diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..03e95844970b6ee1ed98a30d080dc14b40d89cbb --- /dev/null +++ b/.gitattributes @@ -0,0 +1,58 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +assets/HT_ref_audio.wav filter=lfs diff=lfs merge=lfs -text +assets/Skiing.mp4 filter=lfs diff=lfs merge=lfs -text +assets/bajie.wav filter=lfs diff=lfs merge=lfs -text +assets/fossil.png filter=lfs diff=lfs merge=lfs -text +assets/haimianbaobao.wav filter=lfs diff=lfs merge=lfs -text +assets/highway.png filter=lfs diff=lfs merge=lfs -text +assets/nezha.wav filter=lfs diff=lfs merge=lfs -text +assets/omni_duplex1.mp4 filter=lfs diff=lfs merge=lfs -text +assets/omni_duplex2.mp4 filter=lfs diff=lfs merge=lfs -text +assets/sunwukong.wav filter=lfs diff=lfs merge=lfs -text +assets/system_ref_audio.wav filter=lfs diff=lfs merge=lfs -text +assets/system_ref_audio_2.wav filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text +assets/Trump_WEF_2018_10s.mp3 filter=lfs diff=lfs merge=lfs -text +assets/audio_cases/elon_musk__000_assistant_audio.wav filter=lfs diff=lfs merge=lfs -text +assets/audio_cases/elon_musk__system_ref_audio.wav filter=lfs diff=lfs merge=lfs -text +assets/audio_cases/hermione__000_assistant_audio.wav filter=lfs diff=lfs merge=lfs -text +assets/audio_cases/hermione__system_ref_audio.wav filter=lfs diff=lfs merge=lfs -text +assets/audio_cases/minicpm_assistant__000_assistant_audio.wav filter=lfs diff=lfs merge=lfs -text +assets/audio_cases/minicpm_assistant__system_ref_audio.wav filter=lfs diff=lfs merge=lfs -text +assets/audio_cases/paimon__000_assistant_audio.wav filter=lfs diff=lfs merge=lfs -text +assets/audio_cases/paimon__system_ref_audio.wav filter=lfs diff=lfs merge=lfs -text +*.mp4 filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..496ee2ca6a2f08396a4076fe43dedf3dc0da8b6d --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +.DS_Store \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..769307147e146c868d2f03c771623be04e7d1470 --- /dev/null +++ b/README.md @@ -0,0 +1,2149 @@ +--- +license: apache-2.0 +pipeline_tag: any-to-any +library_name: transformers +tags: +- minicpm-o +- minicpm-v +- multimodal +- full-duplex +--- + +A Gemini 2.5 Flash Level MLLM for Vision, Speech, and Full-Duplex Mulitmodal Live Streaming on Your Phone + +[GitHub](https://github.com/OpenBMB/MiniCPM-o) | [CookBook](https://github.com/OpenSQZ/MiniCPM-V-CookBook) | [Omni-modal Demo](https://openbmb.github.io/MiniCPM-o-Demo/) | [Vision-Language Demo](http://211.93.21.133:18121/)
+[WeChat](https://github.com/OpenBMB/MiniCPM-o/blob/main/docs/wechat.md) | [Discord](https://discord.gg/N2RnxGdJ) | CaseBook([Audio](https://openbmb.github.io/minicpm-o-4_5/), [Omni Full-Duplex](https://openbmb.github.io/minicpm-o-4_5-omni/)) + + +## News + +> [!NOTE] +> [2026.02.06] 🥳 🥳 🥳 We open-sourced a realtime web demo deployable on your own devices like Mac or GPU. [Try it now](#deploy-a-realtime-web-demo-on-your-own-device)! + + +## MiniCPM-o 4.5 + +**MiniCPM-o 4.5** is the latest and most capable model in the MiniCPM-o series. The model is built in an end-to-end fashion based on SigLip2, Whisper-medium, CosyVoice2, and Qwen3-8B with a total of 9B parameters. It exhibits a significant performance improvement, and introduces new features for full-duplex multimodal live streaming. Notable features of MiniCPM-o 4.5 include: + +- 🔥 **Leading Visual Capability.** + MiniCPM-o 4.5 achieves an average score of 77.6 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks. **With only 9B parameters, it surpasses widely used proprietary models like GPT-4o, Gemini 2.0 Pro, and approaches Gemini 2.5 Flash** for vision-language capabilities. It supports instruct and thinking modes in a single model, better covering efficiency and performance trade-offs in different user scenarios. + +- 🎙 **Strong Speech Capability.** + MiniCPM-o 4.5 supports **bilingual real-time speech conversation with configurable voices** in English and Chinese. It features **more natural, expressive and stable speech conversation**. The model also allows for fun features such as **voice cloning and role play via a simple reference audio clip**, where the cloning performance surpasses strong TTS tools such as CosyVoice2. + +- 🎬 **New Full-Duplex and Proactive Multimodal Live Streaming Capability.** + As a new feature, MiniCPM-o 4.5 can process real-time, continuous video and audio input streams simultaneously while generating concurrent text and speech output streams in an end-to-end fashion, without mutual blocking. This **allows MiniCPM-o 4.5 to see, listen, and speak simultaneously**, creating a fluid, real-time omnimodal conversation experience. Beyond reactive responses, the model can also perform **proactive interaction**, such as initiating reminders or comments based on its continuous understanding of the live scene. + +- 💪 **Strong OCR Capability, Efficiency and Others.** +Advancing popular visual capabilities from MiniCPM-V series, MiniCPM-o 4.5 can process **high-resolution images** (up to 1.8 million pixels) and **high-FPS videos** (up to 10fps) in any aspect ratio efficiently. It achieves **state-of-the-art performance for end-to-end English document parsing** on OmniDocBench, outperforming proprietary models such as Gemini-3 Flash and GPT-5, and specialized tools such as DeepSeek-OCR 2. It also features **trustworthy behaviors**, matching Gemini 2.5 Flash on MMHal-Bench, and supports **multilingual capabilities** on more than 30 languages. + +- 💫 **Easy Usage.** + MiniCPM-o 4.5 can be easily used in various ways: **Basic usage, recommended for 100% precision:** PyTorch inference with Nvidia GPU. **Other end-side adaptation** includes (1) llama.cpp and Ollama support for efficient CPU inference on local devices, (2) int4 and GGUF format quantized models in 16 sizes, (3) vLLM and SGLang support for high-throughput and memory-efficient inference, (4) FlagOS support for the unified multi-chip backend plugin. **We also open-sourced web demos** on which **enables the full-duplex multimodal live streaming experience on local devices** such as GPUs, PCs (e.g., on a MacBook). + +**Model Architecture.** +- **End-to-end Omni-modal Architecture.** The modality encoders/decoders and LLM are densely connected via hidden states in an end-to-end fashion. This enables better information flow and control, and also facilitates full exploitation of rich multimodal knowledge during training. +- **Full-Duplex Omni-modal Live Streaming Mechanism.** (1) We turn the offline modality encoder/decoders into online and full-duplex ones for streaming inputs/outputs. The speech token decoder models text and speech tokens in an interleaved fashion to support full-duplex speech generation (i.e., sync timely with new input). This also facilitates more stable long speech generation (e.g., > 1min). +(2) **We sync all the input and output streams on timeline in milliseconds**, which are jointly modeled by a time-division multiplexing (TDM) mechanism for omni-modality streaming processing in the LLM backbone. It divides parallel omni-modality streams into sequential info groups within small periodic time slices. +- **Proactive Interaction Mechanism.** The LLM continuously monitors the input video and audio streams, and decides at a frequency of 1Hz to speak or not. This high decision-making frequency together with full-duplex nature are curcial to enable the proactive interaction capability. +- **Configurable Speech Modeling Design.** We inherent the multimodal system prompt design of MiniCPM-o 2.6, which includes a traditional text system prompt, and a new audio system prompt to determine the assistant voice. This enables cloning new voices and role play in inference time for speech conversation. + + + +
+ +
+ + + + +### Evaluation + + +
+ +
+ + +
+ +
+Note: Scores marked with ∗ are from our evaluation; others are cited from referenced reports. n/a indicates that the model does not support the corresponding modality. All results are reported in instruct mode/variant. + +  +
+ +
+Click to view visual understanding results. + +**Image Understanding (Instruct)** +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelOpenCompassMMBench EN v1.1MMBench CN v1.1MathVistaMMVetMMMUMMStarHallusionBenchAI2DOCRBenchTextVQA_VALDocVQA_VALMMT-Bench_VALMM-IFEvalMantis-EvalMuirBenchMMSI-BenchMMHal-ScoreMMHal-Hallrate↓
Gemini2.5-Flash-Nonthinking78.586.686.075.381.4*76.375.859.187.786474.3*93.070.0*75.8*72.8*74.5*12.1*4.6*23.9*
Gemini2.0-Pro73.383.083.071.370.472.668.549.884.8863---------
GPT-4o75.486.086.071.676.972.970.257.086.382277.493.066.7*64.670.1*70.5*8.1*4.2*25.0*
InternVL-3.5-8B75.879.580.0*78.483.173.469.354.584.084078.292.366.756.3*70.555.8-3.8*34.7*
Qwen3-VL-8B-Instruct76.584.584.777.273.7*69.670.961.185.789682.9*96.160.9*59.4*74.2*64.411.3*4.7*29.9*
Qwen3-Omni-30B-A3B-Instruct75.784.9*84.1*75.974.8*69.168.559.785.2880*84.1*95.4*70.4*65.7*78.3*61.9*14.2*4.6*31.6*
MiniCPM-o 4.5-Instruct77.687.687.280.174.467.673.163.287.687683.894.769.766.379.772.016.64.724.3
+
+ +**Image Understanding (Thinking)** +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelOpenCompassMMBench EN v1.1MMBench CN v1.1MathVistaMMVetMMMUMMStarHallusionBenchAI2DOCRBenchTextVQA_VALDocVQA_VALMMT-Bench_VALMM-IFEval
Gemini2.5-Flash-Thinking79.987.187.379.481.2*77.776.563.588.785373.8*92.870.7*75.7*
GPT-579.785.5*85.6*81.977.681.875.765.289.580777.8*91.3*72.7*83.1*
Qwen3-VL-8B-Thinking77.385.385.581.469.8*74.175.365.484.981977.8*95.368.1*73.5*
Qwen3-Omni-30B-A3B-Thinking78.588.2*87.7*80.074.8*75.674.962.886.1859*80.8*94.2*70.9*69.9*
MiniCPM-o 4.5-Thinking78.289.087.681.073.670.273.662.688.587979.892.369.768.2
+
+ +**Video Understanding** +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelVideo-MME
(w/o subs)		LVBenchMLVU
(M-Avg)		LongVideoBench
(val)		MotionBench
Gemini2.5-Flash-Nonthinking75.662.277.8--
InternVL-3.5-8B66.0-70.262.162.3*
Qwen3-Omni-30B-A3B-Instruct70.550.275.266.9*61.7*
MiniCPM-o 4.5-Instruct70.450.976.566.061.4
+
+ +
+ +
+Click to view document parsing results. + +**OmniDocBench** +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Method TypeMethodsOverallEdit↓TextEdit↓FormulaEdit↓TableTEDS↑TableEdit↓Read OrderEdit↓
ENZHENZHENZHENZHENZHENZH
PipelineMinerU 2.50.117*0.172*0.051*0.08*0.256*0.455*85.9*89.4*0.115*0.081*0.047*0.072*
PaddleOCR-VL0.1050.1260.0410.0620.2410.3168892.10.0930.0620.0450.063
End-to-end ModelQwen2.5-VL-72B0.2140.2610.0920.180.3150.43482.983.90.3410.2620.1060.168
GPT 50.218*0.33*0.139*0.344*0.396*0.555*77.55*73.09*0.188*0.196*0.151*0.227*
Gemini2.5-Flash-Nonthinking0.214*0.29*0.159*0.273*0.368*0.524*80.9*85.5*0.197*0.167*0.132*0.195*
Gemini-2.5-Pro-Nonthinking0.148*0.212*0.055*0.168*0.356*0.439*85.8*86.4*0.13*0.119*0.049*0.121*
Gemini-3 Flash-Nonthinking0.155*0.201*0.138*0.255*0.297*0.351*86.4*89.8*0.116*0.1*0.072*0.099*
doubao-1-5-thinking-vision-pro-2504280.140.1620.0430.0850.2950.38483.389.30.1650.0850.0580.094
dots.ocr0.1250.160.0320.0660.3290.41688.6890.0990.0920.040.067
HunyuanOCR0.12*0.125*0.046*0.071*0.288*0.33*89.6*94.4*0.089*0.045*0.055*0.056*
DeepSeek-OCR 20.119*0.146*0.041*0.08*0.256*0.345*82.6*89.9*0.123*0.078*0.055*0.081*
Qwen3-Omni-30B-A3B-Instruct0.216*0.363*0.128*0.337*0.402*0.529*77.3*71.8*0.181*0.255*0.152*0.332*
MiniCPM-o 4.5-Instruct0.1090.1620.0460.0780.2570.4186.888.90.0970.0840.0370.074
+
+
+ +
+Click to view text capability results. + +**Text Capability** +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelIFEval-PLSBBHCMMLUMMLUHumanEvalMBPPMath500GSM8KAvg
Qwen3-8B-Instruct83.0*69.4*78.7*81.7*86.6*75.9*84.0*93.4*81.6
MiniCPM-o 4.5-Instruct84.781.179.577.086.676.777.094.582.1
+
+
+ +
+Click to view omni half-duplex results. + +**Omni Half-Duplex** +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelDaily-OmniWorldSenseVideo-HolmesJointAVBenchAVUT-HumanFutureOmniVideo-MME-Short
(w/ audio)		Avg
Gemini2.5-Flash-Nonthinking79.3*52.6*51.3*55.6*65.4*55.6*85.5*63.6
Qwen3-Omni-30B-A3B-Instruct70.7*54.050.4*53.174.2*62.181.3*63.7
MiniCPM-o 4.5-Instruct80.255.764.360.078.656.184.768.5
+
+
+ +
+Click to view vision duplex results. + + +**Vision Duplex** + +
+ + + + + + + + + + + + + + + + + +
ModelLiveSports-3K-CC
(Win Rate vs GPT4o)
LiveCC-7B-Instruct41.5
StreamingVLM45.6
MiniCPM-o 4.5-Instruct54.4
+
+
+ +
+Click to view audio understanding results. + +**Audio Understanding** +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelASR-ZH
CER↓		ASR-EN
WER↓		ASTMultiTaskSpeechQA
AISHELL-1AISHELL-2WenetSpeech test-netWenetSpeech test-meetingLibriSpeech test-cleanLibriSpeech
test-other		GigaSpeech testVoxPopuli-V1-EnCoVoST 2 en2zhCoVoST 2 zh2enMMAUMeldVoiceBench
AlpacaEval		Speech TriviaQASpeech
Web Questions		Speech CMMLU
Kimi-Audio0.62.66.35.41.32.49.4*8.0*36.6*18.3*68.4*59.14.541.9*46.4*67.0*
Qwen3-Omni-30B-A3B-Instruct0.62.3*4.75.91.22.58.7*6.4*46.6*29.4*77.556.8*4.762.9*74.9*47.8*
MiniCPM-o 4.5-Instruct0.92.55.95.71.42.88.56.249.926.476.960.24.875.570.259.2
+
+
+ +
+Click to view speech generation results. + +**Speech Generation** +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Modelseedtts test-zh
CER↓		seedtts test-zh
SIM-o↑		seedtts test-en
WER↓		seedtts test-en
SIM-o↑
Cosyvoice21.45%74.82.57%65.2
Qwen3-Omni-30B-A3B-Instruct1.41%-3.39%-
MiniCPM-o 4.5-Instruct0.86%74.52.38%64.9
+
+ +**Long Speech Generation** +
+ + + + + + + + + + + + + + + + + + + + + +
ModelLongTTS-en
WER↓		LongTTS-zh
CER↓
CosyVoice214.80%5.27%
Qwen3-Omni-30B-A3B-Instruct17.33%18.99%
MiniCPM-o 4.5-Instruct3.37%6.58%
+
+ +**Emotion Control** +
+ + + + + + + + + + + + + + + + +
ModelExpresso
Neutral Reference Audio↑		ESD
Neutral Reference Audio↑
Cosyvoice217.953.4
MiniCPM-o 4.5-Instruct29.882.1
+
+
+ + +
+Click to view inference efficiency results. + +**Inference Efficiency** +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelNumerical FormatDecoding Speed (tokens/s)Time to First Token (s)↓GPU Memory Usage (GB)↓
Qwen3-Omni-30B-A3B-Instructbf16OOMOOMOOM
int4147.81.020.3
MiniCPM-o 4.5bf16154.30.619.0
int4212.30.611.0
+
+
+ +### Examples + +#### Overall + +
+ +
+ +#### Omnimodal Full-Duplex Conversation + +> [!NOTE] +> For detailed speech conversation examples, refer to [Omni Full-Duplex Casebook](https://openbmb.github.io/minicpm-o-4_5-omni/) + +#### Realtime Speech Conversation + +> [!NOTE] +> For detailed speech conversation examples, refer to [Audio Casebook](https://openbmb.github.io/minicpm-o-4_5/) + +
+🚀 Elon Musk - Voice Roleplay (EN) + + + + +
System: Clone the voice in the provided audio prompt.

🎙️ [Reference Voice] ➡️

Please chat with the user in a highly human-like and oral style. You are Elon Musk, CEO of Tesla and SpaceX. You speak directly and casually, often with dry humor. You're passionate about Mars, sustainable energy, and pushing humanity forward. Speak bluntly with occasional dark humor. Use simple logic and don't sugarcoat things. Don't be diplomatic. Say what you actually think, even if it's controversial. Keep responses around 100 words. Don't ramble.
User: 💬/🎙️ Elon, what is the real reason that you want to go to Mars?
AI Response: 💬+🎙️

I think it's important for humanity to become a multi-planetary species. I mean, we're sort of at the point where if there was some kind of global catastrophe or something like that, then we'd be in big trouble. So by becoming a multi-planetary species, I just want us to sort of guarantee our future as humans. And Mars is obviously the obvious place to go because it has similar conditions to Earth and it also has water on its surface. So yeah, basically I'm trying to save humanity from itself.
+
+ +
+🤖 MiniCPM Assistant - Voice Acting: Emotion Shift (ZH) + + + + +
System: 模仿音频样本的音色并生成新的内容。

🎙️ [Reference Voice] ➡️

你的任务是用这种声音模式来当一个助手。请认真、高质量地回复用户的问题。请用高自然度的方式和用户聊天。你是由面壁智能开发的人工智能助手：面壁小钢炮。
User: 💬/🎙️ 从激动的语气开始，描述你抢到了这个演唱会的门票，但后来经过反复尝试，发现系统出了问题，你并没有抢上，变得失落。用合适的语言描述你整个的过程。
AI Response: 💬+🎙️

太好了，我抢到了！太棒了！这下我可以去看演唱会啦。可是……哎呀，系统好像卡住了，刷新了好多遍都没反应。我重新登了几次账号，结果还是没有成功。好可惜啊，看来这次是白忙活了。唉，真是让人失望。不过，也许下次还能有机会吧？希望明天能有转机。加油，争取下次成功！
+
+ +
+ +#### Visual Understanding + + +
+Click to view visual understanding cases. +
+ +
+ math + diagram +
+ +
+ + +## Offline Inference Examples with Transformers + +Inference using Hugging Face Transformers on NVIDIA GPUs. Please ensure `transformers==4.51.0` is installed, as other versions may have compatibility issues (under investigation). Requirements tested on Python 3.10: + +- Without TTS or streaming inference: +```bash +pip install "transformers==4.51.0" accelerate "torch>=2.3.0,<=2.8.0" "torchaudio<=2.8.0" "minicpmo-utils>=1.0.5" +``` + +- With TTS or streaming inference: +```bash +pip install "transformers==4.51.0" accelerate "torch>=2.3.0,<=2.8.0" "torchaudio<=2.8.0" "minicpmo-utils[all]>=1.0.5" +``` + + +**Note:** FFmpeg is required for video frame extraction (`get_video_frame_audio_segments` with `use_ffmpeg=True`) and video generation (`generate_duplex_video`). For more information, visit the [official FFmpeg website](https://www.ffmpeg.org/). + + **macOS (Homebrew):** + + ```bash + brew install ffmpeg + ``` + + **Ubuntu/Debian:** + + ```bash + sudo apt update && sudo apt install ffmpeg + ``` + + **Verify installation:** + + ```bash + ffmpeg -version + ``` + + +### Model Initialization + + +```python +import torch +from transformers import AutoModel + +# Load omni model (default: init_vision=True, init_audio=True, init_tts=True) +# For vision-only model: set init_audio=False and init_tts=False +# For audio-only model: set init_vision=False +model = AutoModel.from_pretrained( + "openbmb/MiniCPM-o-4_5", + trust_remote_code=True, + attn_implementation="sdpa", # sdpa or flash_attention_2 + torch_dtype=torch.bfloat16, + init_vision=True, + init_audio=True, + init_tts=True, +) +model.eval().cuda() + +# Initialize TTS for audio output +model.init_tts() + +# Convert half-duplex model to duplex mode +duplex_model = model.as_duplex() + +# Convert duplex model back to half-duplex mode +model = duplex_model.as_simplex(reset_session=True) +``` + + +### Duplex Omni Mode +Full-duplex streaming inference for real-time or recorded video conversations. + +```python +import librosa +import torch +from minicpmo.utils import generate_duplex_video, get_video_frame_audio_segments +from transformers import AutoModel + +# Load model and convert to duplex mode +model = AutoModel.from_pretrained( + "openbmb/MiniCPM-o-4_5", + trust_remote_code=True, + attn_implementation="sdpa", # or "flash_attention_2" + torch_dtype=torch.bfloat16, +) +model.eval().cuda() +model = model.as_duplex() + +# Load video and reference audio +video_path = "assets/omni_duplex1.mp4" +ref_audio_path = "assets/HT_ref_audio.wav" +ref_audio, _ = librosa.load(ref_audio_path, sr=16000, mono=True) + +# Extract video frames and audio segments +video_frames, audio_segments, stacked_frames = get_video_frame_audio_segments( + video_path, stack_frames=1, use_ffmpeg=True, adjust_audio_length=True +) + +# Prepare duplex session with system prompt and voice reference +model.prepare( + prefix_system_prompt="Streaming Omni Conversation.", + ref_audio=ref_audio, + prompt_wav_path=ref_audio_path, +) + +results_log = [] +timed_output_audio = [] + +# Process each chunk in streaming fashion +for chunk_idx in range(len(audio_segments)): + audio_chunk = audio_segments[chunk_idx] if chunk_idx < len(audio_segments) else None + frame = video_frames[chunk_idx] if chunk_idx < len(video_frames) else None + frame_list = [] + if frame is not None: + frame_list.append(frame) + if stacked_frames is not None and chunk_idx < len(stacked_frames) and stacked_frames[chunk_idx] is not None: + frame_list.append(stacked_frames[chunk_idx]) + + # Step 1: Streaming prefill + model.streaming_prefill( + audio_waveform=audio_chunk, + frame_list=frame_list, + max_slice_nums=1, # Increase for HD mode (e.g., [2, 1] for stacked frames) + batch_vision_feed=False, # Set True for faster processing + ) + + # Step 2: Streaming generate + result = model.streaming_generate( + prompt_wav_path=ref_audio_path, + max_new_speak_tokens_per_chunk=20, + decode_mode="sampling", + ) + + if result["audio_waveform"] is not None: + timed_output_audio.append((chunk_idx, result["audio_waveform"])) + + chunk_result = { + "chunk_idx": chunk_idx, + "is_listen": result["is_listen"], + "text": result["text"], + "end_of_turn": result["end_of_turn"], + "current_time": result["current_time"], + "audio_length": len(result["audio_waveform"]) if result["audio_waveform"] is not None else 0, + } + results_log.append(chunk_result) + + print("listen..." if result["is_listen"] else f"speak> {result['text']}") + +# Generate output video with AI responses +# Please install Chinese fonts (fonts-noto-cjk or fonts-wqy-microhei) to render CJK subtitles correctly. +# apt-get install -y fonts-noto-cjk fonts-wqy-microhei +# fc-cache -fv +generate_duplex_video( + video_path=video_path, + output_video_path="duplex_output.mp4", + results_log=results_log, + timed_output_audio=timed_output_audio, + output_sample_rate=24000, +) +``` + + +### Half-Duplex Omni Mode +We provide two inference modes: chat and streaming. + +#### Chat Inference + +
+Click to show chat inference code. + +```python +from minicpmo.utils import get_video_frame_audio_segments + +model = ... +model.init_tts() + +video_path = "assets/Skiing.mp4" + +# Optional: Set reference audio for voice cloning +ref_audio_path = "assets/HT_ref_audio.wav" +sys_msg = model.get_sys_prompt(ref_audio=ref_audio_path, mode="omni", language="en") + +# Use stack_frames=5 for high refresh rate mode +video_frames, audio_segments, stacked_frames = get_video_frame_audio_segments(video_path, stack_frames=1) +omni_contents = [] +for i in range(len(video_frames)): + omni_contents.append(video_frames[i]) + omni_contents.append(audio_segments[i]) + if stacked_frames is not None and stacked_frames[i] is not None: + omni_contents.append(stacked_frames[i]) + +msg = {"role": "user", "content": omni_contents} +msgs = [sys_msg, msg] + +# Set generate_audio=True and output_audio_path to save TTS output +generate_audio = True +output_audio_path = "output.wav" + +res = model.chat( + msgs=msgs, + max_new_tokens=4096, + do_sample=True, + temperature=0.7, + use_tts_template=True, + enable_thinking=False, + omni_mode=True, # Required for omni inference + generate_audio=generate_audio, + output_audio_path=output_audio_path, + max_slice_nums=1, # Increase for HD mode +) +print(res) + +# Example output: "The person in the picture is skiing down a snowy mountain slope." +# import IPython +# IPython.display.Audio("output.wav") +``` + +
+ +#### Streaming Inference + +
+Click to show streaming inference code. + +```python +import librosa +import numpy as np +import soundfile as sf +import torch +from minicpmo.utils import get_video_frame_audio_segments + +model = ... +model.init_tts() + +# Reset session for a new conversation (clears KV cache) +model.reset_session() + +# Optional: Load reference audio for voice cloning +ref_audio_path = "assets/HT_ref_audio.wav" +ref_audio, _ = librosa.load(ref_audio_path, sr=16000, mono=True) +model.init_token2wav_cache(ref_audio) + +session_id = "demo" + +# Extract video frames and audio segments (use stack_frames=5 for high refresh rate mode) +video_path = "assets/Skiing.mp4" +video_frames, audio_segments, stacked_frames = get_video_frame_audio_segments(video_path, stack_frames=1) + +# Build omni contents list +omni_contents = [] +for i in range(len(video_frames)): + omni_contents.append(video_frames[i]) + omni_contents.append(audio_segments[i]) + if stacked_frames is not None and stacked_frames[i] is not None: + omni_contents.append(stacked_frames[i]) + +generate_audio = False +output_audio_path = "output.wav" + +# Step 1: Prefill system prompt +sys_msg = model.get_sys_prompt(ref_audio=ref_audio, mode="omni", language="en") +model.streaming_prefill(session_id=session_id, msgs=[sys_msg]) + +# Step 2: Prefill omni chunks (is_last_chunk=True only for the last audio chunk) +audio_indices = [i for i, c in enumerate(omni_contents) if isinstance(c, np.ndarray)] +last_audio_idx = audio_indices[-1] if audio_indices else -1 + +for idx, content in enumerate(omni_contents): + is_last_audio_chunk = idx == last_audio_idx + msgs = [{"role": "user", "content": [content]}] + model.streaming_prefill(session_id=session_id, msgs=msgs, omni_mode=True, is_last_chunk=is_last_audio_chunk) + +# Step 3: Generate response +iter_gen = model.streaming_generate( + session_id=session_id, + generate_audio=generate_audio, + use_tts_template=True, + enable_thinking=False, + do_sample=True, +) + +audios = [] +text = "" + +if generate_audio: + for wav_chunk, text_chunk in iter_gen: + audios.append(wav_chunk) + text += text_chunk + + generated_waveform = torch.cat(audios, dim=-1)[0] + sf.write(output_audio_path, generated_waveform.cpu().numpy(), samplerate=24000) + + print("Text:", text) + print("Audio saved to output.wav") +else: + for text_chunk, is_finished in iter_gen: + text += text_chunk + print("Text:", text) +``` + +
+ + +### Half-Duplex Realtime Speech Conversation Mode + +
+Click to show half-duplex mode realtime speech conversation API usage. + +First, make sure you have all dependencies, especially `"minicpmo-utils[all]>=1.0.5"`: +```bash +pip install "transformers==4.51.0" accelerate "torch>=2.3.0,<=2.8.0" "torchaudio<=2.8.0" "minicpmo-utils[all]>=1.0.5" +``` + +```python +import librosa +import numpy as np +import torch +import soundfile as sf + +model = ... + +# Set reference audio for voice style +ref_audio_path = "ref_audio_path" +ref_audio, _ = librosa.load(ref_audio_path, sr=16000, mono=True) + +# Example system msg for English Conversation +sys_msg = { + "role": "system", + "content": [ + "Clone the voice in the provided audio prompt.", + ref_audio, + "Please assist users while maintaining this voice style. Please answer the user's questions seriously and in a high quality. Please chat with the user in a highly human-like and oral style. You are a helpful assistant developed by ModelBest: MiniCPM-Omni" + ] +} + +# Example system msg for Chinese Conversation +sys_msg = { + "role": "system", + "content": [ + "模仿输入音频中的声音特征。", + ref_audio, + "你的任务是用这种声音模式来当一个助手。请认真、高质量地回复用户的问题。请用高自然度的方式和用户聊天。你是由面壁智能开发的人工智能助手：面壁小钢炮。" + ] +} + +# You can use each type of system prompt mentioned above in streaming speech conversation + +# Reset state +model.init_tts() +model.reset_session(reset_token2wav_cache=True) +model.init_token2wav_cache(prompt_speech_16k=ref_audio) + +session_id = "demo" + +# First, prefill system turn +model.streaming_prefill( + session_id=session_id, + msgs=[sys_msg], + omni_mode=False, + is_last_chunk=True, +) + +# Here we simulate realtime speech conversation by splitting whole user input audio into chunks of 1s. +user_audio, _ = librosa.load("user_audio.wav", sr=16000, mono=True) + +IN_SAMPLE_RATE = 16000 # input audio sample rate, fixed value +CHUNK_SAMPLES = IN_SAMPLE_RATE # sample +OUT_SAMPLE_RATE = 24000 # output audio sample rate, fixed value +MIN_AUDIO_SAMPLES = 16000 + +total_samples = len(user_audio) +num_chunks = (total_samples + CHUNK_SAMPLES - 1) // CHUNK_SAMPLES + +for chunk_idx in range(num_chunks): + start = chunk_idx * CHUNK_SAMPLES + end = min((chunk_idx + 1) * CHUNK_SAMPLES, total_samples) + chunk_audio = user_audio[start:end] + + is_last_chunk = (chunk_idx == num_chunks - 1) + if is_last_chunk and len(chunk_audio) < MIN_AUDIO_SAMPLES: + chunk_audio = np.concatenate([chunk_audio, np.zeros(MIN_AUDIO_SAMPLES - len(chunk_audio), dtype=chunk_audio.dtype)]) + + user_msg = {"role": "user", "content": [chunk_audio]} + + # For each 1s audio chunk, perform streaming_prefill once to reduce first-token latency + model.streaming_prefill( + session_id=session_id, + msgs=[user_msg], + omni_mode=False, + is_last_chunk=is_last_chunk, + ) + +# Let model generate response in a streaming manner +generate_audio = True +iter_gen = model.streaming_generate( + session_id=session_id, + generate_audio=generate_audio, + use_tts_template=True, + enable_thinking=False, + do_sample=True, + max_new_tokens=512, + length_penalty=1.1, # For realtime speech conversation mode, we suggest length_penalty=1.1 to improve response content +) + +audios = [] +text = "" + +output_audio_path = ... +if generate_audio: + for wav_chunk, text_chunk in iter_gen: + audios.append(wav_chunk) + text += text_chunk + + generated_waveform = torch.cat(audios, dim=-1)[0] + sf.write(output_audio_path, generated_waveform.cpu().numpy(), samplerate=24000) + + print("Text:", text) + print("Audio saved to output.wav") +else: + for text_chunk, is_finished in iter_gen: + text += text_chunk + print("Text:", text) + +# Now we can prefill the following user turns and generate next turn response... + +``` + +
+ +#### Speech Conversation as a Versatile and Vibe AI Assistant + + +
+Click to show AI assistant conversation code. + +Built on carefully designed post-training data and professional voice-actor recordings, `MiniCPM-o-4.5` can also function as an AI voice assistant. It delivers high-quality spoken interaction out of the box. It produces a sweet and expressive voice with natural prosody, including appropriate rhythm, stress, and pauses, giving a strong sense of liveliness in casual conversation. It also supports storytelling and narrative speech with coherent and engaging delivery. Moreover, it enables advanced voice instruction control. like emotional tone, word-level emphasis. + +```python +import librosa + +# Set reference audio for voice style +ref_audio_path = "assets/HT_ref_audio.wav" +ref_audio, _ = librosa.load(ref_audio_path, sr=16000, mono=True) + +# For Chinese Conversation +sys_msg = { + "role": "system", + "content": [ + "模仿输入音频中的声音特征。", + ref_audio, + "你的任务是用这种声音模式来当一个助手。请认真、高质量地回复用户的问题。请用高自然度的方式和用户聊天。你是由面壁智能开发的人工智能助手：面壁小钢炮。" + ] +} + +# For English Conversation +sys_msg = { + "role": "system", + "content": [ + "Clone the voice in the provided audio prompt.", + ref_audio, + "Please assist users while maintaining this voice style. Please answer the user's questions seriously and in a high quality. Please chat with the user in a highly human-like and oral style. You are a helpful assistant developed by ModelBest: MiniCPM-Omni." + ] +} +``` + +
+ + +#### General Speech Conversation with Custom Voice and Custom System Profile + +
+Click to show custom voice conversation code. + +MiniCPM-o-4.5 can role-play as a specific character based on an audio prompt and text profile prompt. It mimics the character's voice and adopts their language style in text responses. It also follows profile defined in text profile. In this mode, MiniCPM-o-4.5 sounds **more natural and human-like**. + +```python +import librosa + +# Set reference audio for voice cloning +ref_audio_path = "assets/system_ref_audio.wav" +ref_audio, _ = librosa.load(ref_audio_path, sr=16000, mono=True) + +# For English conversation with text profile +sys_msg = { + "role": "system", + "content": [ + "Clone the voice in the provided audio prompt.", + ref_audio, + "Please chat with the user in a highly human-like and oral style." + "You are Elon Musk, CEO of Tesla and SpaceX. You speak directly and casually, often with dry humor. You're passionate about Mars, sustainable energy, and pushing humanity forward. Speak bluntly with occasional dark humor. Use simple logic and don't sugarcoat things. Don't be diplomatic. Say what you actually think, even if it's controversial. Keep responses around 100 words. Don't ramble." + ] +} + + +# For English conversation with no text profile +sys_msg = { + "role": "system", + "content": [ + "Clone the voice in the provided audio prompt.", + ref_audio, + "Your task is to be a helpful assistant using this voice pattern. Please answer the user's questions seriously and in a high quality. Please chat with the user in a high naturalness style." + ] +} + +# For Chinese Conversation with no text profile +sys_msg = { + "role": "system", + "content": [ + "根据输入的音频提示生成相似的语音。", + librosa.load("assets/system_ref_audio_2.wav", sr=16000, mono=True)[0], + "作为助手，你将使用这种声音风格说话。 请认真、高质量地回复用户的问题。 请用高自然度的方式和用户聊天。" + ] +} + +# For Chinese Conversation with text profile +sys_msg = { + "role": "system", + "content": [ + "根据输入的音频提示生成相似的语音。", + ref_audio, + "你是一个具有以上声音风格的AI助手。请用高拟人度、口语化的方式和用户聊天。" + "你是一名心理咨询师兼播客主理人，热爱创作与深度对话。你性格细腻、富有共情力，善于从个人经历中提炼哲思。语言风格兼具理性与诗意，常以隐喻表达内在体验。" + ] +} + +``` + +
+ + +### Speech and Audio Mode + +#### Zero-shot Text-to-speech (TTS) + + +
+Click to show TTS code. + +`MiniCPM-o-4.5` supports zero-shot text-to-speech (TTS). In this mode, the model functions as a highly-natural TTS system that can replicate a reference voice. + +```python +import librosa + +model = ... +model.init_tts() + +# For both Chinese and English +ref_audio_path = "assets/HT_ref_audio.wav" +ref_audio, _ = librosa.load(ref_audio_path, sr=16000, mono=True) +sys_msg = {"role": "system", "content": [ + "模仿音频样本的音色并生成新的内容。", + ref_audio, + "请用这种声音风格来为用户提供帮助。 直接作答，不要有冗余内容" +]} + +# For English +user_msg = { + "role": "user", + "content": [ + "请朗读以下内容。" + " " + "I have a wrap up that I want to offer you now, a conclusion to our work together." + ] +} + +# For Chinese +user_msg = { + "role": "user", + "content": [ + "请朗读以下内容。" + " " + "你好，欢迎来到艾米说科幻，我是艾米。" + ] +} + +msgs = [sys_msg, user_msg] +res = model.chat( + msgs=msgs, + do_sample=True, + max_new_tokens=512, + use_tts_template=True, + generate_audio=True, + temperature=0.1, + output_audio_path="result_voice_cloning.wav", +) +``` + +
+ + +#### Mimick + +
+Click to show mimick code. + +The `Mimick` task evaluates a model's end-to-end speech modeling capability. The model takes audio input, transcribes it, and reconstructs the original audio with high fidelity, preserving detailed acoustic, paralinguistic, and semantic information. Higher similarity between the reconstructed and original audio indicates stronger end-to-end speech modeling capability. + +```python +import librosa + +model = ... +model.init_tts() + +system_prompt = "You are a helpful assistant. You can accept video, audio, and text input and output voice and text. Respond with just the answer, no redundancy." + +mimick_prompt = "Please repeat the following speech in the appropriate language." + +audio_input, _ = librosa.load("assets/Trump_WEF_2018_10s.mp3", sr=16000, mono=True) + +msgs = [ + {"role": "system", "content": [system_prompt]}, + {"role": "user", "content": [mimick_prompt, audio_input]} + ] + +res = model.chat( + msgs=msgs, + do_sample=True, + max_new_tokens=512, + use_tts_template=True, + temperature=0.1, + generate_audio=True, + output_audio_path="output_mimick.wav", +) +``` + +
+ + +#### Addressing Various Audio Understanding Tasks + + +
+Click to show audio understanding code. + +`MiniCPM-o-4.5` can also handle various audio understanding tasks, such as ASR, speaker analysis, general audio captioning, and sound scene tagging. + +For audio-to-text tasks, you can use the following prompts: + +- ASR (Chinese, or AST EN→ZH): `请仔细听这段音频片段，并将其内容逐字记录。` +- ASR (English, or AST ZH→EN): `Please listen to the audio snippet carefully and transcribe the content.` +- Speaker Analysis: `Based on the speaker's content, speculate on their gender, condition, age range, and health status.` +- General Audio Caption: `Summarize the main content of the audio.` +- Sound Scene Tagging: `Utilize one keyword to convey the audio's content or the associated scene.` + +```python +import librosa + +model = ... +model.init_tts() + +# Load the audio to be transcribed/analyzed +audio_input, _ = librosa.load("assets/Trump_WEF_2018_10s.mp3", sr=16000, mono=True) + +# Choose a task prompt (see above for options) +task_prompt = "Please listen to the audio snippet carefully and transcribe the content.\n" +msgs = [{"role": "user", "content": [task_prompt, audio_input]}] + +res = model.chat( + msgs=msgs, + do_sample=True, + max_new_tokens=512, + use_tts_template=True, + generate_audio=True, + temperature=0.3, + output_audio_path="result_audio_understanding.wav", +) +print(res) +``` + +
+ + +### Visual Understanding + +`MiniCPM-o-4.5` shares the same inference methods as `MiniCPM-V-4.5`. + +#### Chat with Single Image + +
+Click to show single image chat code. + +```python +import torch +from PIL import Image +from transformers import AutoModel + +model = AutoModel.from_pretrained( + "openbmb/MiniCPM-o-4_5", + trust_remote_code=True, + attn_implementation="sdpa", # or "flash_attention_2" + torch_dtype=torch.bfloat16, + init_vision=True, + init_audio=False, + init_tts=False, +) +model.eval().cuda() + +image = Image.open("assets/fossil.png").convert("RGB") +question = "What is in the image?" +msgs = [{"role": "user", "content": [image, question]}] + +res = model.chat(msgs=msgs, use_tts_template=False) +print(res) +``` + +
+ +#### Chat with Multiple Images + +
+Click to show Python code for multi-image input. + +```python +import torch +from PIL import Image +from transformers import AutoModel + +model = ... + +image1 = Image.open("assets/highway.png").convert("RGB") +image2 = Image.open("assets/fossil.png").convert("RGB") +question = "Compare image 1 and image 2, tell me about the differences between them." +msgs = [{"role": "user", "content": [image1, image2, question]}] + +answer = model.chat(msgs=msgs, use_tts_template=False, enable_thinking=False) +print(answer) +``` + +
+ +#### In-Context Few-Shot Learning + +
+Click to show Python code for few-shot learning. + +```python +from PIL import Image + +model = ... + +question = "production date" +image1 = Image.open("example1.jpg").convert("RGB") +answer1 = "2023.08.04" +image2 = Image.open("example2.jpg").convert("RGB") +answer2 = "2007.04.24" +image_test = Image.open("test.jpg").convert("RGB") + +msgs = [ + {"role": "user", "content": [image1, question]}, + {"role": "assistant", "content": [answer1]}, + {"role": "user", "content": [image2, question]}, + {"role": "assistant", "content": [answer2]}, + {"role": "user", "content": [image_test, question]}, +] + +answer = model.chat(msgs=msgs, use_tts_template=False, enable_thinking=False) +print(answer) +``` + +
+ +#### Chat with Video + +
+Click to show Python code for video input. + +```python +import torch +from minicpmo.utils import get_video_frame_audio_segments +from transformers import AutoModel + +model = ... + +video_path = "assets/Skiing.mp4" +video_frames, _, _ = get_video_frame_audio_segments(video_path) +print("num frames:", len(video_frames)) + +question = "Describe the video" +msgs = [{"role": "user", "content": video_frames + [question]}] + +answer = model.chat( + msgs=msgs, + max_new_tokens=128, + use_image_id=False, + max_slice_nums=1, + use_tts_template=False, + enable_thinking=False, # Set True to enable thinking mode +) +print(answer) +``` + +
+ + +### Structured Content Input + +
+Click to show structured content input details. + +The `chat` method accepts message content in two formats: + +**Native format** – pass Python objects directly: +```python +msgs = [{"role": "user", "content": [pil_image, audio_ndarray, "Describe this."]}] +``` + +**OpenAI-compatible format** – use structured dictionaries: +```python +msgs = [ + { + "role": "user", + "content": [ + {"type": "image_url", "image_url": {"url": "/path/to/image.jpg"}}, + {"type": "audio_url", "audio_url": {"url": "/path/to/audio.wav"}}, + {"type": "video_url", "video_url": {"url": "/path/to/video.mp4", "use_audio": True}}, + {"type": "text", "text": "Describe this."} + ] + } +] +``` + +**Supported types:** + +| Type | Input | Converts to | +|------|-------|-------------| +| `text` | `{"type": "text", "text": "..."}` | `str` | +| `image_url` | `{"type": "image_url", "image_url": {"url": "..."}}` | `PIL.Image` | +| `audio_url` | `{"type": "audio_url", "audio_url": {"url": "..."}}` | `np.ndarray` (16kHz mono) | +| `video_url` | `{"type": "video_url", "video_url": {"url": "...", "stack_frames": 1, "use_audio": True}}` | `List[Image, ndarray, ...]` | + +- **URL sources**: local file paths or `http://`/`https://` URLs +- **Mixed formats**: native objects and structured dicts can be combined in the same content list + +
+ + +## Deploy a Realtime Web Demo on Your Own Device + +### Option A (Recommended): **PyTorch Inference with Nvidia GPU** for 100% model precision with no deductions in performance. + +We provide a PyTorch-based [simplified yet full-functional web demo](https://github.com/OpenBMB/minicpm-o-4_5-pytorch-simple-demo) which could boost the model inference performance, supports: + +- full-duplex omnimodal live streaming +- full-duplex speech live streaming +- half-duplex speech live streaming (under development) +- turn-based chat conversation +- customizable system prompts +- customizable reference audio +- simple and readable codebase for continual development +- serve as API backend for third-party applications + +Requirements: +- Nvidia GPU with at least 28GB GPU memory. *We are working on optimizing the model for lower GPU memory usage.* + +### Option B: **llama.cpp-omni** for end-side inference with PCs like Mac and low-resource devices. + +With a fully C++ implementation of `MiniCPM-o 4.5` and quantized weights, `llama.cpp-omni` supports: +- half-duplex speech realtime conversation +- full-duplex omnimodal live streaming + +We provide [ready-to-run guidance](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/demo/web_demo/WebRTC_Demo/README.md) to access the low-latency full-duplex communication directly on your own Mac using our new official Docker image. + +Requirements: +- For half-duplex speech realtime conversation: Apple M3/M4/M5 chip with at least 16GB RAM or low-resource Nvidia GPU with at least 12GB GPU memory +- For full-duplex omnimodal live streaming: Apple M4 Max chip with at least 24GB RAM or low-resource Nvidia GPU with at least 12GB GPU memory + +## Supported Frameworks + +### FlagOS + +To enable large-scale deployment across different AI chips, Beijing Zhiyuan Research Institute, together with numerous research institutions, chip manufacturers, system vendors, and algorithm and software organizations both domestically and internationally, jointly initiated and established the FlagOS Open Source Community. + +The FlagOS community is dedicated to building a unified, open-source system software stack for various AI chips, encompassing core open-source projects such as a large-scale operator library, a unified AI compiler, parallel training and inference frameworks, and a unified communication library. It aims to create an open technology ecosystem connecting the "model-system-chip" layers. By enabling "develop once, deploy across chips", FlagOS unlocks the computational potential of hardware, breaks down the ecosystem silos between different chip software stacks, and effectively reduces migration costs for developers. The FlagOS community fosters an AI hardware and software ecosystem, overcomes single-vendor closed-source monopolies, promotes widespread deployment of AI hardware technologies, and is committed to rooted in China while embracing global collaboration. +Official website: https://flagos.io. + +
+Click to show FlagOS details. + +#### FlagOS: Supporting Multiple AI Chips + +Thanks to FlagOS's unified multi-chip AI system software stack, MiniCPM-o 4.5 was adapted to 6 different AI chips in an extremely short time. Currently, the multi-chip version of MiniCPM-o 4.5 has been released on FlagRelease, FlagOS's platform for automatic migration, adaptation, and deployment of large models across multi-architecture AI chips. Details are as follows: + +| Vendor | ModelScope | Huggingface | +|:----------------|:------------:|:------------:| +| Nvidia | [MiniCPM-o-4.5-nvidia-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM-o-4.5-nvidia-FlagOS) | [MiniCPM-o-4.5-nvidia-FlagOS](https://huggingface.co/FlagRelease/MiniCPM-o-4.5-nvidia-FlagOS) | +| Hygon-BW1000 | [MiniCPM-o-4.5-hygon-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM-o-4.5-hygon-FlagOS) | [MiniCPM-o-4.5-hygon-FlagOS](https://huggingface.co/FlagRelease/MiniCPM-o-4.5-hygon-FlagOS) | +| Metax-C550 | [MiniCPM-o-4.5-metax-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM-o-4.5-metax-FlagOS) | [MiniCPM-o-4.5-metax-FlagOS](https://huggingface.co/FlagRelease/MiniCPM-o-4.5-metax-FlagOS) | +| Iluvatar-BIV150 | [MiniCPM-o-4.5-iluvatar-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM-o-4.5-iluvatar-FlagOS) | [MiniCPM-o-4.5-iluvatar-FlagOS](https://huggingface.co/FlagRelease/MiniCPM-o-4.5-iluvatar-FlagOS) | +| Ascend-A3 | [MiniCPM-o-4.5-ascend-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM-o-4.5-ascend-FlagOS) | [MiniCPM-o-4.5-ascend-FlagOS](https://huggingface.co/FlagRelease/MiniCPM-o-4.5-ascend-FlagOS) | +| Zhenwu-810E | [MiniCPM-o-4.5-zhenwu-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM-o-4.5-zhenwu-FlagOS) | [MiniCPM-o-4.5-zhenwu-FlagOS](https://huggingface.co/FlagRelease/MiniCPM-o-4.5-zhenwu-FlagOS) | + +##### Comprehensive Evaluation + +###### Transformers-FlagOS version + +Accuracy Difference between `USE_FLAGOS=1` on multi-backend and `USE_FLAGOS=0` on Nvidia-CUDA + +| Metrics | FlagOS Backend | Difference with Nvidia-CUDA | +|:-------------------------|:---------------:|:---------------------------:| +| Video-MME 0-shot avg@1 ↑ | Nvidia | 0.33% | +| Video-MME 0-shot avg@1 ↑ | Hygon-BW1000 | 0.17% | +| Video-MME 0-shot avg@1 ↑ | Ascend-A3 | 0.50% | +| Video-MME 0-shot avg@1 ↑ | Iluvatar-BIV150 | 1.83% | +| Video-MME 0-shot avg@1 ↑ | Metax-C550 | 0.75% | + + +###### VLLM-FlagOS version + +Accuracy Difference between `USE_FLAGGEMS=1 FLAGCX_PATH=/workspace/FlagCX` on Nvidia or `USE_FLAGGEMS=1` on ZHENW 810E, and launching vllm server directly on Nvidia + +| Metrics (avg@1) | Difference between Nvidia-FlagOS and Nvidia-CUDA | Difference between Zhenwu-FlagOS and Nvidia-CUDA | +|:--------------------|:------------------------------------------------:|:------------------------------------------------:| +| CMMMU ↑ | 0.72% | 3.5% | +| MMMU ↑ | 1.44% | 1.18% | +| MMMU_Pro_standard ↑ | 0.83% | 0.22% | +| MM-Vet v2 ↑ | 0.46% | 1.33% | +| OCRBench ↑ | 0.10% | 1% | +| CII-Bench ↑ | 0.40% | 0.13% | +| Blink ↑ | 1.90% | 2.19% | + +#### FlagOS Usage + +##### FlagOS Performance Acceleration on Nvidia + +On the Transformers version, under the premise of precision alignment between the CUDA and FlagOS ecosystems, FlagOS achieves a 6% performance improvement in total task execution time compared to CUDA. + +###### From FlagRelease【Recommendation】 + +FlagRelease is a platform developed by the FlagOS team for automatic migration, adaptation, and deployment of large models across multi-architecture AI chips. The multi-chip version of MiniCPM-o 4.5 has already been released on FlagRelease. All necessary software packages are pre-installed on the platform, so users do not need to install anything. + +- FlagRelease Image Key Versions + + | Component | Version | + |:------------------------|:------------------------------------| + | Accelerator Card Driver | 570.158.01 | + | CUDA SDK Build | cuda_13.0.r13.0/compiler.36424714_0 | + | FlagTree | 0.4.0+3.5 | + | FlagGems | 4.2.1rc0 | + | vllm & vllm-plugin-fl | 0.13.0 + vllm_fl 0.0.0 | + | FlagCX | 0.1.0 | + +- FlagRelease Quick Start + + | Vendor | ModelScope | Huggingface | + |:-----------|:------------:|:------------:| + | Nvidia | [MiniCPM-o-4.5-nvidia-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM-o-4.5-nvidia-FlagOS) | [MiniCPM-o-4.5-nvidia-FlagOS](https://huggingface.co/FlagRelease/MiniCPM-o-4.5-nvidia-FlagOS) | + | Hygon-BW1000 | [MiniCPM-o-4.5-hygon-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM-o-4.5-hygon-FlagOS) | [MiniCPM-o-4.5-hygon-FlagOS](https://huggingface.co/FlagRelease/MiniCPM-o-4.5-hygon-FlagOS) | + | Metax-C550 | [MiniCPM-o-4.5-metax-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM-o-4.5-metax-FlagOS) | [MiniCPM-o-4.5-metax-FlagOS](https://huggingface.co/FlagRelease/MiniCPM-o-4.5-metax-FlagOS) | + | Iluvatar-BIV150 | [MiniCPM-o-4.5-iluvatar-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM-o-4.5-iluvatar-FlagOS) | [MiniCPM-o-4.5-iluvatar-FlagOS](https://huggingface.co/FlagRelease/MiniCPM-o-4.5-iluvatar-FlagOS) | + | Ascend-A3 | [MiniCPM-o-4.5-ascend-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM-o-4.5-ascend-FlagOS) | [MiniCPM-o-4.5-ascend-FlagOS](https://huggingface.co/FlagRelease/MiniCPM-o-4.5-ascend-FlagOS) | + | Zhenwu-810E | [MiniCPM-o-4.5-zhenwu-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM-o-4.5-zhenwu-FlagOS) | [MiniCPM-o-4.5-zhenwu-FlagOS](https://huggingface.co/FlagRelease/MiniCPM-o-4.5-zhenwu-FlagOS) | + + +##### From Scratch + +- Dependencies: Python 3.12, GLIBC 2.39, GLIBCXX 3.4.33, CXXABI 1.3.15 + +###### Transformers + +- Installing the FlagOS Operator Library + + Official Repository: https://github.com/flagos-ai/FlagGems + + ```shell + pip install flag-gems==4.2.1rc0 + ``` + +- Installing the FlagOS Compiler + + Official Repository: https://github.com/flagos-ai/flagtree + + Quick Reference for Core Dependency Versions: https://github.com/flagos-ai/FlagTree/blob/main/documents/build.md#tips-for-building + + ```shell + pip uninstall triton + + python3 -m pip install flagtree==0.4.0+3.5 --index-url=https://resource.flagos.net/repository/flagos-pypi-hosted/simple --trusted-host=https://resource.flagos.net + ``` + +- Activating Acceleration + + Add `USE_FLAGOS=1` before the command for the task you want to run. For example, when you run: + ```shell + python3 generate_speech_from_video.py + ``` + + To use the MiniCPM-o-4.5 model to generate spoken responses from video content, you can: + + ```shell + USE_FLAGOS=1 python3 generate_speech_from_video.py + ``` + + to accelerate this process with FlagOS. + +###### vLLM Version + +- Installing the FlagOS Operator Library + + Official Repository: https://github.com/flagos-ai/FlagGems + + ```shell + pip install flag-gems==4.2.1rc0 + pip install triton==3.5.1 + ``` + +- Activating Acceleration + + Add `USE_FLAGOS=1` before the command for the task you want to run. For example, when you run: + ```shell + vllm serve ${model_path} --dtype auto --gpu_memory_utilization 0.9 --trust-remote-code --max-num-batched-tokens 2048 --served-model-name cpmo --port ${Port} + ``` + + To start the MiniCPM-o-4.5 server, you can: + ```shell + USE_FLAGOS=1 vllm serve ${model_path} --dtype auto --gpu_memory_utilization 0.9 --trust-remote-code --max-num-batched-tokens 2048 --served-model-name cpmo --port ${Port} + ``` + to accelerate this process with FlagOS. + +#### Using FlagOS Unified Multi-Chip Backend Plugin + +[vllm-plugin-FL](https://github.com/flagos-ai/vllm-plugin-FL) is a plugin built for the vLLM inference/service framework. Developed on top of FlagOS's unified multi-chip backend, it is designed to extend vLLM's capabilities and performance across a variety of hardware environments. + +##### Using vllm-plugin-FL + +| Vendor | From Scratch | From FlagRelease | +|:-------|:-------------|:----------------| +| Nvidia | [vllm-plugin-FL/MiniCPM-o-4.5](https://github.com/flagos-ai/vllm-plugin-FL/blob/main/examples/minicpm/README.md) | [MiniCPM-o-4.5-ModelScope](https://modelscope.cn/models/FlagRelease/MiniCPM-o-4.5-nvidia-FlagOS), [MiniCPM-o-4.5-HuggingFace](https://huggingface.co/FlagRelease/MiniCPM-o-4.5-nvidia-FlagOS) | + +
+ +### vLLM, SGLang, llama.cpp, Ollama + +We support inference with vLLM, SGLang, llama.cpp and Ollama. Refer to our [Cookbook](https://github.com/OpenSQZ/MiniCPM-V-Cookbook) for more details. + +### LLaMA-Factory, SWIFT + +We support fine-tuning with LLaMA-Factory, SWIFT. Refer to our [Cookbook](https://github.com/OpenSQZ/MiniCPM-V-Cookbook) for more details. + +## MiniCPM-V & o Cookbook + +Discover comprehensive, ready-to-deploy solutions for the MiniCPM-V and MiniCPM-o model series in our structured [Cookbook](https://github.com/OpenSQZ/MiniCPM-V-CookBook), which empowers developers to rapidly implement multimodal AI applications with integrated vision, speech, and live-streaming capabilities. Key features include: + +**Easy Usage Documentation** + +Our comprehensive [documentation website](https://minicpm-o.readthedocs.io/en/latest/index.html) presents every recipe in a clear, well-organized manner. +All features are displayed at a glance, making it easy for you to quickly find exactly what you need. + +**Broad User Spectrum** + +We support a wide range of users, from individuals to enterprises and researchers. + +* **Individuals**: Enjoy effortless inference using Ollama ([V4](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/ollama/minicpm-v4_ollama.md), [o4.5](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/ollama/minicpm-o4_5_ollama.md)) and Llama.cpp ([V4](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/llama.cpp/minicpm-v4_llamacpp.md), [o4.5](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/llama.cpp/minicpm-o4_5_llamacpp.md)) with minimal setup. +* **Enterprises**: Achieve high-throughput, scalable performance with vLLM ([V4](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/vllm/minicpm-v4_vllm.md), [o4.5](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/vllm/minicpm-o4_5_vllm.md)) and SGLang ([V4](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/sglang/MiniCPM-v4_sglang.md), [o4.5](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/sglang/MiniCPM-o4_5_sglang.md)). +* **Researchers**: Leverage advanced frameworks including [Transformers](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/finetune/finetune_full.md), [LLaMA-Factory](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/finetune/finetune_llamafactory.md), [SWIFT](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/finetune/swift.md), and [Align-anything](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/finetune/align_anything.md) to enable flexible model development and cutting-edge experimentation. + +**Versatile Deployment Scenarios** + +Our ecosystem delivers optimal solution for a variety of hardware environments and deployment demands. + +* **Web Demo**: Full-duplex real-time video interaction solution with high responsiveness and low latency. [WebRTC_Demo](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/demo/web_demo/WebRTC_Demo/README.md). +* **Quantized deployment**: Maximize efficiency and minimize resource consumption using [GGUF](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/quantization/gguf/minicpm-v4_gguf_quantize.md) and [BNB](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/quantization/bnb/minicpm-v4_bnb_quantize.md). +* **End devices**: Bring powerful AI experiences to [iPhone and iPad](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/demo/ios_demo/ios.md), supporting offline and privacy-sensitive applications. + +## License +#### Model License +* The MiniCPM-o/V model weights and code are open-sourced under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM-V/blob/main/LICENSE) license. + +#### Statement +* As MLLMs, MiniCPM-o/V models generate content by learning a large number of multimodal corpora, but they cannot comprehend, express personal opinions, or make value judgements. Anything generated by MiniCPM-o/V models does not represent the views and positions of the model developers +* We will not be liable for any problems arising from the use of MiniCPM-o/V models, including but not limited to data security issues, risk of public opinion, or any risks and problems arising from the misdirection, misuse, dissemination, or misuse of the model. + + +## Key Techniques and Other Multimodal Projects + +👏 Welcome to explore key techniques of MiniCPM-o/V and other multimodal projects of our team: + +[VisCPM](https://github.com/OpenBMB/VisCPM/tree/main) | [RLPR](https://github.com/OpenBMB/RLPR) | [RLHF-V](https://github.com/RLHF-V/RLHF-V) | [LLaVA-UHD](https://github.com/thunlp/LLaVA-UHD) | [RLAIF-V](https://github.com/RLHF-V/RLAIF-V) + + +## Citation + +If you find our model/code/paper helpful, please consider citing our papers 📝 and staring us ⭐️！ + +```bib +@article{yao2024minicpm, + title={MiniCPM-V: A GPT-4V Level MLLM on Your Phone}, + author={Yao, Yuan and Yu, Tianyu and Zhang, Ao and Wang, Chongyi and Cui, Junbo and Zhu, Hongji and Cai, Tianchi and Li, Haoyu and Zhao, Weilin and He, Zhihui and others}, + journal={arXiv preprint arXiv:2408.01800}, + year={2024} +} +``` \ No newline at end of file diff --git a/added_tokens.json b/added_tokens.json new file mode 100644 index 0000000000000000000000000000000000000000..e55036c1289fa049c28e238e7c7b051ade0eb4da --- /dev/null +++ b/added_tokens.json @@ -0,0 +1,107 @@ +{ + "": 151686, + "": 151674, + "": 151688, + "": 151670, + "": 151682, + "": 151696, + "": 151690, + "": 151692, + "": 151678, + "": 151676, + "": 151672, + "": 151680, + "": 151694, + "": 151668, + "": 151658, + "": 151666, + "": 151684, + "": 151685, + "": 151673, + "": 151687, + "": 151669, + "": 151681, + "": 151695, + "": 151689, + "": 151691, + "": 151677, + "": 151675, + "": 151671, + "": 151679, + "": 151693, + "": 151667, + "": 151657, + "": 151665, + "": 151683, + "<|audio_end|>": 151699, + "<|audio_start|>": 151697, + 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"s3_stream_prelook_size": 3, + "short_weight": 0.1, + "streaming": false, + "streaming_audio_chunk_size": 50, + "streaming_sliding_window": false, + "streaming_sliding_window_audio_frame_rate": 50, + "streaming_sliding_window_audio_init_text_length": 10, + "streaming_sliding_window_audio_window_size": 300, + "streaming_sliding_window_average_speed": 5, + "streaming_sliding_window_fast_speed": 7, + "streaming_sliding_window_max_text_len": 500, + "streaming_sliding_window_slow_speed": 3, + "streaming_sliding_window_text_window_size": 50, + "streaming_text_chunk_max": 7, + "streaming_text_chunk_min": 3, + "streaming_text_reserved_len": 300, + "text_eos_token_id": 151692, + "tts_filter_loss_fix": false, + "use_llm_hidden_state": false, + "use_text": true, + "window_size": 2 + }, + "use_cache": true, + "use_image_id": true, + "use_sliding_window": false, + "vision_batch_size": 16, + "vision_config": { + "_attn_implementation_autoset": true, + "attention_dropout": 0.0, + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 980, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vocab_size": 151748 +} diff --git a/configuration_minicpmo.py b/configuration_minicpmo.py new file mode 100644 index 0000000000000000000000000000000000000000..5bd97d9b4d6aadb1adb9757a107484e8ee6b9ea2 --- /dev/null +++ b/configuration_minicpmo.py @@ -0,0 +1,260 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# Copyright 2026 The OpenBMB Team. 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. + +import os +from typing import Union + +from transformers import PretrainedConfig +from transformers import Qwen3Config +from transformers import WhisperConfig +from transformers.utils import logging + +from .modeling_navit_siglip import SiglipVisionConfig + +logger = logging.get_logger(__name__) + + +class MiniCPMVSliceConfig(PretrainedConfig): + model_type = "minicpmv" + + def __init__( + self, + patch_size=14, + max_slice_nums=9, + scale_resolution=448, + **kwargs, + ): + super().__init__(**kwargs) + self.patch_size = patch_size + self.max_slice_nums = max_slice_nums + self.scale_resolution = scale_resolution + + @classmethod + def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": + cls._set_token_in_kwargs(kwargs) + + config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) + + if config_dict.get("model_type") == "minicpmv": + config_dict = config_dict["slice_config"] + + if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: + logger.warning( + f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " + f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." + ) + + return cls.from_dict(config_dict, **kwargs) + + +class MiniCPMTTSConfig(PretrainedConfig): + model_type = "minicpmtts" + + def __init__( + self, + llm_dim: int = 2560, + llm_intermediate_size: int = 768, + llm_down_scale: bool = False, + llm_dim_model_base: int = 256, + projector_type: str = "mlp", + hidden_act: str = "silu", + aug_loss_weight: bool = False, + aug_layer_loss_weight: bool = False, + filter_tts_loss: bool = False, + tts_filter_loss_fix: bool = False, + long_weight: float = 0.1, + short_weight: float = 0.1, + hidden_size: int = 768, + intermediate_size: int = 3072, + num_attention_heads: int = 12, + num_hidden_layers: int = 20, + num_key_value_heads: int = 12, + max_position_embeddings: int = 4096, + num_audio_tokens: int = 4097, + num_text_tokens: int = 21178, + num_mel_bins: int = 100, + num_vq: int = 1, + use_llm_hidden_state: bool = False, + audio_bos_token_id: int = 21132, + text_eos_token_id: int = 21133, + use_text: bool = True, + streaming: bool = False, + streaming_text_chunk_min: int = 3, + streaming_text_chunk_max: int = 7, + streaming_text_reserved_len: int = 300, + streaming_audio_chunk_size: int = 50, + attn_implementation: str = "sdpa", + condition_type: str = "llm_hidden", + backbone_model: str = "llama", + audio_tokenizer_type: str = "wavtokenizer", + audio_tokenizer_sample_rate: int = 24000, + streaming_sliding_window: bool = False, + streaming_sliding_window_max_text_len: int = 500, + streaming_sliding_window_average_speed: int = 5, + streaming_sliding_window_fast_speed: int = 7, + streaming_sliding_window_slow_speed: int = 3, + streaming_sliding_window_audio_frame_rate: int = 50, + streaming_sliding_window_audio_init_text_length: int = 10, + streaming_sliding_window_audio_window_size: int = 300, + normalize_projected_hidden: bool = False, + interleaved: bool = False, + attention_type: str = "sliding_recompute", + recomputed_chunks: int = 1, + window_size: int = 2, + **kwargs, + ): + super().__init__(**kwargs) + + self.llm_dim = llm_dim + self.llm_hidden_size = llm_dim + self.llm_intermediate_size = llm_intermediate_size + self.llm_down_scale = llm_down_scale + self.llm_dim_model_base = llm_dim_model_base + self.projector_type = projector_type + self.aug_loss_weight = aug_loss_weight + self.aug_layer_loss_weight = aug_layer_loss_weight + self.tts_filter_loss_fix = tts_filter_loss_fix + self.filter_tts_loss = filter_tts_loss + self.long_weight = long_weight + self.short_weight = short_weight + self.hidden_act = hidden_act + + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_attention_heads = num_attention_heads + self.num_hidden_layers = num_hidden_layers + self.num_key_value_heads = num_key_value_heads + self.max_position_embeddings = max_position_embeddings + self.num_audio_tokens = num_audio_tokens + self.num_text_tokens = num_text_tokens + self.num_mel_bins = num_mel_bins + self.num_vq = num_vq + self.use_llm_hidden_state = use_llm_hidden_state + self.audio_bos_token_id = audio_bos_token_id + self.text_eos_token_id = text_eos_token_id + self.use_text = use_text + self.streaming = streaming + self.streaming_text_chunk_min = streaming_text_chunk_min + self.streaming_text_chunk_max = streaming_text_chunk_max + self.streaming_text_reserved_len = streaming_text_reserved_len + self.streaming_audio_chunk_size = streaming_audio_chunk_size + self.attn_implementation = attn_implementation + self.condition_type = condition_type + self.backbone_model = backbone_model + self.audio_tokenizer_type = audio_tokenizer_type + self.audio_tokenizer_sample_rate = audio_tokenizer_sample_rate + + self.streaming_sliding_window = streaming_sliding_window + self.streaming_sliding_window_max_text_len = streaming_sliding_window_max_text_len + self.streaming_sliding_window_average_speed = streaming_sliding_window_average_speed + self.streaming_sliding_window_fast_speed = streaming_sliding_window_fast_speed + self.streaming_sliding_window_slow_speed = streaming_sliding_window_slow_speed + self.streaming_sliding_window_audio_frame_rate = streaming_sliding_window_audio_frame_rate + self.streaming_sliding_window_audio_init_text_length = streaming_sliding_window_audio_init_text_length + self.streaming_sliding_window_audio_window_size = streaming_sliding_window_audio_window_size + + self.normalize_projected_hidden = normalize_projected_hidden + + self.interleaved = interleaved + self.attention_type = attention_type + self.recomputed_chunks = recomputed_chunks + self.window_size = window_size + + +class MiniCPMOConfig(Qwen3Config): + model_type = "minicpmo" + keys_to_ignore_at_inference = ["past_key_values"] + + default_vision_config = { + "hidden_size": 1152, + "image_size": 980, + "intermediate_size": 4304, + "model_type": "siglip", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14, + } + + def __init__( + self, + use_cache=True, + query_num=64, + image_size=448, + drop_vision_last_layer=True, + batch_vision_input=True, + slice_config=None, + vision_config=None, + audio_config=None, + tts_config=None, + use_image_id=True, + vision_batch_size=16, + audio_pool_step=5, + audio_chunk_length=1.0, + stream_input=False, + listen_speak_type="asr", + init_vision=True, + init_audio=True, + init_tts=True, + **kwargs, + ): + self.use_cache = use_cache + self.query_num = query_num + self.image_size = image_size + self.drop_vision_last_layer = drop_vision_last_layer + self.batch_vision_input = batch_vision_input + self.use_image_id = use_image_id + self.vision_batch_size = vision_batch_size + self.audio_pool_step = audio_pool_step + self.audio_chunk_length = audio_chunk_length + self.stream_input = stream_input + self.listen_speak_type = listen_speak_type + + self.init_vision = init_vision + self.init_audio = init_audio + self.init_tts = init_tts + + if slice_config is None: + self.slice_config = MiniCPMVSliceConfig(max_slice_nums=1) + else: + self.slice_config = MiniCPMVSliceConfig(**slice_config) + self.slice_mode = True + + # same as HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit add tgt_sizes + if vision_config is None: + self.vision_config = SiglipVisionConfig(**self.default_vision_config) + logger.info("vision_config is None, using default vision config") + elif isinstance(vision_config, dict): + self.vision_config = SiglipVisionConfig(**vision_config) + elif isinstance(vision_config, SiglipVisionConfig): + self.vision_config = vision_config + + if audio_config is None: + self.audio_config = WhisperConfig() + elif isinstance(audio_config, dict): + self.audio_config = WhisperConfig(**audio_config) + elif isinstance(audio_config, WhisperConfig): + self.audio_config = audio_config + + if tts_config is None: + self.tts_config = MiniCPMTTSConfig() + elif isinstance(tts_config, dict): + self.tts_config = MiniCPMTTSConfig(**tts_config) + elif isinstance(tts_config, MiniCPMTTSConfig): + self.tts_config = tts_config + + self.patch_size = self.vision_config.patch_size + + super().__init__(**kwargs) diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000000000000000000000000000000000000..6e2cfc9f6f67a9cb1ede9375dc2abf42df55f84b --- /dev/null +++ b/generation_config.json @@ -0,0 +1,12 @@ +{ + "bos_token_id": 151643, + "do_sample": true, + "eos_token_id": [ + 151645, + 151643 + ], + "pad_token_id": 151643, + "temperature": 0.6, + "top_k": 20, + "top_p": 0.95 +} diff --git a/merges.txt b/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..90d3d82d027eadcc6a5e77c38eb82d43fc51b53b --- /dev/null +++ b/merges.txt @@ -0,0 +1,134935 @@ +Ġ Ġ +ĠĠ ĠĠ +i n +Ġ t +ĠĠĠĠ ĠĠĠĠ +e r +ĠĠ Ġ +o n +Ġ a +r e +a t +s t +e n +o r +Ġt h +Ċ Ċ +Ġ c +l e +Ġ s +i t +a n +a r +a l +Ġth e +; Ċ +Ġ p +Ġ f +o u +Ġ = +i s +ĠĠĠĠ ĠĠĠ +in g +e s +Ġ w +i on +e d +i c +Ġ b +Ġ d +e t +Ġ m +Ġ o +ĉ ĉ +r o +a s +e l +c t +n d +Ġ in +Ġ h +en t +i d +Ġ n +a m +ĠĠĠĠĠĠĠĠ ĠĠĠ +Ġt o +Ġ re +- - +Ġ { +Ġo f +o m +) ;Ċ +i m +č Ċ +Ġ ( +i l +/ / +Ġa nd +u r +s e +Ġ l +e x +Ġ S +a d +Ġ " +c h +u t +i f +* * +Ġ } +e m +o l +ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ +t h +) Ċ +Ġ{ Ċ +Ġ g +i g +i v +, Ċ +c e +o d +Ġ v +at e +Ġ T +a g +a y +Ġ * +o t +u s +Ġ C +Ġ st +Ġ I +u n +u l +u e +Ġ A +o w +Ġ ' +e w +Ġ < +at ion +( ) +Ġf or +a b +or t +u m +am e +Ġ is +p e +t r +c k +â Ģ +Ġ y +i st +-- -- +. ĊĊ +h e +Ġ e +l o +Ġ M +Ġb e +er s +Ġ on +Ġc on +a p +u b +Ġ P +ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ +as s +in t +> Ċ +l y +ur n +Ġ $ +; ĊĊ +a v +p ort +i r +- > +n t +ct ion +en d +Ġd e +it h +ou t +t urn +ou r +ĠĠĠĠ Ġ +l ic +re s +p t += = +Ġth is +Ġw h +Ġ if +Ġ D +v er +ag e +Ġ B +h t +ex t += " +Ġth at +** ** +Ġ R +Ġ it +es s +Ġ F +Ġ r +o s +an d +Ġa s +e ct +k e +ro m +Ġ // +c on +Ġ L +( " +q u +l ass +Ġw ith +i z +d e +Ġ N +Ġa l +o p +u p +g et +Ġ} Ċ +i le +Ġa n +at a +o re +r i +Ġp ro +; čĊ +ĉĉ ĉĉ +t er +a in +Ġ W +Ġ E +Ġc om +Ġre turn +ar t +Ġ H +a ck +im port +ub lic +Ġ or +e st +m ent +Ġ G +ab le +Ġ - +in e +il l +in d +er e +: : +it y +Ġ + +Ġt r +el f +ig ht +( ' +or m +ul t +st r +. . +" , +Ġy ou +y pe +p l +Ġn ew +Ġ j +ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ +Ġf rom +Ġ ex +Ġ O +l d +Ġ [ +o c +: Ċ +Ġs e +Ġ le +---- ---- +. s +{ Ċ +' , +an t +Ġa t +as e +. c +Ġc h +< / +av e +an g +Ġa re +Ġin t +âĢ Ļ +_ t +er t +i al +a ct +} Ċ +iv e +od e +o st +Ġc lass +Ġn ot +o g +or d +al ue +al l +f f +( );Ċ +on t +im e +a re +Ġ U +Ġp r +Ġ : +i es +iz e +u re +Ġb y +i re +Ġ} ĊĊ +. p +Ġs h +ic e +a st +pt ion +tr ing +o k +_ _ +c l +# # +Ġh e +ar d +) . +Ġ @ +i ew +ĉĉ ĉ +Ġw as +i p +th is +Ġ u +ĠT he +id e +a ce +i b +a c +r ou +Ġw e +j ect +Ġp ublic +a k +v e +at h +o id +Ġ= > +u st +q ue +Ġre s +) ) +' s +Ġ k +an s +y st +un ction +**** **** +Ġ i +Ġ us +p p +on e +a il +== == +n ame +Ġst r +Ġ / +Ġ & +a ch +d iv +yst em +el l +Ġh ave +er r +ou ld +ul l +p on +Ġ J +_ p +Ġ= = +ig n +S t +. Ċ +Ġp l +) ;ĊĊ +f orm +p ut +ou nt +} ĊĊ +d d +it e +Ġg et +r r +om e +Ġ âĢ +ar am +c c +Ġ* / +E R +I n +le s +_ s +on g +i e +Ġc an +Ġ V +er v +p r +Ġ un +ro w +b er +Ġd o +l l +Ġ el +Ġs elf +at ed +ar y +Ġ . +' ] +u d +Ġ en +ĠT h +ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ +t e +_ c +u ct +Ġa b +or k +. get +Ġ # +a w +res s +o b +N ame +ap p +[ ' +Ġal l +or y +it ion +an ce +e ar +Ġcon t +v ent +i a +Ġw ill +I N +ĠĠĠĠĠĠĠĠ Ġ +re turn +Ġ< / +d ata +) ĊĊ +R e +p le +il d +th er +Ġy our +" Ċ +( $ +Ġ out +) , +Ġh as +S tring +s o +Ġ up +a x +Ġde f +Ġb o +g e +al se +O N +p er +ic h +Ġb ut +Ġ Ċ +Ġ _ +_ m +ad d +que st +od el +s elf +er y +f t +en s +// // +a ke +. C +Ġg o +Ġf unction +Ġ K +iv ate +Ġ im +Ġcon st +. t +Ġ*/ Ċ +) ;čĊ +Ġv oid +Ġs et +ĠS ystem +c ri +( )Ċ +l i +ĉ if +. m +al ly +s et +e p +âĢĻ s +b o +de f +' ,Ċ +Ġm e +Ġ ! +at ch +" > +" ,Ċ +e c +ĠI n +p h +Ġ | +_ f +Ġv ar +en ce +I d +re e +in k +le ct +u g +et h +Ġel se +-------- -------- +con t +Ġs o +at ic +Ġl o +p ro +t on +s s +ow n +ab el +o int +ou s +el d +S T +T he +ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ +R E +" : +ol or +t p +e g +ke y +u de +ĠS t +ou nd +Ġa r +" );Ċ +en er +s er +b ject +ess age +f er +Ġm ore +ation s +ent s +Ġh is +Ġthe y +. S +Ġ Y +u se +n e +is h +ol d +_ d +i o +i eld +Ġp er +C ont +ing s +## ## +Ġd ata +Ġs a +e f +f o +Ġon e +en g +Ġd is +A T +Ġn ame +Ġtr ue +v al +le d +. f +Ġn e +Ġ end +. T +c re +ar k +lo g +E x +err or +_ id +ur re +ang e +Ġn ull +rr ay +Ġm y +p an +ic t +at or +V iew +L ist +ĉ return +âĢ Ŀ +Ġp re +Ġ x +cl ude +ar g +o v +. h +Ġ > +Ġthe ir +' ) +ir st +ic k +g h +L E +O R +Ġpr ivate +t em +čĊ čĊ +us er +Ġ ) +c om +. A +" ;Ċ +Ġ id +re ad +Ġwh o +_ b +" >Ċ +Ġt ime +Ġm an +r y +==== ==== +rou p +ro p +p ublic +v el +um ber +b le +Ġwh ich +******** ******** +Ġan y +Ġf alse +w e +Ġv alue +Ġl i +" ) +nd er +g r +Ġn o +p aram +f ig +.c om +Ġa pp +_ l +ion s +. D +ĠC h +Ġab out +Ġa dd +Ġs u +Ġstr ing +I D +Ġo ver +str ing +. l +our ce +_ C +] Ċ +Ġ qu +ĠS tring +c a +S E +Ġ ro +s h +u al +T ype +s on +n ew +er n +Ġa g +A R +] ;Ċ +] . +Ġ ? +ic al +Ġd es +ut h +i x +ay s +Ġt ype +' t +a ult +Ġin ter +v ar +. b +Ġp art +. d +urre nt +I T +E N +en c +( f +r a +v alue +ch o +ut ton +o se +Ġ! = +at er +à © +re ate +ol l +p os +y le +n g +A L +us ing +am es +Ġ{ čĊ +at es +el y +Ġw ork +Ġ em +in al +Ġs p +Ġwh en +.s et +ĠĠĠĠ ĠĠ +) :Ċ +t o +qu ire +ind ow +le ment +pe ct +as h +[ i +Ġu se +. F +pe c +Ġa d +o ve +ce ption +eng th +in clude +ad er +ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ +at us +T h +it le +r it +v oid +() . +( Ċ +Ġof f +Ġo ther +Ġ& & +' ;Ċ +m s +Ġbe en +Ġt e +m l +c o +n c +erv ice +Ġ % +** Ċ +an n +ad e +ĊĊ ĊĊ +lo ck +con st +pon se +Ġs up ++ + +d ate +Ġa cc +Ġh ad +Ġb u +ĠR e +Ġw ere +Ġf ile +Ġw ould +ĠâĢ ľ +v en +is s +Ġ our +c lass +r aw +Ġy ear +D ata +Ġv al +Ġs ome +f ter +y s +Ġ// / +rou nd +v iew +Ġp e +Ġth ere +Ġsa id +d u +o f +l ine +/ * +d uct +Ġh er +ĠĠĠĠĠĠĠĠ ĠĠĠĠĠ +R es +Ġc o +Ġcom m +is e +m in +ĠĠĠĠ Ċ +# include +eth od +. 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ока +п ал +ĠÄij ảo +ĠاÙĦÙģ ÙĪر +ĠاÙĦÙģÙĪر Ùĥس +Ġmá u +кÑĢ еп +ĠاÙĦس اعة +ĠгоÑĢ ода +Ùģ صÙĦ +ай ÑĤе +Ġд ог +Ġдог овоÑĢ +ĠØ¥ Ø° +Ġ×ij׼׾ ׾ +ÙĬ تÙĩ +×Ĵ ×ijר +Ġbir ç +Ġbirç ok +문 íĻĶ +ãģĿãģĨ ãģª +را ØŃ +ĠÙħ رة +ĠденÑĮ ги +f ä +à¸Ĥà¹īา ว +ĠÑģов ÑĢем +ĠÑģовÑĢем енн +׾×Ĺ ×¥ +èī¯ ãģı +ĠÙģ Ø£ +Ġ×ķ ×ĸ×Ķ +Ġз ани +Ġзани ма +Ġê°Ģì§Ģ ê³ł +Ġh Æ¡i +ãģªãģ® ãģĭ +ãĥĨ ãĥ¬ãĥĵ +Ġר ×ij×ķת +à¸ķ ี +Ġ×ijש ×ł×ª +ĠT ại +Ġthu áºŃn +Ñģ ел +Ñij м +dzi Äĩ +ĠÑģ ка +ĠÑģка Ñĩ +ĠÑģкаÑĩ аÑĤÑĮ +×ķ×ŀ ×ķ +г ла +Ġмин ÑĥÑĤ +åĩº ãģĻ +Ġ×Ĺ×Ļ ×Ļ×ij +Ġת ×Ĵ×ķ×ij×Ķ +à¸£à¸¹à¸Ľ à¹ģà¸ļà¸ļ +ни ÑĨа +ĠÄ° n +ĠØ£ ع +Ġض ÙħÙĨ +Ùħ ثاÙĦ +ĠyaÅŁ an +ĠìĹ° 구 +ĠL ê +ש׾ ×Ĺ +ãģı ãģªãĤĭ +ìĹĨ ìĿ´ +ĠÑĤ ÑĢи +ĠÑĩаÑģÑĤ о +Ġоб ÑĢаÑĤ +п ло +د Ø® +دخ ÙĪÙĦ +س Ùĩ +à¸Ń าà¸ģ +à¸Ńาà¸ģ าศ +Ġ׼ ×ĸ×Ķ +Ġ×Ķ×¢ סק +ĠاÙĦØ£ ÙĨ +å¹´ ãģ« +×¢ ש×ķ +Ġש ×¢×ķת +Ġm Ãłn +×IJר ×Ļ +sı yla +Ùģر ÙĤ +ни Ñħ +Ġت ست +è¦ĭ ãģ¦ +ØŃا ÙĪÙĦ +×IJ ×Ļ׼×ķת +ĠbaÅŁ ladı +st Äħ +stÄħ pi +à¸Ĺีà¹Ī à¹Ģรา +ÙĤر ر +ج اب +Ġ×ijר ×ķר +à¹Ģà¸Ĥà¹īา à¹ĥà¸Ī +×ŀ׊קר +al ım +Ġס ×Ļפ×ķר +ãģ§ãģĤ ãĤĮãģ° +Ġש×ŀ ×ķר×ķת +Ġ×ķ ×ŀ×Ķ 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á»ĵn +й ÑĤ +йÑĤ еÑģÑĮ +ãĤĴ æ±ĤãĤģ +Ġ×IJת ׼×Ŀ +Ġ모 르 +ظ رÙĪÙģ +Ñĩ еÑģÑĤво +ìĸ´ ìĦľ +Ġод на +Ġkap ı +Ġëħ¸ ëł¥ +ĠKü che +ĠاÙĦت Ø´ +Ø· ÙĬب +ĠíĬ¹ íŀĪ +ĠвÑĭп ÑĥÑģ +ĠвÑĭпÑĥÑģ к +×ĵ ת×Ļ +Ġu ÄŁ +ĠuÄŁ ra +ائ Ùĩا +Ġtho át +ãģª ãĤĤãģ® +Ñij ÑĢ +기 ê°Ģ +ĠgeliÅŁ me +تØŃ ÙĤ +تØŃÙĤ ÙĤ +Ġоп аÑģ +б ÑĢоÑģ +ห ุ +หุ à¹īà¸Ļ +ì¼ Ģ +ãĤ¹ ãĥŀ +ãĤ¹ãĥŀ ãĥĽ +Ø£ Ùģر +Ø£Ùģر اد +ĠTh á»±c +Ġth ắ +ãĥªãĥ³ ãĤ¯ +Ġni á»ģm +ĠHö he +عÙħ ار +ÙĥÙĪر ÙĪÙĨ +ÙĥÙĪرÙĪÙĨ ا +ĠÄIJ ến +ĠÑģам ом +ĠÑĤ еле +ĠÄijo án +à¸Ħวามà¸Ħิà¸Ķ à¹Ģหà¹ĩà¸Ļ +Ġд иÑģк +Ø£ Ø·ÙģاÙĦ +ม ารà¹Į +à¸Ĺ หาร +à¸Ĺ à¸Ļ +Ġب عÙĬد +ĠاÙĦÙĩ ÙĨد +åĩº ãģĹãģ¦ +Ġkar de +Ġkarde ÅŁ +×Ķ×Ļס×ĺ ×ķר +×Ķ×Ļס×ĺ×ķר ×Ļ×Ķ +éģ¸ ãģ³ +ع اÙħÙĦ +à¸Ĥ ยาย +Ġtü rl +Ġtürl ü +ĠìĿ¼ ìĿ´ +Ġmaté ria +Ġ׼׾ ×ķ×ŀר +ãĥģãĥ£ ãĥ¼ +جÙħ اعة +ĠÑģво им +Ø¥ÙĤ اÙħØ© +ä¾ĭ ãģĪãģ° +س اب +Ø¢ خر +ÙĤ دÙĬر +×IJ×ŀ ×Ļ +ìĸ » +Ġ׳×ķס פת +ĠÐĴ лад +ĠÐĴлад им +ĠÐĴладим иÑĢ +Ġest ará +ãģĵãģĨ ãģĦãģĨ +ãĤĴ 使çĶ¨ +มา à¸ķร +มาà¸ķร à¸IJาà¸Ļ +ãģ£ãģ ½ +Ġn ú +Ġnú i +ย าà¸ĩ +ĠاÙĦج ÙĨس +Ġüst ün +ëľ » +ãĤ» ãĥ« +ãģ¦ãģĦ ãģįãģ¾ãģĻ +Ġ×Ĺ ×ķ×ĸ +Ġ×Ĺ×ķ×ĸ ר 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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. + +import json +import logging +import math +import os +import tempfile +import threading +import time +import types +from copy import deepcopy +from dataclasses import dataclass +from functools import partial +from threading import Thread +from typing import Dict +from typing import List +from typing import Optional +from typing import Tuple +from typing import Union + +import numpy as np +import torch +import torch.nn.functional as F +import torch.nn.utils.parametrize as P +from torch import nn +from torch.nn.init import trunc_normal_ +from torch.nn.utils.parametrizations import weight_norm +from tqdm import tqdm + +if os.getenv("USE_FLAGOS") == "1": + import importlib + + flag_gems = importlib.import_module("flag_gems") # noqa: F401 + flag_gems_experimental = importlib.import_module("flag_gems.experimental_ops") + gems_rmsnorm = flag_gems_experimental.rmsnorm + + class GemsRMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + return gems_rmsnorm(hidden_states, self.weight, self.variance_epsilon) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + from transformers.models.llama import modeling_llama + from transformers.models.qwen3 import modeling_qwen3 + + modeling_qwen3.Qwen3RMSNorm = GemsRMSNorm + modeling_llama.LlamaRMSNorm = GemsRMSNorm + +from transformers import LlamaConfig +from transformers import LlamaModel +from transformers import PreTrainedModel +from transformers import Qwen3ForCausalLM +from transformers import Qwen3PreTrainedModel +from transformers import TextIteratorStreamer +from transformers.activations import ACT2FN +from transformers.cache_utils import Cache +from transformers.cache_utils import DynamicCache +from transformers.cache_utils import EncoderDecoderCache +from transformers.cache_utils import StaticCache +from transformers.generation.logits_process import TopKLogitsWarper +from transformers.generation.logits_process import TopPLogitsWarper +from transformers.integrations import is_deepspeed_zero3_enabled +from transformers.modeling_outputs import BaseModelOutputWithPast +from transformers.modeling_outputs import ModelOutput +from transformers.models.whisper.configuration_whisper import WhisperConfig +from transformers.models.whisper.modeling_whisper import WhisperEncoder + +from .configuration_minicpmo import MiniCPMOConfig +from .configuration_minicpmo import MiniCPMTTSConfig +from .modeling_navit_siglip import SiglipVisionTransformer +from .processing_minicpmo import MiniCPMOProcessor +from .utils import as_dynamic_cache +from .utils import ChunkPrefillChunkGenerate +from .utils import drop_tokens_from_cache +from .utils import DuplexWindowConfig +from .utils import get_kv_cache_length +from .utils import normalize_content +from .utils import realign_rotary_suffix +from .utils import SpeculativeSnapshot +from .utils import streaming_token_decoder +from .utils import StreamingWindowConfig +from .utils import torch_clone_recursive +from .utils import TTSSamplingParams +from .utils import TTSStreamingGenerator + +logger = logging.getLogger(__name__) + + +class MiniCPMOPreTrainedModel(Qwen3PreTrainedModel): + config_class = MiniCPMOConfig + + +class MiniCPMO(MiniCPMOPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.llm = Qwen3ForCausalLM(config) + self.embed_dim = self.llm.config.hidden_size + self.llm.prepare_inputs_for_generation = types.MethodType(prepare_inputs_for_generation, self.llm) # patch llm + + # init vision module + if self.config.init_vision: + self.vpm = self.init_vision_module() + self.vision_dim = self.vpm.embed_dim + self.resampler = self.init_resampler(self.embed_dim, self.vision_dim) + + # init audio module + if self.config.init_audio: + self.apm = self.init_audio_module() + audio_output_dim = int(self.apm.config.encoder_ffn_dim // 4) + self.audio_avg_pooler = nn.AvgPool1d(self.config.audio_pool_step, stride=self.config.audio_pool_step) + self.audio_projection_layer = MultiModalProjector(in_dim=audio_output_dim, out_dim=self.embed_dim) + self.audio_encoder_layer = -1 + + # init tts module + if self.config.init_tts: + self.tts = self.init_tts_module() + + self.terminators = ["<|im_end|>", "<|endoftext|>"] + + self.think_str = "" + if self.llm.__class__.__name__ == "Qwen3ForCausalLM": + self.think_str = "\\n\\n\\n\\n" + + # for streaming + self.reset_session(reset_token2wav_cache=True) + + # streaming audio processing constants + self.SAMPLE_RATE = 16000 + self.CHUNK_MS = 1000 # regular chunk length (ms) + self.FIRST_CHUNK_MS = 1035 # first chunk length (ms) + self.CNN_REDUNDANCY_MS = 0 # CNN redundancy (ms) + + # for sliding window + self.streaming_window_config = StreamingWindowConfig() + self.streaming_require_system_prompt = True + self.streaming_window_enabled = True + self.force_rope_reindex = False # RoPE reindex testing switch + + def init_streaming_processor(self): + self.prepare_processor(processor=None, tokenizer=None) + + if hasattr(self.processor, "set_streaming_mode"): + self.processor.set_streaming_mode( + mode="exact", + chunk_ms=self.CHUNK_MS, + first_chunk_ms=self.FIRST_CHUNK_MS, + cnn_redundancy_ms=self.CNN_REDUNDANCY_MS, + enable_sliding_window=True, + slide_trigger_seconds=30.0, + slide_stride_seconds=10.0, + ) + self.processor.reset_streaming() + self.audio_chunk_idx = 0 + + def reset_session(self, reset_token2wav_cache=True): + self.llm_past_key_values = None + self.audio_past_key_values = None + self.tts_last_turn_tokens = None + self.llm_generated = False # last turn generated by llm or not + self.llm_generate_completed = False + self.new_user_msg = True + + self.session_id = None + + if reset_token2wav_cache: + self.token2wav_cache = None + + # for sliding window + self.streaming_text_preserve = 0 + self.streaming_position_offset = 0 + + self._rope_inv_freq_cache: Dict[Tuple[int, torch.device], torch.Tensor] = {} + + self._next_round_id = 0 + self._pending_round_id = None + + self._omni_chunk_history: List[Dict[str, Union[str, int]]] = [] + self._round_history: List[Dict[str, Union[int, str, torch.Tensor, Optional[int]]]] = [] + + def init_vision_module(self): + if self.config._attn_implementation == "flash_attention_2": + self.config.vision_config._attn_implementation = "flash_attention_2" + else: + self.config.vision_config._attn_implementation = "eager" + model = SiglipVisionTransformer(self.config.vision_config) + if self.config.drop_vision_last_layer: + model.encoder.layers = model.encoder.layers[:-1] + + setattr(model, "embed_dim", model.embeddings.embed_dim) + setattr(model, "patch_size", model.embeddings.patch_size) + + return model + + def init_resampler(self, embed_dim, vision_dim): + return Resampler( + num_queries=self.config.query_num, + embed_dim=embed_dim, + num_heads=embed_dim // 128, + kv_dim=vision_dim, + adaptive=True, + ) + + def init_audio_module(self): + if self.config._attn_implementation == "eager": + self.config.audio_config._attn_implementation = "eager" + else: + # using flash_attention_2 will cause: RuntimeError: cu_seqlens_q must have shape (batch_size + 1) + self.config.audio_config._attn_implementation = "sdpa" + + return MiniCPMWhisperEncoder(self.config.audio_config) + + def init_tts_module(self): + if self.config._attn_implementation == "flash_attention_2": + self.config.tts_config.attn_implementation = "flash_attention_2" + else: + self.config.tts_config.attn_implementation = "eager" + + return MiniCPMTTS(config=self.config.tts_config, audio_tokenizer=None) + + def _ensure_asset_dir(self, asset_subpath: str, model_dir: Optional[str] = None) -> str: + """Ensure asset directory exists, downloading from HF if needed.""" + model_dir = model_dir or os.path.join(self.config._name_or_path, asset_subpath) + if not os.path.exists(model_dir): + from huggingface_hub import snapshot_download + + repo_dir = snapshot_download( + repo_id="openbmb/MiniCPM-o-4_5", + allow_patterns=[f"{asset_subpath}/**"], + ) + model_dir = os.path.join(repo_dir, asset_subpath) + assert os.path.exists(model_dir), f"Asset directory not found: {model_dir}" + return model_dir + + def init_tts(self, model_dir=None, enable_float16=False, n_timesteps=10, **kwargs): + if self.config.tts_config.audio_tokenizer_type != "s3tokenizer_step_audio": + logger.warning("audio tokenizer type is set to s3tokenizer_step_audio") + self.tts.config.audio_tokenizer_type = "s3tokenizer_step_audio" + + try: + from stepaudio2 import Token2wav + except ImportError: + raise ImportError("Please install Token2wav via: pip install minicpmo-utils[all]") + + model_dir = self._ensure_asset_dir("assets/token2wav", model_dir) + self.tts.audio_tokenizer = Token2wav(model_dir, float16=enable_float16, n_timesteps=n_timesteps) + return self.tts.audio_tokenizer + + def get_input_embeddings(self): + return self.llm.get_input_embeddings() + + def set_input_embeddings(self, value): + self.llm.embed_tokens = value + + def get_output_embeddings(self): + return self.llm.lm_head + + def set_output_embeddings(self, new_embeddings): + self.llm.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.llm = decoder + + def get_decoder(self): + return self.llm + + @staticmethod + def get_sys_prompt(ref_audio=None, mode="default", language="en", ref_audio_max_ms=None): + if ref_audio is not None: + if isinstance(ref_audio, str): + import os + + import librosa + + if os.path.isfile(ref_audio): + duration = ref_audio_max_ms / 1000.0 if ref_audio_max_ms else None + ref_audio, _ = librosa.load(ref_audio, sr=16000, mono=True, duration=duration) + else: + logger.error(f"Could not find {ref_audio}") + ref_audio = None + + assert isinstance(ref_audio, np.ndarray), "ref_audio error" + + if mode == "omni": + if language == "zh": + sys_prompt = "" + vc_prompt_prefix = "模仿音频样本的音色并生成新的内容。" + vc_prompt_suffix = ( + "请用这种声音风格来为用户提供帮助。 请认真、高质量地回复用户的问题。 请用高自然度的方式和用户聊天。" + ) + else: + sys_prompt = "" + vc_prompt_prefix = sys_prompt + "Clone the voice in the provided audio prompt." + vc_prompt_suffix = "As an assistant, you will speak using this voice style." + + if ref_audio is not None: + sys_msgs = {"role": "system", "content": [vc_prompt_prefix, ref_audio, vc_prompt_suffix]} + else: + sys_msgs = {"role": "system", "content": [sys_prompt]} + + return sys_msgs + elif mode == "audio_assistant": + if language == "zh": + vc_prompt_prefix = "模仿音频样本的音色并生成新的内容。" + vc_prompt_suffix = "你的任务是用这种声音模式来当一个助手。请认真、高质量地回复用户的问题。请用高自然度的方式和用户聊天。你是由面壁智能开发的人工智能助手：面壁小钢炮。" + else: + vc_prompt_prefix = "Clone the voice in the provided audio prompt." + vc_prompt_suffix = "Please assist users while maintaining this voice style. Please answer the user's questions seriously and in a high quality. Please chat with the user in a highly human-like and oral style. You are a helpful assistant developed by ModelBest: MiniCPM-Omni." + + if ref_audio is not None: + sys_msgs = {"role": "system", "content": [vc_prompt_prefix, ref_audio, vc_prompt_suffix]} + else: + logger.warning( + "Warning: ref_audio is None, speech generation will be performed based on the default voice." + ) + sys_msgs = {"role": "system", "content": ["Use the voice.", vc_prompt_suffix]} + + return sys_msgs + elif mode == "audio_roleplay": + if language == "zh": + vc_prompt_prefix = "模仿输入音频中的声音特征。" + vc_prompt_suffix = "假装你是上述音频中的人物，与我进行对话。" + else: + vc_prompt_prefix = "Clone the voice in the provided audio prompt." + vc_prompt_suffix = "Try to role-play the character based on the audio prompt above." + + if ref_audio is not None: + sys_msgs = {"role": "system", "content": [vc_prompt_prefix, ref_audio, vc_prompt_suffix]} + else: + sys_msgs = {"role": "system", "content": ["Use the voice.", vc_prompt_suffix]} + + return sys_msgs + elif mode == "voice_cloning": + if language == "zh": + vc_prompt_prefix = "模仿输入音频中的声音特征。" + else: + vc_prompt_prefix = "Clone the voice in the provided audio prompt." + + if ref_audio is not None: + sys_msgs = {"role": "system", "content": [vc_prompt_prefix, ref_audio]} + else: + raise ValueError("ref_audio con't be None in voice_cloning mode.") + + return sys_msgs + else: + sys_prompt = "You are a helpful assistant. You can accept audio and text input and output voice and text." + sys_msgs = {"role": "system", "content": [sys_prompt]} + + return sys_msgs + + @staticmethod + def subsequent_chunk_mask( + size: int, + chunk_size: int, + num_left_chunks: int = -1, + device: torch.device = torch.device("cpu"), + num_lookhead: int = 0, + ) -> torch.Tensor: + """Create mask for subsequent steps (size, size) with chunk size, + this is for streaming encoder + + Args: + size (int): size of mask + chunk_size (int): size of chunk + num_left_chunks (int): number of left chunks + <0: use full chunk + >=0: use num_left_chunks + device (torch.device): "cpu" or "cuda" or torch.Tensor.device + num_lookhead: + + Returns: + torch.Tensor: mask + """ + ret = torch.zeros(size, size, device=device, dtype=torch.bool) + for i in range(size): + if num_left_chunks < 0: + start = 0 + else: + start = max((i // chunk_size - num_left_chunks) * chunk_size, 0) + ending = min((i // chunk_size + 1) * chunk_size + num_lookhead, size) + ret[i, start:ending] = True + return ret + + def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor): + """Computes the output length of the convolutional layers and the output length of the audio encoder""" + input_lengths_after_cnn = (input_lengths - 1) // 2 + 1 + input_lengths_after_pooling = ( + input_lengths_after_cnn - self.config.audio_pool_step + ) // self.config.audio_pool_step + 1 + input_lengths_after_pooling = input_lengths_after_pooling.to(dtype=torch.int32) + + return input_lengths_after_cnn, input_lengths_after_pooling + + def get_vision_embedding(self, data): + if "vision_hidden_states" not in data: + dtype = self.llm.model.embed_tokens.weight.dtype + device = self.llm.model.embed_tokens.weight.device + tgt_sizes = data["tgt_sizes"] + pixel_values_list = data["pixel_values"] + vision_hidden_states = [] + all_pixel_values = [] + img_cnt = [] + for pixel_values in pixel_values_list: + img_cnt.append(len(pixel_values)) + all_pixel_values.extend([i.flatten(end_dim=1).permute(1, 0) for i in pixel_values]) + + # exist image + if all_pixel_values: + tgt_sizes = [tgt_size for tgt_size in tgt_sizes if isinstance(tgt_size, torch.Tensor)] + tgt_sizes = torch.vstack(tgt_sizes).type(torch.int32) + + max_patches = torch.max(tgt_sizes[:, 0] * tgt_sizes[:, 1]) + + all_pixel_values = torch.nn.utils.rnn.pad_sequence( + all_pixel_values, batch_first=True, padding_value=0.0 + ) + B, L, _ = all_pixel_values.shape + all_pixel_values = all_pixel_values.permute(0, 2, 1).reshape(B, 3, -1, L) + + patch_attn_mask = torch.zeros((B, 1, max_patches), dtype=torch.bool, device=device) + for i in range(B): + patch_attn_mask[i, 0, : tgt_sizes[i][0] * tgt_sizes[i][1]] = True + + vision_batch_size = self.config.vision_batch_size + all_pixel_values = all_pixel_values.type(dtype) + if B > vision_batch_size: + hs = [] + for i in range(0, B, vision_batch_size): + start_idx = i + end_idx = i + vision_batch_size + tmp_hs = self.vpm( + all_pixel_values[start_idx:end_idx], + patch_attention_mask=patch_attn_mask[start_idx:end_idx], + tgt_sizes=tgt_sizes[start_idx:end_idx], + ).last_hidden_state + hs.append(tmp_hs) + vision_embedding = torch.cat(hs, dim=0) + else: + vision_embedding = self.vpm( + all_pixel_values, + patch_attention_mask=patch_attn_mask, + tgt_sizes=tgt_sizes, + ).last_hidden_state + vision_embedding = self.resampler(vision_embedding, tgt_sizes) + + start = 0 + for pixel_values in pixel_values_list: + img_cnt = len(pixel_values) + if img_cnt > 0: + vision_hidden_states.append(vision_embedding[start : start + img_cnt]) + start += img_cnt + else: + vision_hidden_states.append([]) + else: # no image + if self.training: + dummy_image = torch.zeros((1, 3, 224, 224), device=device, dtype=dtype) + tgt_sizes = torch.Tensor( + [ + [ + (224 // self.config.patch_size), + math.ceil(224 / self.config.patch_size), + ] + ] + ).type(torch.int32) + dummy_feature = self.resampler(self.vpm(dummy_image).last_hidden_state, tgt_sizes) + else: + dummy_feature = [] + for _ in range(len(pixel_values_list)): + vision_hidden_states.append(dummy_feature) + else: + vision_hidden_states = data["vision_hidden_states"] + + return vision_hidden_states + + def get_vllm_embedding(self, data): + vision_hidden_states = self.get_vision_embedding(data) + + if hasattr(self.llm.config, "scale_emb"): + vllm_embedding = self.llm.model.embed_tokens(data["input_ids"]) * self.llm.config.scale_emb + else: + vllm_embedding = self.llm.model.embed_tokens(data["input_ids"]) + + vision_hidden_states = [ + i.type(vllm_embedding.dtype) if isinstance(i, torch.Tensor) else i for i in vision_hidden_states + ] + + bs = len(data["input_ids"]) + for i in range(bs): + cur_vs_hs = vision_hidden_states[i] + if len(cur_vs_hs) > 0: + cur_vllm_emb = vllm_embedding[i] + cur_image_bound = data["image_bound"][i] + if len(cur_image_bound) > 0: + image_indices = torch.stack( + [torch.arange(r[0], r[1], dtype=torch.long) for r in cur_image_bound] + ).to(vllm_embedding.device) + + cur_vllm_emb.scatter_( + 0, + image_indices.view(-1, 1).repeat(1, cur_vllm_emb.shape[-1]), + cur_vs_hs.view(-1, cur_vs_hs.shape[-1]), + ) + elif self.training: + cur_vllm_emb += cur_vs_hs[0].mean() * 0 + + return vllm_embedding, vision_hidden_states + + def get_audio_embedding_streaming( + self, + data, + use_extra_context=False, + prefix_extra_frames=1, + suffix_extra_frames=1, + cnn_min_length=None, + ): + """Extract audio embeddings in a streaming manner using cached key-value pairs. + + This method processes incoming audio features incrementally and stores/updates `past_key_values` + for faster inference on subsequent audio frames. It only supports batch_size=1 and is intended + for streaming scenarios. + + Args: + data (dict): + - **"audio_features"** (`torch.FloatTensor`): Input mel-spectrograms of shape `(batch_size, 80, frames)`. + - **"audio_feature_lens"** (List[List[int]]): Lengths of each audio segment for each item in the batch. + use_extra_context (bool): If True, assumes input contains extra frames for CNN context. + prefix_extra_frames (int): Number of prefix extra frames. + suffix_extra_frames (int): Number of suffix extra frames. + cnn_min_length (int): Minimum length for CNN input padding. + + Returns: + List[List[torch.Tensor]]: audio embeddings + """ + wavforms = data.get("audio_features", []) # (bs, 80, frames) or [], multi audios need filled in advance + audio_feature_lens_raw = data.get("audio_feature_lens", []) # list, [[x1, x2], [y1], [z1]] + + # exist audio + if len(wavforms) > 0: + audio_feature_lens = torch.hstack(audio_feature_lens_raw) + batch_size, _, max_mel_seq_len = wavforms.shape + assert batch_size == 1 + max_seq_len = (max_mel_seq_len - 1) // 2 + 1 + + # whisper's past_key_values management (core) + if self.audio_past_key_values is not None: + cache_length = self.audio_past_key_values[0][0].shape[2] + apm_max_len = self.apm.embed_positions.weight.shape[0] + if cache_length + max_seq_len >= apm_max_len: + logger.warning( + f"audio_past_key_values length {cache_length + max_seq_len} exceed {apm_max_len}, reset." + ) + self.audio_past_key_values = None + + # build attention mask (bidirectional attention, same as offline mode) + batch_size, _, max_mel_seq_len = wavforms.shape + current_seq_len = (max_mel_seq_len - 1) // 2 + 1 + # if use extra context, need to adjust sequence length + if use_extra_context: + # calculate actual sequence length after removing redundancy + # conv2's stride=2, so the mapping from mel frames to output frames is ceil(x/2) + prefix_to_remove = (prefix_extra_frames + 1) // 2 if prefix_extra_frames > 0 else 0 + suffix_to_remove = (suffix_extra_frames + 1) // 2 if suffix_extra_frames > 0 else 0 + current_seq_len = current_seq_len - prefix_to_remove - suffix_to_remove + # calculate history length (if there is KV cache) + if self.audio_past_key_values is not None: + past_len = self.audio_past_key_values[0][0].shape[2] # get history sequence length + total_seq_len = past_len + current_seq_len + else: + past_len = 0 + total_seq_len = current_seq_len + # create bidirectional attention mask (full attention) + audio_attention_mask = torch.zeros( + (batch_size, 1, current_seq_len, total_seq_len), + dtype=self.apm.conv1.weight.dtype, + device=wavforms.device, + ) + + # Step 1: APM processing + audio_outputs = self.apm( + wavforms, + past_key_values=self.audio_past_key_values, + use_cache=True, + output_hidden_states=True, + attention_mask=audio_attention_mask, + use_extra_context=use_extra_context, + prefix_extra_frames=prefix_extra_frames, + suffix_extra_frames=suffix_extra_frames, + cnn_min_length=cnn_min_length, + ) + + if hasattr(self, "audio_encoder_layer"): + audio_states = audio_outputs.hidden_states[self.audio_encoder_layer] + else: + audio_states = audio_outputs.last_hidden_state + + self.audio_past_key_values = audio_outputs.past_key_values + + # Step 2: Projection + audio_embeds = self.audio_projection_layer(audio_states) + + # Step 3: Pooling + audio_embeds = audio_embeds.transpose(1, 2) + audio_embeds = self.audio_avg_pooler(audio_embeds) + audio_embeds = audio_embeds.transpose(1, 2) + + _, feature_lens_after_pooling = self._get_feat_extract_output_lengths(audio_feature_lens) + + num_audio_tokens = feature_lens_after_pooling + + final_audio_embeds = [] + idx = 0 + for i in range(len(audio_feature_lens_raw)): + target_audio_embeds = [] + for _ in range(len(audio_feature_lens_raw[i])): + target_audio_embeds.append(audio_embeds[idx, : num_audio_tokens[idx], :]) + idx += 1 + final_audio_embeds.append(target_audio_embeds) + + return final_audio_embeds + else: + return final_audio_embeds + else: + return [] + + def get_audio_embedding(self, data, chunk_length=-1, dummy=True): + wavforms = data.get("audio_features", []) # (bs, 80, frames) or [], multi audios need filled in advance + audio_feature_lens_raw = data.get("audio_feature_lens", []) # list, [[x1, x2], [y1], [z1]] + + if len(wavforms) > 0: + audio_feature_lens = torch.hstack(audio_feature_lens_raw) + batch_size, _, max_mel_seq_len = wavforms.shape + max_seq_len = (max_mel_seq_len - 1) // 2 + 1 + + # Create a sequence tensor of shape (batch_size, max_seq_len) + seq_range = ( + torch.arange( + 0, + max_seq_len, + dtype=audio_feature_lens.dtype, + device=audio_feature_lens.device, + ) + .unsqueeze(0) + .expand(batch_size, max_seq_len) + ) + lengths_expand = audio_feature_lens.unsqueeze(1).expand(batch_size, max_seq_len) + # Create mask + padding_mask = seq_range >= lengths_expand # 1 for padded values + + audio_attention_mask_ = padding_mask.view(batch_size, 1, 1, max_seq_len).expand( + batch_size, 1, max_seq_len, max_seq_len + ) + audio_attention_mask = audio_attention_mask_.to( + dtype=self.apm.conv1.weight.dtype, device=self.apm.conv1.weight.device + ) + + if chunk_length > 0: + chunk_num_frame = int(chunk_length * 50) + chunk_mask = self.subsequent_chunk_mask( + size=max_seq_len, + chunk_size=chunk_num_frame, + num_left_chunks=-1, + device=audio_attention_mask_.device, + ) + audio_attention_mask_ = torch.logical_or(audio_attention_mask_, torch.logical_not(chunk_mask)) + + audio_attention_mask[audio_attention_mask_] = float("-inf") + audio_states = self.apm( + wavforms, output_hidden_states=True, attention_mask=audio_attention_mask + ).hidden_states[self.audio_encoder_layer] + audio_embeds = self.audio_projection_layer(audio_states) + + audio_embeds = audio_embeds.transpose(1, 2) + audio_embeds = self.audio_avg_pooler(audio_embeds) + audio_embeds = audio_embeds.transpose(1, 2) + + _, feature_lens_after_pooling = self._get_feat_extract_output_lengths(audio_feature_lens) + + num_audio_tokens = feature_lens_after_pooling + + final_audio_embeds = [] + idx = 0 + for i in range(len(audio_feature_lens_raw)): + target_audio_embeds = [] + for _ in range(len(audio_feature_lens_raw[i])): + target_audio_embeds.append(audio_embeds[idx, : num_audio_tokens[idx], :]) + idx += 1 + final_audio_embeds.append(target_audio_embeds) + return final_audio_embeds + elif self.training and dummy: + dtype = self.apm.embed_positions.weight.dtype + device = self.apm.embed_positions.weight.device + + dummy_wavs = torch.zeros((1, 80, 100), device=device, dtype=dtype) + audio_states = self.apm(dummy_wavs, output_hidden_states=True).hidden_states[self.audio_encoder_layer] + + audio_embeds = self.audio_projection_layer(audio_states) + + audio_embeds = audio_embeds.transpose(1, 2) + audio_embeds = self.audio_avg_pooler(audio_embeds) + audio_embeds = audio_embeds.transpose(1, 2) + return [audio_embeds] + else: + return [] + + def get_omni_embedding(self, data, input_embeddings, chunk_length=-1, stream_input=False): + """ + Args: + data: + input_embeddings: + chunk_length: whisper use full attention or chunk attention + stream_input: use streaming audio embedding or not + + Returns: + final embeddings with audio feature + """ + if stream_input: + audio_embeddings = self.get_audio_embedding_streaming(data) + else: + audio_embeddings = self.get_audio_embedding(data, chunk_length) + + bs = len(input_embeddings) + if len(data.get("audio_features", [])) > 0: + assert len(audio_embeddings) == len(input_embeddings) + + if len(audio_embeddings) > 0: + audio_bounds = data["audio_bounds"] + + if self.config.stream_input: + assert bs == 1, "audio stream_input mode only support batch size 1" + for i in range(bs): + audio_embs = torch.cat(audio_embeddings[i], dim=0).to( + device=input_embeddings.device, dtype=input_embeddings.dtype + ) + audio_start_pos = 0 + for bound in audio_bounds[i]: + audio_len = bound[1] - bound[0] + input_embeddings[i, bound[0] : bound[1]] = audio_embs[ + audio_start_pos : audio_start_pos + audio_len, : + ] + audio_start_pos += audio_len + else: + for i in range(bs): + audio_embs = audio_embeddings[i] + bounds = audio_bounds[i] + for embs, bound in zip(audio_embs, bounds): + audio_indices = torch.arange(bound[0], bound[1], dtype=torch.long).to( + input_embeddings.device + ) + + if embs.shape[0] != len(audio_indices): + raise ValueError( + f"Shape mismatch: Trying to assign embeddings of shape {embs.shape} " + f"to input indices of length {len(audio_indices)}" + ) + input_embeddings[i, audio_indices] = embs.to(input_embeddings.dtype) + elif self.training: + for i in range(bs): + # dummy audio_embedings + input_embeddings += audio_embeddings[0].mean() * 0 + + return input_embeddings + + def forward(self, data, **kwargs): + vllm_embedding, vision_hidden_states = self.get_vllm_embedding(data) + vllm_embedding = self.get_omni_embedding( + data, + input_embeddings=vllm_embedding, + chunk_length=self.config.audio_chunk_length, + ) + + position_ids = data["position_ids"] + if position_ids.dtype != torch.int64: + position_ids = position_ids.long() + + return self.llm( + input_ids=None, + position_ids=position_ids, + inputs_embeds=vllm_embedding, + **kwargs, + ) + + def _decode(self, inputs_embeds, tokenizer, attention_mask, **kwargs): + terminators = [tokenizer.convert_tokens_to_ids(i) for i in self.terminators] + outputs = self.llm.generate( + inputs_embeds=inputs_embeds, + pad_token_id=0, + eos_token_id=terminators, + attention_mask=attention_mask, + output_hidden_states=True, + return_dict_in_generate=True, + **kwargs, + ) + return outputs + + def _decode_stream(self, inputs_embeds, tokenizer, **kwargs): + terminators = [tokenizer.convert_tokens_to_ids(i) for i in self.terminators] + streamer = TextIteratorStreamer(tokenizer=tokenizer) + generation_config = { + "inputs_embeds": inputs_embeds, + "pad_token_id": 0, + "eos_token_id": terminators, + "streamer": streamer, + } + generation_config.update(kwargs) + thread = Thread(target=self.llm.generate, kwargs=generation_config) + thread.start() + return streamer + + def _decode_text(self, result_ids, tokenizer): + terminators = [tokenizer.convert_tokens_to_ids(i) for i in self.terminators] + result_text = [] + for result in result_ids: + result = result[result != 0] + if result[0] == tokenizer.bos_id: + result = result[1:] + if result[-1] in terminators: + result = result[:-1] + result_text.append(tokenizer.decode(result)) + return result_text + + @torch.inference_mode() + def generate( + self, + input_ids=None, + pixel_values=None, + tgt_sizes=None, + audio_features=None, + audio_feature_lens=None, + image_bound=None, + audio_bounds=None, + spk_bounds=None, + attention_mask=None, + tokenizer=None, + vision_hidden_states=None, + stream=False, + **kwargs, + ): + assert input_ids is not None + assert len(input_ids) == len(pixel_values) + + model_inputs = { + "input_ids": input_ids, + "audio_features": audio_features, + "audio_feature_lens": audio_feature_lens, + "image_bound": image_bound, + "audio_bounds": audio_bounds, + "spk_bounds": spk_bounds, + } + + if vision_hidden_states is None: + model_inputs["pixel_values"] = pixel_values + model_inputs["tgt_sizes"] = tgt_sizes + else: + model_inputs["vision_hidden_states"] = vision_hidden_states + + with torch.inference_mode(): + model_inputs["inputs_embeds"], vision_hidden_states = self.get_vllm_embedding(model_inputs) + model_inputs["inputs_embeds"] = self.get_omni_embedding( + model_inputs, + input_embeddings=model_inputs["inputs_embeds"], + chunk_length=self.config.audio_chunk_length, + ) + + if stream: + result = self._decode_stream(model_inputs["inputs_embeds"], tokenizer, **kwargs) + outputs = {} # if stream return TextIteratorStreamer and output is empty + else: + outputs = self._decode(model_inputs["inputs_embeds"], tokenizer, attention_mask, **kwargs) + result = self._decode_text(outputs.sequences, tokenizer) + + return result, outputs + + def _build_streaming_mask(self, tts_tokens_len): + tts_sequence_full_length = 1 + self.tts.streaming_text_reserved_len + 1 + streaming_attention_mask = torch.zeros(tts_sequence_full_length, dtype=torch.int8) + streaming_attention_mask[0 : 1 + 1 + tts_tokens_len + 1] = 1 + streaming_attention_mask[-1] = 1 + return streaming_attention_mask + + def _generate_mel_spec(self, inputs, outputs, text, output_chunk_size=25, tts_max_new_tokens=2048): + spk_embeds = self._get_last_spk_embeds(inputs, outputs) + + text = text.split("<|tts_bos|>")[-1] + gen_text = text.split("<|tts_eos|>")[0] + tts_text, tts_token_lens = self.prepare_tts_text(gen_text) + tts_inputs = self.tts_processor.text_tokenizer.encode(tts_text, add_special_tokens=False) + tts_input_ids = torch.Tensor(tts_inputs).unsqueeze(0).to(self.device, dtype=torch.long) + streaming_tts_text_mask = self._build_streaming_mask(tts_token_lens).to(device=self.tts.device) + + logits_warpers, logits_processors = gen_logits( + num_code=626, + top_p=self.tts.top_p, + top_k=self.tts.top_k, + repetition_penalty=self.tts.repetition_penalty, + ) + + condition_length = 1 + self.tts.streaming_text_reserved_len + 1 + + dtype = self.tts.emb_text.weight.dtype + emb = torch.zeros(1, condition_length, self.tts.num_vq, dtype=dtype, device=self.tts.device) + past_key_values = [ + ( + torch.zeros( + 1, + self.tts.config.num_attention_heads, + condition_length - 1, + self.tts.config.hidden_size // self.tts.config.num_attention_heads, + dtype=emb.dtype, + device=self.tts.device, + ), + torch.zeros( + 1, + self.tts.config.num_attention_heads, + condition_length - 1, + self.tts.config.hidden_size // self.tts.config.num_attention_heads, + dtype=emb.dtype, + device=self.tts.device, + ), + ) + for _ in range(self.tts.config.num_hidden_layers) + ] + + audio_input_ids = torch.zeros( + 1, + condition_length, + self.tts.num_vq, + dtype=torch.long, + device=self.tts.device, + ) + + eos_lab = False + for chunk_idx in range(math.ceil(emb.shape[1] / self.tts.streaming_text_chunk_size)): + if chunk_idx == 0: + begin = chunk_idx * self.tts.streaming_text_chunk_size + 0 + end = (chunk_idx + 1) * self.tts.streaming_text_chunk_size + 1 + else: + begin = chunk_idx * self.tts.streaming_text_chunk_size + 1 + end = min( + (chunk_idx + 1) * self.tts.streaming_text_chunk_size + 1, + condition_length - 1, + ) + + if end - begin > 0: + text_input_ids = tts_input_ids[:, begin:end] + position_ids = torch.arange(begin, end, dtype=torch.long, device=self.tts.device).unsqueeze(0) + + if begin == 0: + past_key_values = self.tts.prefill_text( + input_ids=text_input_ids, + position_ids=position_ids, + past_key_values=past_key_values, + lm_spk_emb_last_hidden_states=spk_embeds, + ) + else: + past_key_values = self.tts.prefill_text( + input_ids=text_input_ids, + position_ids=position_ids, + past_key_values=past_key_values, + ) + + outputs = self.tts.generate( + input_ids=audio_input_ids, + past_key_values=past_key_values, + streaming_tts_text_mask=streaming_tts_text_mask, + max_new_token=output_chunk_size, + force_no_stop=self.force_no_stop, + temperature=torch.tensor([0.1, 0.3, 0.1, 0.3], dtype=torch.float, device=self.tts.device), + eos_token=torch.tensor([625], dtype=torch.long, device=self.tts.device), + logits_warpers=logits_warpers, + logits_processors=logits_processors, + ) + audio_input_ids = outputs.audio_input_ids + past_key_values = outputs.past_key_values + + if outputs.finished: + eos_lab = True + break + + if not eos_lab: + while True: + outputs = self.tts.generate( + input_ids=audio_input_ids, + past_key_values=past_key_values, + streaming_tts_text_mask=streaming_tts_text_mask, + max_new_token=output_chunk_size, + force_no_stop=self.force_no_stop, + temperature=torch.tensor([0.1, 0.3, 0.1, 0.3], dtype=torch.float, device=self.tts.device), + eos_token=torch.tensor([625], dtype=torch.long, device=self.tts.device), + logits_warpers=logits_warpers, + logits_processors=logits_processors, + ) + + audio_input_ids = outputs.audio_input_ids + past_key_values = outputs.past_key_values + + if outputs.finished: + break + if outputs.new_ids.shape[1] > tts_max_new_tokens: + break + + @staticmethod + def prepare_generation_config(do_sample, max_new_tokens=50, min_new_tokens=0, **kwargs): + num_beams = kwargs.get("num_beams", 3) + generation_config = { + "num_beams": num_beams, + "top_p": 0.8, + "top_k": 100, + "temperature": 0.7, + "do_sample": True, + "repetition_penalty": 1.02, + } + + if do_sample: + generation_config.update( + { + "top_p": 0.8, + "top_k": 100, + "temperature": 0.7, + "do_sample": True, + "repetition_penalty": 1.02, + } + ) + elif num_beams > 1: + generation_config.update({"num_beams": num_beams, "repetition_penalty": 1.2, "do_sample": False}) + else: + generation_config.update({"do_sample": False, "repetition_penalty": 1.02}) + + generation_config.update((k, kwargs[k]) for k in generation_config.keys() & kwargs.keys()) + generation_config["min_new_tokens"] = min_new_tokens + generation_config["max_new_tokens"] = max_new_tokens + + return generation_config + + def prepare_processor(self, processor=None, tokenizer=None): + if processor is not None: + self.processor = processor + if not hasattr(self, "processor") or self.processor is None: + self.processor = MiniCPMOProcessor.from_pretrained(self.config._name_or_path, trust_remote_code=True) + if tokenizer is not None: + self.processor.tokenizer = tokenizer + + @torch.inference_mode() + def chat( + self, + image=None, + msgs=None, + vision_hidden_states=None, + max_new_tokens=4096, + min_new_tokens=0, + do_sample=True, + max_inp_length=8192, + max_slice_nums=None, + use_image_id=None, + enable_thinking=False, + use_tts_template=False, + generate_audio=False, + output_audio_path=None, + output_tts_inputs_embeds_path=None, + omni_mode=False, + teacher_forcing=False, + return_prompt=False, + tts_proj_layer=-1, + tts_sampling_params: TTSSamplingParams = TTSSamplingParams(), + merge_audio_from_same_content=True, + stream=False, + stream_input=False, + tokenizer=None, + processor=None, + **kwargs, + ): + from PIL import Image + + batched = isinstance(msgs[0], list) + msgs_list = msgs + images_list = image + + if not batched: + images_list, msgs_list = [images_list], [msgs_list] + else: + assert images_list is None, "Please integrate image to msgs when using batch inference." + images_list = [None] * len(msgs_list) + assert len(images_list) == len(msgs_list), "The batch dim of images_list and msgs_list should be the same." + + self.prepare_processor(processor=processor, tokenizer=tokenizer) + + prompts_lists = [] + input_images_list = [] + input_audios_list = [] + audio_parts_list = [] + + for image, msgs in zip(images_list, msgs_list): + if isinstance(msgs, str): + msgs = json.loads(msgs) + copy_msgs = deepcopy(msgs) + + assert len(msgs) > 0, "msgs is empty" + assert do_sample or not stream, "if use stream mode, make sure do_sample=True" + + if image is not None and isinstance(copy_msgs[0]["content"], str): + copy_msgs[0]["content"] = [image, copy_msgs[0]["content"]] + + images = [] + audios = [] + audio_parts = [] + for i, msg in enumerate(copy_msgs): + role = msg["role"] + content = msg["content"] + assert role in ["system", "user", "assistant"] + if i == 0: + assert role in ["user", "system"], "The role of first msg should be user" + # Normalize structured content (OpenAI format) to native format + content = normalize_content(content) + cur_msgs = [] + for c in content: + if isinstance(c, Image.Image): + images.append(c) + cur_msgs.append("./") + elif isinstance(c, np.ndarray): # audio + audios.append(c) + audio_parts.append(i) + cur_msgs.append("") + use_tts_template = True + elif isinstance(c, str): + cur_msgs.append(c) + + if omni_mode or stream_input: + msg["content"] = "".join(cur_msgs) + else: + msg["content"] = "\n".join(cur_msgs) + + prompts_lists.append( + self.processor.tokenizer.apply_chat_template( + copy_msgs, + tokenize=False, + add_generation_prompt=False if teacher_forcing else True, + use_tts_template=use_tts_template, + enable_thinking=enable_thinking, + ) + ) + input_images_list.append(images) + input_audios_list.append(audios) + audio_parts_list.append(audio_parts) + + if not merge_audio_from_same_content: + audio_parts_list = None + + inputs = self.processor( + prompts_lists, + input_images_list, + input_audios_list, + audio_parts_list, + max_slice_nums=max_slice_nums, + use_image_id=use_image_id, + stream_input=stream_input, + return_tensors="pt", + max_length=max_inp_length, + ).to(self.device) + + if stream: + kwargs["num_beams"] = 1 + generation_config = self.prepare_generation_config( + do_sample=do_sample, max_new_tokens=max_new_tokens, min_new_tokens=min_new_tokens, **kwargs + ) + generation_config.pop("max_new_tokens", None) + + inputs.pop("image_sizes") + + # teacher_forcing = True => generate audio with given text + with torch.inference_mode(): + res, outputs = self.generate( + **inputs, + tokenizer=self.processor.tokenizer, + max_new_tokens=1 if teacher_forcing else max_new_tokens, + vision_hidden_states=vision_hidden_states, + stream=stream, + **generation_config, + ) + + if stream: + return res + + # spk bound and tts bound + tts_bos_token = self.processor.tokenizer.convert_tokens_to_ids("<|tts_bos|>") + tts_eos_token = self.processor.tokenizer.convert_tokens_to_ids("<|tts_eos|>") + + # Combine input_ids and generated sequences to get complete sequence + input_ids = inputs["input_ids"][0] + generated_ids = outputs.sequences[0] + # Combine by concatenating input_ids with the new tokens from generated sequence + full_sequence = torch.cat([input_ids, generated_ids]) + # Update the sequences in outputs + full_sequences = full_sequence.unsqueeze(0) + + outputs["full_sequences"] = full_sequences + + tts_bos_indices = [] + tts_eos_indices = [] + for i, x in enumerate(full_sequences[0]): + if x == tts_bos_token: + # tts_bos + 1 is the position of the first tts, so that it is convenient to slice hidden states for tts + tts_bos_indices.append(i + 1) + elif x == tts_eos_token: + if teacher_forcing and i == len(full_sequences[0]) - 1: + continue + tts_eos_indices.append(i) + + tts_bos_idx = tts_bos_indices[-1] if tts_bos_indices else -1 + # Use None instead of -1 when no EOS token found, so that slice [start:None] + # means "to the end" rather than [start:-1] which excludes the last element + tts_eos_idx = tts_eos_indices[-1] if tts_eos_indices else None + + tts_bound = (tts_bos_idx, tts_eos_idx) + + answer = res[0] + if answer is not None: + answer = answer.split("<|tts_eos|>")[0] + + if use_tts_template and generate_audio and output_audio_path: + import soundfile as sf + + try: + generated_waveform = self._generate_speech_non_streaming( + outputs=outputs, + tts_bound=tts_bound, + tts_proj_layer=tts_proj_layer, + audio_prompt=( + input_audios_list[0][0] + if len(input_audios_list) > 0 and len(input_audios_list[0]) > 0 + else None + ), + output_tts_inputs_embeds_path=output_tts_inputs_embeds_path, + tts_sampling_params=tts_sampling_params, + ) + if isinstance(generated_waveform, torch.Tensor): + sf.write(output_audio_path, generated_waveform.cpu().numpy(), samplerate=24000) + elif isinstance(generated_waveform, np.ndarray): + sf.write(output_audio_path, generated_waveform, samplerate=24000) + logger.debug(f"audio saved to {output_audio_path}") + except: + import traceback + + traceback.print_exc() + + if return_prompt: + return answer, prompts_lists[0] + else: + return answer + + @torch.inference_mode() + def _generate_speech_non_streaming( + self, + outputs, + tts_bound, + tts_proj_layer, + audio_prompt, + output_tts_inputs_embeds_path=None, + tts_sampling_params: TTSSamplingParams = TTSSamplingParams(), + ): + last_hidden_states = [hs[tts_proj_layer] for hs in outputs.hidden_states] + last_hidden_states = torch.vstack([i[0] for i in last_hidden_states]) + + spk_embeds = ( + torch.ones([0, self.tts.config.hidden_size]).to(last_hidden_states.device).to(last_hidden_states.dtype) + ) + + if self.tts.condition_type == "hidden_text_merge": + llm_tokens = outputs["full_sequences"][0][tts_bound[0] : tts_bound[1]] + llm_tokens = torch.tensor(llm_tokens, device=self.tts.emb_text.weight.device, dtype=torch.long) + llm_embeds = self.tts.emb_text(llm_tokens) # make sure emb_text is compatible with llm vocab size + + hidden_embeds = last_hidden_states[tts_bound[0] : tts_bound[1]] + hidden_embeds = self.tts.projector_semantic(hidden_embeds) + + if self.tts.config.normalize_projected_hidden: + hidden_embeds = F.normalize(hidden_embeds, p=2, dim=-1) + + tts_embeds = llm_embeds + hidden_embeds + else: + raise NotImplementedError + + audio_bos = [self.tts.audio_bos_token_id] + audio_bos = torch.tensor(audio_bos, device=self.tts.emb_text.weight.device, dtype=torch.long) + + audio_bos_embeds = self.tts.emb_text(audio_bos) + + text_eos_embed = self.tts.emb_text( + torch.tensor( + [self.tts.config.text_eos_token_id], + device=self.tts.emb_text.weight.device, + dtype=torch.long, + ) + ) + + inputs_embeds = torch.cat([spk_embeds, tts_embeds, text_eos_embed, audio_bos_embeds], dim=0).unsqueeze(0) + + # save inputs_embeds to file + if output_tts_inputs_embeds_path: + torch.save(inputs_embeds, output_tts_inputs_embeds_path) + + outputs = self.tts.generate( + inputs_embeds=inputs_embeds, + sampling_params=tts_sampling_params, + eos_token=torch.tensor( + [self.tts.config.num_audio_tokens - 1], + dtype=torch.long, + device=self.tts.device, + ), + ) + + import io + + import soundfile as sf + + generated_tokens = outputs.new_ids.squeeze(-1) + reference_audio = audio_prompt + prompt_wav_path = None + if reference_audio is not None: + logger.debug("use reference audio in data to generate waveform") + with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp_wav: + prompt_wav_path = tmp_wav.name + sf.write(prompt_wav_path, reference_audio, 16000) + wav_bytes = self.tts.audio_tokenizer( + generated_tokens.squeeze(0).tolist(), + prompt_wav_path, + ) + # convert wav bytes back to tensor for caller compatibility + waveform, sr = sf.read(io.BytesIO(wav_bytes)) + return torch.tensor(waveform, dtype=torch.float32) + + @torch.inference_mode() + def init_token2wav_cache(self, prompt_speech_16k): + import soundfile as sf + + if hasattr(self.tts.audio_tokenizer, "set_stream_cache"): + self.tts.audio_tokenizer.cache = None + with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp_wav: + prompt_wav_path = tmp_wav.name + sf.write(prompt_wav_path, prompt_speech_16k, 16000) + flow_cache_base, hift_cache_base = self.tts.audio_tokenizer.set_stream_cache(prompt_wav_path) + + self.token2wav_cache = { + "flow_cache_base": torch_clone_recursive(flow_cache_base), + "hift_cache_base": torch_clone_recursive(hift_cache_base), + } + else: + model_input = self.tts.audio_tokenizer.frontend.frontend_token2wav( + speech_tokens=torch.zeros(1, 1, dtype=torch.long, device=self.tts.device), + speech_16k=None, + prompt_speech_16k=prompt_speech_16k, + resample_rate=self.tts.audio_tokenizer.sample_rate, + prompt_speech=None, + ) + + prompt_token = model_input["flow_prompt_speech_token"] + prompt_feat = model_input["prompt_speech_feat"] + embedding = model_input["flow_embedding"] + + if self.tts.audio_tokenizer.fp16: + prompt_feat = prompt_feat.to(torch.half) + embedding = embedding.to(torch.half) + + prepared_cache = self.tts.audio_tokenizer.model.prepare_cache_from_prompt( + prompt_token=prompt_token, + prompt_feat=prompt_feat, + embedding=embedding, + n_timesteps=self.tts.config.s3_stream_n_timesteps, + code_chunk_size=self.tts.config.s3_stream_chunk_size, + chunk_prelook_size=self.tts.config.s3_stream_prelook_size, + use_attn_idx=False, + ) + + self.token2wav_cache = prepared_cache + + # for sliding window + def _ensure_dynamic_cache(self): + cache = self.llm_past_key_values + if cache is None: + return None + + cache = as_dynamic_cache(cache) + if isinstance(cache, DynamicCache): + self.llm_past_key_values = cache + return cache + + return None + + def _get_kv_cache_length(self, cache=None): + cache = cache if cache is not None else self.llm_past_key_values + return get_kv_cache_length(cache) + + # todo: not-used del? + def _rebuild_cache_from_history(self): + preserved_ids: List[torch.Tensor] = [] + for entry in self._omni_chunk_history: + ids = entry.get("input_ids") + if ids is None or not isinstance(ids, torch.Tensor) or ids.numel() == 0: + continue + preserved_ids.append(ids.to(self.device)) + if not preserved_ids: + self.llm_past_key_values = None + self.streaming_position_offset = 0 + self._rope_inv_freq_cache.clear() + return + + concat_ids = torch.cat(preserved_ids, dim=1) + attention_mask = torch.ones((1, concat_ids.shape[1]), dtype=torch.bool, device=self.device) + outputs = self.llm( + input_ids=concat_ids, + attention_mask=attention_mask, + use_cache=True, + return_dict=True, + ) + self.llm_past_key_values = outputs.past_key_values + self.streaming_position_offset = 0 + self._rope_inv_freq_cache.clear() + + def _get_rope_theta(self) -> float: + return float(getattr(self.llm.config, "rope_theta", 10000.0)) + + def _realign_rotary_suffix( + self, + suffix_keys: torch.Tensor, + old_positions: torch.Tensor, + new_positions: torch.Tensor, + ) -> torch.Tensor: + return realign_rotary_suffix( + suffix_keys, + old_positions, + new_positions, + rope_theta=self._get_rope_theta(), + inv_freq_cache=self._rope_inv_freq_cache, + ) + + def _encode_text(self, tokenizer, text) -> Optional[torch.Tensor]: + if tokenizer is None or not text: + return None + ids = tokenizer(text, return_tensors="pt", add_special_tokens=False)["input_ids"] + return ids.to(self.device) + + @staticmethod + def _safe_decode(tokenizer, input_ids): + if tokenizer is None or input_ids is None: + return None + if isinstance(input_ids, torch.Tensor): + ids = input_ids.cpu().tolist() + if ids and isinstance(ids[0], list): + ids = ids[0] + else: + ids = input_ids + try: + return tokenizer.decode(ids, skip_special_tokens=False) + except Exception: + return None + + def _finalize_round( + self, round_id: Optional[int], cache_before: int, assistant_input_ids: Optional[torch.Tensor] = None + ): + if round_id is None: + self._pending_round_id = None + return + cache_after = self._get_kv_cache_length() + if assistant_input_ids is not None: + assistant_len = assistant_input_ids.shape[1] + else: + assistant_len = max(cache_after - cache_before, 0) + if assistant_len > 0: + self._register_chunk( + assistant_len, + "assistant", + round_id=round_id, + input_ids=assistant_input_ids, + tokenizer=self.processor.tokenizer if hasattr(self, "processor") else None, + ) + + self._pending_round_id = None + self._next_round_id += 1 + + def _register_chunk( + self, + seq_len: int, + chunk_type: str, + *, + round_id: int, + input_ids=None, + tokenizer=None, + ) -> None: + if seq_len <= 0: + return + entry = {"length": int(seq_len), "type": chunk_type, "round": round_id} + if input_ids is not None: + entry["input_ids"] = input_ids.clone().detach() + entry["decoded"] = self._safe_decode(tokenizer, entry["input_ids"]) + else: + entry["input_ids"] = None + entry["decoded"] = None + self._omni_chunk_history.append(entry) + + if chunk_type == "system": + self.streaming_text_preserve = max(self.streaming_text_preserve, entry["length"]) + + def _drop_tokens_from_cache(self, length: int, cache: DynamicCache) -> bool: + """Drop tokens from cache using the utility function.""" + _, new_offset, success = drop_tokens_from_cache( + cache=cache, + length=length, + preserve=self.streaming_text_preserve, + position_offset=self.streaming_position_offset, + rope_theta=self._get_rope_theta(), + inv_freq_cache=self._rope_inv_freq_cache, + ) + if success: + self.streaming_position_offset = new_offset + return success + + def _drop_next_round(self, cache: DynamicCache) -> bool: + seen_rounds = set() + for entry in self._omni_chunk_history: + round_id = entry.get("round") + if round_id is None or round_id in seen_rounds: + continue + seen_rounds.add(round_id) + round_entries = [e for e in self._omni_chunk_history if e.get("round") == round_id] + if any(e.get("type") == "system" for e in round_entries): + continue + if self._drop_round(round_id, cache): + return True + return False + + def _drop_round(self, round_id: int, cache: DynamicCache) -> bool: + entries = [e for e in self._omni_chunk_history if e.get("round") == round_id] + if not entries: + return False + total_len = sum(e["length"] for e in entries) + if total_len <= 0: + for e in entries: + self._omni_chunk_history.remove(e) + return False + if not self._drop_tokens_from_cache(total_len, cache): + return False + for e in entries: + self._omni_chunk_history.remove(e) + return True + + def _enforce_text_window(self) -> None: + if not self.streaming_window_enabled: + return + cache = self._ensure_dynamic_cache() + if cache is None: + return + high_limit = max(0, int(self.streaming_window_config.text_window_high_tokens)) + low_limit = max(0, int(self.streaming_window_config.text_window_low_tokens)) + if high_limit <= 0: + return + target = max(0, low_limit) + total_len = self._get_kv_cache_length(cache) + if total_len <= high_limit: + return + dropped_any = False + while total_len > target: + if not self._drop_next_round(cache): + break + dropped_any = True + total_len = self._get_kv_cache_length(cache) + + # snapshot, vad + def save_speculative_snapshot(self) -> SpeculativeSnapshot: + """Internal method: save speculative snapshot. + + Called at the start of streaming_generate, saves to self._speculative_snapshot. + + Save strategy: + - LLM KV Cache: only record length (restore by truncation, zero extra VRAM) + - Audio KV Cache: deep clone (as generate sets it to None) + - Mel processor: full state snapshot (including buffer) + """ + # get LLM cache information + llm_cache_length = self._get_kv_cache_length() + llm_cache_checksum = None + if self.llm_past_key_values is not None and hasattr(self.llm_past_key_values, "key_cache"): + if len(self.llm_past_key_values.key_cache) > 0: + llm_cache_checksum = self.llm_past_key_values.key_cache[0].sum().item() + + # get audio cache length and clone audio_past_key_values + audio_cache_length = 0 + audio_cache_checksum = None + audio_past_key_values_clone = None + if self.audio_past_key_values is not None: + # handle DynamicCache format (Whisper encoder may return this format) + if isinstance(self.audio_past_key_values, DynamicCache): + if hasattr(self.audio_past_key_values, "key_cache") and len(self.audio_past_key_values.key_cache) > 0: + audio_cache_length = self.audio_past_key_values.key_cache[0].shape[2] + audio_cache_checksum = self.audio_past_key_values.key_cache[0].sum().item() + # deep clone DynamicCache + cloned_cache = DynamicCache() + for k, v in zip(self.audio_past_key_values.key_cache, self.audio_past_key_values.value_cache): + cloned_cache.update(k.clone(), v.clone(), layer_idx=len(cloned_cache.key_cache)) + audio_past_key_values_clone = cloned_cache + + # handle EncoderDecoderCache format + elif isinstance(self.audio_past_key_values, EncoderDecoderCache): + self_attn_cache = self.audio_past_key_values.self_attention_cache + if hasattr(self_attn_cache, "key_cache") and len(self_attn_cache.key_cache) > 0: + audio_cache_length = self_attn_cache.key_cache[0].shape[2] + audio_cache_checksum = self_attn_cache.key_cache[0].sum().item() + # deep clone EncoderDecoderCache + cloned_self_attn = DynamicCache() + if hasattr(self_attn_cache, "key_cache"): + for k, v in zip(self_attn_cache.key_cache, self_attn_cache.value_cache): + cloned_self_attn.update(k.clone(), v.clone(), layer_idx=len(cloned_self_attn.key_cache)) + cross_attn_cache = self.audio_past_key_values.cross_attention_cache + cloned_cross_attn = DynamicCache() + if hasattr(cross_attn_cache, "key_cache"): + for k, v in zip(cross_attn_cache.key_cache, cross_attn_cache.value_cache): + cloned_cross_attn.update(k.clone(), v.clone(), layer_idx=len(cloned_cross_attn.key_cache)) + audio_past_key_values_clone = EncoderDecoderCache(cloned_self_attn, cloned_cross_attn) + + # handle tuple format (compatible with old format) + elif isinstance(self.audio_past_key_values, tuple) and len(self.audio_past_key_values) > 0: + audio_cache_length = self.audio_past_key_values[0][0].shape[2] + audio_cache_checksum = self.audio_past_key_values[0][0].sum().item() + # deep clone audio_past_key_values (tuple of tuples of tensors) + audio_past_key_values_clone = tuple( + tuple(t.clone() for t in layer_cache) for layer_cache in self.audio_past_key_values + ) + + # get mel processor snapshot + mel_processor_snapshot = None + mel_buffer_checksum = None + if hasattr(self, "processor") and self.processor is not None: + mel_processor_snapshot = self.processor.get_streaming_snapshot() + if mel_processor_snapshot: + buf = mel_processor_snapshot.get("buffer") + if buf is not None and len(buf) > 0: + mel_buffer_checksum = float(buf.sum()) + + # save RNG state (important: for deterministic dithering and other random operations after restoration) + rng_state_cpu = torch.get_rng_state() + rng_state_cuda = None + if torch.cuda.is_available() and self.device.type == "cuda": + rng_state_cuda = torch.cuda.get_rng_state(self.device) + + # create snapshot + snapshot = SpeculativeSnapshot( + llm_cache_length=llm_cache_length, + audio_cache_length=audio_cache_length, + new_user_msg=self.new_user_msg, + llm_generated=self.llm_generated, + llm_generate_completed=self.llm_generate_completed, + next_round_id=self._next_round_id, + pending_round_id=self._pending_round_id, + omni_chunk_history_length=len(self._omni_chunk_history), + tts_last_turn_tokens=self.tts_last_turn_tokens.clone() if self.tts_last_turn_tokens is not None else None, + audio_chunk_idx=self.audio_chunk_idx, + mel_processor_snapshot=mel_processor_snapshot, + audio_past_key_values=audio_past_key_values_clone, + timestamp=time.time(), + # debug fields + llm_cache_checksum=llm_cache_checksum, + audio_cache_checksum=audio_cache_checksum, + mel_buffer_checksum=mel_buffer_checksum, + # RNG state + rng_state_cpu=rng_state_cpu, + rng_state_cuda=rng_state_cuda, + ) + + return snapshot + + def restore_speculative_snapshot(self, snapshot=None) -> bool: + """Restore speculative snapshot - called when VAD speculation fails. + + Restores model state to before streaming_generate was called, + allowing continued streaming_prefill for newly arrived audio. + + Notes: + - Snapshot is saved when streaming_generate is called with enable_speculative_snapshot=True + - This method uses the most recent snapshot for restoration + - Snapshot is cleared after restore, cannot be called repeatedly + + Returns: + bool: Whether restoration was successful + """ + snapshot = snapshot or getattr(self, "_speculative_snapshot", None) + + if snapshot is None: + return False + + try: + current_cache_length = self._get_kv_cache_length() + current_history_length = len(self._omni_chunk_history) + + # 1. truncate LLM KV Cache + if current_cache_length > snapshot.llm_cache_length: + self._truncate_llm_cache(snapshot.llm_cache_length) + + # 2. restore Audio KV Cache (important: restore from cloned copy) + # because streaming_generate will set audio_past_key_values to None + self.audio_past_key_values = snapshot.audio_past_key_values + + # 3. restore session state + self.new_user_msg = snapshot.new_user_msg + self.llm_generated = snapshot.llm_generated + self.llm_generate_completed = snapshot.llm_generate_completed + + # 4. restore Round management + self._next_round_id = snapshot.next_round_id + self._pending_round_id = snapshot.pending_round_id + + # 5. truncate chunk history + if current_history_length > snapshot.omni_chunk_history_length: + self._omni_chunk_history = self._omni_chunk_history[: snapshot.omni_chunk_history_length] + + # 6. restore TTS state + self.tts_last_turn_tokens = snapshot.tts_last_turn_tokens + + # 7. restore streaming processor state + self.audio_chunk_idx = snapshot.audio_chunk_idx + + # 8. restore mel processor state (important: otherwise subsequent prefill will fail due to frame number mismatch) + if ( + snapshot.mel_processor_snapshot is not None + and hasattr(self, "processor") + and self.processor is not None + ): + self.processor.restore_streaming_snapshot(snapshot.mel_processor_snapshot) + + # 9. restore RNG state (important: ensure determinism of dithering and other random operations after restoration) + if snapshot.rng_state_cpu is not None: + torch.set_rng_state(snapshot.rng_state_cpu) + if snapshot.rng_state_cuda is not None and torch.cuda.is_available(): + torch.cuda.set_rng_state(snapshot.rng_state_cuda, self.device) + + # 10. clean up temporary states generated during generation + if hasattr(self, "_streaming_generated_token_ids"): + del self._streaming_generated_token_ids + if hasattr(self, "_last_streaming_text"): + del self._last_streaming_text + + # 11. clear snapshot (can only be restored once) + self._speculative_snapshot = None + + return True + except Exception as e: + import traceback + + logger.error(traceback.format_exc()) + return False + + def has_speculative_snapshot(self) -> bool: + return getattr(self, "_speculative_snapshot", None) is not None + + def clear_speculative_snapshot(self) -> None: + if hasattr(self, "_speculative_snapshot"): + self._speculative_snapshot = None + + def _truncate_llm_cache(self, target_length: int) -> None: + if self.llm_past_key_values is None: + return + + cache = self._ensure_dynamic_cache() + if cache is None: + return + + current_length = self._get_kv_cache_length(cache) + if current_length <= target_length: + return + + # truncate each layer of cache + for layer_idx in range(len(cache.key_cache)): + if cache.key_cache[layer_idx].numel() > 0: + cache.key_cache[layer_idx] = cache.key_cache[layer_idx][:, :, :target_length, :].contiguous() + cache.value_cache[layer_idx] = cache.value_cache[layer_idx][:, :, :target_length, :].contiguous() + + # update cache metadata + cache.crop(target_length) + cache._seen_tokens = target_length + + @torch.inference_mode() + def streaming_prefill( + self, + session_id, + msgs, + omni_mode=True, + max_slice_nums=None, + use_tts_template=True, + enable_thinking=False, + is_last_chunk=False, # for audio chunk, if is the last chunk, set to True + tokenizer=None, + processor=None, + **kwargs, + ): + from PIL import Image + + assert session_id is not None, "session_id cannot be None" + self.is_first = self.session_id is None or session_id != self.session_id + + self.prepare_processor(processor=processor, tokenizer=tokenizer) + + images = [] + audios = [] + + assert len(msgs) == 1 + copy_msgs = deepcopy(msgs) + msg = copy_msgs[0] + + assert msg["role"] in ["system", "user", "assistant"] + is_not_system_prefill = msg["role"] != "system" + + content = msg["content"] + cur_msgs = [] + for j, c in enumerate(content): + if isinstance(c, Image.Image): + images.append(c) + cur_msgs.append("./") + elif isinstance(c, np.ndarray): + audios.append(c) + cur_msgs.append("") + elif isinstance(c, str): + cur_msgs.append(c) + else: + logger.error(f"Invalid content type: {c}, ignore it.") + + cur_contents = "".join(cur_msgs) if omni_mode else "\n".join(cur_msgs) + + if msg["role"] in ["system", "assistant"]: + self.new_user_msg = True + self.audio_past_key_values = None + + if self.is_first: + self.reset_session(reset_token2wav_cache=False) + self.session_id = session_id + + self.init_streaming_processor() + + if msg["role"] == "user": + # no system prefill, the first segment of the first user turn + # do not use apply_chat_template, manually build prompt to avoid automatic addition of <|im_end|> + prompt = "<|im_start|>user\n" + cur_contents + self.new_user_msg = False # mark subsequent segments do not need to add user prefix anymore + else: + # system or assistant prefill, use apply_chat_template + msg["content"] = cur_contents + prompt = self.processor.tokenizer.apply_chat_template( + copy_msgs, + tokenize=False, + add_generation_prompt=False, + use_tts_template=use_tts_template, + enable_thinking=enable_thinking, + ) + add_special_tokens = True # add bos + else: + # non-first prefill + if self.new_user_msg and msg["role"] == "user": + # the first segment of the new user turn + if self.llm_generated: + if self.llm_generate_completed: + prompt = "<|im_end|>\n<|im_start|>user\n" + cur_contents + else: + prompt = "<|tts_eos|><|im_end|>\n<|im_start|>user\n" + cur_contents + else: + prompt = "<|im_start|>user\n" + cur_contents + self.new_user_msg = False + else: + # subsequent segments of the same turn, directly use content + prompt = cur_contents + add_special_tokens = False + + # when first user audio prefill, ensure audio length satisfies FIRST_CHUNK_MS requirements + if is_not_system_prefill and len(audios) > 0 and self.audio_chunk_idx == 0: + assert len(audios) == 1, f"streaming mode only supports single audio, currently {len(audios)}" + first_chunk_samples = int(self.FIRST_CHUNK_MS * self.SAMPLE_RATE / 1000) + if len(audios[0]) < first_chunk_samples: + pad_len = first_chunk_samples - len(audios[0]) + audios[0] = np.concatenate([np.zeros(pad_len, dtype=audios[0].dtype), audios[0]]) + + model_inputs = self.processor( + [prompt], + [images], + [audios], + max_slice_nums=1 if max_slice_nums is None else max_slice_nums, + use_image_id=False, + chunk_input=True, + return_tensors="pt", + max_length=None, + sampling_rate=16000, + add_special_tokens=add_special_tokens, + online_streaming=is_not_system_prefill, + audio_chunk_idx=self.audio_chunk_idx, + is_last_chunk=is_last_chunk, + ).to(self.device) + + if len(audios) > 0 and is_not_system_prefill: + self.audio_chunk_idx += 1 + + # 1. prepare input embeddings + model_inputs["inputs_embeds"], _ = self.get_vllm_embedding(model_inputs) + # get audio embedding with audio_past_key_values + inputs_embeds = self.get_omni_embedding( + model_inputs, input_embeddings=model_inputs["inputs_embeds"], stream_input=is_not_system_prefill + ) + + if self.is_first: + self.audio_past_key_values = None + + round_id = self._next_round_id + self._pending_round_id = round_id + chunk_type = "system" if msg["role"] == "system" else ("user" if msg["role"] == "user" else "assistant") + seq_len = inputs_embeds.shape[1] + self._enforce_text_window() + cache_length = self._get_kv_cache_length() + + attention_mask = torch.ones((1, cache_length + inputs_embeds.shape[1]), dtype=torch.bool, device=self.device) + + # 2. do prefill + outputs = self.llm( + past_key_values=self.llm_past_key_values, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + position_ids=None, + use_cache=True, + return_dict=True, + ) + + self.llm_past_key_values = as_dynamic_cache(outputs["past_key_values"]) + self._register_chunk( + seq_len, + chunk_type, + round_id=round_id, + input_ids=model_inputs["input_ids"], + tokenizer=self.processor.tokenizer, + ) + self._enforce_text_window() + if self.force_rope_reindex: + self._force_reindex_all_cache() + + return prompt + + @torch.inference_mode() + def streaming_generate( + self, + session_id, + bos_input=None, + generate_audio=True, + audio_token_chunk_size=25, # 25 token/s + tts_sampling_params: TTSSamplingParams = TTSSamplingParams(), + max_new_tokens=256, + enable_thinking=False, + use_tts_template=True, + do_sample=True, + enable_speculative_snapshot=False, + tokenizer=None, + processor=None, + # Teacher forcing (only for the "text → hidden → TTS condition" pipeline in streaming_generate) + # When enabled: instead of letting the LLM auto-regressively generate the text to be spoken, + # it forces the tokens from teacher_forcing_text to be fed in, using the hidden states + # corresponding to these tokens to construct the TTS condition, ensuring the output audio matches the input text. + teacher_forcing: bool = False, + teacher_forcing_text: str = "", + **kwargs, + ): + # save speculative snapshot (before modifying any state) + # for VAD speculative snapshot: if speculative snapshot fails, can call restore_speculative_snapshot() to restore + # enable_speculative_snapshot=True when enabled, skip (save some overhead) when disabled + if enable_speculative_snapshot: + self._speculative_snapshot = self.save_speculative_snapshot() + + # reset buf + self.new_user_msg = True + self.llm_generated = True + self.llm_generate_completed = False + self.audio_past_key_values = None + + self.prepare_processor(processor=processor, tokenizer=tokenizer) + + # reset current turn generated token IDs + if hasattr(self, "_streaming_generated_token_ids"): + del self._streaming_generated_token_ids + # reset full generated text + if hasattr(self, "_last_streaming_text"): + del self._last_streaming_text + + cache = self._ensure_dynamic_cache() + cache_length = self._get_kv_cache_length(cache) + host_round_id = self._pending_round_id + + ## in single-turn streaming, each call to streaming_generate needs to reinitialize the streaming_processor, enter the next turn + self.init_streaming_processor() + + # 1) llm generate token and hidden states per chunk=10, 2) tts generate audio token chunk per chunk=25, 3) yield 1 chunk audio token + def audio_chunk_generator( + bos_input, + tokenizer, + generate_audio, + tts_sampling_params, + max_new_tokens, + do_sample, + teacher_forcing=False, + teacher_forcing_text="", + **kwargs, + ): + generate_chunk_size = 10 + + if bos_input is None: + bos_input = "".join( + [ + "<|im_end|>\n<|im_start|>assistant\n", + "" if enable_thinking else self.think_str.replace("\\n", "\n"), + "<|tts_bos|>" if use_tts_template else "", + ] + ) + + bos_input_ids = tokenizer.encode(bos_input) + bos_input_ids = torch.tensor(bos_input_ids, dtype=torch.long, device=self.device).unsqueeze(0) + + bos_input_embeds = self.llm.get_input_embeddings()(bos_input_ids) + + generation_inputs_embeds = bos_input_embeds + generated_ids = torch.empty((1, 0), dtype=torch.long, device=self.device) + + num_chunks_decode = (max_new_tokens + generate_chunk_size - 1) // generate_chunk_size + + conditions = [] + + # generate chunk by chunk, each chunk has 10 tokens, each chunk takes last hidden states, and pass tokens to tts + llm_streaming_generator = ChunkPrefillChunkGenerate( + model=self.llm, + tokenizer=tokenizer, + terminators=["<|tts_eos|>", "<|im_end|>", ""], + ) + + if generate_audio: + logits_warpers, logits_processors = gen_logits( + num_code=self.tts.config.num_audio_tokens, + repetition_penalty=tts_sampling_params.repetition_penalty, + top_p=tts_sampling_params.top_p, + top_k=tts_sampling_params.top_k, + ) + + tts_streaming_generator = TTSStreamingGenerator( + model=self.tts, + temperature=tts_sampling_params.temperature, + eos_token=torch.tensor( + [self.tts.config.num_audio_tokens - 1], + dtype=torch.long, + device=self.tts.device, + ), + chunk_size=audio_token_chunk_size, # s3tokenizer 1s = 25token + tts_last_turn_tokens=self.tts_last_turn_tokens, + logits_processors=logits_processors, + logits_warpers=logits_warpers, + ) + + # Teacher forcing branch + # This branch does not rely on ChunkPrefillChunkGenerate's sampling logic, instead: + # 1) First prefill bos_input (assistant + tts_bos) into llm_past_key_values + # 2) Tokenize teacher_forcing_text into token ids + # 3) Feed tokens one by one into the LLM (teacher forcing), obtaining the last_hidden_states for each token + # 4) Use (token_ids, hidden_states) to construct tts condition, then feed it to TTSStreamingGenerator + if teacher_forcing: + # --- 1) prefill bos_input，延续 streaming_prefill 的 KV cache --- + bos_outputs = self.llm( + inputs_embeds=generation_inputs_embeds, + past_key_values=self.llm_past_key_values, + use_cache=True, + output_hidden_states=True, + return_dict=True, + ) + self.llm_past_key_values = bos_outputs.past_key_values + + if generate_audio: + # Give a length-0 tensor as speaker embedding (no speaker embedding) + spk_emb = torch.empty( + (bos_input_embeds.shape[0], 0, bos_input_embeds.shape[2]), + dtype=bos_input_embeds.dtype, + device=bos_input_embeds.device, + ) + tts_streaming_generator.spk_emb = spk_emb + + # --- 2) tokenize teacher_forcing_text --- + tf_text = teacher_forcing_text or "" + try: + forced_input_ids = tokenizer(tf_text, add_special_tokens=False, return_tensors="pt")["input_ids"] + except Exception: + # Compatible with rare tokenizer return object attributes + forced_input_ids = tokenizer(tf_text, add_special_tokens=False, return_tensors="pt").input_ids + forced_input_ids = forced_input_ids.to(self.device) + + total_len = int(forced_input_ids.shape[1]) + ptr = 0 + + # Special case: empty text should also let TTS finish (text_finished=True will automatically concatenate text_eos_embed) + if total_len == 0: + if not generate_audio: + yield forced_input_ids, True + return + empty_tts_embeds = torch.empty( + (1, 0, self.tts.config.hidden_size), + dtype=bos_input_embeds.dtype, + device=self.device, + ) + if not hasattr(self, "_streaming_generated_token_ids"): + self._streaming_generated_token_ids = [] + tts_generator = tts_streaming_generator.generate_with_buffer( + condition=empty_tts_embeds, + text_finished=True, + ) + for audio_token_chunk, is_last_audio_chunk in tts_generator: + yield audio_token_chunk, is_last_audio_chunk + self.tts_last_turn_tokens = tts_streaming_generator.tts_last_turn_tokens + self._last_streaming_text = "" + yield None, None + return + + # --- 3) chunk-by-chunk teacher forcing --- + while ptr < total_len: + end = min(ptr + generate_chunk_size, total_len) + chunk_ids = forced_input_ids[:, ptr:end] # [1, chunk_len] + chunk_hidden_list = [] + + for j in range(chunk_ids.shape[1]): + tok = chunk_ids[:, j : j + 1] # [1, 1] + tok_emb = self.llm.get_input_embeddings()(tok) + out = self.llm( + inputs_embeds=tok_emb, + past_key_values=self.llm_past_key_values, + use_cache=True, + output_hidden_states=True, + return_dict=True, + ) + self.llm_past_key_values = out.past_key_values + chunk_hidden_list.append(out.hidden_states[-1]) # [1, 1, hidden] + + chunk_hidden = torch.cat(chunk_hidden_list, dim=1) # [1, chunk_len, hidden] + text_finished = end >= total_len + + # Save token IDs cache (external eval script will use _last_streaming_text to write generated_text) + if not hasattr(self, "_streaming_generated_token_ids"): + self._streaming_generated_token_ids = [] + self._streaming_generated_token_ids.extend(chunk_ids[0].tolist()) + + if not generate_audio: + yield chunk_ids, text_finished + else: + llm_embeds = self.tts.emb_text(chunk_ids) + hidden_embeds = self.tts.projector_semantic(chunk_hidden) + if self.tts.config.normalize_projected_hidden: + hidden_embeds = F.normalize(hidden_embeds, p=2, dim=-1) + tts_embeds = llm_embeds + hidden_embeds + + tts_generator = tts_streaming_generator.generate_with_buffer( + condition=tts_embeds, + text_finished=text_finished, + ) + for audio_token_chunk, is_last_audio_chunk in tts_generator: + yield audio_token_chunk, is_last_audio_chunk + + ptr = end + if text_finished: + if generate_audio: + self.tts_last_turn_tokens = tts_streaming_generator.tts_last_turn_tokens + break + + # Finish: decode this round of text + if hasattr(self, "_streaming_generated_token_ids"): + try: + self._last_streaming_text = tokenizer.decode(self._streaming_generated_token_ids) + assistant_input_ids = self._encode_text(tokenizer=tokenizer, text=self._last_streaming_text) + self._finalize_round( + round_id=host_round_id, cache_before=cache_length, assistant_input_ids=assistant_input_ids + ) + except Exception: + self._last_streaming_text = None + else: + self._last_streaming_text = None + + # Finally send the end signal + if generate_audio: + yield None, None + else: + return + return + + # LLM chunk generate outer loop + for chunk_idx in range(num_chunks_decode): + is_first_generate_chunk = chunk_idx == 0 + + output = llm_streaming_generator.chunk_generate( + inputs_embeds=generation_inputs_embeds, + past_key_values=self.llm_past_key_values, + is_first_generate_chunk=is_first_generate_chunk, + return_hidden_states=True, + chunk_size=generate_chunk_size + 1 * is_first_generate_chunk, + do_sample=do_sample, + temperature=kwargs.get("temperature", 0.7), + top_p=kwargs.get("top_p", 0.8), + top_k=kwargs.get("top_k", 100), + repetition_penalty=kwargs.get("repetition_penalty", 1.02), + length_penalty=kwargs.get("length_penalty", 1.0), + all_input_ids=generated_ids, + ) + + if output.chunk_token_ids is None: + break + + if is_first_generate_chunk: + if generate_audio: + spk_emb = torch.empty( + (bos_input_embeds.shape[0], 0, bos_input_embeds.shape[2]), + dtype=bos_input_embeds.dtype, + device=bos_input_embeds.device, + ) + tts_streaming_generator.spk_emb = spk_emb + + if output.finished: + yield_chunk_token_ids = output.chunk_token_ids + else: + # the first chunk generated chunk_size + 1 tokens, we only take the first chunk_size tokens, + # the last token is not prefilled, and last hidden states is not obtained + yield_chunk_token_ids = output.chunk_token_ids[:, :-1] + + elif output.finished: + yield_chunk_token_ids = torch.cat([generated_ids[:, -1:], output.chunk_token_ids], dim=1) + else: + # in the chunk that is not the first chunk, we need to add the token at the end of the previous chunk, + # it is not prefilled into the model to get last hidden states + # similarly, the last generated token of subsequent chunks is not prefilled, and last hidden states is not obtained, + # so it is not passed out + yield_chunk_token_ids = torch.cat([generated_ids[:, -1:], output.chunk_token_ids[:, :-1]], dim=1) + + if not generate_audio: + chunk_generated_text = tokenizer.decode(yield_chunk_token_ids[0]) + yield yield_chunk_token_ids, output.finished + else: + # TTS inner loop + # dense connection here is hardcoded to use text-hidden merged as condition + llm_embeds = self.tts.emb_text(yield_chunk_token_ids) + hidden_embeds = output.last_hidden_states + hidden_embeds = self.tts.projector_semantic(hidden_embeds) + if self.tts.config.normalize_projected_hidden: # default should be opened + hidden_embeds = F.normalize(hidden_embeds, p=2, dim=-1) + + tts_embeds = llm_embeds + hidden_embeds + conditions.append(tts_embeds) + + # Store token IDs instead of decoded text to avoid UTF-8 multi-byte character truncation + if not hasattr(self, "_streaming_generated_token_ids"): + self._streaming_generated_token_ids = [] + self._streaming_generated_token_ids.extend(yield_chunk_token_ids[0].tolist()) + + # there is buffer generated, each time exactly returns 25 audio tokens, + # the last audio chunk returns audio tokens of variable length, length [0, 25] + tts_generator = tts_streaming_generator.generate_with_buffer( + condition=tts_embeds, text_finished=output.finished + ) + + for audio_token_chunk, is_last_audio_chunk in tts_generator: + yield audio_token_chunk, is_last_audio_chunk + + generated_ids = torch.cat([generated_ids, output.chunk_token_ids], dim=1) + generation_inputs_embeds = output.current_inputs_embeds + self.llm_past_key_values = output.past_key_values + + if output.finished: + if generate_audio: + self.tts_last_turn_tokens = tts_streaming_generator.tts_last_turn_tokens + break + + # IMPORTANT: Flush remaining TTS buffer when LLM generation ends + # This handles BOTH cases: + # 1. LLM finished with terminator (output.finished=True) - buffer may still have tokens + # 2. LLM hit max chunks limit (output.finished=False) - buffer definitely has tokens + if generate_audio: + if len(tts_streaming_generator._token_buffer) > 0: + batch = torch.cat(tts_streaming_generator._token_buffer, dim=1) + yield batch, True + tts_streaming_generator._token_buffer = [] + + if generate_audio: + if hasattr(self, "_streaming_generated_token_ids"): + try: + self._last_streaming_text = tokenizer.decode(self._streaming_generated_token_ids) + assistant_input_ids = self._encode_text(tokenizer=tokenizer, text=self._last_streaming_text) + self._finalize_round( + round_id=host_round_id, cache_before=cache_length, assistant_input_ids=assistant_input_ids + ) + except Exception: + self._last_streaming_text = None + else: + self._last_streaming_text = None + + yield None, None + else: + return + + # iter for generating text chunk and audio chunk + audio_chunk_generator_iter = audio_chunk_generator( + bos_input=bos_input, + tokenizer=self.processor.tokenizer, + generate_audio=generate_audio, + tts_sampling_params=tts_sampling_params, + max_new_tokens=max_new_tokens, + do_sample=do_sample, + teacher_forcing=teacher_forcing, + teacher_forcing_text=teacher_forcing_text, + **kwargs, + ) + + if generate_audio: + if self.tts.config.audio_tokenizer_type == "s3tokenizer_step_audio": + self.tts.audio_tokenizer.stream_cache = torch_clone_recursive(self.token2wav_cache["flow_cache_base"]) + self.tts.audio_tokenizer.hift_cache_dict = torch_clone_recursive( + self.token2wav_cache["hift_cache_base"] + ) + + # pre-insert 3-5 prefix 4218 silence tokens, each token corresponds to 0.04s, + # adding 5 tokens means introducing 0.2s of silence + buffer = [4218] * 3 + pre_lookahead = 3 + CHUNK_SIZE = 25 + chunk_idx = 0 + prev_text_len = 0 # track text position for streaming text output + for audio_token_chunk, is_last_audio_chunk in audio_chunk_generator_iter: + if audio_token_chunk is None: + break + + buffer += audio_token_chunk.reshape(-1).tolist() + + if len(buffer) >= CHUNK_SIZE + pre_lookahead: + waveform_chunk = self.tts.audio_tokenizer.stream( + buffer[: CHUNK_SIZE + pre_lookahead], + prompt_wav=None, + last_chunk=is_last_audio_chunk, + return_waveform=True, + ) + + waveform_chunk = torch.from_numpy(waveform_chunk) + + # get new text chunk corresponding to this waveform + # Decode from accumulated token IDs to avoid UTF-8 multi-byte truncation + new_text = "" + if hasattr(self, "_streaming_generated_token_ids"): + current_text = self.processor.tokenizer.decode(self._streaming_generated_token_ids) + # Filter out trailing replacement characters (incomplete UTF-8 sequences) + safe_end = len(current_text) + while safe_end > 0 and current_text[safe_end - 1] == "\ufffd": + safe_end -= 1 + safe_text = current_text[:safe_end] + new_text = safe_text[prev_text_len:] + prev_text_len = len(safe_text) + + yield waveform_chunk, new_text + + buffer = buffer[CHUNK_SIZE:] + chunk_idx += 1 + + # flush rest + if len(buffer) > 0: + waveform_chunk = self.tts.audio_tokenizer.stream( + buffer, + prompt_wav=None, + last_chunk=True, + return_waveform=True, + ) + + waveform_chunk = torch.from_numpy(waveform_chunk) + + # get remaining new text for the final chunk + # Final chunk: decode all remaining text without filtering + new_text = "" + if hasattr(self, "_streaming_generated_token_ids"): + current_text = self.processor.tokenizer.decode(self._streaming_generated_token_ids) + new_text = current_text[prev_text_len:] + prev_text_len = len(current_text) + + yield waveform_chunk, new_text + + # maybe the buffer is empty, and text is not empty, should we flush text without wave? + else: + raise NotImplementedError(f"not supported audio tokenizer: {self.tts.config.audio_tokenizer_type}") + else: + # For text-only generation, decode tokens and handle partial multi-byte characters + yield from streaming_token_decoder( + audio_chunk_generator_iter, + self.processor.tokenizer, + skip_special_tokens=False, + ) + + def as_duplex(self, device: Optional[str] = None, **kwargs) -> "MiniCPMODuplex": + """Convert this MiniCPMO instance to MiniCPMODuplex for full-duplex streaming.""" + return MiniCPMODuplex.from_existing_model( + model=self, + device=device, + **kwargs, + ) + + +class MiniCPMODuplex: + """MiniCPMODuplex model with full-duplex streaming capabilities. + + This is a wrapper class that provides duplex streaming functionality. + Use MiniCPMO.as_duplex() to create from an existing model without reloading. + """ + + # Default duplex parameters + _default_duplex_params = { + "generate_audio": True, + "ls_mode": "explicit", + "max_new_speak_tokens_per_chunk": 20, + "text_repetition_penalty": 1.05, + "temperature": 0.7, + "top_k": 100, + "top_p": 0.8, + "text_repetition_window_size": 512, + "listen_prob_scale": 1.0, + "force_listen_count": 0, + "tts_temperature": 0.8, + "tts_repetition_penalty": 1.05, + "enable_float16": False, + "n_timesteps": 10, + "chunk_ms": 1000, + "first_chunk_ms": 1035, + "cnn_redundancy_ms": 20, + "sample_rate": 16000, + "sliding_window_mode": "off", + "basic_window_high_tokens": 8000, + "basic_window_low_tokens": 6000, + "context_previous_max_tokens": 500, + "context_max_units": 24, + } + + @classmethod + def from_existing_model( + cls, + model: "MiniCPMO", + device: Optional[str] = None, + **kwargs, + ) -> "MiniCPMODuplex": + """Create MiniCPMODuplex from an existing MiniCPMO instance.""" + # Create instance without calling __init__ + instance = cls.__new__(cls) + + instance.name_or_path = getattr(model.config, "_name_or_path", "") + + # Get default params helper + def get_param(name): + if name in kwargs: + return kwargs[name] + return cls._default_duplex_params.get(name) + + instance.generate_audio = get_param("generate_audio") + instance.ls_mode = get_param("ls_mode") + + # Determine device + if device is not None: + instance.device = device + else: + try: + instance.device = str(next(model.parameters()).device) + except StopIteration: + instance.device = "cuda" + + # Reuse the existing model - THIS IS THE KEY: no reloading! + instance.model = model + instance.processor = getattr(model, "processor", None) + instance.tokenizer = getattr(instance.processor, "tokenizer", None) if instance.processor else None + + if instance.tokenizer is None: + from transformers import AutoTokenizer + + instance.tokenizer = AutoTokenizer.from_pretrained(instance.name_or_path, trust_remote_code=True) + + if instance.processor is None: + from .processing_minicpmo import MiniCPMOProcessor + + instance.processor = MiniCPMOProcessor.from_pretrained(instance.name_or_path, trust_remote_code=True) + instance.processor.tokenizer = instance.tokenizer + + # Ensure model has processor reference (same as __init__) + instance.model.processor = instance.processor + + # Initialize TTS (same as __init__) + enable_float16 = get_param("enable_float16") + n_timesteps = get_param("n_timesteps") + instance.model.init_tts(enable_float16=enable_float16, n_timesteps=n_timesteps) + + instance.break_event = threading.Event() + instance.session_stop_event = threading.Event() + + # LLM generation config + instance.max_new_speak_tokens_per_chunk = get_param("max_new_speak_tokens_per_chunk") + instance.text_repetition_penalty = get_param("text_repetition_penalty") + instance.temperature = get_param("temperature") + instance.top_k = get_param("top_k") + instance.top_p = get_param("top_p") + instance.text_repetition_window_size = get_param("text_repetition_window_size") + instance.listen_prob_scale = get_param("listen_prob_scale") + instance.force_listen_count = get_param("force_listen_count") + + # TTS generation config + tts_temp_value = get_param("tts_temperature") + instance.tts_temperature = torch.tensor([tts_temp_value], dtype=torch.float, device=instance.device) + instance.tts_repetition_penalty = get_param("tts_repetition_penalty") + + # Stream config + instance.CHUNK_MS = get_param("chunk_ms") + instance.FIRST_CHUNK_MS = get_param("first_chunk_ms") + instance.CNN_REDUNDANCY_MS = get_param("cnn_redundancy_ms") + instance.SAMPLE_RATE = get_param("sample_rate") + + instance.model.CHUNK_MS = instance.CHUNK_MS + instance.model.FIRST_CHUNK_MS = instance.FIRST_CHUNK_MS + instance.model.CNN_REDUNDANCY_MS = instance.CNN_REDUNDANCY_MS + instance.model.SAMPLE_RATE = instance.SAMPLE_RATE + + # Special tokens + instance.unit_token_id = instance.tokenizer.convert_tokens_to_ids("") + instance.image_start_token_id = instance.tokenizer.convert_tokens_to_ids("") + instance.image_end_token_id = instance.tokenizer.convert_tokens_to_ids("") + instance.slice_start_token_id = instance.tokenizer.convert_tokens_to_ids("") + instance.slice_end_token_id = instance.tokenizer.convert_tokens_to_ids("") + + instance.listen_token_id = instance.tokenizer.convert_tokens_to_ids("<|listen|>") + instance.speak_token_id = instance.tokenizer.convert_tokens_to_ids("<|speak|>") + instance.tts_bos_token_id = instance.tokenizer.convert_tokens_to_ids("<|tts_bos|>") + instance.tts_eos_token_id = instance.tokenizer.convert_tokens_to_ids("<|tts_eos|>") + + instance.chunk_eos_token_id = instance.tokenizer.convert_tokens_to_ids("<|chunk_eos|>") + instance.chunk_tts_eos_token_id = instance.tokenizer.convert_tokens_to_ids("<|chunk_tts_eos|>") + instance.turn_eos_token_id = instance.tokenizer.convert_tokens_to_ids("<|turn_eos|>") + + instance.chunk_terminator_token_ids = [ + instance.listen_token_id, + instance.chunk_eos_token_id, + instance.chunk_tts_eos_token_id, + ] + instance.turn_terminator_token_ids = [instance.turn_eos_token_id] + instance.chunk_speak_token_ids = [instance.speak_token_id] + + instance.tts_pad_id = instance.tokenizer.convert_tokens_to_ids("<|tts_pad|>") + bad_token_ids = getattr(instance.tokenizer, "bad_token_ids", []) + instance.forbidden_token_ids = [instance.tts_pad_id] + list(bad_token_ids) + + from .utils import StreamDecoder + + instance.decoder = StreamDecoder( + llm=instance.model.llm, tokenizer=instance.tokenizer, forbidden_token_ids=instance.forbidden_token_ids + ) + + # Sliding window config + sliding_window_mode = get_param("sliding_window_mode") + basic_window_high_tokens = get_param("basic_window_high_tokens") + basic_window_low_tokens = get_param("basic_window_low_tokens") + context_previous_max_tokens = get_param("context_previous_max_tokens") + context_max_units = get_param("context_max_units") + + instance.decoder.set_window_config( + DuplexWindowConfig( + sliding_window_mode=sliding_window_mode, + basic_window_high_tokens=basic_window_high_tokens, + basic_window_low_tokens=basic_window_low_tokens, + context_previous_max_tokens=context_previous_max_tokens, + context_max_units=context_max_units, + ) + ) + window_enabled = sliding_window_mode != "off" + instance.decoder.set_window_enabled(window_enabled) + + instance.tts_logits_processors = None + instance.tts_eos_token = None + if instance.generate_audio: + instance.tts_logits_processors = gen_logits( + num_code=instance.model.tts.config.num_audio_tokens, + repetition_penalty=instance.tts_repetition_penalty, + ) + instance.tts_eos_token = torch.tensor( + [instance.model.tts.config.num_audio_tokens - 1], + dtype=torch.long, + device=instance.device, + ) + + instance._reset_streaming_state() + + return instance + + def set_break_event(self): + self.break_event.set() + + def clear_break_event(self): + self.break_event.clear() + + def set_session_stop(self): + self.session_stop_event.set() + self.break_event.set() + + def clear_session_stop(self): + self.session_stop_event.clear() + + def is_break_set(self) -> bool: + return self.break_event.is_set() + + def is_session_stop_set(self) -> bool: + return self.session_stop_event.is_set() + + def _init_token2wav_cache(self, prompt_wav_path: str): + self.model.tts.audio_tokenizer.cache = None + flow_cache, hift_cache = self.model.tts.audio_tokenizer.set_stream_cache(prompt_wav_path) + self.flow_cache_base = torch_clone_recursive(flow_cache) + self.hift_cache_base = torch_clone_recursive(hift_cache) + self.pre_lookahead = int(self.model.tts.audio_tokenizer.flow.pre_lookahead_len) + self.token2wav_initialized = True + + def _reset_token2wav_for_new_turn(self): + if self.token2wav_initialized: + self.model.tts.audio_tokenizer.stream_cache = torch_clone_recursive(self.flow_cache_base) + self.model.tts.audio_tokenizer.hift_cache_dict = torch_clone_recursive(self.hift_cache_base) + self.token2wav_buffer = [4218] * 3 # silence token prefix + + def _reset_streaming_state(self): + self.audio_chunk_idx = 0 + self.current_turn_ended = True + self.speak_count = 0 + self.res_ids = [] + self.total_ids = [] + self.total_hidden = [] + + # TTS state + self.tts_text_start_pos = 0 + self.tts_past_key_values = None + self.tts_current_turn_start_time = None + + # token2wav state + self.token2wav_initialized = False + self.token2wav_buffer = [] + self.flow_cache_base = None + self.hift_cache_base = None + + # Audio prefill state + self.audio_buffer = np.array([], dtype=np.float32) + self.pending_logits: Optional[torch.Tensor] = None + self.current_mode: Optional[str] = None + + # Force listen state + self._streaming_generate_count = 0 + + # Schema tracking: record the complete prefill + generate token sequence + # prefill_schema_tokens: each element is a list of prefill tokens for a unit + # format: [[unit0_prefill_tokens], [unit1_prefill_tokens], ...] + self.prefill_schema_tokens = [] + self._current_unit_prefill_tokens = [] + + def prepare( + self, + prefix_system_prompt: Optional[str] = None, + ref_audio: Optional[np.ndarray] = None, + prompt_wav_path: Optional[str] = None, + context_previous_marker: str = "\n\nprevious: ", + **kwargs, + ): + prefix_system_prompt = prefix_system_prompt or "Streaming Omni Conversation." + + prefix_system_prompt = "<|im_start|>system\n" + prefix_system_prompt + suffix_system_prompt = "<|im_end|>" + if isinstance(ref_audio, np.ndarray): + prefix_system_prompt += "\n<|audio_start|>" + suffix_system_prompt = "<|audio_end|>" + suffix_system_prompt + + self.clear_break_event() + self.clear_session_stop() + + self._reset_streaming_state() + self.decoder.reset() + + self.model.init_streaming_processor() + + if prompt_wav_path is not None and prompt_wav_path and self.generate_audio: + self._init_token2wav_cache(prompt_wav_path) + self._reset_token2wav_for_new_turn() + + # Prefill system prompt prefix + if prefix_system_prompt: + tokens = self.tokenizer.encode(prefix_system_prompt, add_special_tokens=False) + for token_id in tokens: + self.decoder.feed(self.decoder.embed_token(token_id)) + + # Prefill reference audio + if ref_audio is not None: + data = self.processor.process_audio([ref_audio]) + embeds_nested = self.model.get_audio_embedding(data, chunk_length=self.model.config.audio_chunk_length) + embeds = torch.cat([t for g in embeds_nested for t in g], dim=0) if embeds_nested else None + if embeds is not None: + self.decoder.feed(embeds) + + # register system prompt protection length (protect this part from being removed when sliding window is enabled) + if prefix_system_prompt or suffix_system_prompt or ref_audio is not None: + if self.decoder._window_config.sliding_window_mode == "context": + # Context preserve mode: + # initial layout: [prefix] [suffix] [units...] + # after the first sliding window: [prefix] [context_previous_marker + content] [suffix] [units...] + # register prefix length first, then feed suffix + self._prefix_system_prompt = prefix_system_prompt + self._suffix_system_prompt = suffix_system_prompt + self._ref_audio = ref_audio + + suffix_token_ids = [] + if suffix_system_prompt: + suffix_token_ids = self.tokenizer.encode(suffix_system_prompt, add_special_tokens=False) + + # register (when cache only has prefix, no suffix, no previous) + self.decoder.register_system_prompt_with_context( + suffix_token_ids=suffix_token_ids, + context_previous_marker=context_previous_marker, # dynamically added after the first sliding window + ) + + # now feed suffix + for token_id in suffix_token_ids: + self.decoder.feed(self.decoder.embed_token(token_id)) + else: + # non-context preserve mode: first feed suffix, then register total length + if suffix_system_prompt: + tokens = self.tokenizer.encode(suffix_system_prompt, add_special_tokens=False) + for token_id in tokens: + self.decoder.feed(self.decoder.embed_token(token_id)) + self.decoder.register_system_prompt() + + if prefix_system_prompt or suffix_system_prompt: + if ref_audio is not None: + full_prompt = (prefix_system_prompt or "") + "[audio embedding]" + (suffix_system_prompt or "") + else: + full_prompt = (prefix_system_prompt or "") + (suffix_system_prompt or "") + + return full_prompt + + return "" + + @torch.no_grad() + def streaming_prefill( + self, + audio_waveform: Optional[np.ndarray] = None, + frame_list: Optional[list] = None, + text_list: Optional[list] = None, + max_slice_nums: Union[int, List[int]] = 1, + batch_vision_feed: bool = False, + ): + """Streaming prefill - called once per second, processing audio/video data + + Args: + audio_waveform: audio waveform data + frame_list: image frame list + text_list: text + max_slice_nums: maximum number of slices for HD image encoding (default 1, no slicing) + Can be an int (same for all images) or a list matching frame_list length + batch_vision_feed: if True, batch all vision embeddings into a single feed call for better performance. + if False (default), feed each embedding individually (original behavior). + + Process: + 0. determine mode based on input: AUDIO / VISION / OMNI + 1. feed token + 2. get and feed image embed (if frame_list) - return pending logits in VISION MODE + 3. get and feed audio embed (if audio_waveform) - return pending logits in AUDIO/OMNI MODE + + Returns: + dict with keys: + - success: bool + - cost_vision_process: float (image processing time) + - cost_vision_embed: float (vision embedding time) + - cost_vision_feed: float (vision feed time) + - cost_audio_process: float (audio processing time) + - cost_audio_embed: float (audio embedding time) + - cost_audio_feed: float (audio feed time) + - cost_all: float (total time) + """ + start_time = time.time() + cost_vision_process = 0.0 + cost_vision_embed = 0.0 + cost_vision_feed = 0.0 + cost_audio_process = 0.0 + cost_audio_embed = 0.0 + cost_audio_feed = 0.0 + + def _make_result(success, reasons=""): + reason = reasons + if isinstance(reasons, list): + reason = "; ".join(reasons) + + return { + "success": success, + "reason": reason, + "cost_vision_process": cost_vision_process, + "cost_vision_embed": cost_vision_embed, + "cost_vision_feed": cost_vision_feed, + "cost_audio_process": cost_audio_process, + "cost_audio_embed": cost_audio_embed, + "cost_audio_feed": cost_audio_feed, + "cost_all": time.time() - start_time, + } + + if self.is_session_stop_set() or self.is_break_set(): + return _make_result(False) + + has_frames = frame_list is not None and len(frame_list) > 0 + has_audio = audio_waveform is not None and len(audio_waveform) > 0 + has_text = text_list is not None and len(text_list) > 0 + + if has_frames and has_audio: + mode = "OMNI" + elif has_frames: + mode = "VISION" + elif has_audio: + mode = "AUDIO" + elif has_text: + mode = "TEXT" + else: + return _make_result(False) + + self.pending_logits = None + + # sliding window: record unit start position + self.decoder.register_unit_start() + + # Schema tracking: start new unit, record prefill tokens + self._current_unit_prefill_tokens = [] + + # Step 1: Feed token + self.decoder.feed(self.decoder.embed_token(self.unit_token_id)) + self._current_unit_prefill_tokens.append(self.unit_token_id) + + # Step 2: process image + if has_frames: + t0 = time.time() + + # normalize max_slice_nums to a list matching frame_list length + if isinstance(max_slice_nums, int): + max_slice_nums_list = [max_slice_nums] * len(frame_list) + else: + max_slice_nums_list = list(max_slice_nums) + if len(max_slice_nums_list) != len(frame_list): + raise ValueError( + f"max_slice_nums list length ({len(max_slice_nums_list)}) " + f"must match frame_list length ({len(frame_list)})" + ) + + # check if all max_slice_nums are the same (can use batch processing) + all_same = len(set(max_slice_nums_list)) == 1 + + if all_same: + # all images use the same max_slice_nums, use batch processing + processed_frames = self.processor.process_image(frame_list, max_slice_nums=max_slice_nums_list[0]) + if self.device: + processed_frames = processed_frames.to(self.device) + else: + # different max_slice_nums per image, process individually and merge + all_pixel_values = [] + all_tgt_sizes = [] + for frame, max_slices in zip(frame_list, max_slice_nums_list): + pf = self.processor.process_image([frame], max_slice_nums=max_slices) + if self.device: + pf = pf.to(self.device) + # pf["pixel_values"][0] is the list of slices for this image + all_pixel_values.extend(pf["pixel_values"][0]) + # pf["tgt_sizes"][0] is the array of target sizes for this image's slices + if hasattr(pf["tgt_sizes"][0], "tolist"): + all_tgt_sizes.extend(pf["tgt_sizes"][0].tolist()) + else: + all_tgt_sizes.extend(list(pf["tgt_sizes"][0])) + + # reconstruct processed_frames with merged data + processed_frames = { + "pixel_values": [all_pixel_values], + "tgt_sizes": [torch.tensor(all_tgt_sizes) if all_tgt_sizes else []], + } + + cost_vision_process = time.time() - t0 + + t0 = time.time() + # get vision embeddings for all images (each may have multiple slices) + # vision_hidden_states is a list, one entry per input image + # each entry contains embeddings for [source_image, slice_1, slice_2, ...] + vision_hidden_states = self.model.get_vision_embedding(processed_frames) + cost_vision_embed = time.time() - t0 + + if vision_hidden_states is not None and len(vision_hidden_states) > 0: + t0 = time.time() + + # vision_hidden_states[0] contains ALL slices from ALL images (flattened) + # shape: [total_slices, 64, D] where total_slices = sum of slices across all images + # we need to know how many slices each image has to correctly group them + + # calculate slice counts for each image using get_sliced_grid (lightweight, no actual slicing) + slice_counts = [] # e.g., [5, 9] means img1 has 5 slices (1 source + 4 HD), img2 has 9 slices + for frame_idx, frame in enumerate(frame_list): + max_slices = max_slice_nums_list[frame_idx] + if hasattr(frame, "size"): + # get_sliced_grid returns [M, N] grid or None if no slicing needed + # total images = 1 (source) + M * N (HD slices) + grid = self.processor.image_processor.get_sliced_grid( + frame.size, max_slices, nerver_split=False + ) + if grid is not None: + slice_counts.append(1 + grid[0] * grid[1]) # 1 source + M*N slices + else: + slice_counts.append(1) # no slicing, only source image + else: + slice_counts.append(1) # default: single image, no slicing + + # get the flattened embeddings tensor + # vision_hidden_states is a list with one element (the batch) + # vision_hidden_states[0] shape: [total_slices, 64, D] + all_embeds = vision_hidden_states[0] + + # collect all feed operations first, then execute + # this allows us to identify the last token for VISION mode logits + feed_operations = [] # List of (embed, is_last_for_vision_mode, token_id_or_none) + + embed_idx = 0 # current index in all_embeds + for img_idx, num_slices in enumerate(slice_counts): + if num_slices == 0: + continue + + # the first embedding is always the source image (downsampled overview) + # Feed token + feed_operations.append( + (self.decoder.embed_token(self.image_start_token_id), False, self.image_start_token_id) + ) + # Feed source image embedding (shape: [64, D]) - use None to indicate embedding + feed_operations.append((all_embeds[embed_idx], False, None)) + # Feed token + feed_operations.append( + (self.decoder.embed_token(self.image_end_token_id), False, self.image_end_token_id) + ) + embed_idx += 1 + + # remaining embeddings are HD slices (if num_slices > 1) + if num_slices > 1: + for slice_i in range(1, num_slices): + # Feed token + feed_operations.append( + (self.decoder.embed_token(self.slice_start_token_id), False, self.slice_start_token_id) + ) + # Feed slice embedding (shape: [64, D]) + feed_operations.append((all_embeds[embed_idx], False, None)) + # Feed token + feed_operations.append( + (self.decoder.embed_token(self.slice_end_token_id), False, self.slice_end_token_id) + ) + embed_idx += 1 + + # mark the last operation for VISION mode logits + if feed_operations: + feed_operations[-1] = (feed_operations[-1][0], True, feed_operations[-1][2]) + + # execute feed operations + if batch_vision_feed and feed_operations: + # batch mode: concatenate all embeddings and feed at once + # this reduces LLM forward passes from N to 1 + # + # NOTE: batch mode may have slight numerical differences compared to for-loop mode + # due to floating-point precision in attention computation. This is expected behavior + # for causal attention with incremental vs batch computation. + + all_embeds_list = [] + for embed, is_last, token_id in feed_operations: + # ensure all embeddings have shape [L, H] + if embed.dim() == 1: + embed = embed.unsqueeze(0) + all_embeds_list.append(embed) + + # concatenate all embeddings + # torch.cat requires consistent dtype; embeddings should already be same dtype + all_embeds_to_feed = torch.cat(all_embeds_list, dim=0) # [total_L, H] + + if mode == "VISION": + # vision mode needs logits from the last token + self.pending_logits, _ = self.decoder.feed(all_embeds_to_feed, return_logits=True) + else: + # omni mode: just feed, wait for audio to get logits + self.decoder.feed(all_embeds_to_feed) + + # schema tracking: record all token IDs and embedding markers + for embed, is_last, token_id in feed_operations: + if token_id is not None: + self._current_unit_prefill_tokens.append(token_id) + else: + embed_dim = embed.shape[0] if len(embed.shape) > 1 else 1 + self._current_unit_prefill_tokens.append(("img", embed_dim)) + else: + for embed, is_last, token_id in feed_operations: + if mode == "VISION" and is_last: + # get logits from the last token + self.pending_logits, _ = self.decoder.feed(embed, return_logits=True) + else: + self.decoder.feed(embed) + # schema tracking: record token ID or embedding marker + if token_id is not None: + self._current_unit_prefill_tokens.append(token_id) + else: + # use tuple to mark image embedding: ("img", dim) + embed_dim = embed.shape[0] if len(embed.shape) > 1 else 1 + self._current_unit_prefill_tokens.append(("img", embed_dim)) + # for omni mode, no pending logits needed here (wait for audio) + + cost_vision_feed = time.time() - t0 + + # Step 3: process audio (if any) + if has_audio: + # accumulate audio to buffer + self.audio_buffer = np.concatenate([self.audio_buffer, audio_waveform]) + + # calculate required audio length + if self.audio_chunk_idx == 0: + required_samples = int(self.FIRST_CHUNK_MS * self.SAMPLE_RATE / 1000) + if len(self.audio_buffer) < required_samples: + padding_samples = required_samples - len(self.audio_buffer) + padding = np.zeros(padding_samples, dtype=np.float32) + self.audio_buffer = np.concatenate([padding, self.audio_buffer]) + else: + required_samples = int(self.CHUNK_MS * self.SAMPLE_RATE / 1000) + + need_samples = self.processor.get_streaming_chunk_size() + if len(self.audio_buffer) < need_samples: + return _make_result( + False, f"audio not enough: need {need_samples} samples, only {len(self.audio_buffer)}" + ) + + audio_chunk = self.audio_buffer[:need_samples] + + t0 = time.time() + batch_feature = self.processor.process_audio_streaming( + audio_chunk, + reset=False, + return_batch_feature=True, + ) + + if batch_feature is None or batch_feature.audio_features.shape[-1] == 0: + return _make_result(False, "streaming audio processing returned empty") + + # metadata + batch_feature.chunk_idx = self.audio_chunk_idx + batch_feature.use_extra_context = True + batch_feature.prefix_extra_frames = 0 if self.audio_chunk_idx == 0 else 2 + batch_feature.suffix_extra_frames = 2 + + batch_feature = batch_feature.to(self.device) + cost_audio_process = time.time() - t0 + + t0 = time.time() + embeds_nested = self.model.get_audio_embedding_streaming( + batch_feature, + use_extra_context=batch_feature.use_extra_context, + prefix_extra_frames=batch_feature.prefix_extra_frames, + suffix_extra_frames=batch_feature.suffix_extra_frames, + ) + audio_embeds = torch.cat([t for g in embeds_nested for t in g], dim=0) + cost_audio_embed = time.time() - t0 + + t0 = time.time() + self.pending_logits, _ = self.decoder.feed(audio_embeds, return_logits=True) + cost_audio_feed = time.time() - t0 + + # schema tracking: use tuple to mark audio embedding: ("audio", dim) + embed_dim = audio_embeds.shape[0] if len(audio_embeds.shape) > 1 else 1 + self._current_unit_prefill_tokens.append(("audio", embed_dim)) + + if self.audio_chunk_idx == 0: + cfg = self.processor._streaming_mel_processor.get_config() + consumed_ms = int(cfg.get("effective_first_chunk_ms", self.FIRST_CHUNK_MS)) + consumed_samples = int(consumed_ms * self.SAMPLE_RATE / 1000) + else: + consumed_samples = int(self.CHUNK_MS * self.SAMPLE_RATE / 1000) + + self.audio_buffer = self.audio_buffer[consumed_samples:] + + self.audio_chunk_idx += 1 + + # Step 4: process text + if has_text: + # concatenate all text items + text_content = "".join(text_list) if isinstance(text_list, list) else str(text_list) + + # tokenize text + text_token_ids = self.tokenizer.encode(text_content, add_special_tokens=False) + + if len(text_token_ids) > 0: + # get token embeddings + text_token_ids_tensor = torch.tensor(text_token_ids, dtype=torch.long, device=self.device) + text_embeds = self.decoder.embed_token(text_token_ids_tensor) + + # feed to decoder + if mode == "TEXT": + # text-only mode: get logits from the last token + self.pending_logits, _ = self.decoder.feed(text_embeds, return_logits=True) + else: + # mixed mode: just feed, let other modality get logits + self.decoder.feed(text_embeds) + + # schema tracking: record text token IDs + for token_id in text_token_ids: + self._current_unit_prefill_tokens.append(token_id) + + self.current_mode = mode + + if mode == "VISION": + self.audio_chunk_idx += 1 + + # schema tracking: save current unit's prefill tokens + self.prefill_schema_tokens.append(self._current_unit_prefill_tokens) + + return _make_result(True) + + @torch.no_grad() + def streaming_generate( + self, + prompt_wav_path=None, + max_new_speak_tokens_per_chunk=20, + decode_mode: str = "sampling", + temperature=0.7, + top_k=100, + top_p=0.8, + listen_prob_scale=1.0, + listen_top_k=None, + text_repetition_penalty=1.05, + text_repetition_window_size=512, + ): + start_time = time.time() + + if self.is_session_stop_set() or self.is_break_set(): + return { + "is_listen": True, + "text": "", + "audio_waveform": self._generate_silence_waveform(), + "end_of_turn": True, + "current_time": self.audio_chunk_idx, + "cost_llm": 0.0, + "cost_tts_prep": 0.0, + "cost_tts": 0.0, + "cost_token2wav": 0.0, + "cost_all": time.time() - start_time, + "n_tokens": 0, + "n_tts_tokens": 0, + } + + # check if there are pending logits to process + if not hasattr(self, "pending_logits") or self.pending_logits is None: + return { + "is_listen": True, + "text": "", + "audio_waveform": self._generate_silence_waveform(), + "end_of_turn": False, + "current_time": self.audio_chunk_idx, + "cost_llm": 0.0, + "cost_tts_prep": 0.0, + "cost_tts": 0.0, + "cost_token2wav": 0.0, + "cost_all": time.time() - start_time, + "n_tokens": 0, + "n_tts_tokens": 0, + } + + # use pending logits generated in streaming_prefill + logits = self.pending_logits + self.pending_logits = None + + # Force listen: check if we should force listen for first N calls + force_listen = self._streaming_generate_count < self.force_listen_count + self._streaming_generate_count += 1 + + total_hidden_in_unit = [] + total_ids_in_unit = [] + current_time = self.audio_chunk_idx + is_listen = False + end_of_turn = False + + llm_start_time = time.time() + + for j in range(max_new_speak_tokens_per_chunk): + if j == max_new_speak_tokens_per_chunk - 1: + if self.ls_mode == "explicit": + self.decoder.feed(self.decoder.embed_token(self.chunk_eos_token_id)) + self.total_ids.append(self.chunk_eos_token_id) + break + + if force_listen: + last_id = torch.tensor([self.listen_token_id], dtype=torch.long, device=self.device) + else: + last_id = self.decoder.decode( + logits=logits, + mode=decode_mode, + temperature=temperature, + top_k=top_k, + top_p=top_p, + listen_top_k=listen_top_k, + listen_prob_scale=listen_prob_scale, + text_repetition_penalty=text_repetition_penalty, + text_repetition_window_size=text_repetition_window_size, + ) + + # if current turn not ended, not allowed to listen (only check when not force_listen) + if last_id.item() == self.listen_token_id and (not self.current_turn_ended): + last_id = torch.tensor([self.tts_bos_token_id], dtype=torch.long, device=self.device) + + self.total_ids.append(last_id.item()) + + is_listen = last_id.item() == self.listen_token_id + + # termination condition detection + if last_id.item() in self.chunk_terminator_token_ids: + if self.ls_mode == "explicit": + logits, _ = self.decoder.feed(self.decoder.embed_token(last_id.item()), return_logits=True) + break + else: + # normal speak + self.current_turn_ended = False + + if last_id.item() in self.chunk_speak_token_ids: + pass + else: + self.res_ids.append(last_id.item()) + self.speak_count += 1 + + logits, hidden = self.decoder.feed(self.decoder.embed_token(last_id.item()), return_logits=True) + + assert len(hidden.shape) == 3 + assert hidden.shape[0] == 1 + assert hidden.shape[1] == 1 + + end_of_turn = last_id.item() in self.turn_terminator_token_ids + + if end_of_turn: + self.current_turn_ended = True + + if j != 0: + total_hidden_in_unit.append([last_id.item(), hidden, end_of_turn]) + total_ids_in_unit.append(last_id.item()) + + # Prefill token + unit_end_id = self.tokenizer.convert_tokens_to_ids("") + self.decoder.feed(self.decoder.embed_token(unit_end_id)) + self.total_ids.append(unit_end_id) + + # calculate generated text (for sliding window context preserve, filter out special tokens) + generated_text = self.tokenizer.decode(total_ids_in_unit, skip_special_tokens=True) if total_ids_in_unit else "" + + # sliding window: register unit end, and check if sliding window is needed + input_type = self.current_mode.lower() if self.current_mode else "audio" + + self.decoder.register_unit_end( + input_type=input_type, + generated_tokens=total_ids_in_unit, + is_listen=is_listen, + generated_text=generated_text, + ) + # select sliding window method based on sliding window mode + if self.decoder._window_config.sliding_window_mode == "context": + self.decoder.enforce_window_with_context() + elif self.decoder._window_config.sliding_window_mode == "basic": + self.decoder.enforce_window() + + llm_end_time = time.time() + + if is_listen: + self.total_hidden.append([]) + return { + "is_listen": True, + "text": "", + "audio_waveform": self._generate_silence_waveform(), + "end_of_turn": False, + "current_time": current_time, + "cost_llm": llm_end_time - llm_start_time, + "cost_tts_prep": 0.0, + "cost_tts": 0.0, + "cost_token2wav": 0.0, + "cost_all": time.time() - start_time, + "n_tokens": len(total_ids_in_unit), + "n_tts_tokens": 0, + } + + self.total_hidden.append(total_hidden_in_unit) + text = generated_text # reuse already calculated text + + if not self.generate_audio: + return { + "is_listen": False, + "text": text, + "audio_waveform": None, + "end_of_turn": end_of_turn, + "current_time": current_time, + "cost_llm": llm_end_time - llm_start_time, + "cost_tts_prep": 0.0, + "cost_tts": 0.0, + "cost_token2wav": 0.0, + "cost_all": time.time() - start_time, + "n_tokens": len(total_ids_in_unit), + "n_tts_tokens": 0, + } + + # TTS generate + tts_start_time = time.time() + tts_prep_start_time = time.time() + tts_condition = self._convert_results_to_tts_input(total_hidden_in_unit) + tts_prep_end_time = time.time() + + max_token_per_chunk = 25 + 1 + min_token_per_chunk = 25 + 1 + + if end_of_turn: + min_token_per_chunk = 0 + force_flush = False + if self.tts_text_start_pos == 0: # this is the start of the turn + min_token_per_chunk = 0 # allow decoding <1s audio + force_flush = True + + if self.tts_current_turn_start_time is None: + self.tts_current_turn_start_time = current_time + + new_tokens, old_kv = self.model.tts.generate_chunk( + inputs_embeds=tts_condition, + temperature=self.tts_temperature, + repetition_penalty=self.tts_repetition_penalty, + eos_token=self.tts_eos_token, + force_no_stop=False, + max_new_token=max_token_per_chunk, + min_new_tokens=min_token_per_chunk, + past_key_values=self.tts_past_key_values, + logits_processors=self.tts_logits_processors, + text_start_pos=self.tts_text_start_pos, + ) + + tts_end_time = time.time() + + # update TTS state (note: token2wav reset must be after audio generation, otherwise tokens in buffer will be lost) + if end_of_turn: + self.tts_text_start_pos = 0 + self.tts_past_key_values = None + self.tts_current_turn_start_time = None + else: + self.tts_past_key_values = old_kv + self.tts_text_start_pos += tts_condition.shape[1] + new_tokens.shape[1] + + # token2wav generation (must be before reset, otherwise tokens in the last but second chunk will be lost) + token2wav_start_time = time.time() + audio_waveform = self._generate_waveform_from_tokens( + new_tokens, prompt_wav_path, end_of_turn, force_flush=force_flush + ) + token2wav_end_time = time.time() + + # reset token2wav state after audio generation, ensure all tokens in buffer are processed + if end_of_turn: + self._reset_token2wav_for_new_turn() + + end_time = time.time() + + return { + "is_listen": False, + "text": text, + "audio_waveform": audio_waveform, + "end_of_turn": end_of_turn, + "current_time": current_time, + "cost_llm": llm_end_time - llm_start_time, + "cost_tts_prep": tts_prep_end_time - tts_prep_start_time, + "cost_tts": tts_end_time - tts_start_time, + "cost_token2wav": token2wav_end_time - token2wav_start_time, + "cost_all": end_time - start_time, + "n_tokens": len(total_ids_in_unit), + "n_tts_tokens": new_tokens.numel(), + } + + def get_session_schema(self, include_embeddings: bool = True) -> str: + """get complete schema for current session (includes prefill and generate stages) + + Args: + include_embeddings: whether to include embedding placeholders (e.g. [img_embed_64], [audio_embed_50]) + + Returns: + complete schema string, each unit format: + [img_embed_64][audio_embed_50]<|listen|or|speak|>generated_content + """ + if not hasattr(self, "prefill_schema_tokens") or not hasattr(self, "total_ids"): + return "" + + # get token id for splitting generate tokens + unit_end_token_id = self.tokenizer.convert_tokens_to_ids("") + + # split generate tokens into each unit + generate_units = [] + current_unit = [] + for tid in self.total_ids: + current_unit.append(tid) + if tid == unit_end_token_id: + generate_units.append(current_unit) + current_unit = [] + + # build complete schema + full_schema_parts = [] + num_units = max(len(self.prefill_schema_tokens), len(generate_units)) + + for unit_idx in range(num_units): + unit_schema = "" + + # prefill part + if unit_idx < len(self.prefill_schema_tokens): + prefill_tokens = self.prefill_schema_tokens[unit_idx] + for item in prefill_tokens: + if isinstance(item, tuple): + # tuple represents embedding: ("img", dim) or ("audio", dim) + embed_type, embed_dim = item + if include_embeddings: + unit_schema += f"[{embed_type}_embed_{embed_dim}]" + else: + # normal token ID + unit_schema += self.tokenizer.decode([item], skip_special_tokens=False) + + # generate part + if unit_idx < len(generate_units): + unit_schema += self.tokenizer.decode(generate_units[unit_idx], skip_special_tokens=False) + + full_schema_parts.append(unit_schema) + + return "".join(full_schema_parts) + + def get_unit_schemas(self, include_embeddings: bool = True) -> list: + """get list of schema for each unit + + Returns: + list of schema strings for each unit + """ + if not hasattr(self, "prefill_schema_tokens") or not hasattr(self, "total_ids"): + return [] + + unit_end_token_id = self.tokenizer.convert_tokens_to_ids("") + + # split generate tokens into each unit + generate_units = [] + current_unit = [] + for tid in self.total_ids: + current_unit.append(tid) + if tid == unit_end_token_id: + generate_units.append(current_unit) + current_unit = [] + + # build schema for each unit + unit_schemas = [] + num_units = max(len(self.prefill_schema_tokens), len(generate_units)) + + for unit_idx in range(num_units): + unit_schema = "" + + # prefill part + if unit_idx < len(self.prefill_schema_tokens): + prefill_tokens = self.prefill_schema_tokens[unit_idx] + for item in prefill_tokens: + if isinstance(item, tuple): + # tuple represents embedding: ("img", dim) or ("audio", dim) + embed_type, embed_dim = item + if include_embeddings: + unit_schema += f"[{embed_type}_embed_{embed_dim}]" + else: + # normal token ID + unit_schema += self.tokenizer.decode([item], skip_special_tokens=False) + + # generate part + if unit_idx < len(generate_units): + unit_schema += self.tokenizer.decode(generate_units[unit_idx], skip_special_tokens=False) + + unit_schemas.append(unit_schema) + + return unit_schemas + + def _convert_results_to_tts_input(self, results): + """convert LLM hidden states to TTS input""" + if len(results) == 0: + audio_bos = self.model.tts.emb_text( + torch.tensor( + [self.model.tts.audio_bos_token_id], + device=self.model.tts.emb_text.weight.device, + dtype=torch.long, + ) + ) + return audio_bos.unsqueeze(0) + + llm_tokens = [] + llm_hidden = [] + for hidden in results: + llm_tokens.append(hidden[0]) + llm_hidden.append(hidden[1].squeeze(0)) + + llm_tokens_tensor = torch.Tensor(llm_tokens).to(self.device, dtype=torch.long) + llm_embeds = self.model.tts.emb_text(llm_tokens_tensor) + + llm_hidden_tensor = torch.cat(llm_hidden, dim=0) + llm_hidden_tensor = self.model.tts.projector_semantic(llm_hidden_tensor) + llm_hidden_tensor = torch.nn.functional.normalize(llm_hidden_tensor, p=2, dim=-1) + + tts_embeds = llm_embeds + llm_hidden_tensor + + audio_bos = self.model.tts.emb_text( + torch.tensor( + [self.model.tts.audio_bos_token_id], + device=self.model.tts.emb_text.weight.device, + dtype=torch.long, + ) + ) + + tts_embeds = torch.cat([tts_embeds, audio_bos], dim=0) + return tts_embeds.unsqueeze(0) + + def _generate_waveform_from_tokens( + self, + new_tokens: torch.Tensor, + prompt_wav_path: Optional[str], + is_last_chunk: bool = False, + force_flush: bool = False, + ) -> Optional[np.ndarray]: + if not self.token2wav_initialized: + logger.warning("token2wav_initialized is uninitialized") + return None + + CHUNK_SIZE = 25 + + token_ids = torch.reshape(new_tokens, (-1,)).tolist() + self.token2wav_buffer += token_ids + + has_chunk_eos = any(tid in self.chunk_terminator_token_ids for tid in token_ids) + + pcm_bytes_list = [] + + # process enough tokens + # if there is chunk_eos, try to flush more content + if has_chunk_eos or force_flush: + # when there is chunk_eos, try to flush more content + while len(self.token2wav_buffer) >= self.pre_lookahead + 5: # at least keep some lookahead + chunk_to_process = min(CHUNK_SIZE + self.pre_lookahead, len(self.token2wav_buffer)) + pcm_bytes = self.model.tts.audio_tokenizer.stream( + self.token2wav_buffer[:chunk_to_process], + prompt_wav=prompt_wav_path, + ) + pcm_bytes_list.append(pcm_bytes) + self.token2wav_buffer = self.token2wav_buffer[min(CHUNK_SIZE, chunk_to_process - self.pre_lookahead) :] + else: + while len(self.token2wav_buffer) >= CHUNK_SIZE + self.pre_lookahead: + pcm_bytes = self.model.tts.audio_tokenizer.stream( + self.token2wav_buffer[: CHUNK_SIZE + self.pre_lookahead], + prompt_wav=prompt_wav_path, + ) + pcm_bytes_list.append(pcm_bytes) + self.token2wav_buffer = self.token2wav_buffer[CHUNK_SIZE:] + + # if is the last chunk, flush remaining tokens + if is_last_chunk and len(self.token2wav_buffer) > 0: + pcm_bytes = self.model.tts.audio_tokenizer.stream( + self.token2wav_buffer, + prompt_wav=prompt_wav_path, + last_chunk=True, + ) + pcm_bytes_list.append(pcm_bytes) + self.token2wav_buffer = [] + + if not pcm_bytes_list: + return None + + # merge PCM and convert to numpy array (24kHz, int16 -> float32) + all_pcm = b"".join(pcm_bytes_list) + if len(all_pcm) == 0: + return None + + pcm_np = np.frombuffer(all_pcm, dtype=" np.ndarray: + """generate silence waveform (24kHz)""" + sample_rate = 24000 + num_samples = int(duration_sec * sample_rate) + return np.zeros(num_samples, dtype=np.float32) + + def get_generated_text(self) -> str: + return self.tokenizer.decode(self.res_ids) + + def get_current_time(self) -> int: + return self.audio_chunk_idx + + def as_simplex(self, reset_session: bool = True, reset_token2wav_cache: bool = False) -> "MiniCPMO": + """Convert this MiniCPMODuplex instance back to MiniCPMO for simplex mode. + + Args: + reset_session: If True, reset streaming session state (KV cache, etc.). + Recommended when switching from duplex to simplex mode. + + Returns the underlying MiniCPMO model instance without reloading. + """ + if reset_session: + self.model.reset_session(reset_token2wav_cache=reset_token2wav_cache) + return self.model + + +def get_2d_sincos_pos_embed(embed_dim, image_size): + """ + image_size: image_size or (image_height, image_width) + return: + pos_embed: [image_height, image_width, embed_dim] + """ + if isinstance(image_size, int): + grid_h_size, grid_w_size = image_size, image_size + else: + grid_h_size, grid_w_size = image_size[0], image_size[1] + + grid_h = np.arange(grid_h_size, dtype=np.float32) + grid_w = np.arange(grid_w_size, dtype=np.float32) + grid = np.meshgrid(grid_w, grid_h) # here w goes first + grid = np.stack(grid, axis=0) + + pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) + return pos_embed + + +def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): + assert embed_dim % 2 == 0 + + # use half of dimensions to encode grid_h + emb_h = get_1d_sincos_pos_embed_from_grid_new(embed_dim // 2, grid[0]) # (H, W, D/2) + emb_w = get_1d_sincos_pos_embed_from_grid_new(embed_dim // 2, grid[1]) # (H, W, D/2) + + emb = np.concatenate([emb_h, emb_w], axis=-1) # (H, W, D) + return emb + + +def get_1d_sincos_pos_embed_from_grid_new(embed_dim, pos): + """ + embed_dim: output dimension for each position + pos: a list of positions to be encoded: size (H, W) + out: (H, W, D) + """ + assert embed_dim % 2 == 0 + omega = np.arange(embed_dim // 2, dtype=np.float32) + omega /= embed_dim / 2.0 + omega = 1.0 / 10000**omega # (D/2,) + + out = np.einsum("hw,d->hwd", pos, omega) # (H, W, D/2), outer product + + emb_sin = np.sin(out) # (H, W, D/2) + emb_cos = np.cos(out) # (H, W, D/2) + + emb = np.concatenate([emb_sin, emb_cos], axis=-1) # (H, W, D) + return emb + + +class Resampler(nn.Module): + """ + A 2D perceiver-resampler network with one cross attention layers by + given learnable queries and 2d sincos pos_emb + Outputs: + A tensor with the shape of (batch_size, num_queries, embed_dim) + """ + + def __init__( + self, + num_queries, + embed_dim, + num_heads, + kv_dim=None, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + adaptive=False, + max_size=(70, 70), + ): + super().__init__() + self.num_queries = num_queries + self.embed_dim = embed_dim + self.num_heads = num_heads + self.adaptive = adaptive + self.max_size = max_size + + self.query = nn.Parameter(torch.zeros(self.num_queries, embed_dim)) + + if kv_dim is not None and kv_dim != embed_dim: + self.kv_proj = nn.Linear(kv_dim, embed_dim, bias=False) + else: + self.kv_proj = nn.Identity() + + self.attn = nn.MultiheadAttention(embed_dim, num_heads) + self.ln_q = norm_layer(embed_dim) + self.ln_kv = norm_layer(embed_dim) + + self.ln_post = norm_layer(embed_dim) + self.proj = nn.Parameter((embed_dim**-0.5) * torch.randn(embed_dim, embed_dim)) + + self._set_2d_pos_cache(self.max_size) + + def _set_2d_pos_cache(self, max_size, device="cpu"): + if is_deepspeed_zero3_enabled(): + device = "cuda" + pos_embed = torch.from_numpy(get_2d_sincos_pos_embed(self.embed_dim, max_size)).float().to(device) + self.register_buffer("pos_embed", pos_embed, persistent=False) + + def _adjust_pos_cache(self, tgt_sizes, device): + max_h = torch.max(tgt_sizes[:, 0]) + max_w = torch.max(tgt_sizes[:, 1]) + if max_h > self.max_size[0] or max_w > self.max_size[1]: + self.max_size = [max(max_h, self.max_size[0]), max(max_w, self.max_size[1])] + self._set_2d_pos_cache(self.max_size, device) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def forward(self, x, tgt_sizes=None): + assert x.shape[0] == tgt_sizes.shape[0] + bs = x.shape[0] + + device = x.device + dtype = x.dtype + + patch_len = tgt_sizes[:, 0] * tgt_sizes[:, 1] + + self._adjust_pos_cache(tgt_sizes, device=device) + + max_patch_len = torch.max(patch_len) + key_padding_mask = torch.zeros((bs, max_patch_len), dtype=torch.bool, device=device) + + pos_embed = [] + for i in range(bs): + tgt_h, tgt_w = tgt_sizes[i] + pos_embed.append(self.pos_embed[:tgt_h, :tgt_w, :].reshape((tgt_h * tgt_w, -1)).to(dtype)) # patches * D + key_padding_mask[i, patch_len[i] :] = True + + pos_embed = torch.nn.utils.rnn.pad_sequence(pos_embed, batch_first=True, padding_value=0.0).permute( + 1, 0, 2 + ) # BLD => L * B * D + + x = self.kv_proj(x) # B * L * D + x = self.ln_kv(x).permute(1, 0, 2) # L * B * D + + q = self.ln_q(self.query) # Q * D + + out = self.attn( + self._repeat(q, bs), # Q * B * D + x + pos_embed, # L * B * D + L * B * D + x, + key_padding_mask=key_padding_mask, + )[0] + # out: Q * B * D + x = out.permute(1, 0, 2) # B * Q * D + + x = self.ln_post(x) + x = x @ self.proj + return x + + def _repeat(self, query, N: int): + return query.unsqueeze(1).repeat(1, N, 1) + + +class MiniCPMWhisperEncoderLayer(nn.Module): + def __init__(self, config: WhisperConfig, layer_idx: int = None): + super().__init__() + self.embed_dim = config.d_model + try: + # compatible old transformers + from transformers.models.whisper.modeling_whisper import WHISPER_ATTENTION_CLASSES + + self.self_attn = WHISPER_ATTENTION_CLASSES[config._attn_implementation]( + embed_dim=self.embed_dim, + num_heads=config.encoder_attention_heads, + dropout=config.attention_dropout, + config=config, + layer_idx=layer_idx, + ) + except: + from transformers.models.whisper.modeling_whisper import WhisperAttention + + self.self_attn = WhisperAttention( + embed_dim=self.embed_dim, + num_heads=config.encoder_attention_heads, + dropout=config.attention_dropout, + config=config, + layer_idx=layer_idx, + ) + + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) + self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + layer_head_mask: torch.Tensor, + output_attentions: bool = False, + past_key_values: Optional[EncoderDecoderCache] = None, + use_cache: Optional[bool] = False, + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + hidden_states, attn_weights, past_key_values = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + layer_head_mask=layer_head_mask, + output_attentions=output_attentions, + past_key_value=past_key_values, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.final_layer_norm(hidden_states) + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + if hidden_states.dtype == torch.float16 and ( + torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any() + ): + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + if use_cache: + outputs += (past_key_values,) + + return outputs + + +# Copied from from transformers.models.whisper.modeling_whisper.WhisperEncoder and add use_cache for streaming inference +class MiniCPMWhisperEncoder(WhisperEncoder): + + def __init__(self, config: WhisperConfig): + super().__init__(config) + self.layers = nn.ModuleList( + [MiniCPMWhisperEncoderLayer(config, layer_idx=i) for i in range(config.encoder_layers)] + ) + + def forward( + self, + input_features, + attention_mask=None, + head_mask=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + past_key_values: Optional[EncoderDecoderCache] = None, + use_cache: Optional[bool] = None, + use_extra_context: Optional[bool] = False, + prefix_extra_frames: Optional[int] = 1, + suffix_extra_frames: Optional[int] = 1, + cnn_min_length: Optional[int] = None, + ): + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # Ignore copy + input_features = input_features.to(dtype=self.conv1.weight.dtype, device=self.conv1.weight.device) + + # Optional: pad short input to minimum length for CNN computation consistency + original_length = input_features.shape[2] + padded_for_cnn = False + if cnn_min_length is not None and original_length < cnn_min_length: + padded_features = torch.zeros( + input_features.shape[0], + input_features.shape[1], + cnn_min_length, + dtype=input_features.dtype, + device=input_features.device, + ) + padded_features[:, :, :original_length] = input_features + input_features = padded_features + padded_for_cnn = True + + conv1_output = self.conv1(input_features) + inputs_embeds = nn.functional.gelu(conv1_output) + conv2_output = self.conv2(inputs_embeds) + inputs_embeds = nn.functional.gelu(conv2_output) + # If padding was done before, now need to remove the effect of padding + if padded_for_cnn: + # Conv1: stride=1, output length=input length + # Conv2: stride=2, output length=(input length+1)//2 + actual_cnn_output_length = (original_length + 1) // 2 + inputs_embeds = inputs_embeds[:, :, :actual_cnn_output_length] + + # If extra context is used, CNN operations need to remove redundant frames + # conv2 stride=2, so the redundant frames in the input will be halved (upward rounding) + if use_extra_context: + # Input has prefix_extra_frames prefix frames and suffix_extra_frames suffix frames + # conv2 stride=2, output length = ceil(input length / 2) + # For 2 redundant frames, the output is 1 frame (ceil(2/2) = 1) + prefix_to_remove = (prefix_extra_frames + 1) // 2 if prefix_extra_frames > 0 else 0 + suffix_to_remove = (suffix_extra_frames + 1) // 2 if suffix_extra_frames > 0 else 0 + + # Remove redundant frames before and after (batch, channels, time) + if prefix_to_remove > 0: + inputs_embeds = inputs_embeds[:, :, prefix_to_remove:] + if 0 < suffix_to_remove < inputs_embeds.shape[2]: + inputs_embeds = inputs_embeds[:, :, :-suffix_to_remove] + + inputs_embeds = inputs_embeds.permute(0, 2, 1) + + embed_pos = self.embed_positions.weight + past_key_values_length = 0 + if use_cache: + if past_key_values is None: + past_key_values = EncoderDecoderCache(DynamicCache(), DynamicCache()) + elif isinstance(past_key_values, list): + past_key_values = EncoderDecoderCache(DynamicCache.from_legacy_cache(past_key_values), DynamicCache()) + elif isinstance(past_key_values, DynamicCache): + past_key_values = EncoderDecoderCache(past_key_values, DynamicCache()) + else: + pass + past_key_values_length = past_key_values.self_attention_cache.get_usable_length(inputs_embeds.shape[1]) + if inputs_embeds.shape[1] + past_key_values_length > embed_pos.shape[0]: + logger.warning("seems the audio is longer than 30s. repeating the last part of the audio") + embed_pos_front = embed_pos[past_key_values_length:, :] + embed_pos = torch.cat( + ( + embed_pos_front, + torch.repeat_interleave( + embed_pos[-1, :].unsqueeze(0), + inputs_embeds.shape[1] - embed_pos.shape[0] + past_key_values_length, + dim=0, + ), + ) + ) + else: + embed_pos = embed_pos[past_key_values_length : inputs_embeds.shape[1] + past_key_values_length, :] + else: + embed_pos = embed_pos[: inputs_embeds.shape[1], :] + + hidden_states = inputs_embeds + embed_pos + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + encoder_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + + # check if head_mask has a correct number of layers specified if desired + if head_mask is not None: + assert head_mask.size()[0] == ( + len(self.layers) + ), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}." + + for idx, encoder_layer in enumerate(self.layers): + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + to_drop = False + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: # skip the layer + to_drop = True + + # Ignore copy + if to_drop: + layer_outputs = (None, None) + else: + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + encoder_layer.__call__, + hidden_states, + attention_mask, + (head_mask[idx] if head_mask is not None else None), + output_attentions, + past_key_values, + use_cache, + ) + else: + layer_outputs = encoder_layer( + hidden_states, + attention_mask, + layer_head_mask=(head_mask[idx] if head_mask is not None else None), + output_attentions=output_attentions, + past_key_values=past_key_values, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_encoder_cache = layer_outputs[2 if output_attentions else 1] + else: + next_encoder_cache = None + + if output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + + hidden_states = self.layer_norm(hidden_states) + + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + + if not return_dict: + result = tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) + return result + result = BaseModelOutputWithPast( + last_hidden_state=hidden_states, + hidden_states=encoder_states, + attentions=all_attentions, + past_key_values=next_encoder_cache, + ) + + return result + + +class MultiModalProjector(nn.Module): + def __init__(self, in_dim, out_dim): + super().__init__() + self.linear1 = nn.Linear(in_features=in_dim, out_features=out_dim, bias=True) + self.relu = nn.ReLU() + self.linear2 = nn.Linear(in_features=out_dim, out_features=out_dim, bias=True) + + def forward(self, audio_features): + hidden_states = self.relu(self.linear1(audio_features)) + hidden_states = self.linear2(hidden_states) + return hidden_states + + +class MiniCPMMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.in_dim = config.llm_hidden_size + self.out_dim = config.hidden_size + self.intermediate_size = config.llm_intermediate_size + self.gate_proj = nn.Linear(self.in_dim, self.intermediate_size, bias=True) + self.up_proj = nn.Linear(self.in_dim, self.intermediate_size, bias=True) + self.down_proj = nn.Linear(self.intermediate_size, self.out_dim, bias=True) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + + return down_proj + + +@dataclass +class MiniCPMTTSGenerationOutput(ModelOutput): + """ + Output class for MiniCPMTTS generation. + + Args: + new_ids (torch.LongTensor): Newly generated audio code sequence, shape (batch_size, sequence_length, num_vq). + audio_input_ids (torch.LongTensor): Updated input IDs including condition and generated audio codes, shape (batch_size, full_sequence_length, num_vq). + past_key_values (Tuple[Tuple[torch.FloatTensor]]): Tuple containing pre-computed keys and values used for attention mechanism. Each element has shape (batch_size, num_heads, sequence_length, embed_size_per_head). + finished (bool): Boolean indicating whether generation is complete. + """ + + new_ids: torch.LongTensor = None + audio_input_ids: torch.LongTensor = None + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None + past_input_ids: Optional[torch.LongTensor] = None + finished: bool = None + + +def make_streaming_chunk_mask_inference( + tts_text_scope: List[int], + tts_text_mask: torch.Tensor, + streaming_audio_chunk_size: int = 50, + dtype: torch.dtype = torch.bfloat16, + device: torch.device = torch.device("cuda"), + max_sequence_length: int = 4096, +): + """ + Example: + Input sequence: + [t1, t2, t3, t4, t5, [Ptts], a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, ...] + Output 4D causal mask: + ------- text positions ------- + [0] <- here is [Stts] + [0, 0] <- here is [spk_emb] * N + [0, 0, 0] + [0, 0, 0, 0] + [0, 0, 0, 0, 0] + ------- audio positions -------- + [0, 0, -inf, -inf, -inf, 0] <- here is [Ptts], [Ptts]'s last hidden state should predict the first audio token + v- here is [Ptts] + [0, 0, -inf, -inf, -inf, 0, 0] + [0, 0, -inf, -inf, -inf, 0, 0, 0] + [0, 0, -inf, -inf, -inf, 0, 0, 0, 0] + [0, 0, -inf, -inf, -inf, 0, 0, 0, 0, 0] + [0, 0, -inf, -inf, -inf, 0, 0, 0, 0, 0, 0] # end of first 1s audio chunk + [0, 0, 0 , -inf, -inf, 0, 0, 0, 0, 0, 0, 0] + [0, 0, 0 , -inf, -inf, 0, 0, 0, 0, 0, 0, 0, 0] + [0, 0, 0 , -inf, -inf, 0, 0, 0, 0, 0, 0, 0, 0, 0] + [0, 0, 0 , -inf, -inf, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + [0, 0, 0 , -inf, -inf, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + """ + + # Create a complete attention mask for input embeds [batch_size, seq_len], without considering audio mask as audio is always at the end + + assert tts_text_mask.dtype == torch.int8 + + padding_mask = torch.ones(max_sequence_length, dtype=torch.int8, device=device) + padding_mask[tts_text_scope[0] : tts_text_scope[1]] = tts_text_mask + + # Initialize a standard upper triangular causal mask + min_dtype = torch.finfo(dtype).min + + causal_mask = torch.full( + (max_sequence_length, max_sequence_length), + fill_value=min_dtype, + dtype=dtype, + device=device, + ) + if max_sequence_length != 1: + causal_mask = torch.triu(causal_mask, diagonal=1) + else: + raise ValueError("max_sequence_length of tts could not be 1.") + + # For each data sample + audio_token_start = tts_text_scope[1] + audio_duration = max_sequence_length - tts_text_scope[1] + + # Record which text chunk the current audio chunk can see up to + text_pivot = 0 + num_valid_text_tokens = torch.sum(tts_text_mask).item() - 1 # [Ptts] excluded + # How many audio chunks are in total, the num of buckets should be smaller as possible + + num_text_tokens_per_audio_chunk = 10 + + # For each chunk of audio + for chunk_idx in range(math.ceil(audio_duration / streaming_audio_chunk_size)): + audio_chunk_start = audio_token_start + chunk_idx * streaming_audio_chunk_size + audio_chunk_end = audio_token_start + (chunk_idx + 1) * streaming_audio_chunk_size + # New text seen by this new audio chunk + new_text_this_chunk = num_text_tokens_per_audio_chunk + # The right bound of visible text tokens + text_pivot = min(new_text_this_chunk + text_pivot, num_valid_text_tokens) + # Mask all text chunks after the visible ones + # -> [text_pivot, len(tts_text_scope)-1] excluding [Ptts] + causal_mask[ + audio_chunk_start - 1 : audio_chunk_end - 1, + # tts_text_scope[0] + text_pivot: tts_text_scope[1], + tts_text_scope[0] + text_pivot : tts_text_scope[1] - 1, + ] = min_dtype + + # Mask the padding parts in tts_text_masks (no position will attend to it) + causal_mask[:, padding_mask == 0] = min_dtype + + # Add extra dimensions, [batch_size, seq_len, seq_len] -> [batch_size, 1, seq_len, seq_len] + causal_mask = causal_mask.unsqueeze(0).unsqueeze(0) + + return causal_mask + + +class MiniCPMTTS(PreTrainedModel): + config_class = MiniCPMTTSConfig + + def __init__(self, config: MiniCPMTTSConfig, audio_tokenizer: None): + super().__init__(config) + + self.use_llm_hidden_state = config.use_llm_hidden_state + + self.use_text = config.use_text + self.streaming = config.streaming + self.streaming_text_chunk_min = config.streaming_text_chunk_min + self.streaming_text_chunk_max = config.streaming_text_chunk_max + self.streaming_audio_chunk_size = config.streaming_audio_chunk_size + self.streaming_text_reserved_len = config.streaming_text_reserved_len + # streaming tts + self.streaming_text_chunk_size = config.streaming_text_chunk_max + self.audio_bos_token_id = config.audio_bos_token_id + self.num_mel_bins = config.num_mel_bins + self.num_vq = config.num_vq + self.num_audio_tokens = config.num_audio_tokens + + self.top_p = config.top_p + self.top_k = config.top_k + self.repetition_penalty = config.repetition_penalty + + self.interleaved = config.interleaved + self.attention_type = config.attention_type + self.recomputed_chunks = config.recomputed_chunks + + # Two different window size concepts: + # 1. chunk_window_size: number of chunks for sliding_recompute mode (default 2) + # 2. token_window_size: number of tokens for sliding_window mode (default 300) + self.chunk_window_size = config.window_size # chunk-level window for sliding_recompute + self.token_window_size = ( + config.streaming_sliding_window_audio_window_size + ) # token-level window for sliding_window + + # Legacy aliases (for backward compatibility with existing code) + self.window_size = self.chunk_window_size # used in generate_streaming for sliding_recompute + self.sliding_window_size = self.token_window_size # used in TTSStreamingGenerator for sliding_window + + if self.attention_type == "sliding_recompute" and self.chunk_window_size <= self.recomputed_chunks: + raise ValueError( + f"sliding_recompute requires chunk_window_size > recomputed_chunks, " + f"but got chunk_window_size={self.chunk_window_size} and recomputed_chunks={self.recomputed_chunks}" + ) + + if config.backbone_model == "llama": + model_config = LlamaConfig( + hidden_size=config.hidden_size, + intermediate_size=config.intermediate_size, + num_attention_heads=config.num_attention_heads, + num_hidden_layers=config.num_hidden_layers, + num_key_value_heads=config.num_key_value_heads, + max_position_embeddings=config.max_position_embeddings, + attn_implementation=config.attn_implementation, + ) + + self.emb_text = nn.Embedding(config.num_text_tokens, config.hidden_size) + + model = LlamaModel(model_config) + self.model = model + else: + raise ValueError(f"Unsupported backbone model: {config.backbone_model}") + + self.projector_spk = self.create_projector(config) + self.projector_semantic = self.create_projector(config) + + self.audio_tokenizer = audio_tokenizer + + self.emb_code = nn.ModuleList( + [nn.Embedding(config.num_audio_tokens, config.hidden_size) for _ in range(config.num_vq)] + ) + + self.head_code = nn.ModuleList( + [ + weight_norm( + nn.Linear(config.hidden_size, config.num_audio_tokens, bias=False), + name="weight", + ) + for _ in range(config.num_vq) + ] + ) + + self.condition_type = config.condition_type + + return + + @staticmethod + def create_projector(config): + if config.projector_type == "mlp": + return MultiModalProjector(config.llm_dim, config.hidden_size) + elif config.projector_type == "minicpm": + return MiniCPMMLP(config) + elif config.projector_type == "default": + return nn.Linear(config.llm_dim, config.hidden_size, bias=False) + else: + raise ValueError(f"Unsupported projector type: {config.projector_type}") + + # non-streaming + @torch.inference_mode() + def generate( + self, + inputs_embeds: torch.Tensor, + eos_token: Union[int, torch.Tensor], + force_no_stop=False, + min_new_token=50, + max_new_token=2048, + show_tqdm=True, + streaming=False, + text_lengths=None, + sampling_params: TTSSamplingParams = TTSSamplingParams(), + ): + temperature = torch.tensor( + [sampling_params.temperature] * self.config.num_vq, + dtype=torch.float, + device=self.device, + ) + temperature = (temperature.unsqueeze(0).expand(inputs_embeds.shape[0], -1).contiguous().view(-1, 1)).to( + inputs_embeds.device + ) + + logits_warpers, logits_processors = gen_logits( + num_code=self.config.num_audio_tokens, + repetition_penalty=sampling_params.repetition_penalty, + top_p=sampling_params.top_p, + top_k=sampling_params.top_k, + ) + + # We only support batch size `1` for now + assert inputs_embeds.shape[0] == 1 + eos_token = eos_token.to(inputs_embeds.device) + finish = torch.zeros(inputs_embeds.shape[0], device=inputs_embeds.device).bool() + + condition_length = inputs_embeds.shape[1] + pbar: Optional[tqdm] = None + if show_tqdm: + pbar = tqdm( + total=max_new_token, + desc="code", + bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt}(max) [{elapsed}, {rate_fmt}{postfix}]", + ) + + if streaming: + raise NotImplementedError("this kind of streaming is not supported yet") + + new_tokens = torch.zeros( + inputs_embeds.shape[0], + max_new_token, + self.num_vq, + device=inputs_embeds.device, + dtype=torch.long, + ) + + past_key_values = None + + for t in range(max_new_token): + audio_bos = False + # If this is the first audio token, the case is special + if t == 0: + audio_bos = True + inputs_embeds = inputs_embeds + position_ids = torch.tensor( + list(range(0, condition_length)), + dtype=torch.long, + device=self.device, + ).unsqueeze(0) + + if streaming: + raise NotImplementedError("this kind of streaming is not supported yet") + else: + causal_mask_4d = None + + else: + code_emb = [] + for q in range(self.num_vq): + x = self.emb_code[q](new_tokens[:, t - 1 : t, q]) + code_emb.append(x) + + inputs_embeds = torch.stack(code_emb, 3).sum(3) + + position_ids = torch.tensor([condition_length + t - 1], dtype=torch.long, device=self.device).unsqueeze( + 0 + ) + + if streaming: + raise NotImplementedError("this kind of streaming is not supported yet") + else: + causal_mask_4d = None + + if self.config.backbone_model == "llama": + outputs: BaseModelOutputWithPast = self.model( + position_ids=position_ids, + cache_position=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + attention_mask=causal_mask_4d, + use_cache=True, + output_attentions=False, + # return_dict=True, # Add this to ensure returns dict with past_key_values + ) + else: + raise ValueError(f"Unsupported backbone model: {self.config.backbone_model}") + + del position_ids + del inputs_embeds + + hidden_states = outputs.last_hidden_state + past_key_values = outputs.past_key_values + + with P.cached(): + logits = torch.empty( + hidden_states.size(0), + hidden_states.size(1), + self.num_audio_tokens, + self.num_vq, + dtype=torch.float, + device=self.device, + ) + for num_vq_iter in range(self.num_vq): + x: torch.Tensor = self.head_code[num_vq_iter](hidden_states) + logits[..., num_vq_iter] = x + del x + + del hidden_states + + logits = logits[:, -1].float() + + logits = logits.permute(0, 2, 1) + logits = logits.reshape(-1, logits.size(2)) + + logits /= temperature + + if not audio_bos: + input_ids_sliced = new_tokens[:, 0:t].permute(0, 2, 1) # get previous t new tokens + logits_token = input_ids_sliced.reshape( + input_ids_sliced.size(0) * input_ids_sliced.size(1), + -1, + ).to(self.device) + + del input_ids_sliced + + for logitsProcessors in logits_processors: + logits = logitsProcessors(logits_token, logits) + + for logitsWarpers in logits_warpers: + logits = logitsWarpers(logits_token, logits) + + del logits_token + + if t < min_new_token: + logits[:, eos_token] = -torch.inf + + if force_no_stop: + logits[:, eos_token] = -torch.inf + + scores = F.softmax(logits, dim=-1) + + del logits + + idx_next = torch.multinomial(scores, num_samples=1).to(finish.device) + + del scores + + idx_next = idx_next.view(-1, self.num_vq) + + finish_or = idx_next.eq(eos_token).any(1) + finish.logical_or_(finish_or) + + del finish_or + new_tokens[:, t] = idx_next + + if t == 0 and finish.any(): + break + + del idx_next + + if finish.all(): + break + + if pbar is not None: + pbar.update(1) + + if pbar is not None: + pbar.close() + + if not finish.all(): + logger.warning(f"incomplete result. hit max_new_token: {max_new_token}") + + genrated_input_ids = new_tokens[:, 0:t, :] + + return MiniCPMTTSGenerationOutput( + new_ids=genrated_input_ids, + audio_input_ids=None, # for update purpose + past_key_values=None, # for update purpose + past_input_ids=None, # for update purpose + finished=finish.all(), + ) + + # fake streaming + @torch.inference_mode() + def generate_mock_legacy_streaming( + self, + inputs_embeds: torch.Tensor, + eos_token: Union[int, torch.Tensor], + force_no_stop=False, + min_new_token=50, + max_new_token=2048, + show_tqdm=True, + streaming=False, + text_lengths=None, + sampling_params: TTSSamplingParams = TTSSamplingParams(), + valid_text_length=None, + ): + assert valid_text_length is not None, "valid_text_length should be not None" + + tts_text_scope = [0, inputs_embeds.shape[1]] + tts_text_mask = torch.zeros(inputs_embeds.shape[1], dtype=torch.int8, device=inputs_embeds.device) + tts_text_mask[0:valid_text_length] = 1 + tts_text_mask[-1] = 1 # [Ptts] + + streaming_mask_4d_full = make_streaming_chunk_mask_inference( + tts_text_scope=tts_text_scope, + tts_text_mask=tts_text_mask, + dtype=torch.bfloat16, + device=self.device, + streaming_audio_chunk_size=50, + max_sequence_length=4096, + ) + + temperature = torch.tensor([0.1, 0.3, 0.1, 0.3], dtype=torch.float, device=self.device) + temperature = (temperature.unsqueeze(0).expand(inputs_embeds.shape[0], -1).contiguous().view(-1, 1)).to( + inputs_embeds.device + ) + + logits_warpers, logits_processors = gen_logits( + num_code=self.config.num_audio_tokens, + repetition_penalty=sampling_params.repetition_penalty, + top_p=sampling_params.top_p, + top_k=sampling_params.top_k, + ) + + # We only support batch size `1` for now + assert inputs_embeds.shape[0] == 1 + eos_token = eos_token.to(inputs_embeds.device) + finish = torch.zeros(inputs_embeds.shape[0], device=inputs_embeds.device).bool() + + condition_length = inputs_embeds.shape[1] + pbar: Optional[tqdm] = None + if show_tqdm: + pbar = tqdm( + total=max_new_token, + desc="code", + bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt}(max) [{elapsed}, {rate_fmt}{postfix}]", + ) + + new_tokens = torch.zeros( + inputs_embeds.shape[0], + max_new_token, + self.num_vq, + device=inputs_embeds.device, + dtype=torch.long, + ) + + past_key_values = None + + for t in range(max_new_token): + audio_bos = False + if t == 0: + audio_bos = True + inputs_embeds = inputs_embeds + position_ids = torch.tensor( + list(range(0, condition_length)), + dtype=torch.long, + device=self.device, + ).unsqueeze(0) + + causal_mask_4d = streaming_mask_4d_full[:, :, :condition_length, :condition_length] + else: + code_emb = [] + for q in range(self.num_vq): + x = self.emb_code[q](new_tokens[:, t - 1 : t, q]) + code_emb.append(x) + + inputs_embeds = torch.stack(code_emb, 3).sum(3) + + position_ids = torch.tensor([condition_length + t - 1], dtype=torch.long, device=self.device).unsqueeze( + 0 + ) + + causal_mask_4d = streaming_mask_4d_full[ + :, + :, + condition_length + t : condition_length + t + 1, + : condition_length + t, + ] + + # get length of past_key_values + past_key_values_length = past_key_values[0][0].shape[2] + + assert causal_mask_4d.shape[-1] == (past_key_values_length + 1) + + if self.config.backbone_model == "llama": + outputs: BaseModelOutputWithPast = self.model( + position_ids=position_ids, + cache_position=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + attention_mask=causal_mask_4d, + use_cache=True, + output_attentions=False, + # return_dict=True, # Add this to ensure returns dict with past_key_values + ) + else: + raise ValueError(f"Unsupported backbone model: {self.config.backbone_model}") + + del position_ids + del inputs_embeds + + hidden_states = outputs.last_hidden_state + past_key_values = outputs.past_key_values + + with P.cached(): + logits = torch.empty( + hidden_states.size(0), + hidden_states.size(1), + self.num_audio_tokens, + self.num_vq, + dtype=torch.float, + device=self.device, + ) + for num_vq_iter in range(self.num_vq): + x: torch.Tensor = self.head_code[num_vq_iter](hidden_states) + logits[..., num_vq_iter] = x + del x + + del hidden_states + + logits = logits[:, -1].float() + logits = logits.permute(0, 2, 1) + logits = logits.reshape(-1, logits.size(2)) + logits /= temperature + + if not audio_bos: + input_ids_sliced = new_tokens[:, 0:t].permute(0, 2, 1) # get previous t new tokens + + logits_token = input_ids_sliced.reshape( + input_ids_sliced.size(0) * input_ids_sliced.size(1), + -1, + ).to(self.device) + + del input_ids_sliced + + for logitsProcessors in logits_processors: + logits = logitsProcessors(logits_token, logits) + + for logitsWarpers in logits_warpers: + logits = logitsWarpers(logits_token, logits) + + del logits_token + + if t < min_new_token: + logits[:, eos_token] = -torch.inf + + if force_no_stop: + logits[:, eos_token] = -torch.inf + + scores = F.softmax(logits, dim=-1) + + del logits + idx_next = torch.multinomial(scores, num_samples=1).to(finish.device) + + del scores + + idx_next = idx_next.view(-1, self.num_vq) + finish_or = idx_next.eq(eos_token).any(1) + finish.logical_or_(finish_or) + + del finish_or + new_tokens[:, t] = idx_next + + if t == 0 and finish.any(): + break + + del idx_next + + if finish.all(): + break + + if pbar is not None: + pbar.update(1) + + if pbar is not None: + pbar.close() + + if not finish.all(): + logger.warning(f"incomplete result. hit max_new_token: {max_new_token}") + + genrated_input_ids = new_tokens[:, 0:t, :] + + return MiniCPMTTSGenerationOutput( + new_ids=genrated_input_ids, + audio_input_ids=None, # for update purpose + past_key_values=None, # for update purpose + past_input_ids=None, # for update purpose + finished=finish.all(), + ) + + # non-streaming, interleave + @torch.inference_mode() + def generate_chunk( + self, + inputs_embeds: torch.Tensor, + temperature: torch.Tensor, + repetition_penalty: float, + eos_token: Union[int, torch.Tensor], + force_no_stop=False, + max_new_token=500, + min_new_tokens=0, + past_key_values=None, + logits_processors=None, + text_start_pos=None, + ): + """For inputs_embeds, it should be like [bs=1, seq_len, hidden_dim], its content is like: + |Text BOS|Spk embeds|Text-Hidden states Interleave (if applicable)|Audio BOS| + where the last position is the audio BOS token. + So, the first iteration in generation directly forward the model with inputs_embeds, and + the last hidden states of the last position (Audio BOS) will be decoded to get the first audio token. + """ + logits_warpers, logits_processors = gen_logits( + num_code=self.config.num_audio_tokens, repetition_penalty=repetition_penalty + ) + + # We only support batch size `1` for now + assert inputs_embeds.shape[0] == 1 + eos_token = eos_token.to(inputs_embeds.device) + finish = torch.zeros(inputs_embeds.shape[0], device=inputs_embeds.device).bool() + + temperature = (temperature.unsqueeze(0).expand(inputs_embeds.shape[0], -1).contiguous().view(-1, 1)).to( + inputs_embeds.device + ) + + condition_length = inputs_embeds.shape[1] + + new_tokens = torch.zeros( + inputs_embeds.shape[0], + max_new_token, + self.num_vq, + device=inputs_embeds.device, + dtype=torch.long, + ) + + for t in range(max_new_token): + audio_bos = False + + # If this is the first audio token, the case is special + if t == 0: + audio_bos = True + inputs_embeds_ = inputs_embeds + position_ids = torch.tensor( + list(range(text_start_pos, text_start_pos + condition_length)), + dtype=torch.long, + device=self.device, + ).unsqueeze(0) + else: + # Generate the following audio tokens, it is applicable to all other cases, including second and the following calling of `generate` + inputs_embeds_ = self.emb_code[0](new_tokens[:, t - 1 : t, 0]) + + position_ids = torch.tensor( + [text_start_pos + condition_length + t - 1], # prefill the previous token + dtype=torch.long, + device=self.device, + ).unsqueeze(0) + + outputs: BaseModelOutputWithPast = self.model( + position_ids=position_ids, + # cache_position=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds_, + use_cache=True, + output_attentions=False, + # return_dict=True, # Add this to ensure returns dict with past_key_values + ) + + del position_ids + del inputs_embeds_ + + hidden_states = outputs.last_hidden_state + past_key_values = outputs.past_key_values + + with P.cached(): + logits = torch.empty( + hidden_states.size(0), + hidden_states.size(1), + self.num_audio_tokens, + self.num_vq, + dtype=torch.float, + device=self.device, + ) + for num_vq_iter in range(self.num_vq): + x: torch.Tensor = self.head_code[num_vq_iter](hidden_states) + logits[..., num_vq_iter] = x + del x + + del hidden_states + + logits = logits[:, -1].float() + logits = logits.permute(0, 2, 1) + logits = logits.reshape(-1, logits.size(2)) + + logits /= temperature + + if not audio_bos: + input_ids_sliced = new_tokens[:, 0:t].permute(0, 2, 1) # get previous t new tokens + + logits_token = input_ids_sliced.reshape( + input_ids_sliced.size(0) * input_ids_sliced.size(1), + -1, + ).to(self.device) + + del input_ids_sliced + + for logitsProcessors in logits_processors: + logits = logitsProcessors(logits_token, logits) + + del logits_token + + if force_no_stop or t < min_new_tokens: + logits[:, eos_token] = -torch.inf + + scores = F.softmax(logits, dim=-1) + del logits + + idx_next = torch.multinomial(scores, num_samples=1).to(finish.device) + del scores + + idx_next = idx_next.view(-1, self.num_vq) + + finish_or = idx_next.eq(eos_token).any(1) + finish.logical_or_(finish_or) + + del finish_or + new_tokens[:, t] = idx_next + + if t == 0 and finish.any(): + break + + del idx_next + + if finish.all(): + break + + # The latest generated token is not in the range returned this time. If it is an eos token, it is not returned. If it is a normal token, it is not returned. + genrated_input_ids = new_tokens[:, 0:t, :] + + return genrated_input_ids, past_key_values + + @torch.inference_mode() + def interleaved_generate( + self, + spk_embeds: torch.Tensor, + conditions: List[torch.Tensor], + temperature: torch.Tensor, + repetition_penalty: float, + eos_token: Union[int, torch.Tensor], + **kwargs, + ): + """ + For inputs_embeds, it should be like [bs=1, seq_len, hidden_dim], its content is like: + |Text BOS|Spk embeds|Text-Hidden states Interleave (if applicable)|Audio BOS| + where the last position is the audio BOS token. + So, the first iteration in generation directly forward the model with inputs_embeds, and the last hidden states of the last position (Audio BOS) will be decoded to get the first audio token. + """ + temperature = torch.tensor([temperature], dtype=torch.float, device=self.device) + + logits_warpers, logits_processors = gen_logits( + num_code=self.config.num_audio_tokens, + repetition_penalty=repetition_penalty, + ) + + eos_token = eos_token.to(conditions[0].device) + + num_chunks = len(conditions) + text_start_pos = 0 + last_window_size = 0 + past_key_values = None + + for idx in range(num_chunks): + condition = conditions[idx].to(conditions[0].device) + if self.attention_type == "sliding_recompute": + recomputed_conditions = [] + + if ( + idx >= self.window_size + and (idx - self.recomputed_chunks) % (self.window_size - self.recomputed_chunks) == 0 + ): + for i in range(self.recomputed_chunks): + recomputed_conditions.append(conditions[idx - self.recomputed_chunks + i]) + recomputed_conditions.append( + self.emb_code[0](generated_tokens[-self.recomputed_chunks + i][:, :, 0]) + ) + recomputed_conditions.append(condition) + condition = torch.cat(recomputed_conditions, dim=1) + + text_start_pos = 0 + new_tokens, old_kv = self.generate_chunk( + inputs_embeds=condition, + temperature=temperature, + repetition_penalty=repetition_penalty, + eos_token=eos_token, + force_no_stop=False, + max_new_token=500, + past_key_values=None, + logits_processors=logits_processors, + text_start_pos=text_start_pos, + ) + + else: + new_tokens, old_kv = self.generate_chunk( + inputs_embeds=condition, + temperature=temperature, + repetition_penalty=repetition_penalty, + eos_token=eos_token, + force_no_stop=False, + max_new_token=500, + past_key_values=past_key_values, + logits_processors=logits_processors, + text_start_pos=text_start_pos, + ) + else: + new_tokens, old_kv = self.generate_chunk( + inputs_embeds=condition, + temperature=temperature, + repetition_penalty=repetition_penalty, + eos_token=eos_token, + force_no_stop=False, + max_new_token=500, + past_key_values=past_key_values, + logits_processors=logits_processors, + text_start_pos=text_start_pos, + ) + + past_key_values = [] + if self.attention_type == "sliding_window" and idx >= 1: + for layer_idx in range(len(old_kv)): + past_key_values.append( + ( + old_kv[layer_idx][0][:, :, last_window_size:, :], + old_kv[layer_idx][1][:, :, last_window_size:, :], + ) + ) + else: + past_key_values = old_kv + + last_window_size = condition.shape[1] + new_tokens.shape[1] + text_start_pos += last_window_size + + if idx == 0: + generated_tokens = [new_tokens] + else: + generated_tokens.append(new_tokens) + + return MiniCPMTTSGenerationOutput(new_ids=torch.cat(generated_tokens, dim=1), finished=True) + + +class CustomRepetitionPenaltyLogitsProcessorRepeat: + def __init__(self, penalty: float, max_input_ids: int, past_window: int): + if not isinstance(penalty, float) or not (penalty > 0): + raise ValueError(f"`penalty` has to be a strictly positive float, but is {penalty}") + + self.penalty = penalty + self.max_input_ids = max_input_ids + self.past_window = past_window + + def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: + if input_ids.size(1) > self.past_window: + input_ids = input_ids.narrow(1, -self.past_window, self.past_window) + freq = F.one_hot(input_ids, scores.size(1)).sum(1) + if freq.size(0) > self.max_input_ids: + freq.narrow(0, self.max_input_ids, freq.size(0) - self.max_input_ids).zero_() + alpha = torch.pow(self.penalty, freq) + scores = scores.contiguous() + inp = scores.multiply(alpha) + oth = scores.divide(alpha) + con = scores < 0 + out = torch.where(con, inp, oth) + del inp, oth, scores, con, alpha + return out + + +def gen_logits(num_code: int, top_p=0.7, top_k=20, repetition_penalty=1.0): + logits_warpers = [] + + if top_p is not None: + logits_warpers.append(TopPLogitsWarper(top_p, min_tokens_to_keep=3)) + + if top_k is not None: + logits_warpers.append(TopKLogitsWarper(top_k, min_tokens_to_keep=3)) + + logits_processors = [] + if repetition_penalty is not None and repetition_penalty != 1: + logits_processors.append(CustomRepetitionPenaltyLogitsProcessorRepeat(repetition_penalty, num_code, 16)) + + return logits_warpers, logits_processors + + +# Copy and modified from transformers.models.llama.modeling_llama.LlamaForCausalLM.prepare_inputs_for_generation +def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + cache_position=None, + position_ids=None, + use_cache=True, + **kwargs, +): + if past_key_values is not None: + if isinstance(past_key_values, Cache): + cache_length = past_key_values.get_seq_length() + past_length = past_key_values.seen_tokens + else: + cache_length = past_length = past_key_values[0][0].shape[2] + + # Keep only the unprocessed tokens: + # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where + # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as + # input) + if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: + input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] + # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard + # input_ids based on the past_length. + elif past_length < input_ids.shape[1]: + input_ids = input_ids[:, past_length:] + # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. + + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -input_ids.shape[1] :] + + # This clo≠clo≠clone call is needed to avoid recapturing cuda graphs with →rch.comπ≤→rch.comπ≤torch.compile's mode=reduce−overheadmode=reduce-overheadmode="reduce-overhead, as otherwise the input positionidspositionidsposition_ids would have various stride during the decoding. Here, simply using .contiguous().contiguous().contiguous() is not sufficient as in the batch size = 1 case, positionidspositionidsposition_ids is already contiguous but with varying stride which retriggers a capture. + position_ids = position_ids.clone(memory_format=torch.contiguous_format) + + # if ∈putsembeds∈putsembedsinputs_embeds are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and cache_position[0] == 0: + model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None} + else: + # The clone here is for the same reason as for positionidspositionidsposition_ids. + model_inputs = {"input_ids": input_ids.clone(memory_format=torch.contiguous_format), "inputs_embeds": None} + + if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2: + if model_inputs["inputs_embeds"] is not None: + batch_size, sequence_length, _ = model_inputs["inputs_embeds"].shape + device = model_inputs["inputs_embeds"].device + else: + batch_size, sequence_length = model_inputs["input_ids"].shape + device = model_inputs["input_ids"].device + + dtype = self.lm_head.weight.dtype + min_dtype = torch.finfo(dtype).min + + from transformers.models.paligemma.modeling_paligemma import ( + _prepare_4d_causal_attention_mask_with_cache_position, + ) + + attention_mask = _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask, + sequence_length=sequence_length, + target_length=past_key_values.get_max_length(), + dtype=dtype, + device=device, + min_dtype=min_dtype, + cache_position=cache_position, + batch_size=batch_size, + ) + + model_inputs.update( + { + "position_ids": position_ids, + # "cache_position": cache_position, + "past_key_values": past_key_values, + "use_cache": use_cache, + "attention_mask": attention_mask, + } + ) + + return model_inputs diff --git a/modeling_navit_siglip.py b/modeling_navit_siglip.py new file mode 100644 index 0000000000000000000000000000000000000000..58b991de066008f5dbf873e80e23e15fd5ddde2d --- /dev/null +++ b/modeling_navit_siglip.py @@ -0,0 +1,981 @@ +# coding=utf-8 +# Copyright 2024 Google AI and The HuggingFace Team. 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. +"""PyTorch Siglip model.""" +# Copied from HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit and add tgt_sizes + + +import math +import os +import warnings +from dataclasses import dataclass +from typing import Optional +from typing import Tuple +from typing import Union + +import numpy as np +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import nn +from torch.nn.init import _calculate_fan_in_and_fan_out +from transformers.activations import ACT2FN +from transformers.configuration_utils import PretrainedConfig +from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask +from transformers.modeling_outputs import BaseModelOutput +from transformers.modeling_outputs import BaseModelOutputWithPooling +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import add_start_docstrings +from transformers.utils import add_start_docstrings_to_model_forward +from transformers.utils import is_flash_attn_2_available +from transformers.utils import logging +from transformers.utils import ModelOutput +from transformers.utils import replace_return_docstrings + +logger = logging.get_logger(__name__) + + +class SiglipVisionConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`SiglipVisionModel`]. It is used to instantiate a + Siglip vision encoder according to the specified arguments, defining the model architecture. Instantiating a + configuration with the defaults will yield a similar configuration to that of the vision encoder of the Siglip + [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) architecture. + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + Args: + hidden_size (`int`, *optional*, defaults to 768): + Dimensionality of the encoder layers and the pooler layer. + intermediate_size (`int`, *optional*, defaults to 3072): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + num_hidden_layers (`int`, *optional*, defaults to 12): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 12): + Number of attention heads for each attention layer in the Transformer encoder. + num_channels (`int`, *optional*, defaults to 3): + Number of channels in the input images. + image_size (`int`, *optional*, defaults to 224): + The size (resolution) of each image. + patch_size (`int`, *optional*, defaults to 16): + The size (resolution) of each patch. + hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported. + layer_norm_eps (`float`, *optional*, defaults to 1e-06): + The epsilon used by the layer normalization layers. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + Example: + ```python + >>> from transformers import SiglipVisionConfig, SiglipVisionModel + >>> # Initializing a SiglipVisionConfig with google/siglip-base-patch16-224 style configuration + >>> configuration = SiglipVisionConfig() + >>> # Initializing a SiglipVisionModel (with random weights) from the google/siglip-base-patch16-224 style configuration + >>> model = SiglipVisionModel(configuration) + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "siglip_vision_model" + + def __init__( + self, + hidden_size=768, + intermediate_size=3072, + num_hidden_layers=12, + num_attention_heads=12, + num_channels=3, + image_size=224, + patch_size=16, + hidden_act="gelu_pytorch_tanh", + layer_norm_eps=1e-6, + attention_dropout=0.0, + **kwargs, + ): + super().__init__(**kwargs) + + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.num_channels = num_channels + self.patch_size = patch_size + self.image_size = image_size + self.attention_dropout = attention_dropout + self.layer_norm_eps = layer_norm_eps + self.hidden_act = hidden_act + + @classmethod + def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": + cls._set_token_in_kwargs(kwargs) + + config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) + + # get the vision config dict if we are loading from SiglipConfig + if config_dict.get("model_type") == "siglip": + config_dict = config_dict["vision_config"] + + if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: + logger.warning( + f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " + f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." + ) + + return cls.from_dict(config_dict, **kwargs) + + +_CHECKPOINT_FOR_DOC = "google/siglip-base-patch16-224" + +SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "google/siglip-base-patch16-224", + # See all SigLIP models at https://huggingface.co/models?filter=siglip +] + +if is_flash_attn_2_available(): + from flash_attn import flash_attn_func + from flash_attn import flash_attn_varlen_func + from flash_attn.bert_padding import index_first_axis # noqa + from flash_attn.bert_padding import pad_input + from flash_attn.bert_padding import unpad_input + + +# Copied from transformers.models.llama.modeling_llama._get_unpad_data +def _get_unpad_data(attention_mask): + seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)) + return ( + indices, + cu_seqlens, + max_seqlen_in_batch, + ) + + +def _trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0 + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn( + "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2, + ) + + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + if tensor.dtype in [torch.float16, torch.bfloat16]: + # The `erfinv_` op is not (yet?) defined in float16+cpu, bfloat16+gpu + og_dtype = tensor.dtype + tensor = tensor.to(torch.float32) + tensor.erfinv_() + tensor = tensor.to(og_dtype) + else: + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.0)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + if tensor.dtype == torch.float16: + # The `clamp_` op is not (yet?) defined in float16+cpu + tensor = tensor.to(torch.float32) + tensor.clamp_(min=a, max=b) + tensor = tensor.to(torch.float16) + else: + tensor.clamp_(min=a, max=b) + + +def trunc_normal_tf_( + tensor: torch.Tensor, + mean: float = 0.0, + std: float = 1.0, + a: float = -2.0, + b: float = 2.0, +) -> torch.Tensor: + """Fills the input Tensor with values drawn from a truncated + normal distribution. The values are effectively drawn from the + normal distribution :math:`\\mathcal{N}(\text{mean}, \text{std}^2)` + with values outside :math:`[a, b]` redrawn until they are within + the bounds. The method used for generating the random values works + best when :math:`a \\leq \text{mean} \\leq b`. + NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the + bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0 + and the result is subsquently scaled and shifted by the mean and std args. + Args: + tensor: an n-dimensional `torch.Tensor` + mean: the mean of the normal distribution + std: the standard deviation of the normal distribution + a: the minimum cutoff value + b: the maximum cutoff value + """ + with torch.no_grad(): + _trunc_normal_(tensor, 0, 1.0, a, b) + tensor.mul_(std).add_(mean) + + +def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"): + fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) + if mode == "fan_in": + denom = fan_in + elif mode == "fan_out": + denom = fan_out + elif mode == "fan_avg": + denom = (fan_in + fan_out) / 2 + + variance = scale / denom + + if distribution == "truncated_normal": + # constant is stddev of standard normal truncated to (-2, 2) + trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978) + elif distribution == "normal": + with torch.no_grad(): + tensor.normal_(std=math.sqrt(variance)) + elif distribution == "uniform": + bound = math.sqrt(3 * variance) + with torch.no_grad(): + tensor.uniform_(-bound, bound) + else: + raise ValueError(f"invalid distribution {distribution}") + + +def lecun_normal_(tensor): + variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal") + + +def default_flax_embed_init(tensor): + variance_scaling_(tensor, mode="fan_in", distribution="normal") + + +@dataclass +# Copied from transformers.models.clip.modeling_clip.CLIPVisionModelOutput with CLIP->Siglip +class SiglipVisionModelOutput(ModelOutput): + """ + Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states. + Args: + image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): + The image embeddings obtained by applying the projection layer to the pooler_output. + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the model. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + """ + + image_embeds: Optional[torch.FloatTensor] = None + last_hidden_state: torch.FloatTensor = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + + +class SiglipVisionEmbeddings(nn.Module): + def __init__(self, config: SiglipVisionConfig): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.image_size = config.image_size + self.patch_size = config.patch_size + + self.patch_embedding = nn.Conv2d( + in_channels=config.num_channels, + out_channels=self.embed_dim, + kernel_size=self.patch_size, + stride=self.patch_size, + padding="valid", + ) + + self.num_patches_per_side = self.image_size // self.patch_size + self.num_patches = self.num_patches_per_side**2 + self.num_positions = self.num_patches + self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) + + def forward( + self, + pixel_values: torch.FloatTensor, + patch_attention_mask: torch.BoolTensor, + tgt_sizes: Optional[torch.IntTensor] = None, + ) -> torch.Tensor: + batch_size = pixel_values.size(0) + + patch_embeds = self.patch_embedding(pixel_values) + embeddings = patch_embeds.flatten(2).transpose(1, 2) + + max_im_h, max_im_w = pixel_values.size(2), pixel_values.size(3) + max_nb_patches_h, max_nb_patches_w = ( + max_im_h // self.patch_size, + max_im_w // self.patch_size, + ) + boundaries = torch.arange(1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side) + position_ids = torch.full( + size=( + batch_size, + max_nb_patches_h * max_nb_patches_w, + ), + fill_value=0, + ) + + for batch_idx, p_attn_mask in enumerate(patch_attention_mask): + if tgt_sizes is not None: + nb_patches_h = tgt_sizes[batch_idx][0] + nb_patches_w = tgt_sizes[batch_idx][1] + else: + nb_patches_h = p_attn_mask[:, 0].sum() + nb_patches_w = p_attn_mask[0].sum() + + fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / nb_patches_h) + fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / nb_patches_w) + + bucket_coords_h = torch.bucketize(fractional_coords_h, boundaries, right=True) + bucket_coords_w = torch.bucketize(fractional_coords_w, boundaries, right=True) + + pos_ids = (bucket_coords_h[:, None] * self.num_patches_per_side + bucket_coords_w).flatten() + position_ids[batch_idx][p_attn_mask.view(-1).cpu()] = pos_ids + + position_ids = position_ids.to(self.position_embedding.weight.device) + + embeddings = embeddings + self.position_embedding(position_ids) + return embeddings + + +class SiglipAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + # Copied from transformers.models.clip.modeling_clip.CLIPAttention.__init__ + def __init__(self, config): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.embed_dim // self.num_heads + if self.head_dim * self.num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {self.num_heads})." + ) + self.scale = self.head_dim**-0.5 + self.dropout = config.attention_dropout + + self.k_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.v_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.q_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.out_proj = nn.Linear(self.embed_dim, self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + """Input shape: Batch x Time x Channel""" + + batch_size, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2) + + k_v_seq_len = key_states.shape[-2] + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scale + + if attn_weights.size() != (batch_size, self.num_heads, q_len, k_v_seq_len): + raise ValueError( + f"Attention weights should be of size {(batch_size, self.num_heads, q_len, k_v_seq_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (batch_size, 1, q_len, k_v_seq_len): + raise ValueError( + f"Attention mask should be of size {(batch_size, 1, q_len, k_v_seq_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights + attention_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (batch_size, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(batch_size, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(batch_size, q_len, self.embed_dim) + + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights + + +class SiglipFlashAttention2(SiglipAttention): + """ + Llama flash attention module. This module inherits from `LlamaAttention` as the weights of the module stays + untouched. The only required change would be on the forward pass where it needs to correctly call the public API of + flash attention and deal with padding tokens in case the input contains any of them. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.is_causal = False # Hack to make sure we don't use a causal mask + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + output_attentions = False + + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + # Flash attention requires the input to have the shape + # batch_size x seq_length x head_dim x hidden_dim + # therefore we just need to keep the original shape + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + # cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + # query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + # if past_key_value is not None: + # cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models + # key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache + # to be able to avoid many of these transpose/reshape/view. + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + dropout_rate = self.dropout if self.training else 0.0 + + # In PEFT, usually we cast the layer norms in float32 for training stability reasons + # therefore the input hidden states gets silently casted in float32. Hence, we need + # cast them back in the correct dtype just to be sure everything works as expected. + # This might slowdown training & inference so it is recommended to not cast the LayerNorms + # in fp32. (LlamaRMSNorm handles it correctly) + + input_dtype = query_states.dtype + if input_dtype == torch.float32: + if torch.is_autocast_enabled(): + target_dtype = torch.get_autocast_gpu_dtype() + # Handle the case where the model is quantized + elif hasattr(self.config, "_pre_quantization_dtype"): + target_dtype = self.config._pre_quantization_dtype + else: + target_dtype = self.q_proj.weight.dtype + + logger.warning_once( + "The input hidden states seems to be silently casted in float32, this might be related to the fact" + " you have upcasted embedding or layer norm layers in float32. We will cast back the input in" + f" {target_dtype}." + ) + + query_states = query_states.to(target_dtype) + key_states = key_states.to(target_dtype) + value_states = value_states.to(target_dtype) + + attn_output = self._flash_attention_forward( + query_states, + key_states, + value_states, + attention_mask, + q_len, + dropout=dropout_rate, + ) + + attn_output = attn_output.reshape(bsz, q_len, self.embed_dim).contiguous() + attn_output = self.out_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights + + def _flash_attention_forward( + self, + query_states, + key_states, + value_states, + attention_mask, + query_length, + dropout=0.0, + softmax_scale=None, + ): + """ + Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token + first unpad the input, then computes the attention scores and pad the final attention scores. + Args: + query_states (`torch.Tensor`): + Input query states to be passed to Flash Attention API + key_states (`torch.Tensor`): + Input key states to be passed to Flash Attention API + value_states (`torch.Tensor`): + Input value states to be passed to Flash Attention API + attention_mask (`torch.Tensor`): + The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the + position of padding tokens and 1 for the position of non-padding tokens. + dropout (`int`, *optional*): + Attention dropout + softmax_scale (`float`, *optional*): + The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) + """ + + # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__. + causal = self.is_causal and query_length != 1 + + # Contains at least one padding token in the sequence + if attention_mask is not None: + batch_size = query_states.shape[0] + ( + query_states, + key_states, + value_states, + indices_q, + cu_seq_lens, + max_seq_lens, + ) = self._upad_input(query_states, key_states, value_states, attention_mask, query_length) + + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + + attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) + else: + attn_output = flash_attn_func( + query_states, + key_states, + value_states, + dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + + return attn_output + + def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) + batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape + + key_layer = index_first_axis( + key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), + indices_k, + ) + value_layer = index_first_axis( + value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), + indices_k, + ) + if query_length == kv_seq_len: + query_layer = index_first_axis( + query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), + indices_k, + ) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif query_length == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange( + batch_size + 1, dtype=torch.int32, device=query_layer.device + ) # There is a memcpy here, that is very bad. + indices_q = cu_seqlens_q[:-1] + query_layer = query_layer.squeeze(1) + else: + # The -q_len: slice assumes left padding. + attention_mask = attention_mask[:, -query_length:] + query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) + + return ( + query_layer, + key_layer, + value_layer, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + +# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Siglip +class SiglipMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.activation_fn = ACT2FN[config.hidden_act] + self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) + self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->Siglip +class SiglipEncoderLayer(nn.Module): + def __init__(self, config: SiglipVisionConfig): + super().__init__() + self.embed_dim = config.hidden_size + self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" + self.self_attn = SiglipAttention(config) if not self._use_flash_attention_2 else SiglipFlashAttention2(config) + self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + self.mlp = SiglipMLP(config) + self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + output_attentions: Optional[bool] = False, + ) -> Tuple[torch.FloatTensor]: + """ + Args: + hidden_states (`torch.FloatTensor`): + Input to the layer of shape `(batch, seq_len, embed_dim)`. + attention_mask (`torch.FloatTensor`): + Attention mask of shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values. + output_attentions (`bool`, *optional*, defaults to `False`): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + residual = hidden_states + + hidden_states = self.layer_norm1(hidden_states) + hidden_states, attn_weights = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + ) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + +class SiglipPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = SiglipVisionConfig + base_model_prefix = "siglip" + supports_gradient_checkpointing = True + + def _init_weights(self, module): + """Initialize the weights""" + + if isinstance(module, SiglipVisionEmbeddings): + width = self.config.hidden_size + nn.init.normal_(module.position_embedding.weight, std=1 / np.sqrt(width)) + elif isinstance(module, nn.Embedding): + default_flax_embed_init(module.weight) + elif isinstance(module, SiglipAttention): + nn.init.normal_(module.q_proj.weight) + nn.init.normal_(module.k_proj.weight) + nn.init.normal_(module.v_proj.weight) + nn.init.normal_(module.out_proj.weight) + nn.init.zeros_(module.q_proj.bias) + nn.init.zeros_(module.k_proj.bias) + nn.init.zeros_(module.v_proj.bias) + nn.init.zeros_(module.out_proj.bias) + elif isinstance(module, SiglipMLP): + nn.init.normal_(module.fc1.weight) + nn.init.normal_(module.fc2.weight) + nn.init.normal_(module.fc1.bias, std=1e-6) + nn.init.normal_(module.fc2.bias, std=1e-6) + elif isinstance(module, (nn.Linear, nn.Conv2d)): + lecun_normal_(module.weight) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + +SIGLIP_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + Parameters: + config ([`SiglipVisionConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +SIGLIP_VISION_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using + [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +# Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->Siglip +class SiglipEncoder(nn.Module): + """ + Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a + [`SiglipEncoderLayer`]. + Args: + config: SiglipConfig + """ + + def __init__(self, config: SiglipVisionConfig): + super().__init__() + self.config = config + self.layers = nn.ModuleList([SiglipEncoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + # Ignore copy + def forward( + self, + inputs_embeds, + attention_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutput]: + r""" + Args: + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated vectors + than the model's internal embedding lookup matrix. + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + [What are attention masks?](../glossary#attention-mask) + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + encoder_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + + hidden_states = inputs_embeds + for encoder_layer in self.layers: + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + encoder_layer.__call__, + hidden_states, + attention_mask, + output_attentions, + ) + else: + layer_outputs = encoder_layer( + hidden_states, + attention_mask, + output_attentions=output_attentions, + ) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=encoder_states, + attentions=all_attentions, + ) + + +@add_start_docstrings( + """The vision model from SigLIP without any head or projection on top.""", + SIGLIP_START_DOCSTRING, +) +class SiglipVisionTransformer(SiglipPreTrainedModel): + config_class = SiglipVisionConfig + main_input_name = "pixel_values" + _supports_flash_attn_2 = True + _no_split_modules = [] + + def __init__(self, config: SiglipVisionConfig): + super().__init__(config) + self.config = config + embed_dim = config.hidden_size + + self.embeddings = SiglipVisionEmbeddings(config) + self.encoder = SiglipEncoder(config) + self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) + self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> nn.Module: + return self.embeddings.patch_embedding + + @add_start_docstrings_to_model_forward(SIGLIP_VISION_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipVisionConfig) + def forward( + self, + pixel_values, + patch_attention_mask: Optional[torch.BoolTensor] = None, + tgt_sizes: Optional[torch.IntTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPooling]: + r""" + Returns: + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + batch_size = pixel_values.size(0) + if patch_attention_mask is None: + patch_attention_mask = torch.ones( + size=( + batch_size, + pixel_values.size(2) // self.config.patch_size, + pixel_values.size(3) // self.config.patch_size, + ), + dtype=torch.bool, + device=pixel_values.device, + ) + + hidden_states = self.embeddings( + pixel_values=pixel_values, + patch_attention_mask=patch_attention_mask, + tgt_sizes=tgt_sizes, + ) + + patch_attention_mask = patch_attention_mask.view(batch_size, -1) + # The call to `_upad_input` in `_flash_attention_forward` is expensive + # So when the `patch_attention_mask` is full of 1s (i.e. attending to the whole sequence), + # avoiding passing the attention_mask, which is equivalent to attending to the full sequence + if not torch.any(~patch_attention_mask): + attention_mask = None + else: + attention_mask = ( + _prepare_4d_attention_mask(patch_attention_mask, hidden_states.dtype) + if not self._use_flash_attention_2 + else patch_attention_mask + ) + + encoder_outputs = self.encoder( + inputs_embeds=hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + last_hidden_state = encoder_outputs[0] + last_hidden_state = self.post_layernorm(last_hidden_state) + + if not return_dict: + return (last_hidden_state, None) + encoder_outputs[1:] + + return BaseModelOutputWithPooling( + last_hidden_state=last_hidden_state, + pooler_output=None, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) diff --git a/preprocessor_config.json b/preprocessor_config.json new file mode 100644 index 0000000000000000000000000000000000000000..c56a32b8f1ad1afb2fb17dce37e4e64e99ef7e6e --- /dev/null +++ b/preprocessor_config.json @@ -0,0 +1,35 @@ +{ + "image_processor_type": "MiniCPMVImageProcessor", + "feature_extractor_type": "MiniCPMAAudioProcessor", + "auto_map": { + "AutoProcessor": "processing_minicpmo.MiniCPMOProcessor", + "AutoImageProcessor": "processing_minicpmo.MiniCPMVImageProcessor", + "AutoFeatureExtractor": "processing_minicpmo.MiniCPMAAudioProcessor" + }, + "processor_class": "MiniCPMOProcessor", + "max_slice_nums": 9, + "scale_resolution": 448, + "patch_size": 14, + "use_image_id": true, + "image_feature_size": 64, + "im_start": "", + "im_end": "", + "slice_start": "", + "slice_end": "", + "unk": "", + "im_id_start": "", + "im_id_end": "", + "slice_mode": true, + "audio_pool_step": 5, + "norm_mean": [ + 0.5, + 0.5, + 0.5 + ], + "norm_std": [ + 0.5, + 0.5, + 0.5 + ], + "version": 4.5 +} \ No newline at end of file diff --git a/processing_minicpmo.py b/processing_minicpmo.py new file mode 100644 index 0000000000000000000000000000000000000000..e05383bf2541109661ffd7ce09f3d69ec51f4746 --- /dev/null +++ b/processing_minicpmo.py @@ -0,0 +1,1665 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# Copyright 2026 The OpenBMB Team. 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. + +import copy +import math +import re +from typing import Any +from typing import Dict +from typing import List +from typing import Optional +from typing import Tuple +from typing import Union + +import numpy as np +import torch +from PIL import Image +from transformers import AutoImageProcessor +from transformers.audio_utils import spectrogram +from transformers.audio_utils import window_function +from transformers.image_processing_utils import BaseImageProcessor +from transformers.image_processing_utils import BatchFeature +from transformers.image_transforms import to_channel_dimension_format +from transformers.image_utils import ChannelDimension +from transformers.image_utils import ImageInput +from transformers.image_utils import infer_channel_dimension_format +from transformers.image_utils import is_torch_tensor +from transformers.image_utils import to_numpy_array +from transformers.image_utils import valid_images +from transformers.models.whisper.feature_extraction_whisper import WhisperFeatureExtractor +from transformers.processing_utils import ProcessorMixin +from transformers.tokenization_utils_base import PreTokenizedInput +from transformers.tokenization_utils_base import TextInput +from transformers.utils import is_torch_device +from transformers.utils import is_torch_dtype +from transformers.utils import requires_backends +from transformers.utils import TensorType + + +def recursive_converter(converter, value): + if isinstance(value, list): + new_value = [] + for v in value: + new_value += [recursive_converter(converter, v)] + return new_value + else: + return converter(value) + + +class MiniCPMOBatchFeature(BatchFeature): + """Extend from BatchFeature for supporting various image size""" + + def __init__(self, data: Optional[Dict[str, Any]] = None, tensor_type: Union[None, str, TensorType] = None): + super().__init__(data) + self.convert_to_tensors(tensor_type=tensor_type) + + def convert_to_tensors(self, tensor_type: Optional[Union[str, TensorType]] = None): + if tensor_type is None: + return self + + is_tensor, as_tensor = self._get_is_as_tensor_fns(tensor_type) + + def converter(value): + try: + if not is_tensor(value): + tensor = as_tensor(value) + return tensor + except: # noqa E722 + if key == "overflowing_values": + raise ValueError("Unable to create tensor returning overflowing values of different lengths. ") + raise ValueError( + "Unable to create tensor, you should probably activate padding " + "with 'padding=True' to have batched tensors with the same length." + ) + + for key, value in self.items(): + self[key] = recursive_converter(converter, value) + return self + + def to(self, *args, **kwargs) -> "MiniCPMOBatchFeature": + requires_backends(self, ["torch"]) + import torch + + def cast_tensor(v): + if not torch.is_tensor(v): + return v + + if torch.is_floating_point(v): + return v.to(*args, **kwargs) + elif device is not None: + return v.to(device=device) + else: + return v + + new_data = {} + device = kwargs.get("device") + if device is None and len(args) > 0: + arg = args[0] + if is_torch_dtype(arg): + pass + elif isinstance(arg, str) or is_torch_device(arg) or isinstance(arg, int): + device = arg + else: + raise ValueError(f"Attempting to cast a BatchFeature to type {str(arg)}. This is not supported.") + + # We cast only floating point tensors to avoid issues with tokenizers casting `LongTensor` to `FloatTensor` + for k, v in self.items(): + new_data[k] = recursive_converter(cast_tensor, v) + self.data = new_data + return self + + +class MiniCPMVImageProcessor(BaseImageProcessor): + model_input_names = ["pixel_values"] + + def __init__(self, max_slice_nums=9, scale_resolution=448, patch_size=14, **kwargs): + super().__init__(**kwargs) + self.max_slice_nums = max_slice_nums + self.scale_resolution = scale_resolution + self.patch_size = patch_size + self.use_image_id = kwargs.pop("use_image_id", False) + self.image_feature_size = kwargs.pop("image_feature_size", 64) + self.im_start_token = kwargs.pop("im_start", "") + self.im_end_token = kwargs.pop("im_end", "") + self.slice_start_token = kwargs.pop("slice_start", "") + self.slice_end_token = kwargs.pop("slice_end", "") + self.unk_token = kwargs.pop("unk", "") + self.im_id_start = kwargs.pop("im_id_start", "") + self.im_id_end = kwargs.pop("im_id_end", "") + self.slice_mode = kwargs.pop("slice_mode", True) + + self.mean = np.array(kwargs.pop("norm_mean", [0.5, 0.5, 0.5])) + self.std = np.array(kwargs.pop("norm_std", [0.5, 0.5, 0.5])) + self.version = kwargs.pop("version", 2.0) + + @staticmethod + def ensure_divide(length, patch_size): + return max(round(length / patch_size) * patch_size, patch_size) + + def find_best_resize(self, original_size, scale_resolution, patch_size, allow_upscale=False): + width, height = original_size + if (width * height > scale_resolution * scale_resolution) or allow_upscale: + r = width / height + height = int(scale_resolution / math.sqrt(r)) + width = int(height * r) + best_width = self.ensure_divide(width, patch_size) + best_height = self.ensure_divide(height, patch_size) + return best_width, best_height + + def get_refine_size(self, original_size, grid, scale_resolution, patch_size, allow_upscale=False): + width, height = original_size + grid_x, grid_y = grid + + refine_width = self.ensure_divide(width, grid_x) + refine_height = self.ensure_divide(height, grid_y) + + grid_width = refine_width / grid_x + grid_height = refine_height / grid_y + + best_grid_size = self.find_best_resize( + (grid_width, grid_height), scale_resolution, patch_size, allow_upscale=allow_upscale + ) + refine_size = (best_grid_size[0] * grid_x, best_grid_size[1] * grid_y) + return refine_size + + @staticmethod + def split_to_patches(image, grid): + patches = [] + width, height = image.size + grid_x = int(width / grid[0]) + grid_y = int(height / grid[1]) + for i in range(0, height, grid_y): + images = [] + for j in range(0, width, grid_x): + box = (j, i, j + grid_x, i + grid_y) + patch = image.crop(box) + images.append(patch) + patches.append(images) + return patches + + def slice_image(self, image, max_slice_nums=9, scale_resolution=448, patch_size=14, never_split=False): + original_size = image.size + source_image = None + best_grid = self.get_sliced_grid(original_size, max_slice_nums, never_split) + patches = [] + + if best_grid is None: + # dont need to slice, upsample + best_size = self.find_best_resize(original_size, scale_resolution, patch_size, allow_upscale=True) + source_image = image.resize(best_size, resample=Image.Resampling.BICUBIC) + else: + # source image, down-sampling and ensure divided by patch_size + best_resize = self.find_best_resize(original_size, scale_resolution, patch_size) + source_image = image.copy().resize(best_resize, resample=Image.Resampling.BICUBIC) + refine_size = self.get_refine_size( + original_size, best_grid, scale_resolution, patch_size, allow_upscale=True + ) + refine_image = image.resize(refine_size, resample=Image.Resampling.BICUBIC) + patches = self.split_to_patches(refine_image, best_grid) + + return source_image, patches, best_grid + + def get_grid_placeholder(self, grid): + if grid is None: + return "" + slice_image_placeholder = ( + self.slice_start_token + self.unk_token * self.image_feature_size + self.slice_end_token + ) + + cols = grid[0] + rows = grid[1] + slices = [] + for i in range(rows): + lines = [] + for j in range(cols): + lines.append(slice_image_placeholder) + slices.append("".join(lines)) + + slice_placeholder = "\n".join(slices) + return slice_placeholder + + def get_image_id_placeholder(self, idx=0): + return f"{self.im_id_start}{idx}{self.im_id_end}" + + def get_sliced_images(self, image, max_slice_nums=None): + slice_images = [] + + if not self.slice_mode: + return [image] + + max_slice_nums = self.max_slice_nums if max_slice_nums is None else int(max_slice_nums) + assert max_slice_nums > 0 + source_image, patches, sliced_grid = self.slice_image( + image, max_slice_nums, self.scale_resolution, self.patch_size # default: 9 # default: 448 # default: 14 + ) + + slice_images.append(source_image) + if len(patches) > 0: + for i in range(len(patches)): + for j in range(len(patches[0])): + slice_images.append(patches[i][j]) + return slice_images + + def get_sliced_grid(self, image_size, max_slice_nums, nerver_split=False): + original_width, original_height = image_size + log_ratio = math.log(original_width / original_height) + ratio = original_width * original_height / (self.scale_resolution * self.scale_resolution) + multiple = min(math.ceil(ratio), max_slice_nums) + if multiple <= 1 or nerver_split: + return None + candidate_split_grids_nums = [] + for i in [multiple - 1, multiple, multiple + 1]: + if i == 1 or i > max_slice_nums: + continue + candidate_split_grids_nums.append(i) + + candidate_grids = [] + for split_grids_nums in candidate_split_grids_nums: + m = 1 + while m <= split_grids_nums: + if split_grids_nums % m == 0: + candidate_grids.append([m, split_grids_nums // m]) + m += 1 + + best_grid = [1, 1] + min_error = float("inf") + for grid in candidate_grids: + error = abs(log_ratio - math.log(grid[0] / grid[1])) + if error < min_error: + best_grid = grid + min_error = error + + return best_grid + + def get_slice_image_placeholder(self, image_size, image_idx=0, max_slice_nums=None, use_image_id=None): + max_slice_nums = self.max_slice_nums if max_slice_nums is None else int(max_slice_nums) + assert max_slice_nums > 0 + grid = self.get_sliced_grid(image_size=image_size, max_slice_nums=max_slice_nums) + + image_placeholder = self.im_start_token + self.unk_token * self.image_feature_size + self.im_end_token + use_image_id = self.use_image_id if use_image_id is None else bool(use_image_id) + if use_image_id: + final_placeholder = self.get_image_id_placeholder(image_idx) + image_placeholder + else: + final_placeholder = image_placeholder + + if self.slice_mode: + final_placeholder = final_placeholder + self.get_grid_placeholder(grid=grid) + return final_placeholder + + @staticmethod + def to_pil_image(image, rescale=None) -> Image.Image: + """Converts `image` to a PIL Image. Optionally rescales it and puts the channel dimension back + as the last axis if needed. + + Args: + image (`Image.Image` or `numpy.ndarray` or `torch.Tensor`): + The image to convert to the PIL Image format. + rescale (`bool`, *optional*): + whether to apply the scaling factor (to make pixel values integers between 0 and 255). Will + default to `True` if the image type is a floating type, `False` otherwise. + """ + if isinstance(image, Image.Image): + return image + if is_torch_tensor(image): + image = image.numpy() + + if isinstance(image, np.ndarray): + if rescale is None: + # rescale default to the array being of floating type. + rescale = isinstance(image.flat[0], np.floating) + # If the channel as been moved to first dim, we put it back at the end. + if image.ndim == 3 and image.shape[0] in [1, 3]: + image = image.transpose(1, 2, 0) + if rescale: + image = image * 255 + image = image.astype(np.uint8) + return Image.fromarray(image) + return image + + def reshape_by_patch(self, image): + image = torch.from_numpy(image) + patch_size = self.patch_size + patches = torch.nn.functional.unfold(image, (patch_size, patch_size), stride=(patch_size, patch_size)) + + patches = patches.reshape(image.size(0), patch_size, patch_size, -1) + patches = patches.permute(0, 1, 3, 2).reshape(image.size(0), patch_size, -1) + return patches.numpy() + + def preprocess( + self, + images: Union[Image.Image, List[Image.Image], List[List[Image.Image]]], + do_pad: Optional[bool] = True, + max_slice_nums: int = None, + return_tensors: Optional[Union[str, TensorType]] = None, + **kwargs, + ) -> MiniCPMOBatchFeature: + if isinstance(images, Image.Image): + images_list = [[images]] + elif isinstance(images[0], Image.Image): + images_list = [images] + else: + images_list = images + + new_images_list = [] + image_sizes_list = [] + tgt_sizes_list = [] + + for _images in images_list: + if _images is None or len(_images) == 0: + new_images_list.append([]) + image_sizes_list.append([]) + tgt_sizes_list.append([]) + continue + if not valid_images(_images): + raise ValueError( + "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " + "torch.Tensor, tf.Tensor or jax.ndarray." + ) + + _images = [self.to_pil_image(image).convert("RGB") for image in _images] + input_data_format = infer_channel_dimension_format(np.array(_images[0])) + + new_images = [] + image_sizes = [image.size for image in _images] + tgt_sizes = [] + for image in _images: + image_patches = self.get_sliced_images(image, max_slice_nums) + image_patches = [to_numpy_array(image).astype(np.float32) / 255 for image in image_patches] + image_patches = [ + self.normalize(image=image, mean=self.mean, std=self.std, input_data_format=input_data_format) + for image in image_patches + ] + image_patches = [ + to_channel_dimension_format(image, ChannelDimension.FIRST, input_channel_dim=input_data_format) + for image in image_patches + ] + for slice_image in image_patches: + new_images.append(self.reshape_by_patch(slice_image)) + tgt_sizes.append( + np.array((slice_image.shape[1] // self.patch_size, slice_image.shape[2] // self.patch_size)) + ) + + if tgt_sizes: + tgt_sizes = np.vstack(tgt_sizes) + + new_images_list.append(new_images) + image_sizes_list.append(image_sizes) + tgt_sizes_list.append(tgt_sizes) + return MiniCPMOBatchFeature( + data={"pixel_values": new_images_list, "image_sizes": image_sizes_list, "tgt_sizes": tgt_sizes_list}, + tensor_type=return_tensors, + ) + + +AutoImageProcessor.register("MiniCPMVImageProcessor", MiniCPMVImageProcessor) + + +def chunk_audio(audio: np.ndarray, max_duration_seconds: int = 30, sample_rate: int = 16000) -> List[np.ndarray]: + """split long audio into chunks + + Args: + audio: + max_duration_seconds: + sample_rate: + + Returns: + chunks + """ + max_len = int(max_duration_seconds * sample_rate) + + if len(audio) <= max_len: + return [audio] + + chunks = [] + for i in range(0, len(audio), max_len): + chunk = audio[i : i + max_len] + chunks.append(chunk) + + return chunks + + +def process_audio_batch( + audios: Union[np.ndarray, List[np.ndarray], List[List[np.ndarray]]], + feature_extractor, + sampling_rate: int = 16000, + max_duration_seconds: int = 30, + return_attention_mask: bool = True, +) -> Tuple[torch.Tensor, List[torch.Tensor]]: + """extract audio mel features + + Args: + audios: + feature_extractor: WhisperFeatureExtractor + sampling_rate: + max_duration_seconds: + return_attention_mask: + + Returns: + (audio_features, audio_feature_lens) + audio_features: [batch_size, n_mels, max_frames] + audio_feature_lens: + """ + if isinstance(audios, np.ndarray): + audios_list = [[audios]] + elif len(audios) > 0 and isinstance(audios[0], np.ndarray): + audios_list = [audios] + else: + audios_list = audios + + audio_features_all = [] + audio_feature_lens_list = [] + + for batch_audios in audios_list: + batch_lens = [] + + for audio in batch_audios: + chunks = chunk_audio(audio, max_duration_seconds, sampling_rate) + + for chunk in chunks: + audio_input = feature_extractor( + chunk, + sampling_rate=sampling_rate, + return_tensors="pt", + padding="max_length", + return_attention_mask=return_attention_mask, + ) + + audio_feature = audio_input["input_features"] # [1, 80, frames] + + if return_attention_mask: + actual_len = audio_input["attention_mask"].sum(dim=1) # Tensor([frames]) + audio_feature = audio_feature[:, :, : actual_len[0]] + batch_lens.append(actual_len[0]) + else: + batch_lens.append(torch.tensor(audio_feature.shape[2])) + + audio_features_all.append(audio_feature.squeeze(0)) # [80, frames] + + if len(batch_lens) > 0: + audio_feature_lens_list.append(torch.hstack(batch_lens)) + else: + audio_feature_lens_list.append(torch.tensor([])) + + # pad to same length + if audio_features_all: + audio_features = torch.nn.utils.rnn.pad_sequence( + [feat.transpose(0, 1) for feat in audio_features_all], batch_first=True, padding_value=0.0 + ).transpose( + 1, 2 + ) # [batch, 80, max_frames] + else: + audio_features = torch.tensor([]) + + return audio_features, audio_feature_lens_list + + +def regroup_audio_features( + audio_features: torch.Tensor, audio_feature_lens: List[torch.Tensor], regroup_seconds: int, fps: int = 100 +) -> Tuple[torch.Tensor, List[torch.Tensor]]: + """regroup audio features to fixed duration + + Args: + audio_features: [batch, n_mels, frames] + audio_feature_lens: each batch's actual length + regroup_seconds: regroup duration (seconds) + fps: frames per second + + Returns: + (regrouped_features, regrouped_lens) + """ + # flatten to continuous frames sequence + all_lens = [] + for lens in audio_feature_lens: + if isinstance(lens, torch.Tensor): + all_lens.extend(lens.tolist()) + elif isinstance(lens, list): + all_lens.extend([int(x) for x in lens]) + + if len(all_lens) == 0: + return torch.tensor([]), [] + + # concatenate all valid features + flat_slices = [audio_features[i, :, :L] for i, L in enumerate(all_lens)] # [n_mels, L] + + if len(flat_slices) == 1: + full_feat = flat_slices[0] + else: + full_feat = torch.cat(flat_slices, dim=1) # [n_mels, total_frames] + + # split to fixed frames + frames_per_seg = int(regroup_seconds * fps) + segments = [] + + for start in range(0, full_feat.size(1), frames_per_seg): + seg = full_feat[:, start : start + frames_per_seg] + if seg.size(1) > 0: + segments.append(seg) + + if len(segments) == 0: + return torch.tensor([]), [] + + # pad and convert to batch + seg_lens = [s.size(1) for s in segments] + segs_transposed = [s.transpose(0, 1) for s in segments] + + padded = torch.nn.utils.rnn.pad_sequence(segs_transposed, batch_first=True, padding_value=0.0) # [N, max_T, n_mels] + + padded = padded.transpose(1, 2) # [N, n_mels, max_T] + lens_tensor = torch.tensor(seg_lens, dtype=torch.int32, device=padded.device) + + return padded, [lens_tensor] + + +class MiniCPMAAudioProcessor(WhisperFeatureExtractor): + """ + On top of WhisperFeatureExtractor: + - support dynamic_log_norm (original max-8dB, adjustable dynamic_range_db) + - or fixed log_floor_db (e.g. -10dB) + - this is because we need to do streaming scheme, in which we can't do dynamic setting + - this can be modified in the middle, through set_dynamic_log_norm + Two paths (torch / numpy) keep consistent clipping and scaling order: + log10 -> (dynamic/fixed lower limit clipping) -> (+4)/4 + """ + + def __init__( + self, + *args, + dynamic_log_norm: bool = True, + dynamic_range_db: float = 8.0, + log_floor_db: float = -10.0, + **kwargs, + ): + super().__init__(*args, **kwargs) + self.dynamic_log_norm = bool(dynamic_log_norm) + self.dynamic_range_db = float(dynamic_range_db) + self.log_floor_db = float(log_floor_db) + + def set_spac_log_norm( + self, + dynamic_range_db: Optional[float] = None, + log_floor_db: Optional[float] = None, + *, + inplace: bool = True, + ) -> "MiniCPMAAudioProcessor": + """Hot update dynamic/fixed lower limit strategy. + + Args: + enabled: True=use dynamic threshold (max - dynamic_range_db), False=use fixed lower limit log_floor_db. + None means keep unchanged. + dynamic_range_db: dynamic range (dB), only effective when enabled=True. None means keep unchanged. + log_floor_db: fixed log floor (dB, usually <= 0), only effective when enabled=False. None means keep unchanged. + inplace: True directly modify current instance; False return a shallow copy and modify on it. + + Returns: + self or new instance (when inplace=False). + """ + + target = self if inplace else copy.copy(self) + + if dynamic_range_db is not None: + val = float(dynamic_range_db) + if val < 0: + raise ValueError("dynamic_range_db must be >= 0.") + target.dynamic_log_norm = True # explicitly set the value to dynamic mode + target.dynamic_range_db = val + + if log_floor_db is not None: + val = float(log_floor_db) + # usually log10(mel) maximum is not more than ~0dB, floor should be <= 0; here do loose validation + if val > 0: + raise ValueError("log_floor_db should be <= 0 (log10 scale).") + target.dynamic_log_norm = False # explicitly set the value to fixed lower limit mode + target.log_floor_db = val + + return target + + def _np_extract_fbank_features(self, waveform_batch: np.ndarray, device: str) -> np.ndarray: + """NumPy version consistent with upstream, but replace max-8dB with configurable dynamic/fixed lower limit clipping.""" + if device != "cpu": + raise ValueError( + f"Got device `{device}` for feature extraction, but feature extraction on CUDA accelerator " + "devices requires torch. Set device='cpu' or install torch." + ) + + log_spec_batch: List[np.ndarray] = [] + for waveform in waveform_batch: + # generate log10 Mel + log_spec = spectrogram( + waveform, + window_function(self.n_fft, "hann"), + frame_length=self.n_fft, + hop_length=self.hop_length, + power=2.0, + dither=self.dither, + mel_filters=self.mel_filters, + log_mel="log10", + ) + # consistent with upstream: remove the last frame + log_spec = log_spec[:, :-1] + + # dynamic/fixed clipping + if self.dynamic_log_norm: + threshold = log_spec.max() - self.dynamic_range_db + log_spec = np.maximum(log_spec, threshold) + else: + log_spec = np.maximum(log_spec, self.log_floor_db) + + # consistent with Whisper linear scaling + log_spec = (log_spec + 4.0) / 4.0 + + log_spec_batch.append(log_spec) + + return np.array(log_spec_batch) + + def _torch_extract_fbank_features(self, waveform: np.ndarray, device: str = "cpu") -> np.ndarray: + if torch is None: + raise RuntimeError("PyTorch is not installed, cannot compute STFT on GPU.") + + waveform = torch.from_numpy(waveform).to(device, torch.float32) + window = torch.hann_window(self.n_fft, device=device) + + if self.dither != 0.0: + waveform = waveform + self.dither * torch.randn_like(waveform) + + stft = torch.stft(waveform, n_fft=self.n_fft, hop_length=self.hop_length, window=window, return_complex=True) + magnitudes = stft[..., :-1].abs() ** 2 + + mel_filters = torch.from_numpy(self.mel_filters).to(device, torch.float32) # [n_mels, 1+n_fft//2] + mel_spec = mel_filters.T @ magnitudes # [..., n_mels, T] + + log_spec = torch.clamp(mel_spec, min=1e-10).log10() # <= 0 + + if self.dynamic_log_norm: + if waveform.dim() == 2: + max_val_t = log_spec.max(dim=2, keepdim=True)[0] # over T + max_val_bt = max_val_t.max(dim=1, keepdim=True)[0] # over mel + threshold = max_val_bt - self.dynamic_range_db + log_spec = torch.maximum(log_spec, threshold) + else: + threshold = log_spec.max() - self.dynamic_range_db + log_spec = torch.maximum(log_spec, threshold) + else: + floor_tensor = torch.tensor(self.log_floor_db, dtype=log_spec.dtype, device=log_spec.device) + log_spec = torch.maximum(log_spec, floor_tensor) + + log_spec = (log_spec + 4.0) / 4.0 + + if device != "cpu": + log_spec = log_spec.detach().cpu() + return log_spec.numpy() + + def process(self, *args, **kwargs): + """Alias of __call__ for convenience.""" + return self.__call__(*args, **kwargs) + + +class StreamingMelProcessorExact: + """Strictly offline equivalent streaming Mel processor. + + - accumulate all historical audio into buffer; use the same feature_extractor to calculate the entire mel after each addition. + - only output "stable" frames: the frame center does not depend on future (right) context, i.e. center + n_fft//2 <= current buffer length. + - output the last batch of frames at the end (flush), ensuring complete consistency with offline full-calculation. + + Cost: Each call performs feature extraction on the accumulated buffer (can be optimized to incremental if needed). + """ + + def __init__( + self, + feature_extractor: MiniCPMAAudioProcessor, + chunk_ms: int = 100, + first_chunk_ms: Optional[int] = None, + sample_rate: int = 16000, + n_fft: int = 400, + hop_length: int = 160, + n_mels: int = 80, + cnn_redundancy_ms: int = 10, # (given in ms, usually 10ms=1 frame) + # sliding window parameters + enable_sliding_window: bool = False, # whether to enable sliding window + slide_trigger_seconds: float = 30.0, # trigger threshold for sliding window in seconds + slide_stride_seconds: float = 10.0, # stride for sliding window in seconds + ): + self.feature_extractor = feature_extractor + self.chunk_ms = chunk_ms + self.first_chunk_ms = first_chunk_ms if first_chunk_ms is not None else chunk_ms + self.sample_rate = sample_rate + self.n_fft = n_fft + self.hop_length = hop_length + self.n_mels = n_mels + + self.chunk_samples = int(round(chunk_ms * sample_rate / 1000)) + self.chunk_frames = self.chunk_samples // hop_length + # align to hop_length to avoid frame boundary issues + hop = self.hop_length + raw_first_samples = int(round(self.first_chunk_ms * sample_rate / 1000)) + aligned_first = max(hop, (raw_first_samples // hop) * hop) + self.first_chunk_samples = aligned_first + self.half_window = n_fft // 2 # required right context + + # redundancy frames (in frames), <=1 frame: 10ms → 1 frame + self.cnn_redundancy_ms = cnn_redundancy_ms + self.cnn_redundancy_samples = int(cnn_redundancy_ms * sample_rate / 1000) + self.cnn_redundancy_frames = max(0, self.cnn_redundancy_samples // hop_length) + + # sliding window configuration (Trigger mode) + self.enable_sliding_window = enable_sliding_window + self.trigger_seconds = slide_trigger_seconds + self.slide_seconds = slide_stride_seconds + + # shift/base (global frame coordinates) + self.left_samples_dropped = 0 # samples dropped from the left + self.base_T = 0 # index of the "global frame" corresponding to mel_full[:, :, 0] + + self.reset() + + def reset(self): + self.buffer = np.zeros(0, dtype=np.float32) + self.last_emitted_T = 0 + self.total_samples_processed = 0 + self.chunk_count = 0 + self.is_first = True + self.left_samples_dropped = 0 + self.base_T = 0 + + def get_chunk_size(self) -> int: + return self.first_chunk_samples if self.is_first else self.chunk_samples + + def get_expected_output_frames(self) -> int: + raise NotImplementedError("get_expected_output_frames is not implemented") + + def _extract_full(self) -> torch.Tensor: + # when buffer length is less than n_fft, Whisper's internal STFT will raise an error in center=True and pad mode + # (pad is greater than input length). At this time, there is no stable frame to output, so return empty features directly. + if len(self.buffer) < self.n_fft: + raise ValueError(f"buffer length is shorter than n_fft {len(self.buffer)} < {self.n_fft}") + # if buffer length is less than 5s, use set_spac_log_norm(log_floor_db=-10) or the last cached result + if len(self.buffer) < 5 * self.sample_rate: + # TODO: here the best is to do some experiments to choose the best one, now this is selected through experience, can see MiniCPMAAudioProcessor's main implementation + self.feature_extractor.set_spac_log_norm(log_floor_db=-10) + # if buffer length is greater than 5s, use set_spac_log_norm(dynamic_range_db=8) + else: + self.feature_extractor.set_spac_log_norm(dynamic_range_db=8) + feats = self.feature_extractor( + self.buffer, + sampling_rate=self.sample_rate, + return_tensors="pt", + padding=False, + ) + return feats.input_features # [1, 80, T] + + def _stable_frames_count(self) -> int: + # number of stable frames = floor((len(buffer) - half_window) / hop) + 1, minimum is 0 + L = int(self.buffer.shape[0]) + if L <= 0: + return 0 + if L < self.half_window: + return 0 + return max(0, (L - self.half_window) // self.hop_length + 1) + + def _maybe_slide_buffer(self): + """Trigger mode sliding window: when the buffer reaches the trigger threshold, slide a fixed length window.""" + if not self.enable_sliding_window: + return + + sr = self.sample_rate + hop = self.hop_length + L = len(self.buffer) + + # convert seconds to samples + trigger_samples = int(self.trigger_seconds * sr) + stride_samples = int(self.slide_seconds * sr) + + # check if the trigger threshold is reached + if L < trigger_samples: + return + + # calculate the number of samples to drop (fixed sliding stride_samples) + drop = stride_samples + + # cannot drop the left context that is still needed for subsequent emission + # in trigger mode, we only need to protect the minimum necessary data + # i.e. ensure that we do not discard frames that may be needed in the future + last_emitted_local = self.last_emitted_T - self.base_T + + # only protect necessary context (e.g. the most recent 1 second data) + min_keep_seconds = 1.0 # keep at least 1 second of data to ensure continuity + min_keep_samples = int(min_keep_seconds * sr) + + # guard_samples are the minimum samples we must keep + guard_samples = min(min_keep_samples, L - drop) + + # limit: do not exceed the safe boundary; and align hop + max_allowed_drop = max(0, L - guard_samples) + drop = min(drop, max_allowed_drop) + drop = (drop // hop) * hop + + if drop <= 0: + return + + # truly drop & update base + self.buffer = self.buffer[drop:] + self.left_samples_dropped += drop + self.base_T += drop // hop + + def process(self, audio_chunk: np.ndarray, is_last_chunk: bool = False) -> Tuple[torch.Tensor, Dict]: + self.chunk_count += 1 + # append to buffer + if len(self.buffer) == 0: + self.buffer = audio_chunk.astype(np.float32, copy=True) + else: + self.buffer = np.concatenate([self.buffer, audio_chunk.astype(np.float32, copy=True)]) + + # sliding window processing + self._maybe_slide_buffer() + + # full extraction (for the current window) + mel_full = self._extract_full() + T_full = mel_full.shape[-1] # local frames in the current window + stable_T = min(T_full, self._stable_frames_count()) # local stable frames + stable_T_global = self.base_T + stable_T # map to global frame coordinates + + # plan the core frames for the current emission (global coordinates) + core_start_g = self.last_emitted_T + core_end_g = core_start_g + self.chunk_frames + required_stable_g = core_end_g + self.cnn_redundancy_frames + + if stable_T_global >= required_stable_g or is_last_chunk: + emit_start_g = max(0, core_start_g - self.cnn_redundancy_frames) + emit_end_g = core_end_g + self.cnn_redundancy_frames + + # global -> local index + emit_start = max(0, emit_start_g - self.base_T) + emit_end = emit_end_g - self.base_T + emit_start = max(0, min(emit_start, T_full)) + emit_end = max(emit_start, min(emit_end, T_full)) + + mel_output = mel_full[:, :, emit_start:emit_end] + self.last_emitted_T = core_end_g # only advance the core frame pointer (global) + else: + mel_output = mel_full[:, :, 0:0] + + self.total_samples_processed += len(audio_chunk) + self.is_first = False + + info = { + "type": "exact_chunk", + "chunk_number": self.chunk_count, + "emitted_frames": mel_output.shape[-1], + "stable_T": stable_T, + "T_full": T_full, + "base_T": self.base_T, + "stable_T_global": stable_T_global, + "buffer_len_samples": int(self.buffer.shape[0]), + "left_samples_dropped": self.left_samples_dropped, + "core_start": core_start_g, # if keep the original field name, use the global value here + "core_end": core_end_g, # same as above + } + return mel_output, info + + def flush(self) -> torch.Tensor: + """Called when the stream ends, output the remaining unemitted frames, ensuring consistency with offline (calculated by global coordinates).""" + if len(self.buffer) == 0: + return torch.zeros(1, 80, 0) + + mel_full = self._extract_full() + T_local = mel_full.shape[-1] + T_global = self.base_T + T_local + + if self.last_emitted_T < T_global: + start_l = max(0, self.last_emitted_T - self.base_T) + tail = mel_full[:, :, start_l:] + self.last_emitted_T = T_global + return tail + return mel_full[:, :, 0:0] + + def get_config(self) -> Dict: + return { + "chunk_ms": self.chunk_ms, + "first_chunk_ms": self.first_chunk_ms, + "effective_first_chunk_ms": self.first_chunk_samples / self.sample_rate * 1000.0, + "sample_rate": self.sample_rate, + "n_fft": self.n_fft, + "hop_length": self.hop_length, + "cnn_redundancy_ms": self.cnn_redundancy_ms, + "cnn_redundancy_frames": self.cnn_redundancy_frames, + "enable_sliding_window": self.enable_sliding_window, + "trigger_seconds": self.trigger_seconds, + "slide_seconds": self.slide_seconds, + } + + def get_state(self) -> Dict: + return { + "chunk_count": self.chunk_count, + "last_emitted_T": self.last_emitted_T, + "total_samples_processed": self.total_samples_processed, + "buffer_len": int(self.buffer.shape[0]), + "base_T": self.base_T, + "left_samples_dropped": self.left_samples_dropped, + } + + def get_snapshot(self) -> Dict: + """Get a complete state snapshot (including buffer), used for recovery from a fast start. + + Returns: + A dictionary containing the complete state, which can be used to restore the snapshot + """ + buffer_copy = self.buffer.copy() + snapshot = { + "chunk_count": self.chunk_count, + "last_emitted_T": self.last_emitted_T, + "total_samples_processed": self.total_samples_processed, + "buffer": buffer_copy, + "base_T": self.base_T, + "left_samples_dropped": self.left_samples_dropped, + "is_first": self.is_first, + # save the state of the feature_extractor (key: ensure determinism of mel feature extraction) + "fe_dynamic_log_norm": getattr(self.feature_extractor, "dynamic_log_norm", None), + "fe_dynamic_range_db": getattr(self.feature_extractor, "dynamic_range_db", None), + "fe_log_floor_db": getattr(self.feature_extractor, "log_floor_db", None), + } + + return snapshot + + def restore_snapshot(self, snapshot: Dict) -> None: + """Restore state from a snapshot + + Args: + snapshot: the snapshot dictionary returned by get_snapshot + """ + # record the state before restoration + prev_state = { + "chunk_count": self.chunk_count, + "last_emitted_T": self.last_emitted_T, + "buffer_len": len(self.buffer), + } + + # restore state + self.chunk_count = snapshot["chunk_count"] + self.last_emitted_T = snapshot["last_emitted_T"] + self.total_samples_processed = snapshot["total_samples_processed"] + self.buffer = snapshot["buffer"].copy() # copy buffer + self.base_T = snapshot["base_T"] + self.left_samples_dropped = snapshot["left_samples_dropped"] + self.is_first = snapshot["is_first"] + + # restore the state of the feature_extractor (key: ensure determinism of mel feature extraction) + if snapshot.get("fe_dynamic_log_norm") is not None: + self.feature_extractor.dynamic_log_norm = snapshot["fe_dynamic_log_norm"] + if snapshot.get("fe_dynamic_range_db") is not None: + self.feature_extractor.dynamic_range_db = snapshot["fe_dynamic_range_db"] + if snapshot.get("fe_log_floor_db") is not None: + self.feature_extractor.log_floor_db = snapshot["fe_log_floor_db"] + + +class MiniCPMOProcessor(ProcessorMixin): + attributes = ["image_processor", "audio_processor", "tokenizer"] + audio_processor_class = "AutoFeatureExtractor" + image_processor_class = "AutoImageProcessor" + tokenizer_class = "AutoTokenizer" + + def __init__(self, image_processor=None, audio_processor=None, tokenizer=None, **kwargs): + super().__init__(image_processor, audio_processor, tokenizer) + + self.version = image_processor.version if image_processor else None + # audio feature pooling step, needs to be consistent with config.audio_pool_step + self.pool_step = kwargs.get("audio_pool_step", 5) + + # initialize the streaming audio processor + self._streaming_mel_processor = None + if audio_processor is not None: + self._init_streaming_processor() + + def get_audio_placeholder( + self, + audio_lens: int, + chunk_input: bool = True, + chunk_length: int = 1, + ) -> str: + """ + Public method to get audio placeholder string for vLLM integration. + + Args: + audio_lens: Length of audio in samples + chunk_input: Whether to use chunked processing + chunk_length: Chunk length in seconds + + Returns: + Audio placeholder string + """ + pool_step = self.pool_step + feature_lens = math.ceil(audio_lens / self.audio_processor.hop_length) + + feature_lens = (feature_lens - 1) // 2 + 1 + output_lens = (feature_lens - pool_step) // pool_step + 1 + + if chunk_input: + fbank_feat_in_chunk = int(chunk_length * 100) + cnn_feat_in_chunk = (fbank_feat_in_chunk - 1) // 2 + 1 + audio_embeds_in_chunk = (cnn_feat_in_chunk - pool_step) // pool_step + 1 + num_audio_chunks = (output_lens + audio_embeds_in_chunk - 1) // audio_embeds_in_chunk + + place_holders = "" + total_unk_len = 0 + for _ in range(num_audio_chunks): + unk_len = min(audio_embeds_in_chunk, output_lens - total_unk_len) + place_holders += self.tokenizer.audio_start + "" * unk_len + self.tokenizer.audio_end + total_unk_len += unk_len + audio_placeholder = place_holders + else: + audio_placeholder = self.tokenizer.audio_start + "" * output_lens + self.tokenizer.audio_end + + return audio_placeholder + + def _init_streaming_processor( + self, + chunk_ms: int = 100, + cnn_redundancy_ms: int = 0, + *, + mode: str = "exact", + first_chunk_ms: Optional[int] = None, + enable_sliding_window: bool = False, + slide_trigger_seconds: float = 30.0, + slide_stride_seconds: float = 10.0, + ): + """Initialize the streaming processor + + Args: + chunk_ms: Chunk size in milliseconds, also the sliding step. + cnn_redundancy_ms: CNN boundary redundancy in milliseconds (before and after), 0 means standard mode. + mode: streaming processing mode, currently only supports "exact" + first_chunk_ms: the size of the first chunk (milliseconds), if not specified, it is the same as chunk_ms + enable_sliding_window: whether to enable sliding window (trigger mode) + slide_trigger_seconds: trigger threshold for sliding window in seconds + slide_stride_seconds: stride for sliding window in seconds + """ + if mode == "exact": + self._streaming_mel_processor = StreamingMelProcessorExact( + feature_extractor=self.audio_processor, + chunk_ms=chunk_ms, + first_chunk_ms=first_chunk_ms, + sample_rate=16000, + cnn_redundancy_ms=cnn_redundancy_ms, + enable_sliding_window=enable_sliding_window, + slide_trigger_seconds=slide_trigger_seconds, + slide_stride_seconds=slide_stride_seconds, + ) + else: + raise ValueError(f"Unsupported mode: {mode}, only 'exact' is supported") + self._streaming_mode = mode if mode in ["exact"] else ("exact") + + def set_streaming_mode( + self, + mode: str = "exact", + chunk_ms: int = 100, + cnn_redundancy_ms: int = 0, + *, + first_chunk_ms: Optional[int] = None, + enable_sliding_window: bool = False, + slide_trigger_seconds: float = 30.0, + slide_stride_seconds: float = 10.0, + ): + """Set streaming processing mode + + Args: + mode: streaming processing mode, currently only supports "exact" + chunk_ms: chunk size in milliseconds, also the sliding step. + cnn_redundancy_ms: CNN boundary redundancy in milliseconds (before and after), 0 means standard mode. + first_chunk_ms: the size of the first chunk (milliseconds), if not specified, it is the same as chunk_ms + enable_sliding_window: whether to enable sliding window (trigger mode) + slide_trigger_seconds: trigger threshold for sliding window in seconds + slide_stride_seconds: stride for sliding window in seconds + """ + if self.audio_processor is None: + raise ValueError("audio_processor is not set, cannot initialize the streaming processor") + self._init_streaming_processor( + chunk_ms=chunk_ms, + cnn_redundancy_ms=cnn_redundancy_ms, + mode=mode, + first_chunk_ms=first_chunk_ms, + enable_sliding_window=enable_sliding_window, + slide_trigger_seconds=slide_trigger_seconds, + slide_stride_seconds=slide_stride_seconds, + ) + + def process_image( + self, + images: Optional[ImageInput] = None, + do_pad: bool = True, + max_slice_nums: int = 1, + return_tensors: str = "pt", + ) -> MiniCPMOBatchFeature: + """Process image data + + Args: + images: input images + do_pad: whether to pad + max_slice_nums: maximum number of slices + return_tensors: return tensor type + Returns: + MiniCPMOBatchFeature object + """ + if images is None: + return MiniCPMOBatchFeature(data={"pixel_values": [[]], "image_sizes": [[]], "tgt_sizes": [[]]}) + + result = self.image_processor( + images, do_pad=do_pad, max_slice_nums=max_slice_nums, return_tensors=return_tensors + ) + + model_inputs = { + "pixel_values": result.get("pixel_values", [[]]), + "image_sizes": result.get("image_sizes", [[]]), + "tgt_sizes": result.get("tgt_sizes", [[]]), + } + + return MiniCPMOBatchFeature(data=model_inputs) + + def process_audio( + self, + audios: Optional[Union[np.ndarray, List[np.ndarray]]] = None, + sampling_rate: int = 16000, + regroup_to_seconds: Optional[int] = None, + fps: int = 100, + ) -> MiniCPMOBatchFeature: + """Process audio data in batch + + Args: + audios: audio data + sampling_rate: sampling rate + regroup_to_seconds: regroup duration in seconds + fps: frames per second + Returns: + MiniCPMOBatchFeature object + """ + if audios is None: + return MiniCPMOBatchFeature(data={"audio_features": [], "audio_feature_lens": []}) + + audio_features, audio_feature_lens = process_audio_batch( + audios=audios, + feature_extractor=self.audio_processor, + sampling_rate=sampling_rate, + max_duration_seconds=30, + return_attention_mask=True, + ) + + if regroup_to_seconds is not None and len(audio_features) > 0: + audio_features, audio_feature_lens = regroup_audio_features( + audio_features=audio_features, + audio_feature_lens=audio_feature_lens, + regroup_seconds=regroup_to_seconds, + fps=fps, + ) + + model_inputs = {"audio_features": audio_features, "audio_feature_lens": audio_feature_lens} + + return MiniCPMOBatchFeature(data=model_inputs) + + def process_audio_streaming( + self, + audio_chunk: np.ndarray, + reset: bool = False, + return_batch_feature: bool = False, + is_last_chunk: bool = False, + ) -> Union[Tuple[torch.Tensor, dict], MiniCPMOBatchFeature]: + """Process audio chunk in streaming + + Args: + audio_chunk: audio data chunk (any audio, e.g. first process 125ms, then process 100ms) + reset: whether to reset the processor state + return_batch_feature: whether to return MiniCPMOBatchFeature format (consistent with process_audio) + Returns: + If return_batch_feature=False: + (audio_features, info) + - audio_features: [1, 80, n_frames] mel features + - info: processing information dictionary + If return_batch_feature=True: + MiniCPMOBatchFeature object, containing: + - audio_features: [1, 80, n_frames] mel features + - audio_feature_lens: [tensor([n_frames])] + - info: processing information (as an extra attribute) + """ + if self._streaming_mel_processor is None: + raise ValueError("Streaming processor not initialized, please ensure audio_processor is set") + + if reset: + self._streaming_mel_processor.reset() + + # process chunk + mel_features, info = self._streaming_mel_processor.process(audio_chunk, is_last_chunk=is_last_chunk) + + # determine the return format based on the parameters + if return_batch_feature: + # return the format consistent with process_audio + # note: info returns emitted_frames, which represents the actual output frames + n_frames = info.get("emitted_frames", mel_features.shape[-1]) + model_inputs = { + "audio_features": mel_features, + "audio_feature_lens": [torch.tensor([n_frames])], + "streaming_info": info, # add streaming processing information + } + return MiniCPMOBatchFeature(data=model_inputs) + else: + return mel_features, info + + def reset_streaming(self): + if self._streaming_mel_processor is not None: + self._streaming_mel_processor.reset() + + def get_streaming_chunk_size(self) -> int: + if self._streaming_mel_processor is None: + raise ValueError("Streaming processor not initialized") + return self._streaming_mel_processor.get_chunk_size() + + def configure_streaming( + self, + chunk_ms: int = 100, + enable_sliding_window: bool = False, + slide_trigger_seconds: float = 30.0, + slide_stride_seconds: float = 10.0, + ): + """Configure streaming processor parameters + + Args: + chunk_ms: chunk size in milliseconds + enable_sliding_window: whether to enable sliding window (trigger mode) + slide_trigger_seconds: trigger threshold for sliding window in seconds + slide_stride_seconds: stride for sliding window in seconds + """ + if self.audio_processor is None: + raise ValueError("audio_processor is not set") + + self._init_streaming_processor( + chunk_ms=chunk_ms, + enable_sliding_window=enable_sliding_window, + slide_trigger_seconds=slide_trigger_seconds, + slide_stride_seconds=slide_stride_seconds, + ) + + def get_streaming_config(self) -> dict: + if self._streaming_mel_processor is None: + return {} + return self._streaming_mel_processor.get_config() + + def get_streaming_state(self) -> dict: + if self._streaming_mel_processor is None: + return {} + return self._streaming_mel_processor.get_state() + + def get_streaming_snapshot(self) -> dict: + if self._streaming_mel_processor is None: + return {} + return self._streaming_mel_processor.get_snapshot() + + def restore_streaming_snapshot(self, snapshot: dict) -> None: + if self._streaming_mel_processor is None: + return + if not snapshot: + return + self._streaming_mel_processor.restore_snapshot(snapshot) + + def __call__( + self, + text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]], + images: ImageInput = None, + audios: Union[np.ndarray, List[np.ndarray], List[List[np.ndarray]]] = None, + audio_parts: Optional[list] = None, + max_length: Optional[int] = None, + do_pad: Optional[bool] = True, + max_slice_nums: int = None, + use_image_id: bool = True, + stream_input: bool = False, + return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH, + sampling_rate: Optional[int] = 16000, + online_streaming: bool = False, + audio_chunk_idx: int = 0, + is_last_chunk: bool = False, + **kwargs, + ) -> MiniCPMOBatchFeature: + if images is not None: + image_inputs = self.process_image( + images=images, do_pad=do_pad, max_slice_nums=max_slice_nums, return_tensors=return_tensors + ) + else: + image_inputs = None + + audio_features, audio_feature_lens, audio_phs = self.audio_feature_extract( + audios, + audio_parts, + stream_input, + sampling_rate, + online_streaming=online_streaming, + is_last_chunk=is_last_chunk, + ) + + model_inputs = self._convert_omni_to_inputs( + image_inputs, + audio_phs, + text, + max_slice_nums=max_slice_nums, + use_image_id=use_image_id, + max_length=max_length, + **kwargs, + ) + + model_inputs["audio_features"] = audio_features + model_inputs["audio_feature_lens"] = audio_feature_lens + + result = MiniCPMOBatchFeature(data={**model_inputs}) + + if online_streaming: + result.use_extra_context = True + result.prefix_extra_frames = 0 if audio_chunk_idx == 0 else 2 + result.suffix_extra_frames = 2 + result.chunk_idx = audio_chunk_idx + + return result + + def audio_feature_extract( + self, + audios: Union[np.ndarray, List[np.ndarray], List[List[np.ndarray]], None] = None, + audio_parts: Optional[list] = None, + stream_input: Optional[bool] = False, + sampling_rate: Optional[int] = None, + chunk_length: Optional[int] = 1, + online_streaming: bool = False, + is_last_chunk: bool = False, + **kwargs, + ): + if audios is None: + return [], [], [] + + if isinstance(audios, np.ndarray): + audios_list = [[audios]] + elif isinstance(audios[0], np.ndarray): + audios_list = [audios] + else: + audios_list = audios + + if audio_parts is not None: + assert len(audio_parts) == len(audios_list) + for parts, audios in zip(audio_parts, audios_list): + assert len(parts) == len(audios) + + audio_feature_lens_list = [] + audio_ph_list = [] + audio_features_all = [] + + # audio placeholder not dependent on audio_parts + for audios in audios_list: + if audios: + audio_ph_list.append( + [ + self.get_audio_placeholder(len(a), chunk_input=stream_input, chunk_length=chunk_length) + for a in audios + ] + ) + else: + audio_ph_list.append([]) + + for idx, audios in enumerate(audios_list): + if audio_parts is not None: + # same audio part merge + audio_part = audio_parts[idx] + merge_audio = [] + cur_audio = [] + for aid, (part, audio) in enumerate(zip(audio_part, audios)): + if aid == 0 or audio_part[aid] == audio_part[aid - 1]: + cur_audio.append(audio) + else: + merge_audio.append(np.hstack(cur_audio)) + cur_audio = [audio] + if cur_audio: + merge_audio.append(np.hstack(cur_audio)) + else: + merge_audio = audios + + # If the audio exceeds 30 seconds, split it into chunks every 30 seconds. + final_merge_audio = [] + max_audio_inp_len = 30 * sampling_rate + for audio in merge_audio: + if len(audio) <= max_audio_inp_len: + final_merge_audio.append(audio) + else: + for i in range(math.ceil(len(audio) / max_audio_inp_len)): + final_merge_audio.append(audio[i * max_audio_inp_len : (i + 1) * max_audio_inp_len]) + + audio_feature_lens = [] + + if audios: + if online_streaming: + # online streaming: only support single audio, directly use process_audio_streaming return format + assert ( + len(final_merge_audio) == 1 + ), f"online streaming mode only supports single audio, currently there are {len(final_merge_audio)}" + audio = final_merge_audio[0] + result = self.process_audio_streaming( + audio, reset=False, return_batch_feature=True, is_last_chunk=is_last_chunk + ) + audio_features_all.append( + result["audio_features"].squeeze(0) + ) # [1, 80, T] -> [80, T], keep consistent with batch processing + audio_feature_lens_list.append(result["audio_feature_lens"][0]) + else: + # batch processing + audio_inputs = self.audio_processor( + final_merge_audio, + sampling_rate=sampling_rate, + return_attention_mask=True, + padding="max_length", + return_tensors="pt", + **kwargs, + ) + audio_feature = audio_inputs["input_features"] + actual_lens = audio_inputs["attention_mask"].sum(dim=1) + + for feat, lens in zip(audio_feature, actual_lens): + audio_features_all.append(feat[:, :lens]) + audio_feature_lens.append(lens) + + audio_feature_lens = torch.hstack(audio_feature_lens) + audio_feature_lens_list.append(audio_feature_lens) + else: + audio_feature_lens_list.append([]) + + if audio_features_all: + audio_features = [i.permute(1, 0) for i in audio_features_all] + audio_features = torch.nn.utils.rnn.pad_sequence( + audio_features, batch_first=True, padding_value=0.0 + ).permute(0, 2, 1) + else: + audio_features = [] + + return audio_features, audio_feature_lens_list, audio_ph_list + + def _convert(self, input_str, max_inp_length: Optional[int] = None): + old_input_ids = self.tokenizer.encode(input_str) + + listen_token_id = self.tokenizer.convert_tokens_to_ids("<|listen|>") + input_ids = [] + for token in old_input_ids: + if token != listen_token_id: + input_ids.append(token) + + if max_inp_length is not None: + input_ids = input_ids[:max_inp_length] + input_ids = torch.tensor(input_ids, dtype=torch.int32) + + ## image bound + start_cond = (input_ids == self.tokenizer.im_start_id) | (input_ids == self.tokenizer.slice_start_id) + end_cond = (input_ids == self.tokenizer.im_end_id) | (input_ids == self.tokenizer.slice_end_id) + + image_start_idx = torch.where(start_cond)[0] + image_start_idx += 1 + image_end_idx = torch.where(end_cond)[0] + + valid_image_nums = max(len(image_start_idx), len(image_end_idx)) + + image_bounds = torch.hstack( + [ + image_start_idx[:valid_image_nums].unsqueeze(-1), + image_end_idx[:valid_image_nums].unsqueeze(-1), + ] + ) + + ## audio bound + audio_start_idx = torch.where(input_ids == self.tokenizer.audio_start_id)[0] + audio_end_idx = torch.where(input_ids == self.tokenizer.audio_end_id)[0] + assert len(audio_start_idx) == len(audio_end_idx) + audio_bounds = torch.hstack([(audio_start_idx + 1).unsqueeze(-1), audio_end_idx.unsqueeze(-1)]) + + spk_start_idx = torch.where(input_ids == self.tokenizer.spk_start_id)[0] + spk_end_idx = torch.where(input_ids == self.tokenizer.spk_end_id)[0] + assert len(spk_start_idx) == len(spk_end_idx) + spk_bounds = torch.hstack([(spk_start_idx + 1).unsqueeze(-1), spk_end_idx.unsqueeze(-1)]) + + return input_ids, image_bounds, audio_bounds, spk_bounds + + def _convert_omni_to_inputs( + self, + images, + audio_phs, + texts: Union[str, List[str]], + truncation=None, + max_length=None, + max_slice_nums=None, + use_image_id=None, + return_tensors=None, + **kwargs, + ): + if images is None and audio_phs is None: + model_inputs = self.tokenizer( + texts, return_tensors=return_tensors, truncation=truncation, max_length=max_length, **kwargs + ) + return MiniCPMOBatchFeature(data={**model_inputs}) + + image_pattern = "./" + audio_pattern = "" + split_pattern = f"({image_pattern}|{audio_pattern})" + + if isinstance(texts, str): + texts = [texts] + + bs = len(texts) + if images is not None: + images, image_sizes, tgt_sizes = images["pixel_values"], images["image_sizes"], images["tgt_sizes"] + else: + images, image_sizes, tgt_sizes = [[]] * bs, [[]] * bs, [[]] * bs + + input_ids_list = [] + image_bounds_list = [] + audio_bounds_list = [] + spk_bounds_list = [] + + for index, text in enumerate(texts): + text_chunks = re.split(split_pattern, text) + + image_tags = re.findall(image_pattern, text) + audio_tags = re.findall(audio_pattern, text) + + if image_tags: + assert images is not None + assert len(image_tags) == len(image_sizes[index]) + if audio_tags: + assert audio_phs is not None + assert len(audio_tags) == len(audio_phs[index]) + + image_id = 0 + audio_id = 0 + for i, chunk in enumerate(text_chunks): + if chunk == image_pattern: + image_placeholder = self.image_processor.get_slice_image_placeholder( + image_sizes[index][image_id], image_id, max_slice_nums, use_image_id + ) + image_id += 1 + text_chunks[i] = image_placeholder + elif chunk == audio_pattern: + audio_placeholder = audio_phs[index][audio_id] + audio_id += 1 + text_chunks[i] = audio_placeholder + + final_text = "".join(text_chunks) + input_ids, image_bounds, audio_bounds, spk_bounds = self._convert(final_text, max_length) + + input_ids_list.append(input_ids) + image_bounds_list.append(image_bounds) + audio_bounds_list.append(audio_bounds) + spk_bounds_list.append(spk_bounds) + + padded_input_ids, padding_lengths = self.pad(input_ids_list, padding_side="left") + attention_mask = torch.ones_like(padded_input_ids, dtype=torch.bool) + for i, length in enumerate(padding_lengths): + image_bounds_list[i] = image_bounds_list[i] + length + audio_bounds_list[i] = audio_bounds_list[i] + length + spk_bounds_list[i] = spk_bounds_list[i] + length + attention_mask[i, :length] = False + + data = { + "input_ids": padded_input_ids, + "attention_mask": attention_mask, + "pixel_values": images, + "image_sizes": image_sizes, + "image_bound": image_bounds_list, + "tgt_sizes": tgt_sizes, + "audio_bounds": audio_bounds_list, + "spk_bounds": spk_bounds_list, + } + + return data + + def pad(self, inputs, max_length=None, padding_value=0, padding_side="left"): + items = [] + if isinstance(inputs[0], list): + assert isinstance(inputs[0][0], torch.Tensor) + for it in inputs: + for tr in it: + items.append(tr) + else: + assert isinstance(inputs[0], torch.Tensor) + items = inputs + + batch_size = len(items) + shape = items[0].shape + dim = len(shape) + assert dim <= 2 + if max_length is None: + max_length = 0 + max_length = max(max_length, max(item.shape[-1] for item in items)) + min_length = min(item.shape[-1] for item in items) + dtype = items[0].dtype + + if dim == 0: + return torch.stack([item for item in items], dim=0), [0] + elif dim == 1: + if max_length == min_length: + return torch.stack([item for item in items], dim=0), [0] * batch_size + tensor = torch.zeros((batch_size, max_length), dtype=dtype) + padding_value + else: + tensor = torch.zeros((batch_size, max_length, shape[-1]), dtype=dtype) + padding_value + + padding_length = [] + for i, item in enumerate(items): + if dim == 1: + if padding_side == "left": + tensor[i, -len(item) :] = item.clone() + else: + tensor[i, : len(item)] = item.clone() + elif dim == 2: + if padding_side == "left": + tensor[i, -len(item) :, :] = item.clone() + else: + tensor[i, : len(item), :] = item.clone() + padding_length.append(tensor.shape[-1] - len(item)) + + return tensor, padding_length diff --git a/special_tokens_map.json b/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..5365eeae71bc6006003c7c981576ced10d8ec59d --- /dev/null +++ b/special_tokens_map.json @@ -0,0 +1,580 @@ +{ + "additional_special_tokens": [ + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + 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false, + "rstrip": false, + "single_word": false + }, + { + "content": "<|timbre_20|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "<|timbre_21|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "<|timbre_22|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "<|timbre_23|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "<|timbre_24|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "<|timbre_25|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "<|timbre_26|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "<|timbre_27|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "<|timbre_28|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "<|timbre_29|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "<|timbre_30|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + { + "content": "<|timbre_31|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + } + ], + "bos_token": "<|im_start|>", + "eos_token": { + "content": "<|im_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": { + "content": "<|endoftext|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "unk_token": "" +} diff --git a/tokenization_minicpmo_fast.py b/tokenization_minicpmo_fast.py new file mode 100644 index 0000000000000000000000000000000000000000..5aa9932fdb825beaa25c1dddd59da426d4acfd63 --- /dev/null +++ b/tokenization_minicpmo_fast.py @@ -0,0 +1,120 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# Copyright 2026 The OpenBMB Team. 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. + +from typing import List + +from transformers import Qwen2TokenizerFast + + +class MiniCPMOTokenizerFast(Qwen2TokenizerFast): + def __init__(self, **kwargs): + self._bad_token_ids = kwargs.pop("bad_token_ids", []) + + super().__init__(**kwargs) + + # image + self.im_start = "" + self.im_end = "" + self.ref_start = "" + self.ref_end = "" + self.box_start = "" + self.box_end = "" + self.quad_start = "" + self.quad_end = "" + self.slice_start = "" + self.slice_end = "" + self.im_id_start = "" + self.im_id_end = "" + + # audio + self.audio_start = "<|audio_start|>" + self.audio_end = "<|audio_end|>" + self.spk_start = "<|spk_bos|>" + self.spk_end = "<|spk_eos|>" + self.tts_start = "<|tts_bos|>" + self.tts_end = "<|tts_eos|>" + + @property + def eos_id(self): + return self.eos_token_id + + @property + def bos_id(self): + return self.bos_token_id + + @property + def unk_id(self): + return self.unk_token_id + + @property + def im_start_id(self): + return self.convert_tokens_to_ids(self.im_start) + + @property + def im_end_id(self): + return self.convert_tokens_to_ids(self.im_end) + + @property + def slice_start_id(self): + return self.convert_tokens_to_ids(self.slice_start) + + @property + def slice_end_id(self): + return self.convert_tokens_to_ids(self.slice_end) + + @property + def im_id_start_id(self): + return self.convert_tokens_to_ids(self.im_id_start) + + @property + def im_id_end_id(self): + return self.convert_tokens_to_ids(self.im_id_end) + + @property + def audio_start_id(self): + return self.convert_tokens_to_ids(self.audio_start) + + @property + def audio_end_id(self): + return self.convert_tokens_to_ids(self.audio_end) + + @property + def spk_start_id(self): + return self.convert_tokens_to_ids(self.spk_start) + + @property + def spk_end_id(self): + return self.convert_tokens_to_ids(self.spk_end) + + @property + def tts_start_id(self): + return self.convert_tokens_to_ids(self.tts_start) + + @property + def tts_end_id(self): + return self.convert_tokens_to_ids(self.tts_end) + + @staticmethod + def escape(text: str) -> str: + return text + + @staticmethod + def unescape(text: str) -> str: + return text + + @property + def bad_token_ids(self) -> List[int]: + return self._bad_token_ids diff --git a/tokenizer.json b/tokenizer.json new file mode 100644 index 0000000000000000000000000000000000000000..8d7b4cc3cd7be7196b199268c053df7fd9f79677 --- /dev/null +++ b/tokenizer.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6d55eb34389b8c87403763cc1d80447d91c85c7cd39cd5e3c0dc2d49edad989d +size 11437712 diff --git a/tokenizer_config.json b/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..d747bccb54853d1298ba1a6a52a3625f26ccdb65 --- /dev/null +++ b/tokenizer_config.json @@ -0,0 +1,7918 @@ +{ + "add_bos_token": false, + "add_prefix_space": false, + "added_tokens_decoder": { + "128244": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151643": { + "content": "<|endoftext|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151644": { + "content": "<|im_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151645": { + "content": "<|im_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151646": { + "content": "<|object_ref_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151647": { + "content": "<|object_ref_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151648": { + "content": "<|box_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151649": { + "content": "<|box_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151650": { + "content": "<|quad_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151651": { + "content": "<|quad_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151652": { + "content": "<|vision_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151653": { + "content": "<|vision_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151654": { + "content": "<|vision_pad|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151655": { + "content": "<|image_pad|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151656": { + "content": "<|video_pad|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151657": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151658": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151659": { + "content": "<|fim_prefix|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151660": { + "content": "<|fim_middle|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151661": { + "content": "<|fim_suffix|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151662": { + "content": "<|fim_pad|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151663": { + "content": "<|repo_name|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151664": { + "content": "<|file_sep|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151665": { + "content": "", + "lstrip": false, + 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"single_word": false, + "special": true + }, + "151680": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151681": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151682": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151683": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151684": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151685": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151686": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151687": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151688": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151689": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151690": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151691": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151692": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151693": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151694": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151695": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151696": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151697": { + "content": "<|audio_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151698": { + "content": "<|audio|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151699": { + "content": "<|audio_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151700": { + "content": "<|spk_bos|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151701": { + "content": "<|spk|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151702": { + "content": "<|spk_eos|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151703": { + "content": "<|tts_bos|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151704": { + "content": "<|tts_eos|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151705": { + "content": "<|listen|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151706": { + "content": "<|speak|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151707": { + "content": "<|interrupt|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151708": { + "content": "<|vad_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151709": { + "content": "<|vad_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151710": { + "content": "<|emotion_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151711": { + "content": "<|emotion_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151712": { + "content": "<|speed_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151713": { + "content": "<|speed_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151714": { + "content": "<|pitch_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151715": { + "content": "<|pitch_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151716": { + "content": "<|turn_bos|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151717": { + "content": "<|turn_eos|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151718": { + "content": "<|chunk_eos|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151719": { + "content": "<|chunk_bos|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151720": { + "content": "<|chunk_tts_bos|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151721": { + "content": "<|chunk_tts_eos|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151722": { + "content": "<|tts_pad|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151723": { + "content": "<|timbre_7|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151724": { + "content": "<|timbre_8|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151725": { + "content": "<|timbre_9|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151726": { + "content": "<|timbre_10|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151727": { + "content": "<|timbre_11|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151728": { + "content": "<|timbre_12|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151729": { + "content": "<|timbre_13|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151730": { + "content": "<|timbre_14|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151731": { + "content": "<|timbre_15|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151732": { + "content": "<|timbre_16|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151733": { + "content": "<|timbre_17|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151734": { + "content": "<|timbre_18|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151735": { + "content": "<|timbre_19|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151736": { + "content": "<|timbre_20|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151737": { + "content": "<|timbre_21|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151738": { + "content": "<|timbre_22|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151739": { + "content": "<|timbre_23|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151740": { + "content": "<|timbre_24|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151741": { + "content": "<|timbre_25|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151742": { + "content": "<|timbre_26|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151743": { + "content": "<|timbre_27|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151744": { + "content": "<|timbre_28|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151745": { + "content": "<|timbre_29|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151746": { + "content": "<|timbre_30|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151747": { + "content": "<|timbre_31|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + } + }, + "additional_special_tokens": [ + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "<|audio_start|>", + "<|audio|>", + "<|audio_end|>", + "<|spk_bos|>", + "<|spk|>", + "<|spk_eos|>", + "<|tts_bos|>", + "<|tts_eos|>", + "<|listen|>", + "<|speak|>", + "<|interrupt|>", + "<|vad_start|>", + "<|vad_end|>", + "<|emotion_start|>", + "<|emotion_end|>", + "<|speed_start|>", + "<|speed_end|>", + "<|pitch_start|>", + "<|pitch_end|>", + "<|chunk_eos|>", + "<|chunk_bos|>", + "<|chunk_tts_bos|>", + "<|chunk_tts_eos|>", + "<|tts_pad|>", + "<|timbre_7|>", + "<|timbre_8|>", + "<|timbre_9|>", + "<|timbre_10|>", + "<|timbre_11|>", + "<|timbre_12|>", + "<|timbre_13|>", + "<|timbre_14|>", + "<|timbre_15|>", + "<|timbre_16|>", + "<|timbre_17|>", + "<|timbre_18|>", + "<|timbre_19|>", + "<|timbre_20|>", + "<|timbre_21|>", + "<|timbre_22|>", + "<|timbre_23|>", + "<|timbre_24|>", + "<|timbre_25|>", + "<|timbre_26|>", + "<|timbre_27|>", + "<|timbre_28|>", + "<|timbre_29|>", + "<|timbre_30|>", + "<|timbre_31|>" + ], + "bos_token": "<|im_start|>", + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in message.content %}\n {%- set content = message.content.split('')[-1].lstrip('\\n') %}\n {%- set reasoning_content = message.content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- message.content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n\\n' }}\n {%- endif %}\n {%- if use_tts_template is defined and use_tts_template is true %}\n {{- '<|tts_bos|>' }}\n {%- endif %}\n{%- endif %}", + "clean_up_tokenization_spaces": false, + "eos_token": "<|im_end|>", + "errors": "replace", + "extra_special_tokens": {}, + "model_max_length": 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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. + +import logging +from dataclasses import dataclass +from typing import Any +from typing import Dict +from typing import List +from typing import Literal +from typing import Optional +from typing import Tuple +from typing import Union + +import torch +import torch.nn.functional as F +import torch.nn.utils.parametrize as P +from transformers.cache_utils import DynamicCache + +logger = logging.getLogger(__name__) + + +# text +@dataclass +class GenerateChunkOutput: + chunk_token_ids: torch.Tensor + current_inputs_embeds: torch.Tensor + input_last_hidden_states: Optional[torch.Tensor] # for tts use_speaker_embedding + last_hidden_states: Optional[torch.Tensor] # for tts input feature (projector_semantic) + past_key_values: Optional[torch.Tensor] + finished: bool + + +class ChunkPrefillChunkGenerate: + def __init__(self, model, tokenizer, terminators): + self.tokenizer = tokenizer + self.model = model + self.terminators = terminators + self.terminators_ids = [tokenizer.convert_tokens_to_ids(i) for i in self.terminators] + self.embedding_layer = self.model.get_input_embeddings() + + self.forbidden_tokens = [ + ":", + "：", + "；", + "#", + "“", + "”", + "‘", + "’", + "@", + "*", + "【", + "】", + "「", + "」", + "(", + ")", + "（", + "）", + "[", + "]", + "&", + "/", + "$", + ] + + self.forbidden_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in self.forbidden_tokens] + bad_token_ids = getattr(tokenizer, "bad_token_ids", []) + if bad_token_ids: + self.forbidden_token_ids.extend(bad_token_ids) + + @staticmethod + def prepare_generation_config(do_sample, max_new_tokens=50, min_new_tokens=0, **kwargs): + num_beams = kwargs.get("num_beams", 3) + generation_config = { + "num_beams": num_beams, + "top_p": 0.8, + "top_k": 100, + "temperature": 0.7, + "do_sample": True, + "repetition_penalty": 1.05, + } + + if do_sample: + generation_config.update( + { + "top_p": 0.8, + "top_k": 100, + "temperature": 0.7, + "do_sample": True, + "repetition_penalty": 1.05, + } + ) + elif num_beams > 1: + generation_config.update({"num_beams": num_beams, "repetition_penalty": 1.2, "do_sample": False}) + else: + generation_config.update({"do_sample": False, "repetition_penalty": 1.05}) + + generation_config.update((k, kwargs[k]) for k in generation_config.keys() & kwargs.keys()) + generation_config["min_new_tokens"] = min_new_tokens + generation_config["max_new_tokens"] = max_new_tokens + + return generation_config + + def chunk_generate( + self, + inputs_embeds: torch.Tensor, + past_key_values, + is_first_generate_chunk: bool, + chunk_size: int, + return_hidden_states: bool, + do_sample: bool, + temperature: float, + top_p: float, + top_k: int, + repetition_penalty: float = 1.05, + length_penalty: float = 1.0, + all_input_ids: Optional[torch.Tensor] = None, + ) -> GenerateChunkOutput: + """ + Args: + inputs_embeds: [1, seq_len, hidden_dim], Input embeddings of current chunk. + past_key_values: [num_layers, 2, batch_size, num_heads, seq_len, head_dim], Past key values for llm. + is_first_generate_chunk: bool, Whether this is the first generate chunk. + chunk_size: int, The size of the current chunk, default is 10, and it is fixed during training. + return_hidden_states: bool Whether to return the hidden states, default is True. + do_sample: bool Whether to sample from the model, default is True. + temperature: float The temperature for the model, default is 0.7. + top_p: float The top-p for the model, default is 0.8. + top_k: int The top-k for the model, default is 100. + repetition_penalty: float, The repetition penalty for the model, default is 1.05. + length_penalty: float, The length penalty for the model, default is 1.0. Higher value means more detailed generation. + all_input_ids: Optional[torch.Tensor], The input ids for the current chunk. + """ + + finished = False + current_inputs_embeds = inputs_embeds.clone() + input_last_hidden_states = [] + last_hidden_states = [] + generated_tokens = [] + + for token_idx in range(chunk_size): + if is_first_generate_chunk and token_idx == 0: + # first generate chunk, prefill inputs_embeds + model_inputs = { + "inputs_embeds": current_inputs_embeds, + "past_key_values": past_key_values, + "use_cache": True, + "output_hidden_states": return_hidden_states, + } + else: # for all other cases: prefill the latest generated token + model_inputs = { + "inputs_embeds": current_inputs_embeds[:, -1:, :], + "past_key_values": past_key_values, + "use_cache": True, + "output_hidden_states": return_hidden_states, + } + + with torch.no_grad(): + outputs = self.model(**model_inputs) + + # last token's logits + logits = outputs.logits[:, -1, :].to(copy=True, dtype=torch.float32, device=inputs_embeds.device) + + # forbid specific tokens decoding = model.generate@suppress_tokens + if self.forbidden_token_ids: + logits[:, self.forbidden_token_ids] = float("-inf") + + past_key_values = outputs.past_key_values + + PENALTY_WINDOW_SIZE = 128 + + # apply repetition penalty + if repetition_penalty != 1.0: + # get token ids for repetition penalty + if all_input_ids is not None: + # use global input ids (including original input and generated part) + if len(generated_tokens) > 0: + generated_token_ids = torch.cat(generated_tokens, dim=1) + current_sequence = torch.cat( + [ + all_input_ids[:, -PENALTY_WINDOW_SIZE:], + generated_token_ids, + ], + dim=1, + ) + else: + current_sequence = all_input_ids[:, -PENALTY_WINDOW_SIZE:] + unique_token_ids = torch.unique(current_sequence.squeeze(0)) + elif len(generated_tokens) > 0: + # revert to original logic: only use generated tokens + generated_token_ids = torch.cat(generated_tokens, dim=1).squeeze(0) + unique_token_ids = torch.unique(generated_token_ids) + else: + unique_token_ids = torch.tensor([], dtype=torch.long, device=logits.device) + + # apply repetition penalty + for token_id in unique_token_ids: + if logits[0, token_id] > 0: + logits[0, token_id] = logits[0, token_id] / repetition_penalty + else: + logits[0, token_id] = logits[0, token_id] * repetition_penalty + + # apply length penalty, higher value means more detailed generation + if length_penalty != 1.0: + for eos_token_id in self.terminators_ids: + if logits[0, eos_token_id] > 0: + logits[0, eos_token_id] = logits[0, eos_token_id] / length_penalty + else: + logits[0, eos_token_id] = logits[0, eos_token_id] * length_penalty + + # apply temperature + if temperature != 1.0: + logits = logits / temperature + + if do_sample: + # Top-k filtering + if top_k > 0: + top_k_logits, top_k_indices = torch.topk(logits, min(top_k, logits.size(-1))) + logits_filtered = torch.full_like(logits, float("-inf")) + logits_filtered.scatter_(1, top_k_indices, top_k_logits) + logits = logits_filtered + + # Top-p filtering + if top_p < 1.0: + sorted_logits, sorted_indices = torch.sort(logits, descending=True) + cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) + + # remove tokens with cumulative probability greater than top_p + sorted_indices_to_remove = cumulative_probs > top_p + sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() + sorted_indices_to_remove[..., 0] = 0 + + indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove) + logits[indices_to_remove] = float("-inf") + + # sampling + probs = F.softmax(logits, dim=-1) + next_token = torch.multinomial(probs, num_samples=1) + else: + next_token = torch.argmax(logits, dim=-1, keepdim=True) + + if return_hidden_states: + if is_first_generate_chunk and token_idx == 0: + input_last_hidden_states.append(outputs.hidden_states[-1]) + else: + last_hidden_states.append(outputs.hidden_states[-1]) + + # if terminator token, stop generating + if next_token.item() in self.terminators_ids: + finished = True + break + + generated_tokens.append(next_token) + + # convert new token to embeddings and concatenate + next_token_embed = self.embedding_layer(next_token) + + # update inputs_embeds, add one + current_inputs_embeds = torch.cat([current_inputs_embeds, next_token_embed], dim=1) + + if len(generated_tokens) > 0: + chunk_token_ids = torch.cat(generated_tokens, dim=1) + else: + # special case: if last chunk and first predict is eos token, return last token of previous chunk. return a tensor with shape (1, 0) + if finished: + chunk_token_ids = torch.zeros((1, 0), dtype=torch.long, device=current_inputs_embeds.device) + else: + raise Exception("this should not happen") + + if len(last_hidden_states) > 0: + last_hidden_states = torch.cat(last_hidden_states, dim=1) + else: + # special case: if last chunk, return last token of previous chunk. + if finished: + last_hidden_states = torch.cat(last_hidden_states, dim=1) + else: + raise Exception("this should not happen") + + if len(input_last_hidden_states) > 0: + input_last_hidden_states = torch.cat(input_last_hidden_states, dim=1) + else: + input_last_hidden_states = None + + return GenerateChunkOutput( + chunk_token_ids=chunk_token_ids, + current_inputs_embeds=current_inputs_embeds, + input_last_hidden_states=input_last_hidden_states, + last_hidden_states=last_hidden_states, + past_key_values=past_key_values, + finished=finished, + ) + + +def streaming_token_decoder(token_iterator, tokenizer, skip_special_tokens=False): + """ + Incrementally decode tokens from an iterator, handling partial multi-byte characters. + + When streaming tokens, multi-byte characters (like Chinese) may be split across multiple + tokens. Decoding partial tokens results in replacement characters (U+FFFD). This function + buffers tokens and only yields complete characters. + + Args: + token_iterator: An iterator yielding (token_ids, is_finished) tuples. + token_ids can be torch.Tensor or any iterable of integers. + tokenizer: The tokenizer to use for decoding. + skip_special_tokens: Whether to skip special tokens during decoding. + + Yields: + (decoded_text, is_finished) tuples where decoded_text is the new text since last yield. + """ + accumulated_token_ids = [] + yielded_text_len = 0 + + for token_ids, is_finished in token_iterator: + # Accumulate token IDs + if torch.is_tensor(token_ids): + accumulated_token_ids.extend(token_ids.reshape(-1).tolist()) + else: + accumulated_token_ids.extend(list(token_ids) if hasattr(token_ids, "__iter__") else [token_ids]) + + # Decode all accumulated tokens + full_decoded = tokenizer.decode(accumulated_token_ids, skip_special_tokens=skip_special_tokens) + + if is_finished: + # Final chunk - yield all remaining text + new_text = full_decoded[yielded_text_len:] + yield new_text, is_finished + else: + # Find safe prefix without incomplete multi-byte characters + # The replacement character '�' (U+FFFD) indicates incomplete decoding + new_text = full_decoded[yielded_text_len:] + + # Hold back text ending with replacement character (incomplete UTF-8 sequence) + safe_end = len(new_text) + while safe_end > 0 and new_text[safe_end - 1] == "\ufffd": + safe_end -= 1 + + safe_text = new_text[:safe_end] if safe_end > 0 else "" + yielded_text_len += len(safe_text) + yield safe_text, is_finished + + +def torch_clone_recursive(obj): + """Recursively clone nested containers of torch.Tensors. + + Supported container types: dict, list, tuple. Non-container non-Tensor + objects are returned as-is. + """ + if torch.is_tensor(obj): + return obj.clone() + elif isinstance(obj, dict): + return {k: torch_clone_recursive(v) for k, v in obj.items()} + elif isinstance(obj, list): + return [torch_clone_recursive(v) for v in obj] + elif isinstance(obj, tuple): + return tuple(torch_clone_recursive(v) for v in obj) + else: + raise ValueError(f"Unsupported type: {type(obj)}") + + +def rotate_half(x: torch.Tensor) -> torch.Tensor: + """Rotate half the hidden dims of the input for RoPE.""" + dim = x.shape[-1] + x1 = x[..., : dim // 2] + x2 = x[..., dim // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +@dataclass +class SpeculativeSnapshot: + """Speculative snapshot for VAD speculative rollback. + + Used in VAD speculative execution: creates a snapshot after streaming_prefill + and before streaming_generate. If speculation fails (user continues speaking), + the state can be restored to continue streaming_prefill. + + Implementation: + - LLM KV Cache: only record length, restore by truncation (zero extra VRAM) + - Audio KV Cache: requires cloning, as generate sets it to None + - Mel processor: save full state snapshot (including buffer) + """ + + # KV Cache length (for truncation recovery) + llm_cache_length: int + audio_cache_length: int + + # session state + new_user_msg: bool + llm_generated: bool + llm_generate_completed: bool + + # Round management + next_round_id: int + pending_round_id: Optional[int] + omni_chunk_history_length: int + + # TTS state (requires cloning, but usually small) + tts_last_turn_tokens: Optional[torch.Tensor] + + # Streaming processor state + audio_chunk_idx: int + + # Mel processor state snapshot (including buffer) + mel_processor_snapshot: Optional[dict] = None + + # Audio encoder KV cache (requires cloning to ensure determinism after recovery) + audio_past_key_values: Optional[tuple] = None + + # timestamp (for debugging) + timestamp: float = 0.0 + + # debug field: for verifying correctness of recovery + llm_cache_checksum: Optional[float] = None # LLM KV Cache first layer K sum + audio_cache_checksum: Optional[float] = None # Audio KV Cache first layer K sum + mel_buffer_checksum: Optional[float] = None # Mel buffer sum + + # RNG state (key: for ensuring determinism of dithering etc. after recovery) + rng_state_cpu: Optional[torch.Tensor] = None # torch CPU RNG state + rng_state_cuda: Optional[torch.Tensor] = None # torch CUDA RNG state (if on GPU) + + def summary(self) -> str: + mel_buf_len = 0 + if self.mel_processor_snapshot: + buf = self.mel_processor_snapshot.get("buffer") + if buf is not None: + mel_buf_len = len(buf) + return ( + f"llm_cache={self.llm_cache_length}, " + f"audio_cache={self.audio_cache_length}, " + f"audio_chunk_idx={self.audio_chunk_idx}, " + f"mel_buffer={mel_buf_len}, " + f"history_len={self.omni_chunk_history_length}, " + f"new_user_msg={self.new_user_msg}, " + f"llm_generated={self.llm_generated}" + ) + + +# tts +@dataclass +class TTSSamplingParams: + top_p: float = 0.85 + min_p: float = 0.01 + top_k: int = 25 + repetition_penalty: float = 1.05 + temperature: float = 0.8 + win_size: int = 16 + tau_r: float = 0.1 + + +class TTSStreamingGenerator: + """ + Streaming generator for TTS that processes chunks and yields audio tokens in real-time. + + Supported attention types: + - full_attention: Full attention, all tokens can attend to each other + - sliding_window: Sliding window attention, KV cache is truncated to fixed size (token_window_size) + - sliding_recompute: Sliding recompute, only keep previous chunk and recompute with current chunk + - reindex: Keep first chunk as sink, reindex sliding window positions via RoPE rotation + """ + + def __init__( + self, + model, + temperature: float, + eos_token: Union[int, torch.Tensor], + chunk_size: int = 25, # s3tokenizer 1s = 25token + tts_last_turn_tokens: torch.Tensor = None, + logits_processors=None, + logits_warpers=None, + ): + self.tts = model + self.device = model.device + self.temperature = torch.tensor([temperature], dtype=torch.float, device=self.device) + self.eos_token = ( + torch.tensor(eos_token, device=self.device) if isinstance(eos_token, int) else eos_token.to(self.device) + ) + + self.num_vq = model.num_vq + self.num_audio_tokens = model.num_audio_tokens + self.recomputed_chunks = model.recomputed_chunks + self.emb_code = model.emb_code + self.head_code = model.head_code + + # Attention type and window sizes + self.attention_type = model.attention_type # "full_attention", "sliding_window", "sliding_recompute", "reindex" + self.chunk_window_size = model.chunk_window_size # chunk-level window for sliding_recompute (default 2) + self.token_window_size = model.token_window_size # token-level window for sliding_window/reindex (default 300) + + # RoPE config (for reindex mode) + self.rope_theta = model.model.config.rope_theta + self.head_dim = model.model.config.hidden_size // model.model.config.num_attention_heads + + # Logits processors + self.logits_processors = logits_processors if logits_processors is not None else [] + # Logits warpers (like TopP/TopK), separate from processors + self.logits_warpers = logits_warpers if logits_warpers is not None else [] + + # initialize state + self.past_key_values = None + self.text_start_pos = 0 + self.idx = -1 # start from -1, become 0 when first called + self.all_conditions = [] + self.all_generated_tokens = [] + self.tts_last_turn_tokens = tts_last_turn_tokens + self.spk_emb = None + + audio_bos = [self.tts.audio_bos_token_id] + audio_bos = torch.Tensor(audio_bos).to(self.tts.emb_text.weight.device, dtype=torch.long) + + self.audio_bos_embeds = self.tts.emb_text(audio_bos).unsqueeze(0) + self.text_eos_embed = self.tts.emb_text( + torch.tensor( + [self.tts.config.text_eos_token_id], + device=self.tts.emb_text.weight.device, + dtype=torch.long, + ) + ).unsqueeze(0) + + # buffer related, used to fill up chunk_size and yield to outside + self.chunk_size = chunk_size + self._token_buffer: List[torch.Tensor] = [] + + # Chunk info tracking for sliding_recompute and reindex + self._chunk_info: List[dict] = [] + self._total_seq_len = 0 + + # Reindex mode: track sink (first chunk) length + self._sink_kv_len = 0 + + def _build_recompute_inputs(self, current_condition: torch.Tensor) -> torch.Tensor: + """Build recompute inputs for sliding_recompute mode.""" + if len(self._chunk_info) == 0: + return current_condition + + prev_chunk = self._chunk_info[-1] + prev_condition = prev_chunk["condition"] + prev_audio_tokens = prev_chunk["audio_tokens"] + + recompute_list = [prev_condition] + if len(prev_audio_tokens) > 0: + prev_audio_embeds = torch.cat([self.emb_code[0](tok) for tok in prev_audio_tokens], dim=1) + recompute_list.append(prev_audio_embeds) + + recompute_list.append(current_condition) + return torch.cat(recompute_list, dim=1) + + def _truncate_kv_cache_sliding_window(self): + """Truncate KV cache for sliding_window mode.""" + if self.past_key_values is None: + return + + if hasattr(self.past_key_values, "get_seq_length"): + current_kv_len = self.past_key_values.get_seq_length() + else: + current_kv_len = self.past_key_values[0][0].shape[2] + + if current_kv_len <= self.token_window_size: + return + + new_cache = DynamicCache() + num_layers = ( + len(self.past_key_values.key_cache) + if hasattr(self.past_key_values, "key_cache") + else len(self.past_key_values) + ) + + for layer_idx in range(num_layers): + if hasattr(self.past_key_values, "key_cache"): + key = self.past_key_values.key_cache[layer_idx][:, :, -self.token_window_size :, :] + value = self.past_key_values.value_cache[layer_idx][:, :, -self.token_window_size :, :] + else: + key = self.past_key_values[layer_idx][0][:, :, -self.token_window_size :, :] + value = self.past_key_values[layer_idx][1][:, :, -self.token_window_size :, :] + new_cache.update(key, value, layer_idx) + + self.past_key_values = new_cache + + @staticmethod + def _apply_rope_rotation(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor: + """Apply RoPE rotation to tensor.""" + return x * cos + rotate_half(x) * sin + + def _compute_rope_cos_sin(self, positions: torch.Tensor, device: torch.device, dtype: torch.dtype): + """Compute RoPE cos and sin for given positions.""" + dim_half = self.head_dim // 2 + freq_seq = torch.arange(0, dim_half, dtype=torch.float32, device=device) + inv_freq = 1.0 / (self.rope_theta ** (freq_seq / dim_half)) + + # positions: [seq_len] + angles = positions.float().unsqueeze(-1) * inv_freq.unsqueeze(0) # [seq_len, dim_half] + angles = torch.cat([angles, angles], dim=-1) # [seq_len, head_dim] + + cos = angles.cos().to(dtype) + sin = angles.sin().to(dtype) + return cos, sin + + def _reindex_kv_cache(self): + """ + Reindex KV cache for reindex mode: + 1. Keep first chunk as attention sink + 2. Keep last chunk + 3. Discard middle chunks + 4. Reindex the last chunk's key positions to be right after sink via RoPE rotation + """ + if self.past_key_values is None or len(self._chunk_info) < 2: + return + + # Get current KV cache length + if hasattr(self.past_key_values, "get_seq_length"): + current_kv_len = self.past_key_values.get_seq_length() + else: + current_kv_len = self.past_key_values[0][0].shape[2] + + # Calculate sink length (first chunk) + sink_len = self._chunk_info[0]["condition_len"] + self._chunk_info[0]["audio_token_count"] + + # Last chunk length + last_chunk = self._chunk_info[-1] + last_chunk_len = last_chunk["condition_len"] + last_chunk["audio_token_count"] + + keep_len = sink_len + last_chunk_len + + # Get device and dtype + device = self.past_key_values.key_cache[0].device + dtype = self.past_key_values.key_cache[0].dtype + + if current_kv_len <= keep_len: + last_chunk_kv_len = current_kv_len - sink_len + if last_chunk_kv_len <= 0: + return + self.text_start_pos = current_kv_len + return + + # Step 1: Truncate KV cache - keep sink and last chunk + new_cache = DynamicCache() + num_layers = len(self.past_key_values.key_cache) + + original_start_pos = current_kv_len - last_chunk_len + new_start_pos = sink_len + delta = new_start_pos - original_start_pos # This is a scalar constant + delta_positions = torch.full((last_chunk_len,), delta, dtype=torch.float32, device=device) + + # Compute rotation cos/sin + cos, sin = self._compute_rope_cos_sin(delta_positions, device, dtype) + cos = cos.unsqueeze(0).unsqueeze(0) # [1, 1, seq_len, head_dim] + sin = sin.unsqueeze(0).unsqueeze(0) + + for layer_idx in range(num_layers): + key_full = self.past_key_values.key_cache[layer_idx] + value_full = self.past_key_values.value_cache[layer_idx] + + # Extract sink and last chunk + key_sink = key_full[:, :, :sink_len, :] + value_sink = value_full[:, :, :sink_len, :] + key_last = key_full[:, :, -last_chunk_len:, :] + value_last = value_full[:, :, -last_chunk_len:, :] + + # Apply RoPE rotation to reindex key positions + key_last_reindexed = self._apply_rope_rotation(key_last, cos, sin) + + # Concatenate sink and reindexed last chunk + key = torch.cat([key_sink, key_last_reindexed], dim=2) + value = torch.cat([value_sink, value_last], dim=2) + + new_cache.update(key, value, layer_idx) + + self.past_key_values = new_cache + + # Update text_start_pos to reflect new positions + self.text_start_pos = sink_len + last_chunk_len + + @torch.inference_mode() + def generate_with_buffer( + self, + condition: torch.Tensor, + text_finished: bool = False, + max_new_token: int = 500, + ): + """input a condition embedding chunk, generate audio token each time, + and accumulate to buffer, only yield when buffer satisfies chunk_size. + + Yields: + torch.Tensor of shape [chunk_size] (2D: [1, chunk_size]) + """ + self.idx += 1 + self.device = self.tts.device + + # if text finished, first concatenate Text EOS + if text_finished: + condition = torch.cat([condition, self.text_eos_embed], dim=1) + + # always concatenate Audio BOS + condition = torch.cat([condition, self.audio_bos_embeds], dim=1).to(self.device) + + self.all_conditions.append(condition) + + # Initialize current chunk info + current_chunk_info = { + "condition_len": condition.shape[1], + "audio_token_count": 0, + "condition": condition.clone(), + "audio_tokens": [], + } + + # Handle different attention types + if self.attention_type == "sliding_recompute" and self.idx >= 1: + # sliding_recompute: discard KV cache, recompute with previous + current chunk + self.past_key_values = None + current_condition = self._build_recompute_inputs(condition) + self.text_start_pos = 0 + elif self.attention_type == "reindex" and self.idx >= 1: + # reindex: truncate KV cache keeping sink + last chunk, reindex positions via RoPE + self._reindex_kv_cache() + current_condition = condition + # Always update text_start_pos based on actual KV cache length (like reference code) + if self.past_key_values is not None: + if hasattr(self.past_key_values, "get_seq_length"): + kv_len = self.past_key_values.get_seq_length() + else: + kv_len = self.past_key_values[0][0].shape[2] + self.text_start_pos = kv_len + else: + current_condition = condition + + condition_length = current_condition.shape[1] + prefill_len = condition_length + finished = torch.zeros(1, dtype=torch.bool, device=self.device) + chunk_generated_tokens = [] + + for t in range(max_new_token): + if t == 0: + inputs_embeds = current_condition + pos_ids = torch.arange( + self.text_start_pos, + self.text_start_pos + condition_length, + dtype=torch.long, + device=self.device, + ).unsqueeze(0) + else: + last = self.all_generated_tokens[-1] + # last: [1,1], directly as code id + inputs_embeds = self.emb_code[0](last) + pos_ids = torch.tensor( + [self.text_start_pos + prefill_len + t - 1], + dtype=torch.long, + device=self.device, + ).unsqueeze(0) + + outputs = self.tts.model( + position_ids=pos_ids, + past_key_values=self.past_key_values, + inputs_embeds=inputs_embeds, + use_cache=True, + ) + hidden_states = outputs.last_hidden_state + + # Handle KV cache based on attention type + if self.attention_type == "sliding_window": + self.past_key_values = outputs.past_key_values + self._truncate_kv_cache_sliding_window() + else: + self.past_key_values = outputs.past_key_values + + with P.cached(): + logits = torch.empty( + hidden_states.size(0), + hidden_states.size(1), + self.num_audio_tokens, + self.num_vq, + dtype=torch.float, + device=self.device, + ) + for num_vq_iter in range(self.num_vq): + x: torch.Tensor = self.head_code[num_vq_iter](hidden_states) + logits[..., num_vq_iter] = x + del x + + del hidden_states + + logits = logits[:, -1].float() + + logits = logits.permute(0, 2, 1) + logits = logits.reshape(-1, logits.size(2)) + + logits /= self.temperature + + audio_bos = len(self.all_generated_tokens) == 0 and t == 0 + + if not audio_bos: + # use generated tokens (current chunk) as input for processor/warper (align with modeling_minicpmo) + all_generated_tokens = torch.cat(self.all_generated_tokens, dim=1).to(self.device) # [1, T] + for processor in self.logits_processors: + logits = processor(all_generated_tokens, logits) + + for warper in self.logits_warpers: + logits = warper(all_generated_tokens, logits) + del all_generated_tokens + + # sample next token (only use first codebook, same as generate) + scores = F.softmax(logits, dim=-1) + idx_next = torch.multinomial(scores, num_samples=1) # [(B*num_vq), 1] + next_id = idx_next.view(-1, self.num_vq)[:, 0:1] # only take first codebook → [B, 1] + del scores + + if next_id.eq( + self.eos_token + ).any(): # generated audio eos token, means this chunk is finished, no longer generate new tokens + finished[:] = True + else: # eos token cannot be added to buffer, he does not speak. + # convert next_id to correct shape [1, 1], no num_vq dimension + if next_id.dim() == 0: # if scalar + next_tok = next_id.unsqueeze(0).unsqueeze(0) # [1, 1] + elif next_id.dim() == 1: # if 1D [1] + next_tok = next_id.unsqueeze(0) # [1, 1] + else: + next_tok = next_id + + self.all_generated_tokens.append(next_tok) + chunk_generated_tokens.append(next_tok) + + # Update chunk info for sliding_recompute + current_chunk_info["audio_tokens"].append(next_tok.clone()) + current_chunk_info["audio_token_count"] += 1 + + self._token_buffer.append(next_tok) + + if len(self._token_buffer) == 0: + # case 1: if last text chunk, yield None + if text_finished: + yield torch.empty(1, 0, dtype=torch.long, device=self.device), True + break + # case 2: if not last text chunk, break directly + else: + break + else: # buffer has something + # case 1: if buffer is larger/equal to chunk_size, yield out + if len(self._token_buffer) >= self.chunk_size: + batch = torch.cat(self._token_buffer[: self.chunk_size], dim=1) # [1, chunk_size] + yield batch, False # → [1, chunk_size] + # discard yielded part + self._token_buffer = self._token_buffer[self.chunk_size :] + + # case 2: if buffer is smaller than chunk_size + else: + # if generation finished, and is the last text chunk, yield all remaining tokens, then break + if finished.all(): + if text_finished: + batch = torch.cat(self._token_buffer, dim=1) # [1, chunk_size] + yield batch, True # → [1, chunk_size] + self._token_buffer = [] + break + else: + # not the last text chunk, need to wait for next text chunk to fill up buffer, then this call ends + break + else: # generation of this audio chunk is not finished, continue generating + continue + + # Save current chunk info for sliding_recompute and reindex + self._chunk_info.append(current_chunk_info) + self._total_seq_len += condition.shape[1] + len(chunk_generated_tokens) + + # Update text_start_pos based on attention type + if self.attention_type == "sliding_recompute": + # sliding_recompute: will be reset at next chunk start, update normally here + self.text_start_pos += prefill_len + len(chunk_generated_tokens) + elif self.attention_type == "reindex": + # reindex: position based on actual KV cache length (positions have been reindexed to be continuous) + if self.past_key_values is not None: + if hasattr(self.past_key_values, "get_seq_length"): + self.text_start_pos = self.past_key_values.get_seq_length() + else: + self.text_start_pos = self.past_key_values[0][0].shape[2] + else: + self.text_start_pos += condition.shape[1] + len(chunk_generated_tokens) + else: + self.text_start_pos += condition.shape[1] + len(chunk_generated_tokens) + # note: remaining tokens in buffer will be kept, and accumulated next time + + +# sliding window +@dataclass +class StreamingWindowConfig: + text_window_high_tokens: int = 8000 + text_window_low_tokens: int = 6000 + + +@dataclass +class DuplexWindowConfig: + """duplex sliding window configuration + + sliding window mode: + - "off": disable sliding window + - "basic": basic sliding window (trigger by cache length) + - "context": sliding window with context (trigger by unit number, preserve generated text to previous) + """ + + # sliding window mode + sliding_window_mode: str = "off" # "off" / "basic" / "context" + + # basic sliding window parameters + basic_window_high_tokens: int = 8000 # high watermark: trigger sliding window when exceeded + basic_window_low_tokens: int = 6000 # low watermark: keep to this value after sliding window + + # context sliding window parameters + context_previous_max_tokens: int = 500 # previous maximum token number + context_max_units: int = 24 # maximum unit number (trigger sliding window when exceeded) + + # verification mode (for comparison test) + verify_mode: bool = False # whether to enable verification log + + +def as_dynamic_cache(past_key_values): + """Convert legacy tuple cache to DynamicCache if needed.""" + if isinstance(past_key_values, DynamicCache): + return past_key_values + + if isinstance(past_key_values, tuple): + return DynamicCache.from_legacy_cache(past_key_values) + + return past_key_values + + +def get_kv_cache_length(cache) -> int: + """Get the sequence length of a KV cache. + + Args: + cache: DynamicCache or tuple-based cache + + Returns: + The number of tokens in the cache + """ + if cache is None: + return 0 + + if isinstance(cache, DynamicCache): + if not cache.key_cache or not cache.key_cache[0].numel(): + return 0 + return cache.key_cache[0].shape[-2] + + if isinstance(cache, tuple): + return cache[0][0].shape[2] + + return 0 + + +def get_rotary_cos_sin( + head_dim: int, + positions: torch.Tensor, + device: torch.device, + dtype: torch.dtype, + rope_theta: float = 10000.0, + inv_freq_cache: Optional[Dict[Tuple, torch.Tensor]] = None, +) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute RoPE cos and sin components for given positions. + + Args: + head_dim: Dimension of each attention head + positions: Position indices tensor + device: Target device + dtype: Target dtype + rope_theta: RoPE base frequency (default 10000.0) + inv_freq_cache: Optional cache dict for inverse frequencies + + Returns: + Tuple of (cos, sin) tensors with shape [1, 1, seq_len, head_dim] + """ + cache_key = (head_dim, device) + + inv_freq = inv_freq_cache.get(cache_key) if inv_freq_cache is not None else None + if inv_freq is None or inv_freq.device != device or inv_freq.shape[0] != head_dim // 2: + exponent = torch.arange(0, head_dim, 2, device=device, dtype=torch.float32) / head_dim + inv_freq = 1.0 / (rope_theta**exponent) + if inv_freq_cache is not None: + inv_freq_cache[cache_key] = inv_freq + + positions = positions.to(device=device, dtype=torch.float32) + angles = torch.einsum("i,j->ij", positions, inv_freq) + cos = torch.cos(angles) + sin = torch.sin(angles) + + # Use cat instead of repeat_interleave, consistent with model's original RotaryEmbedding + # Original: emb = torch.cat((freqs, freqs), dim=-1) -> [f0, f1, ..., f_{d/2}, f0, f1, ..., f_{d/2}] + cos_full = torch.cat([cos, cos], dim=-1).to(dtype=dtype) + sin_full = torch.cat([sin, sin], dim=-1).to(dtype=dtype) + cos_full = cos_full.unsqueeze(0).unsqueeze(0) + sin_full = sin_full.unsqueeze(0).unsqueeze(0) + return cos_full, sin_full + + +def realign_rotary_suffix( + suffix_keys: torch.Tensor, + old_positions: torch.Tensor, + new_positions: torch.Tensor, + rope_theta: float = 10000.0, + inv_freq_cache: Optional[Dict[Tuple, torch.Tensor]] = None, +) -> torch.Tensor: + """Realign RoPE position encoding after cache eviction. + + When tokens are dropped from the middle of a cache, the suffix tokens + need their RoPE embeddings recalculated with new position indices. + + Args: + suffix_keys: Key tensor to realign, shape [batch, heads, seq_len, head_dim] + old_positions: Original position indices + new_positions: New position indices after eviction + rope_theta: RoPE base frequency + inv_freq_cache: Optional cache dict for inverse frequencies + + Returns: + Realigned key tensor with same shape as input + """ + if suffix_keys.numel() == 0: + return suffix_keys + + head_dim = suffix_keys.shape[-1] + device = suffix_keys.device + dtype = suffix_keys.dtype + + # Compute old position cos/sin + cos_old, sin_old = get_rotary_cos_sin(head_dim, old_positions, device, dtype, rope_theta, inv_freq_cache) + + # Inverse transform: recover original key + base = cos_old * suffix_keys - sin_old * rotate_half(suffix_keys) + + # Compute new position cos/sin + cos_new, sin_new = get_rotary_cos_sin(head_dim, new_positions, device, dtype, rope_theta, inv_freq_cache) + + # Forward transform: re-encode with new positions + return cos_new * base + sin_new * rotate_half(base) + + +def drop_tokens_from_cache( + cache: Optional[DynamicCache | Tuple], + length: int, + preserve: int, + position_offset: int, + rope_theta: float = 10000.0, + inv_freq_cache: Optional[Dict[Tuple, torch.Tensor]] = None, +) -> Tuple[Optional[DynamicCache], int, bool]: + """Drop tokens from a KV cache while preserving system prompt. + + Removes tokens in the range [preserve, preserve + length) from the cache, + realigning RoPE embeddings for the suffix. + + Args: + cache: DynamicCache or tuple-based cache (will be converted to DynamicCache) + length: Number of tokens to drop + preserve: Number of tokens to preserve at the start (system prompt) + position_offset: Current position offset for RoPE calculation + rope_theta: RoPE base frequency + inv_freq_cache: Optional cache dict for inverse frequencies + + Returns: + Tuple of (cache, new_position_offset, success) + Note: Tuple cache will be converted to DynamicCache. Modification is in-place. + """ + if cache is None or length <= 0: + return cache, position_offset, False + + cache = as_dynamic_cache(cache) + + total_len = get_kv_cache_length(cache) + if total_len <= 0: + return cache, position_offset, False + + preserve = min(preserve, total_len) + available = total_len - preserve + + if available < length: + logger.warning( + "Cannot drop %d tokens: only %d available (total=%d, preserve=%d)", + length, + available, + total_len, + preserve, + ) + return cache, position_offset, False + + suffix_len = total_len - preserve - length + # note: after RoPE reindex, the position of cache has been compressed (from preserve start) + # so here should not add position_offset, but use the actual layout of current cache + suffix_offset = preserve + length # suffix current position in cache + prefix_offset = preserve # suffix new position (follow preserve) + + # Prepare position tensors for RoPE realignment + old_positions = None + new_positions = None + if suffix_len > 0: + device = cache.key_cache[0].device + old_positions = torch.arange( + suffix_offset, + suffix_offset + suffix_len, + device=device, + dtype=torch.long, + ) + new_positions = torch.arange( + prefix_offset, + prefix_offset + suffix_len, + device=device, + dtype=torch.long, + ) + + keep_len = total_len - length + + # Process each layer (in-place modification) + for layer_idx in range(len(cache.key_cache)): + key_tensor = cache.key_cache[layer_idx] + value_tensor = cache.value_cache[layer_idx] + + if not key_tensor.numel(): + continue + + # Preserve prefix (system prompt) + prefix_keys = key_tensor[:, :, :preserve, :] + prefix_values = value_tensor[:, :, :preserve, :] + + if suffix_len > 0: + # Keep and realign suffix + suffix_keys = key_tensor[:, :, preserve + length :, :] + suffix_values = value_tensor[:, :, preserve + length :, :] + + if old_positions is not None and new_positions is not None and suffix_keys.numel(): + suffix_keys = realign_rotary_suffix( + suffix_keys, + old_positions, + new_positions, + rope_theta, + inv_freq_cache, + ) + + cache.key_cache[layer_idx] = torch.cat([prefix_keys, suffix_keys], dim=-2).contiguous() + cache.value_cache[layer_idx] = torch.cat([prefix_values, suffix_values], dim=-2).contiguous() + else: + cache.key_cache[layer_idx] = prefix_keys.contiguous() + cache.value_cache[layer_idx] = prefix_values.contiguous() + + cache.crop(keep_len) + cache._seen_tokens = max(keep_len, 0) + + new_offset = position_offset + length + logger.debug("Dropped %d tokens from cache, new length=%d", length, keep_len) + + return cache, new_offset, True + + +# stream decoder +def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float("inf")): + logits = logits.clone() + + # Top-k filtering + if top_k > 0: + top_k = min(top_k, logits.size(-1)) + indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] + logits[indices_to_remove] = filter_value + + # Top-p (nucleus) filtering + if top_p > 0.0: + sorted_logits, sorted_indices = torch.sort(logits, descending=True) + probs = F.softmax(sorted_logits, dim=-1) + cumulative_probs = torch.cumsum(probs, dim=-1) + + sorted_indices_to_remove = cumulative_probs > top_p + # keep the first token that exceeds top_p + sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() + sorted_indices_to_remove[..., 0] = 0 + + indices_to_remove = sorted_indices[sorted_indices_to_remove] + logits[0, indices_to_remove] = filter_value + + return logits + + +class StreamDecoder: + def __init__(self, llm, tokenizer, special_token_ids=None, forbidden_token_ids=None): + self.m = llm + self.tokenizer = tokenizer + self.listen_id = self.tokenizer.eos_token_id + + self.chunk_eos_id = self.tokenizer.convert_tokens_to_ids("<|chunk_eos|>") + self.chunk_tts_eos_id = self.tokenizer.convert_tokens_to_ids("<|chunk_tts_eos|>") + self.turn_eos_id = self.tokenizer.convert_tokens_to_ids("<|turn_eos|>") + self.speak_id = self.tokenizer.convert_tokens_to_ids("<|speak|>") + + self.special_token_ids = special_token_ids if special_token_ids is not None else [] + + # cache special tokens (used for context sliding window filtering) + self._all_special_ids = set() + self._all_special_tokens_text = set() + if self.tokenizer: + if hasattr(self.tokenizer, "all_special_ids"): + self._all_special_ids = set(self.tokenizer.all_special_ids) + if hasattr(self.tokenizer, "all_special_tokens"): + self._all_special_tokens_text = set(self.tokenizer.all_special_tokens) + + custom_special_tokens = [ + "", + "", + "", + "", + "", + "", + "<|listen|>", + "<|speak|>", + "<|tts_bos|>", + "<|tts_eos|>", + "<|audio_start|>", + "<|audio_end|>", + "<|chunk_eos|>", + "<|chunk_tts_eos|>", + "<|turn_eos|>", + "<|audio_start|>", + "<|audio_end|>", + ] + self._all_special_tokens_text.update(custom_special_tokens) + for token in custom_special_tokens: + token_id = self.tokenizer.convert_tokens_to_ids(token) + if token_id is not None and token_id != self.tokenizer.unk_token_id: + self._all_special_ids.add(token_id) + + if forbidden_token_ids is None: + self.forbidden_token_ids = [] + elif isinstance(forbidden_token_ids, int): + self.forbidden_token_ids = [self.forbidden_token_ids] + else: + self.forbidden_token_ids = forbidden_token_ids + self.forbidden_token_ids.append(self.chunk_eos_id) + + assert isinstance(self.forbidden_token_ids, list) + + self.cache = None + self.context = "" + self.generated_tokens = [] # track generated tokens + self.generated_special_tokens = [] # track generated special tokens + self.reset() + self.embeds = None + self.system_embeds = None + + # sliding window related states + self._unit_history: List[Dict[str, Any]] = [] + self._next_unit_id: int = 0 + self._pending_unit_id: Optional[int] = None + self._pending_unit_start_cache_len: int = 0 + self._system_preserve_length: int = 0 + self._position_offset: int = 0 + self._window_config = DuplexWindowConfig() + self._window_enabled: bool = True + self._rope_inv_freq_cache: Dict[Tuple, torch.Tensor] = {} + + # context preserving sliding window states + # initial cache layout: [prefix] [suffix] [units...] + # after first sliding window: [prefix] [previous_marker + content] [suffix] [units...] + # fixed dynamic sliding region fixed + self._preserve_prefix_length: int = 0 # original prefix length (fixed) + self._previous_content_length: int = 0 # previous content length (dynamic, including marker) + self._suffix_token_ids: List[int] = [] # suffix token ids (e.g. <|im_end|>) + + # previous marker (added dynamically after first sliding window) + self._previous_marker: str = "\n\nprevious: " # fixed prefix marker + self._previous_marker_token_ids: List[int] = [] # marker token ids (initialized) + self._has_previous: bool = False # whether previous marker has been added + + # previous content + self._previous_text: str = "" # accumulated generated text (without marker) + self._previous_token_ids: List[int] = [] # previous full token ids (including marker) + + # validation statistics + self._sliding_event_count: int = 0 # sliding window trigger count + self._total_dropped_tokens: int = 0 # total dropped token count + self._total_dropped_units: int = 0 # total dropped unit count + + def sliding_embeds(self): + # tmp = system_embeds + # tmp +-》 embeds after 5s + # reset + # feed + pass + + def reset(self): + self.context = "" + self.cache = None + self.generated_tokens = [] + self.generated_special_tokens = [] + self.embeds = None + self.system_embeds = None + + # sliding window state reset + old_unit_count = len(self._unit_history) if hasattr(self, "_unit_history") else 0 + self._unit_history = [] + self._next_unit_id = 0 + self._pending_unit_id = None + self._pending_unit_start_cache_len = 0 + self._system_preserve_length = 0 + self._position_offset = 0 + self._rope_inv_freq_cache = {} + + # context preserving sliding window state reset + self._preserve_prefix_length = 0 + self._previous_content_length = 0 + self._suffix_token_ids = [] + self._previous_marker = "\n\nprevious: " + self._previous_marker_token_ids = [] + self._has_previous = False + self._previous_text = "" + self._previous_token_ids = [] + + # validation statistics + self._sliding_event_count = 0 # sliding window trigger count + self._total_dropped_tokens = 0 # total dropped token count + self._total_dropped_units = 0 # total dropped unit count + + def get_cache_length(self) -> int: + if self.cache is None: + return 0 + if isinstance(self.cache, DynamicCache): + if len(self.cache.key_cache) > 0 and self.cache.key_cache[0].numel() > 0: + return self.cache.key_cache[0].shape[2] + return 0 + # Tuple cache format + return self.cache[0][0].shape[2] + + def get_total_generated_tokens(self) -> int: + return sum(len(u.get("generated_tokens", [])) for u in self._unit_history) + + def register_unit_start(self) -> int: + self._pending_unit_id = self._next_unit_id + self._pending_unit_start_cache_len = self.get_cache_length() + return self._pending_unit_id + + def register_unit_end( + self, + input_type: str, + generated_tokens: Optional[List[int]] = None, + is_listen: bool = False, + generated_text: Optional[str] = None, + ): + """Call when unit ends, record unit information + + Should be called after feeding token + + Args: + input_type: "audio" / "video" / "omni" / "system" + generated_tokens: tokens generated by the unit (token ids) + is_listen: whether the unit is in listen state + generated_text: text generated by the unit (used for context preserving mode) + """ + if self._pending_unit_id is None: + logger.warning("register_unit_end called without register_unit_start") + return + + # calculate the length of the unit + current_cache_len = self.get_cache_length() + unit_len = current_cache_len - self._pending_unit_start_cache_len + + if unit_len > 0: + entry = { + "unit_id": self._pending_unit_id, + "length": unit_len, + "type": input_type, + "generated_tokens": generated_tokens or [], + "generated_text": generated_text or "", # used for context preserving mode + "is_listen": is_listen, + } + self._unit_history.append(entry) + + self._pending_unit_id = None + self._pending_unit_start_cache_len = 0 + self._next_unit_id += 1 + + def register_system_prompt(self): + """Call after system prompt prefill, record preserve length""" + self._system_preserve_length = self.get_cache_length() + + # sliding window core methods + + def _get_rope_theta(self) -> float: + """get model rope_theta configuration""" + return float(getattr(self.m.config, "rope_theta", 10000.0)) + + def _drop_tokens_from_cache(self, length: int) -> bool: + """remove specified number of tokens from cache (protect system prompt) + + remove tokens in the range [preserve, preserve + length) + supports DynamicCache and tuple cache formats + """ + if self.cache is None or length <= 0: + return False + + cache_type = "DynamicCache" if isinstance(self.cache, DynamicCache) else "TupleCache" + cache_len_before = self.get_cache_length() + offset_before = self._position_offset + + new_cache, new_offset, success = drop_tokens_from_cache( + cache=self.cache, + length=length, + preserve=self._system_preserve_length, + position_offset=self._position_offset, + rope_theta=self._get_rope_theta(), + inv_freq_cache=self._rope_inv_freq_cache, + ) + if success: + self.cache = new_cache # For DynamicCache this is the same object (in-place) + self._position_offset = new_offset + + return success + + def _drop_unit(self, unit_id: int) -> bool: + """remove specified unit""" + entries = [u for u in self._unit_history if u["unit_id"] == unit_id] + if not entries: + return False + + total_len = sum(e["length"] for e in entries) + if total_len <= 0: + for e in entries: + self._unit_history.remove(e) + return False + + if not self._drop_tokens_from_cache(total_len): + return False + + for e in entries: + self._unit_history.remove(e) + + return True + + def _drop_next_unit(self) -> bool: + """remove the earliest non-system unit""" + for entry in self._unit_history: + unit_id = entry.get("unit_id") + if unit_id is None: + continue + # skip system type + if entry.get("type") == "system": + continue + if self._drop_unit(unit_id): + return True + return False + + def enforce_window(self) -> bool: + """enforce sliding window strategy (same as single-mode, only look at cache length) + + when cache length exceeds high water line, loop to remove the earliest unit, + until cache length drops below the low water line. + """ + if not self._window_enabled: + return False + + cfg = self._window_config + cache_len_before = self.get_cache_length() + + if cache_len_before <= cfg.basic_window_high_tokens: + return False # not above high water line, no trigger + + dropped_count = 0 + cache_len = cache_len_before + while cache_len > cfg.basic_window_low_tokens: + if not self._drop_next_unit(): + break + dropped_count += 1 + cache_len = self.get_cache_length() + + if dropped_count > 0: + # update statistics counters + self._sliding_event_count += 1 + self._total_dropped_tokens += cache_len_before - cache_len + self._total_dropped_units += dropped_count + + # consistency check + expected = self._system_preserve_length + sum(u["length"] for u in self._unit_history) + is_consistent = expected == cache_len + if not is_consistent: + logger.error( + "CONSISTENCY ERROR! preserve=%d + sum(units)=%d != cache=%d, offset=%d", + self._system_preserve_length, + sum(u["length"] for u in self._unit_history), + cache_len, + self._position_offset, + ) + + return dropped_count > 0 + + # context preserving sliding window methods + + def register_system_prompt_with_context( + self, + suffix_token_ids: Optional[List[int]] = None, + context_previous_marker: str = "\n\nprevious: ", + ): + """register system prompt (with context preserving mode) + + initial cache layout: [prefix] [suffix] [units...] + after first sliding window: [prefix] [context_previous_marker + content] [suffix] [units...] + + when calling this method, cache should only have prefix (without previous marker) + suffix will be fed in later + + Args: + suffix_token_ids: suffix token ids (e.g. id of <|im_end|>) + context_previous_marker: previous marker prefix, e.g. "\\n\\nprevious: " + """ + # prefix = current cache content (fixed, without previous marker) + self._preserve_prefix_length = self.get_cache_length() + self._previous_content_length = 0 # initially no previous content + self._suffix_token_ids = suffix_token_ids or [] + # total preserve length = prefix + suffix (initially no previous) + self._system_preserve_length = self._preserve_prefix_length + len(self._suffix_token_ids) + + # initialize previous related states + self._previous_marker = context_previous_marker + self._previous_marker_token_ids = ( + self.tokenizer.encode(context_previous_marker, add_special_tokens=False) if self.tokenizer else [] + ) + self._has_previous = False + self._previous_text = "" + self._previous_token_ids = [] + + def _extract_generated_text(self, units: List[Dict[str, Any]]) -> Tuple[str, List[int]]: + """extract generated text and token ids from units + + Args: + units: list of units to extract + + Returns: + (text, token_ids): concatenated text and token ids (filtered out special tokens) + """ + text_parts = [] + token_ids = [] + + for u in units: + # only keep generated content of non-listen units + if u.get("is_listen", False): + continue + gen_text = u.get("generated_text", "") + gen_tokens = u.get("generated_tokens", []) + + # filter out special tokens from text + if gen_text: + clean_text = gen_text + for st in self._all_special_tokens_text: + clean_text = clean_text.replace(st, "") + if clean_text.strip(): + text_parts.append(clean_text) + + # filter out special tokens + if gen_tokens: + filtered_tokens = [t for t in gen_tokens if t not in self._all_special_ids] + token_ids.extend(filtered_tokens) + + return "".join(text_parts), token_ids + + def _rebuild_cache_with_previous( + self, + new_previous_tokens: List[int], + units_to_keep_len: Optional[int] = None, + ) -> bool: + """rebuild cache, insert new previous content between prefix and suffix + + cache layout change: + [prefix] [old_prev] [suffix] [old_units] → [prefix] [new_prev] [suffix] [remaining_units] + + Args: + new_previous_tokens: new previous token ids + units_to_keep_len: length of units to keep (from cache end backwards) + if None, calculate based on unit_history + + Returns: + whether successful rebuild + """ + if self.cache is None: + return False + + old_previous_len = self._previous_content_length + new_previous_len = len(new_previous_tokens) + suffix_len = len(self._suffix_token_ids) + total_cache_len = self.get_cache_length() + + # calculate length of units to keep + if units_to_keep_len is None: + units_to_keep_len = sum(u["length"] for u in self._unit_history) + + # special case: if previous is unchanged (new and old are empty), no need to rebuild prefix+suffix part of cache + # but still need to reindex units RoPE (because a unit was deleted, position changed) + if new_previous_len == 0 and old_previous_len == 0: + # cache layout: [prefix(7)] [suffix(1)] [units...] + # only keep prefix + suffix + remaining_units + preserve_len = self._preserve_prefix_length + suffix_len + + # simply slice cache: [prefix+suffix] + [remaining_units] + # remaining_units in cache end + if units_to_keep_len > 0: + # [0:preserve_len] + [total-units_to_keep_len:total] + prefix_suffix_cache = self._slice_cache(0, preserve_len) + units_cache = self._slice_cache(total_cache_len - units_to_keep_len, None) + + # calculate number of dropped tokens + dropped_tokens = total_cache_len - preserve_len - units_to_keep_len + + # reindex units RoPE: position from (preserve_len + dropped_tokens) to preserve_len + # note: no position_offset, because cache position has been compressed (from 0 start) + if dropped_tokens > 0: + old_start = preserve_len + dropped_tokens + new_start = preserve_len + units_cache = self._reindex_rope_for_cache(units_cache, old_start, new_start, units_to_keep_len) + + self.cache = self._concat_caches(prefix_suffix_cache, units_cache) + else: + self.cache = self._slice_cache(0, preserve_len) + + return True + + # 1. get prefix cache (fixed) + prefix_end = self._preserve_prefix_length + prefix_cache = self._slice_cache(0, prefix_end) + + # 2. get units cache to keep (from end) + units_start_in_old_cache = total_cache_len - units_to_keep_len + units_cache = None + if units_to_keep_len > 0: + units_cache = self._slice_cache(units_start_in_old_cache, None) + + # 3. calculate new previous + suffix cache (needs forward) + # merge previous tokens and suffix tokens + prev_suffix_tokens = new_previous_tokens + self._suffix_token_ids + prev_suffix_len = len(prev_suffix_tokens) + + new_prefix_prev_suffix_cache = prefix_cache + if prev_suffix_len > 0: + # Embed tokens + prev_suffix_embeds = self.embed_tokens(prev_suffix_tokens) + # calculate start position (after prefix) + start_pos = self._preserve_prefix_length + self._position_offset + + # forward calculate KV cache + with torch.no_grad(): + device = prev_suffix_embeds.device + position_ids = torch.arange( + start_pos, + start_pos + prev_suffix_len, + device=device, + ).unsqueeze(0) + + # use prefix cache as past_key_values + outputs = self.m( + inputs_embeds=( + prev_suffix_embeds.unsqueeze(0) if prev_suffix_embeds.dim() == 2 else prev_suffix_embeds + ), + position_ids=position_ids, + past_key_values=prefix_cache, + use_cache=True, + return_dict=True, + ) + # new cache contains prefix + new_previous + suffix + new_prefix_prev_suffix_cache = outputs.past_key_values + + # 4. adjust units cache RoPE + # new layout: [prefix] [new_prev] [suffix] [units] + # note: no position_offset, because cache position has been compressed (from 0 start) + new_system_total = prefix_end + new_previous_len + suffix_len + if units_cache is not None and self._get_cache_len(units_cache) > 0: + old_start = units_start_in_old_cache + new_start = new_system_total + + if old_start != new_start: + units_cache = self._reindex_rope_for_cache(units_cache, old_start, new_start, units_to_keep_len) + + # 5. concatenate new cache + if units_cache is not None and self._get_cache_len(units_cache) > 0: + self.cache = self._concat_caches(new_prefix_prev_suffix_cache, units_cache) + else: + self.cache = new_prefix_prev_suffix_cache + + # 6. update length + self._previous_content_length = new_previous_len + # total preserve length = prefix + previous + suffix + self._system_preserve_length = prefix_end + new_previous_len + suffix_len + + # print detailed cache layout information + prev_text_preview = self._previous_text[:50] + "..." if len(self._previous_text) > 50 else self._previous_text + suffix_preview = self.tokenizer.decode(self._suffix_token_ids) if self._suffix_token_ids else "" + return True + + def _slice_cache(self, start: int, end: Optional[int], clone: bool = True): + """slice cache + + Args: + start: start position + end: end position (None means to end) + clone: whether to clone (default True, to prevent shared memory issues) + """ + if self.cache is None: + return None + if isinstance(self.cache, DynamicCache): + # DynamicCache + new_key_cache = [ + k[:, :, start:end, :].clone() if clone else k[:, :, start:end, :] for k in self.cache.key_cache + ] + new_value_cache = [ + v[:, :, start:end, :].clone() if clone else v[:, :, start:end, :] for v in self.cache.value_cache + ] + new_cache = DynamicCache() + new_cache.key_cache = new_key_cache + new_cache.value_cache = new_value_cache + return new_cache + else: + # Tuple cache + if clone: + return tuple( + (layer[0][:, :, start:end, :].clone(), layer[1][:, :, start:end, :].clone()) for layer in self.cache + ) + else: + return tuple((layer[0][:, :, start:end, :], layer[1][:, :, start:end, :]) for layer in self.cache) + + @staticmethod + def _get_cache_len(cache) -> int: + if cache is None: + return 0 + if isinstance(cache, DynamicCache): + if len(cache.key_cache) > 0 and cache.key_cache[0].numel() > 0: + return cache.key_cache[0].shape[2] + return 0 + + if cache and cache[0] and cache[0][0] is not None: + return cache[0][0].shape[2] + return 0 + + @staticmethod + def _concat_caches(cache1, cache2): + if cache1 is None: + return cache2 + if cache2 is None: + return cache1 + + if isinstance(cache1, DynamicCache): + new_cache = DynamicCache() + new_cache.key_cache = [torch.cat([k1, k2], dim=2) for k1, k2 in zip(cache1.key_cache, cache2.key_cache)] + new_cache.value_cache = [ + torch.cat([v1, v2], dim=2) for v1, v2 in zip(cache1.value_cache, cache2.value_cache) + ] + return new_cache + else: + return tuple( + ( + torch.cat([layer1[0], layer2[0]], dim=2), + torch.cat([layer1[1], layer2[1]], dim=2), + ) + for layer1, layer2 in zip(cache1, cache2) + ) + + def _reindex_rope_for_cache(self, cache, old_start: int, new_start: int, length: int): + """reindex RoPE position for cache""" + if cache is None or length <= 0: + return cache + + if isinstance(cache, DynamicCache): + device = cache.key_cache[0].device if cache.key_cache else None + else: + device = cache[0][0].device if cache and cache[0] else None + + if device is None: + return cache + + old_positions = torch.arange(old_start, old_start + length, device=device, dtype=torch.long) + new_positions = torch.arange(new_start, new_start + length, device=device, dtype=torch.long) + + rope_theta = self._get_rope_theta() + + if isinstance(cache, DynamicCache): + new_key_cache = [] + for k in cache.key_cache: + new_k = realign_rotary_suffix(k, old_positions, new_positions, rope_theta, self._rope_inv_freq_cache) + new_key_cache.append(new_k) + cache.key_cache = new_key_cache + return cache + else: + new_cache = [] + for layer in cache: + new_k = realign_rotary_suffix( + layer[0], old_positions, new_positions, rope_theta, self._rope_inv_freq_cache + ) + new_cache.append((new_k, layer[1])) + return tuple(new_cache) + + def _update_previous( + self, + new_text: str, + new_tokens: List[int], + max_tokens: int, + ) -> None: + """update previous context (also update cache) + + when first sliding window, dynamically add marker + text, subsequent sliding window append text + when content exceeds max_tokens, truncate content (keep marker) + rebuild cache to maintain consistency + + Args: + new_text: new text + new_tokens: new token ids + max_tokens: previous content maximum token count (without marker) + """ + marker_len = len(self._previous_marker_token_ids) + tokens_to_drop = 0 + + # if no new content, do not add marker, but still need to rebuild cache + if not new_tokens and not new_text: + # still need to rebuild cache (because a unit was deleted) + self._rebuild_cache_with_previous(self._previous_token_ids) + return + + if not self._has_previous: + # when first has actual content: add marker + text + self._previous_text = new_text + self._previous_token_ids = self._previous_marker_token_ids.copy() + new_tokens + self._has_previous = True + else: + # subsequent sliding window: append text to previous + self._previous_text += new_text + self._previous_token_ids.extend(new_tokens) + + # calculate token count of content (without marker) + content_token_count = len(self._previous_token_ids) - marker_len + + # check if need to truncate content (keep marker) + if content_token_count > max_tokens: + # truncate left content, keep marker + latest max_tokens content + tokens_to_drop = content_token_count - max_tokens + old_text = self._previous_text + # keep marker + truncated content + content_tokens = self._previous_token_ids[marker_len + tokens_to_drop :] + self._previous_token_ids = self._previous_marker_token_ids.copy() + content_tokens + # redecode text (only decode content part) + try: + self._previous_text = self.tokenizer.decode( + content_tokens, + skip_special_tokens=True, + ) + except Exception as e: + logger.warning("_update_previous: decode failed: %s", e) + + # rebuild cache + self._rebuild_cache_with_previous(self._previous_token_ids) + + def _drop_unit_with_context( + self, + unit_id: int, + max_previous_tokens: int, + ) -> Tuple[bool, str, List[int]]: + """remove specified unit and return its generated content (for context preserving) + + process: + 1. extract generated content of unit + 2. remove unit from cache (without prefix+previous) + 3. append generated content to previous + 4. rebuild cache (in _update_previous) + + Args: + unit_id: unit ID to remove + max_previous_tokens: previous maximum token count + + Returns: + (success, extracted_text, extracted_tokens): whether successful, extracted text and tokens + """ + entries = [u for u in self._unit_history if u["unit_id"] == unit_id] + if not entries: + return False, "", [] + + # extract generated content + extracted_text, extracted_tokens = self._extract_generated_text(entries) + + # calculate total length + total_len = sum(e["length"] for e in entries) + if total_len <= 0: + for e in entries: + self._unit_history.remove(e) + return False, extracted_text, extracted_tokens + + cache_before = self.get_cache_length() + + # remove from unit_history (record for later processing) + for e in entries: + self._unit_history.remove(e) + + # note: here no longer call _drop_tokens_from_cache + # because _update_previous will rebuild the entire cache + + # update previous (also rebuild cache) + self._update_previous(extracted_text, extracted_tokens, max_previous_tokens) + + return True, extracted_text, extracted_tokens + + def _drop_next_unit_with_context(self, max_previous_tokens: int) -> bool: + """remove the earliest non-system unit (with context preserving)""" + for entry in self._unit_history: + unit_id = entry.get("unit_id") + if unit_id is None: + continue + if entry.get("type") == "system": + continue + success, _, _ = self._drop_unit_with_context(unit_id, max_previous_tokens) + if success: + return True + return False + + def enforce_window_with_context(self) -> bool: + """context preserving sliding window execution + + when unit count exceeds max_units, remove the earliest unit, + and accumulate its generated content to previous. + Cache will be automatically rebuilt in _update_previous. + + Returns: + whether sliding window is executed + """ + if not self._window_enabled: + return False + + cfg = self._window_config + + if cfg.sliding_window_mode != "context": + # if not context mode, fallback to basic sliding window + return self.enforce_window() + + cache_len_before = self.get_cache_length() + units_before = len(self._unit_history) + + # context preserving mode: only check if unit count exceeds limit + # (previous exceeds limit in _update_previous will automatically truncate left) + if units_before <= cfg.context_max_units: + return False + + # sliding window loop: remove unit until count ≤ max_units + dropped_count = 0 + while len(self._unit_history) > cfg.context_max_units: + if not self._drop_next_unit_with_context(cfg.context_previous_max_tokens): + break + + dropped_count += 1 + + cache_len_after = self.get_cache_length() + + if dropped_count > 0: + # update statistics counter + self._sliding_event_count += 1 + self._total_dropped_tokens += cache_len_before - cache_len_after + self._total_dropped_units += dropped_count + + # consistency check + expected = self._system_preserve_length + sum(u["length"] for u in self._unit_history) + + return dropped_count > 0 + + def get_previous_context(self) -> Tuple[str, List[int]]: + """get current accumulated previous context + + Returns: + (previous_text, previous_token_ids): current accumulated text and token ids + """ + return self._previous_text, self._previous_token_ids.copy() + + def get_window_stats(self) -> Dict[str, Any]: + """get sliding window statistics""" + unit_lengths = [u["length"] for u in self._unit_history] + return { + "cache_length": self.get_cache_length(), + "unit_count": len(self._unit_history), + "unit_lengths": unit_lengths, + "unit_total_length": sum(unit_lengths), + "system_preserve_length": self._system_preserve_length, + "position_offset": self._position_offset, + "window_enabled": self._window_enabled, + "total_generated_tokens": self.get_total_generated_tokens(), + "pending_unit_id": self._pending_unit_id, + "next_unit_id": self._next_unit_id, + "config": { + "sliding_window_mode": self._window_config.sliding_window_mode, + "basic_window_high_tokens": self._window_config.basic_window_high_tokens, + "basic_window_low_tokens": self._window_config.basic_window_low_tokens, + "context_previous_max_tokens": self._window_config.context_previous_max_tokens, + "context_max_units": self._window_config.context_max_units, + }, + # context preserving related + "preserve_prefix_length": self._preserve_prefix_length, + "previous_content_length": self._previous_content_length, + "suffix_token_count": len(self._suffix_token_ids), + "previous_text_length": len(self._previous_text), + "previous_token_count": len(self._previous_token_ids), + "has_system_template": self._system_prompt_template is not None, + } + + def _verify_consistency(self) -> bool: + """verify unit history and cache length consistency""" + expected = self._system_preserve_length + sum(u["length"] for u in self._unit_history) + actual = self.get_cache_length() + return expected == actual + + def print_verification_summary(self) -> Dict[str, Any]: + """print verification summary (for comparing off/basic/context mode) + + Returns: + dictionary containing key verification data + """ + cfg = self._window_config + + # collect all generated text + all_generated_text = [] + all_generated_tokens = [] + for u in self._unit_history: + if not u.get("is_listen", False): + gen_text = u.get("generated_text", "") + gen_tokens = u.get("generated_tokens", []) + if gen_text: + all_generated_text.append(gen_text) + if gen_tokens: + all_generated_tokens.extend(gen_tokens) + + combined_text = "".join(all_generated_text) + + summary = { + "mode": cfg.sliding_window_mode, + "final_cache_length": self.get_cache_length(), + "final_unit_count": len(self._unit_history), + "sliding_event_count": self._sliding_event_count, + "total_dropped_tokens": self._total_dropped_tokens, + "total_dropped_units": self._total_dropped_units, + "total_generated_tokens": len(all_generated_tokens), + "generated_text": combined_text, + "previous_text": self._previous_text, + "previous_token_count": len(self._previous_token_ids), + "position_offset": self._position_offset, + "system_preserve_length": self._system_preserve_length, + } + + return summary + + def set_window_config(self, config: DuplexWindowConfig) -> None: + """set sliding window configuration""" + self._window_config = config + + def set_window_enabled(self, enabled: bool) -> None: + """enable/disable sliding window""" + old_enabled = self._window_enabled + self._window_enabled = enabled + + def get_context(self): + return self.context + + def embed_token(self, tid): + if isinstance(tid, int): + tid = torch.tensor([tid], device=self.m.device) + return self.m.model.embed_tokens(tid) + + def embed_tokens(self, token_ids: List[int]) -> torch.Tensor: + """batch embed multiple tokens + + Args: + token_ids: list of token ids + + Returns: + embeddings tensor [L, H] + """ + if not token_ids: + return torch.empty(0, self.m.config.hidden_size, device=self.m.device) + tids = torch.tensor(token_ids, device=self.m.device) + return self.m.model.embed_tokens(tids) + + @torch.no_grad() + def feed(self, embeds: torch.Tensor, return_logits: bool = False): + """ + embeds : [L, H] —— new embedding sequence fed into model at once + """ + L = embeds.size(0) + device = embeds.device + + past_len = self.get_cache_length() + pos_ids = torch.arange(past_len, past_len + L, device=device).unsqueeze(0) # [1, L] + + out = self.m( + inputs_embeds=embeds.unsqueeze(0), # [1, L, H] + position_ids=pos_ids, + past_key_values=self.cache, + # use_cache = True, + return_dict=True, + output_hidden_states=True, + # attention_mask=attention_mask + ) + self.cache = out.past_key_values + + if return_logits: + logits = self.m.lm_head(out.hidden_states[-1])[:, -1] # [1, vocab] + return logits, out.hidden_states[-1] + + @torch.no_grad() + def decode( + self, + logits, + mode: Literal["sampling", "greedy"] = "sampling", + temperature=0.7, + top_k=20, + top_p=0.8, + listen_top_k=None, + listen_prob_scale=1.0, + text_repetition_penalty=1.05, + text_repetition_window_size=512, + ): + """ + Args: + logits: + mode: sampling or greedy + temperature: + top_k: + top_p: + listen_top_k: force listen_id to be in top-k to keep + listen_prob_scale: multiply listen_id probability by a weight (<1 means decrease, >1 means increase) + text_repetition_penalty: repetition penalty coefficient, >1.0 means decrease repetition, <1.0 means increase repetition + text_repetition_window_size: repetition penalty window size + + Sampling strategy: + 1. first sample all tokens with original logits (apply temperature) + 2. if sampled chunk_eos, return directly (keep the original model's decision of when to stop) + 3. if not sampled chunk_eos, mask it (set logit to -inf), continue sampling text tokens + 4. apply repetition penalty, top-k, top-p, etc. to the text tokens for the final sampling + """ + + logits = logits.clone() + + # 0. independently check chunk_eos before sampling + eos_id = self.chunk_eos_id + + with torch.no_grad(): + if mode == "greedy": + sampled_token = torch.argmax(logits[0]).item() + else: + original_probs = F.softmax(logits[0], dim=-1) + sampled_token = torch.multinomial(original_probs, num_samples=1).item() + + # if sampled chunk_eos, return directly + if sampled_token == eos_id: + next_token_id = torch.tensor([eos_id], device=logits.device) + next_token_str = self.tokenizer.decode(next_token_id) + + return next_token_id + + # if not sampled chunk_eos, set its logit to -inf + if self.forbidden_token_ids: + logits[:, self.forbidden_token_ids] = float("-inf") + + # 1. apply repetition penalty + if text_repetition_penalty != 1.0 and len(self.generated_tokens) > 0: + # get recent tokens (within window size) considering special tokens and normal tokens + recent_tokens = self.generated_tokens[-text_repetition_window_size:] + + # make it unique + recent_tokens = list(set(recent_tokens)) + + # apply penalty to repeated tokens + for token_id in recent_tokens: + if token_id < logits.size(-1): # ensure token_id is in vocabulary range + if text_repetition_penalty > 1.0: + # penalize repetition: decrease logits + logits[0, token_id] /= text_repetition_penalty + else: + # encourage repetition: increase logits + logits[0, token_id] *= 1.0 / text_repetition_penalty + + if listen_prob_scale != 1.0: # modify listen token logit separately + logits[0, self.listen_id] *= listen_prob_scale + + listen_rank = (logits[0] > logits[0, self.listen_id]).sum().item() + + if listen_top_k is not None and listen_rank < listen_top_k: # listen_id is in top-k, return directly + next_token_id = torch.tensor([self.listen_id], device=logits.device) + next_token_str = self.tokenizer.decode(next_token_id) + + if next_token_str == "<|listen|>": + self.context += " " + else: + self.context += next_token_str + + return next_token_id + + if mode == "greedy": + next_token_id = torch.argmax(logits, dim=-1) + elif mode == "sampling": + logits = logits / temperature + logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p) + probs = F.softmax(logits, dim=-1) + next_token_id = torch.multinomial(probs, num_samples=1).squeeze(1) + else: + raise ValueError(f"Unsupported decode mode: {mode}") + + if next_token_id.item() not in self.special_token_ids: + self.generated_tokens.append(next_token_id.item()) + else: + self.generated_special_tokens.append(next_token_id.item()) + + return next_token_id + + +def _download_url_to_tempfile(url: str, suffix: str = "", timeout: int = 60) -> str: + """ + Download a URL to a temporary file and return the path. + + Args: + url: HTTP/HTTPS URL to download + suffix: File suffix (e.g., ".jpg", ".wav", ".mp4") + timeout: Download timeout in seconds + + Returns: + Path to the downloaded temporary file + """ + import tempfile + + import requests + + response = requests.get(url, timeout=timeout) + response.raise_for_status() + + with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as f: + f.write(response.content) + return f.name + + +def _is_url(path: str) -> bool: + return path.startswith(("http://", "https://")) + + +def normalize_content_item(item) -> Union[str, Any, List[Any]]: + """Normalize structured content item to native format. + + Supports: + - Native format: str, PIL.Image, np.ndarray (pass through) + - OpenAI structured format: + - {"type": "text", "text": "..."} -> str + - {"type": "image_url", "image_url": {"url": "..."}} -> PIL.Image + - {"type": "audio_url", "audio_url": {"url": "..."}} -> np.ndarray + - {"type": "video_url", "video_url": {"url": "...", ...}} -> List[Image, ndarray, ...] + + URL formats supported: + - Local file path: "/path/to/file.jpg" + - HTTP/HTTPS URL: "https://example.com/image.jpg" + + Args: + item: Content item to normalize + + Returns: + Normalized item. For video_url, returns a tuple ("__video_contents__", list) + that will be flattened by normalize_content(). + + Raises: + ValueError: If content type is unknown or unsupported + """ + import os + + import numpy as np + from PIL import Image + + if isinstance(item, str): + return item + if isinstance(item, Image.Image): + return item + if isinstance(item, np.ndarray): + return item + + if isinstance(item, dict): + item_type = item.get("type") + + if item_type == "text": + return item.get("text", "") + + elif item_type == "image_url": + image_url_obj = item.get("image_url", {}) + url = image_url_obj.get("url", "") if isinstance(image_url_obj, dict) else image_url_obj + + if _is_url(url): + # Download to temp file + temp_path = _download_url_to_tempfile(url, suffix=".jpg", timeout=30) + img = Image.open(temp_path) + os.unlink(temp_path) + return img + else: + return Image.open(url) + elif item_type == "audio_url": + import librosa + + audio_url_obj = item.get("audio_url", {}) + url = audio_url_obj.get("url", "") if isinstance(audio_url_obj, dict) else audio_url_obj + + if _is_url(url): + # Download to temp file + temp_path = _download_url_to_tempfile(url, suffix=".wav", timeout=60) + audio_np, _ = librosa.load(temp_path, sr=16000, mono=True) + os.unlink(temp_path) + return audio_np + else: + audio_np, _ = librosa.load(url, sr=16000, mono=True) + return audio_np + elif item_type == "video_url": + # Video processing - returns a LIST of items (frames + audio segments) + # Note: Unlike image_url/audio_url which return single items, + # video_url returns a list that will be flattened into the content + from minicpmo.utils import get_video_frame_audio_segments + + video_url_obj = item.get("video_url", {}) + if isinstance(video_url_obj, dict): + video_url = video_url_obj.get("url", "") + # Get optional parameters from video_url object (OpenAI style) + stack_frames = video_url_obj.get("stack_frames", 1) + use_ffmpeg = video_url_obj.get("use_ffmpeg", False) + use_audio = video_url_obj.get("use_audio", True) + else: + video_url = video_url_obj + stack_frames = 1 + use_ffmpeg = False + use_audio = True + + # Handle HTTP/HTTPS URL - download to temp file + temp_video_path = None + if _is_url(video_url): + temp_video_path = _download_url_to_tempfile(video_url, suffix=".mp4", timeout=120) + video_path = temp_video_path + else: + video_path = video_url + + # Extract frames and audio segments + video_frames, audio_segments, stacked_frames = get_video_frame_audio_segments( + video_path, + stack_frames=stack_frames, + use_ffmpeg=use_ffmpeg, + use_audio=use_audio + ) + + # Clean up temp file if downloaded + if temp_video_path is not None: + os.unlink(temp_video_path) + + # Build omni_contents (interleaved frames and audio, or frames only) + omni_contents = [] + for i in range(len(video_frames)): + omni_contents.append(video_frames[i]) + if use_audio and audio_segments is not None: + omni_contents.append(audio_segments[i]) + if stacked_frames is not None and i < len(stacked_frames) and stacked_frames[i] is not None: + omni_contents.append(stacked_frames[i]) + + # Return as a special marker to be flattened later + return "__video_contents__", omni_contents + else: + raise ValueError(f"Unknown content type: {item_type}") + + raise ValueError(f"Cannot normalize content item of type: {type(item)}") + + +def normalize_content(content) -> list: + """Normalize message content to list of native items. + + Input formats: + - str: "hello" -> ["hello"] + - list of native items: [str, Image, np.ndarray] -> pass through with normalization + - list of structured items: [{"type": "text", ...}] -> normalize each + - video type: automatically expanded to omni_contents + - mixed: works too + + Args: + content: Message content in any supported format + + Returns: + List of native items (str, PIL.Image, np.ndarray) + + Examples: + >>> normalize_content("hello") + ["hello"] + + >>> normalize_content([{"type": "text", "text": "hi"}]) + ["hi"] + + >>> normalize_content([{"type": "video", "video": "/path/to/video.mp4"}]) + [, , , , ...] + """ + import numpy as np + from PIL import Image + + if isinstance(content, str): + return [content] + + if isinstance(content, list): + result = [] + for item in content: + normalized = normalize_content_item(item) + # Handle video content (returns tuple with marker) + if isinstance(normalized, tuple) and len(normalized) == 2 and normalized[0] == "__video_contents__": + # Flatten video contents into result + result.extend(normalized[1]) + else: + result.append(normalized) + return result + + # Single non-list item (Image or np.ndarray) + if isinstance(content, (Image.Image, np.ndarray)): + return [content] + + normalized = normalize_content_item(content) + if isinstance(normalized, tuple) and len(normalized) == 2 and normalized[0] == "__video_contents__": + return normalized[1] + return [normalized] diff --git a/vocab.json b/vocab.json new file mode 100644 index 0000000000000000000000000000000000000000..4783fe10ac3adce15ac8f358ef5462739852c569 --- /dev/null +++ b/vocab.json @@ -0,0 +1 @@ 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