL/rwkv-4-world/resolve/main/RWKV-4-World-7B-v1-20230626-ctx4096.pth"
+    },
+    {
+      "name": "RWKV-4-World-CHNtuned-7B-v1-20230709-ctx4096.pth",
+      "desc": {
+        "en": "Global Languages 7B v1 Enhanced Chinese",
+        "zh": "全球语言 7B v1 中文增强"
+      },
+      "size": 15035393458,
+      "SHA256": "52d33e8352a40158d21425fee4f68df1515d6324056f788d2c78a366ef578ffa",
+      "lastUpdated": "2023-07-09T18:23:33",
+      "url": "https://huggingface.co/BlinkDL/rwkv-4-world/blob/main/RWKV-4-World-CHNtuned-7B-v1-20230709-ctx4096.pth",
+      "downloadUrl": "https://huggingface.co/BlinkDL/rwkv-4-world/resolve/main/RWKV-4-World-CHNtuned-7B-v1-20230709-ctx4096.pth"
+    },
+    {
+      "name": "Readflow-RWKV-4-World-CHNtuned-7B-v1-20230709-ctx32k.pth",
+      "desc": {
+        "en": "Global Languages 7B v1 Enhanced Chinese Ctx32k Summary Ability",
+        "zh": "全球语言 7B v1 中文增强 32k上下文 总结能力"
+      },
+      "size": 15035391543,
+      "SHA256": "1bd1de8cdbd56b67e1374588fe5d202884049c71278ffcb12f5c4efbdb422ee1",
+      "lastUpdated": "2023-07-20T06:11:29",
+      "url": "https://huggingface.co/xiaol/readflow-rwkv-4-world-ctx32k/blob/main/Readflow-RWKV-4-World-CHNtuned-7B-v1-20230709-ctx32k.pth",
+      "downloadUrl": "https://huggingface.co/xiaol/readflow-rwkv-4-world-ctx32k/resolve/main/Readflow-RWKV-4-World-CHNtuned-7B-v1-20230709-ctx32k.pth"
+    },
+    {
+      "name": "RWKV-4-World-JPNtuned-7B-v1-20230718-ctx4096.pth",
+      "desc": {
+        "en": "Global Languages 7B v1 Enhanced Japanese",
+        "zh": "全球语言 7B v1 日文增强"
+      },
+      "size": 15035393458,
+      "SHA256": "3e4c7664ce893ac1f6bb59cd76664fb5c872cb076bb82dbd534db0555b6e9fa5",
+      "lastUpdated": "2023-07-18T20:01:12",
+      "url": "https://huggingface.co/BlinkDL/rwkv-4-world/blob/main/RWKV-4-World-JPNtuned-7B-v1-20230718-ctx4096.pth",
+      "downloadUrl": "https://huggingface.co/BlinkDL/rwkv-4-world/resolve/main/RWKV-4-World-JPNtuned-7B-v1-20230718-ctx4096.pth"
     },
     {
       "name": "RWKV-4-Novel-7B-v1-ChnEng-ChnPro-20230410-ctx4096.pth",
@@ -489,6 +550,30 @@
       "lastUpdated": "2023-05-23T11:22:41",
       "url": "https://huggingface.co/BlinkDL/rwkv-4-raven/blob/main/RWKV-4-Raven-14B-v12-Eng98%25-Other2%25-20230523-ctx8192.pth",
       "downloadUrl": "https://huggingface.co/BlinkDL/rwkv-4-raven/resolve/main/RWKV-4-Raven-14B-v12-Eng98%25-Other2%25-20230523-ctx8192.pth"
+    },
+    {
+      "name": "RWKV-4-MIDI-120M-v1-20230714-ctx4096.pth",
+      "desc": {
+        "en": "Music 120M v1",
+        "zh": "作曲 120M v1"
+      },
+      "size": 239224753,
+      "SHA256": "161d27dcf50d0958d230601ba1e0f8e7dd9c236105e92d2b833496412ace430c",
+      "lastUpdated": "2023-07-15T08:03:36",
+      "url": "https://huggingface.co/BlinkDL/rwkv-4-music/blob/main/RWKV-4-MIDI-120M-v1-20230714-ctx4096.pth",
+      "downloadUrl": "https://huggingface.co/BlinkDL/rwkv-4-music/resolve/main/RWKV-4-MIDI-120M-v1-20230714-ctx4096.pth"
+    },
+    {
+      "name": "RWKV-4-MIDI-560M-v1-20230717-ctx4096.pth",
+      "desc": {
+        "en": "Music 560M v1",
+        "zh": "作曲 560M v1"
+      },
+      "size": 1130577457,
+      "SHA256": "62b21841b24af38ef176e9e9d895d9fff730cea8aa0623f53a1784d74ce828d6",
+      "lastUpdated": "2023-07-17T15:02:08",
+      "url": "https://huggingface.co/BlinkDL/rwkv-4-music/blob/main/RWKV-4-MIDI-560M-v1-20230717-ctx4096.pth",
+      "downloadUrl": "https://huggingface.co/BlinkDL/rwkv-4-music/resolve/main/RWKV-4-MIDI-560M-v1-20230717-ctx4096.pth"
     }
   ]
 }

midi/sample.txt

@@ -0,0 +1 @@
+<start> p:24:a p:2a:a p:31:a p:39:a p:3b:a p:45:a b:26:a g:3e:a g:3e:a g:42:a g:42:a g:45:a g:45:a pi:3e:a pi:42:a pi:45:a t14 p:24:0 p:2a:0 p:31:0 p:39:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a t14 p:2a:0 p:3b:0 p:45:0 b:26:0 g:3e:0 g:3e:0 g:42:0 g:42:0 g:45:0 g:45:0 pi:3e:0 pi:42:0 pi:45:0 t2 p:2e:a p:3b:a p:45:a b:26:a g:3e:a g:3e:a g:42:a g:42:a g:45:a g:45:a pi:3e:a pi:42:a pi:45:a t14 p:2e:0 p:3b:0 p:45:0 g:3e:0 g:3e:0 g:42:0 g:42:0 g:45:0 g:45:0 pi:3e:0 pi:42:0 pi:45:0 t2 p:2e:a p:3b:a p:45:a g:3e:a g:3e:a g:42:a g:42:a g:45:a g:45:a pi:3e:a pi:42:a pi:45:a t14 p:2e:0 p:3b:0 p:45:0 b:26:0 g:3e:0 g:3e:0 g:42:0 g:42:0 g:45:0 g:45:0 pi:3e:0 pi:42:0 pi:45:0 t2 p:26:a p:2a:a p:3b:a p:45:a t14 p:26:0 p:2a:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a b:26:a g:3e:a g:3e:a g:42:a g:42:a g:45:a g:45:a pi:3e:a pi:42:a pi:45:a t14 p:2a:0 p:3b:0 p:45:0 b:26:0 t2 p:24:a p:2a:a p:3b:a p:45:a b:2d:a t14 p:24:0 p:2a:0 p:3b:0 p:45:0 b:2d:0 g:3e:0 g:3e:0 g:42:0 g:42:0 g:45:0 g:45:0 pi:3e:0 pi:42:0 pi:45:0 t2 p:24:a p:2a:a p:3b:a p:45:a b:21:a g:39:a g:39:a g:3d:a g:3d:a g:40:a g:40:a pi:39:a pi:3d:a pi:40:a t14 p:24:0 p:2a:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a t14 p:2a:0 p:3b:0 p:45:0 b:21:0 g:39:0 g:39:0 g:3d:0 g:3d:0 g:40:0 g:40:0 pi:39:0 pi:3d:0 pi:40:0 t2 p:24:a p:2e:a p:3b:a p:45:a b:21:a g:39:a g:39:a g:3d:a g:3d:a g:40:a g:40:a pi:39:a pi:3d:a pi:40:a t14 p:24:0 p:2e:0 p:3b:0 p:45:0 b:21:0 g:39:0 g:39:0 g:3d:0 g:3d:0 g:40:0 g:40:0 pi:39:0 pi:3d:0 pi:40:0 t2 p:24:a p:2a:a p:3b:a p:45:a b:21:a g:39:a g:39:a g:3d:a g:3d:a g:40:a g:40:a pi:39:a pi:3d:a pi:40:a t14 p:24:0 p:2a:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a t14 p:2a:0 p:3b:0 p:45:0 b:21:0 g:39:0 g:39:0 g:3d:0 g:3d:0 g:40:0 g:40:0 pi:39:0 pi:3d:0 pi:40:0 t2 p:26:a p:2a:a p:3b:a p:45:a b:21:a g:39:a g:39:a g:3d:a g:3d:a g:40:a g:40:a pi:39:a pi:3d:a pi:40:a t14 p:26:0 p:2a:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a t14 p:2a:0 p:3b:0 p:45:0 b:21:0 g:39:0 g:39:0 g:3d:0 g:3d:0 g:40:0 g:40:0 pi:39:0 pi:3d:0 pi:40:0 t2 p:26:a p:2e:a p:31:a p:39:a p:3b:a p:45:a b:21:a g:39:a g:39:a g:3d:a g:3d:a g:40:a g:40:a pi:39:a pi:3d:a pi:40:a t14 p:26:0 p:2e:0 p:31:0 p:39:0 p:3b:0 p:45:0 b:21:0 t2 p:26:a p:2e:a p:31:a p:39:a p:3b:a p:45:a b:21:a t14 p:26:0 p:2e:0 p:31:0 p:39:0 p:3b:0 p:45:0 b:21:0 g:39:0 g:39:0 g:3d:0 g:3d:0 g:40:0 g:40:0 pi:39:0 pi:3d:0 pi:40:0 t2 p:24:a p:2a:a p:31:a p:39:a p:3b:a p:45:a b:1f:a g:3b:a g:3b:a g:3e:a g:3e:a g:43:a g:43:a pi:3b:a pi:3e:a pi:43:a t14 p:24:0 p:2a:0 p:31:0 p:39:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a t14 p:2a:0 p:3b:0 p:45:0 b:1f:0 g:3b:0 g:3b:0 g:3e:0 g:3e:0 g:43:0 g:43:0 pi:3b:0 pi:3e:0 pi:43:0 t2 p:2e:a p:3b:a p:45:a b:1f:a g:3b:a g:3b:a g:3e:a g:3e:a g:43:a g:43:a pi:3b:a pi:3e:a pi:43:a t14 p:2e:0 p:3b:0 p:45:0 g:3b:0 g:3b:0 g:3e:0 g:3e:0 g:43:0 g:43:0 pi:3b:0 pi:3e:0 pi:43:0 t2 p:2e:a p:3b:a p:45:a g:3b:a g:3b:a g:3e:a g:3e:a g:43:a g:43:a pi:3b:a pi:3e:a pi:43:a t14 p:2e:0 p:3b:0 p:45:0 b:1f:0 g:3b:0 g:3b:0 g:3e:0 g:3e:0 g:43:0 g:43:0 pi:3b:0 pi:3e:0 pi:43:0 t2 p:26:a p:2a:a p:3b:a p:45:a t14 p:26:0 p:2a:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a b:1f:a g:3b:a g:3b:a g:3e:a g:3e:a g:43:a g:43:a pi:3b:a pi:3e:a pi:43:a t14 p:2a:0 p:3b:0 p:45:0 b:1f:0 t2 p:24:a p:2a:a p:3b:a p:45:a b:1f:a t14 p:24:0 p:2a:0 p:3b:0 p:45:0 b:1f:0 g:3b:0 g:3b:0 g:3e:0 g:3e:0 g:43:0 g:43:0 pi:3b:0 pi:3e:0 pi:43:0 t2 p:24:a p:2e:a p:3b:a p:45:a b:26:a g:39:a g:39:a g:3e:a g:3e:a g:42:a g:42:a pi:39:a pi:3e:a pi:42:a t14 p:24:0 p:2e:0 p:3b:0 p:45:0 t2 p:2a:a p:3b:a p:45:a t14 p:2a:0 p:3b:0 <end>

vendor.yml

@@ -2,5 +2,8 @@
 - ^backend-python/wkv_cuda_utils/
 - ^backend-python/get-pip\.py
 - ^backend-python/convert_model\.py
+- ^backend-python/utils/midi\.py
 - ^build/
+- ^finetune/lora/
+- ^finetune/json2binidx_tool/
 - ^frontend/wailsjs/