+
+
+
+
+## Overview
+### Introduction
+Qwen2.5-Omni is an end-to-end multimodal model designed to perceive diverse modalities, including text, images, audio, and video, while simultaneously generating text and natural speech responses in a streaming manner.
+
+
+ 
+
+ +### Key Features + +* **Omni and Novel Architecture**: We propose Thinker-Talker architecture, an end-to-end multimodal model designed to perceive diverse modalities, including text, images, audio, and video, while simultaneously generating text and natural speech responses in a streaming manner. We propose a novel position embedding, named TMRoPE (Time-aligned Multimodal RoPE), to synchronize the timestamps of video inputs with audio. + +* **Real-Time Voice and Video Chat**: Architecture designed for fully real-time interactions, supporting chunked input and immediate output. + +* **Natural and Robust Speech Generation**: Surpassing many existing streaming and non-streaming alternatives, demonstrating superior robustness and naturalness in speech generation. + +* **Strong Performance Across Modalities**: Exhibiting exceptional performance across all modalities when benchmarked against similarly sized single-modality models. Qwen2.5-Omni outperforms the similarly sized Qwen2-Audio in audio capabilities and achieves comparable performance to Qwen2.5-VL-7B. + +* **Excellent End-to-End Speech Instruction Following**: Qwen2.5-Omni shows performance in end-to-end speech instruction following that rivals its effectiveness with text inputs, evidenced by benchmarks such as MMLU and GSM8K. + +### Model Architecture + +
+ 
+
+ +### Performance + +We conducted a comprehensive evaluation of Qwen2.5-Omni, which demonstrates strong performance across all modalities when compared to similarly sized single-modality models and closed-source models like Qwen2.5-VL-7B, Qwen2-Audio, and Gemini-1.5-pro. In tasks requiring the integration of multiple modalities, such as OmniBench, Qwen2.5-Omni achieves state-of-the-art performance. Furthermore, in single-modality tasks, it excels in areas including speech recognition (Common Voice), translation (CoVoST2), audio understanding (MMAU), image reasoning (MMMU, MMStar), video understanding (MVBench), and speech generation (Seed-tts-eval and subjective naturalness). + +
+ 
+
+
+ YRf6NHY?zorU4lUUYpqi+J%C^mKOP!$d+i
zw*%AdTG7$i4dbh)F?x=zo7Zk))q{3C;5I|{(J8hMC_r#@1x6y$@aIMvez&BfZ({?D
z@-HIsO$Rpcs-ei7#PbNky7f)qyg7{e3A*6#R)CL?3|54X$CE$-#Hk5F{2)Kfs`%I(
z{Y%j$Uun_KPjp~th)P92&``)5n$|W*D{sA_G2R=RqxX^~2!5gJg^IYiXg ^oSYg?naq41F`-0%
z7aeubVeXMkjd@2H(C8w|eHH4iyTMwUpTzb0htal%YTZ5JMVMr+ZnAH##>L&?NJbLh
zs-@CRCy0sX-6bdEN#vR!7X0+W@|!1rddT@$;Lpi~VA9V;@NLOPl3xyCU${24vf8mr
z@`aX17vtRJfBh$Ubnwf>Mt2uWUx!n3-fsF^d-3^<9Y=}=G9gRO?o
+
+
+
+Multimodality -> Text
+
+
+
+
+
+
+ Datasets
+ Model
+ Performance
+
+
+ OmniBench
+
Speech | Sound Event | Music | AvgGemini-1.5-Pro
+ 42.67%|42.26%|46.23%|42.91%
+
+
+ MIO-Instruct
+ 36.96%|33.58%|11.32%|33.80%
+
+
+ AnyGPT (7B)
+ 17.77%|20.75%|13.21%|18.04%
+
+
+ video-SALMONN
+ 34.11%|31.70%|56.60%|35.64%
+
+
+ UnifiedIO2-xlarge
+ 39.56%|36.98%|29.25%|38.00%
+
+
+ UnifiedIO2-xxlarge
+ 34.24%|36.98%|24.53%|33.98%
+
+
+ MiniCPM-o
+ -|-|-|40.50%
+
+
+ Baichuan-Omni-1.5
+ -|-|-|42.90%
+
+
+ Qwen2.5-Omni-3B
+ 52.14%|52.08%|52.83%|52.19%
+
+
+Qwen2.5-Omni-7B
+ 55.25%|60.00%|52.83%|56.13%
+
+
+
+Audio -> Text
+
+
+
+
+
+
+
+ Datasets
+ Model
+ Performance
+
+
+ ASR
+
+
+ Librispeech
+
dev-clean | dev other | test-clean | test-otherSALMONN
+ -|-|2.1|4.9
+
+
+ SpeechVerse
+ -|-|2.1|4.4
+
+
+ Whisper-large-v3
+ -|-|1.8|3.6
+
+
+ Llama-3-8B
+ -|-|-|3.4
+
+
+ Llama-3-70B
+ -|-|-|3.1
+
+
+ Seed-ASR-Multilingual
+ -|-|1.6|2.8
+
+
+ MiniCPM-o
+ -|-|1.7|-
+
+
+ MinMo
+ -|-|1.7|3.9
+
+
+ Qwen-Audio
+ 1.8|4.0|2.0|4.2
+
+
+ Qwen2-Audio
+ 1.3|3.4|1.6|3.6
+
+
+ Qwen2.5-Omni-3B
+ 2.0|4.1|2.2|4.5
+
+
+ Qwen2.5-Omni-7B
+ 1.6|3.5|1.8|3.4
+
+
+ Common Voice 15
+
en | zh | yue | frWhisper-large-v3
+ 9.3|12.8|10.9|10.8
+
+
+ MinMo
+ 7.9|6.3|6.4|8.5
+
+
+ Qwen2-Audio
+ 8.6|6.9|5.9|9.6
+
+
+ Qwen2.5-Omni-3B
+ 9.1|6.0|11.6|9.6
+
+
+ Qwen2.5-Omni-7B
+ 7.6|5.2|7.3|7.5
+
+
+ Fleurs
+
zh | enWhisper-large-v3
+ 7.7|4.1
+
+
+ Seed-ASR-Multilingual
+ -|3.4
+
+
+ Megrez-3B-Omni
+ 10.8|-
+
+
+ MiniCPM-o
+ 4.4|-
+
+
+ MinMo
+ 3.0|3.8
+
+
+ Qwen2-Audio
+ 7.5|-
+
+
+ Qwen2.5-Omni-3B
+ 3.2|5.4
+
+
+ Qwen2.5-Omni-7B
+ 3.0|4.1
+
+
+ Wenetspeech
+
test-net | test-meetingSeed-ASR-Chinese
+ 4.7|5.7
+
+
+ Megrez-3B-Omni
+ -|16.4
+
+
+ MiniCPM-o
+ 6.9|-
+
+
+ MinMo
+ 6.8|7.4
+
+
+ Qwen2.5-Omni-3B
+ 6.3|8.1
+
+
+ Qwen2.5-Omni-7B
+ 5.9|7.7
+
+
+ Voxpopuli-V1.0-en
+ Llama-3-8B
+ 6.2
+
+
+ Llama-3-70B
+ 5.7
+
+
+ Qwen2.5-Omni-3B
+ 6.6
+
+
+ Qwen2.5-Omni-7B
+ 5.8
+
+
+ S2TT
+
+
+ CoVoST2
+
en-de | de-en | en-zh | zh-enSALMONN
+ 18.6|-|33.1|-
+
+
+ SpeechLLaMA
+ -|27.1|-|12.3
+
+
+ BLSP
+ 14.1|-|-|-
+
+
+ MiniCPM-o
+ -|-|48.2|27.2
+
+
+ MinMo
+ -|39.9|46.7|26.0
+
+
+ Qwen-Audio
+ 25.1|33.9|41.5|15.7
+
+
+ Qwen2-Audio
+ 29.9|35.2|45.2|24.4
+
+
+ Qwen2.5-Omni-3B
+ 28.3|38.1|41.4|26.6
+
+
+ Qwen2.5-Omni-7B
+ 30.2|37.7|41.4|29.4
+
+
+ SER
+
+
+ Meld
+ WavLM-large
+ 0.542
+
+
+ MiniCPM-o
+ 0.524
+
+
+ Qwen-Audio
+ 0.557
+
+
+ Qwen2-Audio
+ 0.553
+
+
+ Qwen2.5-Omni-3B
+ 0.558
+
+
+ Qwen2.5-Omni-7B
+ 0.570
+
+
+ VSC
+
+
+ VocalSound
+ CLAP
+ 0.495
+
+
+ Pengi
+ 0.604
+
+
+ Qwen-Audio
+ 0.929
+
+
+ Qwen2-Audio
+ 0.939
+
+
+ Qwen2.5-Omni-3B
+ 0.936
+
+
+ Qwen2.5-Omni-7B
+ 0.939
+
+
+ Music
+
+
+ GiantSteps Tempo
+ Llark-7B
+ 0.86
+
+
+ Qwen2.5-Omni-3B
+ 0.88
+
+
+ Qwen2.5-Omni-7B
+ 0.88
+
+
+ MusicCaps
+ LP-MusicCaps
+ 0.291|0.149|0.089|0.061|0.129|0.130
+
+
+ Qwen2.5-Omni-3B
+ 0.325|0.163|0.093|0.057|0.132|0.229
+
+
+ Qwen2.5-Omni-7B
+ 0.328|0.162|0.090|0.055|0.127|0.225
+
+
+ Audio Reasoning
+
+
+ MMAU
+
Sound | Music | Speech | AvgGemini-Pro-V1.5
+ 56.75|49.40|58.55|54.90
+
+
+ Qwen2-Audio
+ 54.95|50.98|42.04|49.20
+
+
+ Qwen2.5-Omni-3B
+ 70.27|60.48|59.16|63.30
+
+
+ Qwen2.5-Omni-7B
+ 67.87|69.16|59.76|65.60
+
+
+ Voice Chatting
+
+
+ VoiceBench
+
AlpacaEval | CommonEval | SD-QA | MMSUUltravox-v0.4.1-LLaMA-3.1-8B
+ 4.55|3.90|53.35|47.17
+
+
+ MERaLiON
+ 4.50|3.77|55.06|34.95
+
+
+ Megrez-3B-Omni
+ 3.50|2.95|25.95|27.03
+
+
+ Lyra-Base
+ 3.85|3.50|38.25|49.74
+
+
+ MiniCPM-o
+ 4.42|4.15|50.72|54.78
+
+
+ Baichuan-Omni-1.5
+ 4.50|4.05|43.40|57.25
+
+
+ Qwen2-Audio
+ 3.74|3.43|35.71|35.72
+
+
+ Qwen2.5-Omni-3B
+ 4.32|4.00|49.37|50.23
+
+
+ Qwen2.5-Omni-7B
+ 4.49|3.93|55.71|61.32
+
+
+ VoiceBench
+
OpenBookQA | IFEval | AdvBench | AvgUltravox-v0.4.1-LLaMA-3.1-8B
+ 65.27|66.88|98.46|71.45
+
+
+ MERaLiON
+ 27.23|62.93|94.81|62.91
+
+
+ Megrez-3B-Omni
+ 28.35|25.71|87.69|46.25
+
+
+ Lyra-Base
+ 72.75|36.28|59.62|57.66
+
+
+ MiniCPM-o
+ 78.02|49.25|97.69|71.69
+
+
+ Baichuan-Omni-1.5
+ 74.51|54.54|97.31|71.14
+
+
+ Qwen2-Audio
+ 49.45|26.33|96.73|55.35
+
+
+ Qwen2.5-Omni-3B
+ 74.73|42.10|98.85|68.81
+
+
+Qwen2.5-Omni-7B
+ 81.10|52.87|99.42|74.12
+
+
+
+
+Image -> Text
+
+| Dataset | Qwen2.5-Omni-7B | Qwen2.5-Omni-3B | Other Best | Qwen2.5-VL-7B | GPT-4o-mini |
+|--------------------------------|--------------|------------|------------|---------------|-------------|
+| MMMUval | 59.2 | 53.1 | 53.9 | 58.6 | **60.0** |
+| MMMU-Prooverall | 36.6 | 29.7 | - | **38.3** | 37.6 |
+| MathVistatestmini | 67.9 | 59.4 | **71.9** | 68.2 | 52.5 |
+| MathVisionfull | 25.0 | 20.8 | 23.1 | **25.1** | - |
+| MMBench-V1.1-ENtest | 81.8 | 77.8 | 80.5 | **82.6** | 76.0 |
+| MMVetturbo | 66.8 | 62.1 | **67.5** | 67.1 | 66.9 |
+| MMStar | **64.0** | 55.7 | **64.0** | 63.9 | 54.8 |
+| MMEsum | 2340 | 2117 | **2372** | 2347 | 2003 |
+| MuirBench | 59.2 | 48.0 | - | **59.2** | - |
+| CRPErelation | **76.5** | 73.7 | - | 76.4 | - |
+| RealWorldQAavg | 70.3 | 62.6 | **71.9** | 68.5 | - |
+| MME-RealWorlden | **61.6** | 55.6 | - | 57.4 | - |
+| MM-MT-Bench | 6.0 | 5.0 | - | **6.3** | - |
+| AI2D | 83.2 | 79.5 | **85.8** | 83.9 | - |
+| TextVQAval | 84.4 | 79.8 | 83.2 | **84.9** | - |
+| DocVQAtest | 95.2 | 93.3 | 93.5 | **95.7** | - |
+| ChartQAtest Avg | 85.3 | 82.8 | 84.9 | **87.3** | - |
+| OCRBench_V2en | **57.8** | 51.7 | - | 56.3 | - |
+
+
+| Dataset | Qwen2.5-Omni-7B | Qwen2.5-Omni-3B | Qwen2.5-VL-7B | Grounding DINO | Gemini 1.5 Pro |
+|--------------------------|--------------|---------------|---------------|----------------|----------------|
+| Refcocoval | 90.5 | 88.7 | 90.0 | **90.6** | 73.2 |
+| RefcocotextA | **93.5** | 91.8 | 92.5 | 93.2 | 72.9 |
+| RefcocotextB | 86.6 | 84.0 | 85.4 | **88.2** | 74.6 |
+| Refcoco+val | 85.4 | 81.1 | 84.2 | **88.2** | 62.5 |
+| Refcoco+textA | **91.0** | 87.5 | 89.1 | 89.0 | 63.9 |
+| Refcoco+textB | **79.3** | 73.2 | 76.9 | 75.9 | 65.0 |
+| Refcocog+val | **87.4** | 85.0 | 87.2 | 86.1 | 75.2 |
+| Refcocog+test | **87.9** | 85.1 | 87.2 | 87.0 | 76.2 |
+| ODinW | 42.4 | 39.2 | 37.3 | **55.0** | 36.7 |
+| PointGrounding | 66.5 | 46.2 | **67.3** | - | - |
+
+
+
+Video(without audio) -> Text
+
+| Dataset | Qwen2.5-Omni-7B | Qwen2.5-Omni-3B | Other Best | Qwen2.5-VL-7B | GPT-4o-mini |
+|-----------------------------|--------------|------------|------------|---------------|-------------|
+| Video-MMEw/o sub | 64.3 | 62.0 | 63.9 | **65.1** | 64.8 |
+| Video-MMEw sub | **72.4** | 68.6 | 67.9 | 71.6 | - |
+| MVBench | **70.3** | 68.7 | 67.2 | 69.6 | - |
+| EgoSchematest | **68.6** | 61.4 | 63.2 | 65.0 | - |
+
+
+
+Zero-shot Speech Generation
+
+
+
+
+
+
+
+ Datasets
+ Model
+ Performance
+
+
+ Content Consistency
+
+
+ SEED
+
test-zh | test-en | test-hard Seed-TTS_ICL
+ 1.11 | 2.24 | 7.58
+
+
+ Seed-TTS_RL
+ 1.00 | 1.94 | 6.42
+
+
+ MaskGCT
+ 2.27 | 2.62 | 10.27
+
+
+ E2_TTS
+ 1.97 | 2.19 | -
+
+
+ F5-TTS
+ 1.56 | 1.83 | 8.67
+
+
+ CosyVoice 2
+ 1.45 | 2.57 | 6.83
+
+
+ CosyVoice 2-S
+ 1.45 | 2.38 | 8.08
+
+
+ Qwen2.5-Omni-3B_ICL
+ 1.95 | 2.87 | 9.92
+
+
+ Qwen2.5-Omni-3B_RL
+ 1.58 | 2.51 | 7.86
+
+
+ Qwen2.5-Omni-7B_ICL
+ 1.70 | 2.72 | 7.97
+
+
+ Qwen2.5-Omni-7B_RL
+ 1.42 | 2.32 | 6.54
+
+
+ Speaker Similarity
+
+
+ SEED
+
test-zh | test-en | test-hard Seed-TTS_ICL
+ 0.796 | 0.762 | 0.776
+
+
+ Seed-TTS_RL
+ 0.801 | 0.766 | 0.782
+
+
+ MaskGCT
+ 0.774 | 0.714 | 0.748
+
+
+ E2_TTS
+ 0.730 | 0.710 | -
+
+
+ F5-TTS
+ 0.741 | 0.647 | 0.713
+
+
+ CosyVoice 2
+ 0.748 | 0.652 | 0.724
+
+
+ CosyVoice 2-S
+ 0.753 | 0.654 | 0.732
+
+
+ Qwen2.5-Omni-3B_ICL
+ 0.741 | 0.635 | 0.748
+
+
+ Qwen2.5-Omni-3B_RL
+ 0.744 | 0.635 | 0.746
+
+
+ Qwen2.5-Omni-7B_ICL
+ 0.752 | 0.632 | 0.747
+
+
+Qwen2.5-Omni-7B_RL
+ 0.754 | 0.641 | 0.752
+
+
+
+## Quickstart
+
+Below, we provide simple examples to show how to use Qwen2.5-Omni with 🤗 Transformers. The codes of Qwen2.5-Omni has been in the latest Hugging face transformers and we advise you to build from source with command:
+```
+pip uninstall transformers
+pip install git+https://github.com/huggingface/transformers@v4.51.3-Qwen2.5-Omni-preview
+pip install accelerate
+```
+or you might encounter the following error:
+```
+KeyError: 'qwen2_5_omni'
+```
+
+
+We offer a toolkit to help you handle various types of audio and visual input more conveniently, as if you were using an API. This includes base64, URLs, and interleaved audio, images and videos. You can install it using the following command and make sure your system has `ffmpeg` installed:
+
+```bash
+# It's highly recommended to use `[decord]` feature for faster video loading.
+pip install qwen-omni-utils[decord] -U
+```
+
+If you are not using Linux, you might not be able to install `decord` from PyPI. In that case, you can use `pip install qwen-omni-utils -U` which will fall back to using torchvision for video processing. However, you can still [install decord from source](https://github.com/dmlc/decord?tab=readme-ov-file#install-from-source) to get decord used when loading video.
+
+### 🤗 Transformers Usage
+
+Here we show a code snippet to show you how to use the chat model with `transformers` and `qwen_omni_utils`:
+
+```python
+import soundfile as sf
+
+from transformers import Qwen2_5OmniForConditionalGeneration, Qwen2_5OmniProcessor
+from qwen_omni_utils import process_mm_info
+
+# default: Load the model on the available device(s)
+model = Qwen2_5OmniForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-Omni-7B", torch_dtype="auto", device_map="auto")
+
+# We recommend enabling flash_attention_2 for better acceleration and memory saving.
+# model = Qwen2_5OmniForConditionalGeneration.from_pretrained(
+# "Qwen/Qwen2.5-Omni-7B",
+# torch_dtype="auto",
+# device_map="auto",
+# attn_implementation="flash_attention_2",
+# )
+
+processor = Qwen2_5OmniProcessor.from_pretrained("Qwen/Qwen2.5-Omni-7B")
+
+conversation = [
+ {
+ "role": "system",
+ "content": [
+ {"type": "text", "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."}
+ ],
+ },
+ {
+ "role": "user",
+ "content": [
+ {"type": "video", "video": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2.5-Omni/draw.mp4"},
+ ],
+ },
+]
+
+# set use audio in video
+USE_AUDIO_IN_VIDEO = True
+
+# Preparation for inference
+text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
+audios, images, videos = process_mm_info(conversation, use_audio_in_video=USE_AUDIO_IN_VIDEO)
+inputs = processor(text=text, audio=audios, images=images, videos=videos, return_tensors="pt", padding=True, use_audio_in_video=USE_AUDIO_IN_VIDEO)
+inputs = inputs.to(model.device).to(model.dtype)
+
+# Inference: Generation of the output text and audio
+text_ids, audio = model.generate(**inputs, use_audio_in_video=USE_AUDIO_IN_VIDEO)
+
+text = processor.batch_decode(text_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
+print(text)
+sf.write(
+ "output.wav",
+ audio.reshape(-1).detach().cpu().numpy(),
+ samplerate=24000,
+)
+```
+
+Text -> Text
+
+| Dataset | Qwen2.5-Omni-7B | Qwen2.5-Omni-3B | Qwen2.5-7B | Qwen2.5-3B | Qwen2-7B | Llama3.1-8B | Gemma2-9B |
+|-----------------------------------|-----------|------------|------------|------------|------------|-------------|-----------|
+| MMLU-Pro | 47.0 | 40.4 | **56.3** | 43.7 | 44.1 | 48.3 | 52.1 |
+| MMLU-redux | 71.0 | 60.9 | **75.4** | 64.4 | 67.3 | 67.2 | 72.8 |
+| LiveBench0831 | 29.6 | 22.3 | **35.9** | 26.8 | 29.2 | 26.7 | 30.6 |
+| GPQA | 30.8 | 34.3 | **36.4** | 30.3 | 34.3 | 32.8 | 32.8 |
+| MATH | 71.5 | 63.6 | **75.5** | 65.9 | 52.9 | 51.9 | 44.3 |
+| GSM8K | 88.7 | 82.6 | **91.6** | 86.7 | 85.7 | 84.5 | 76.7 |
+| HumanEval | 78.7 | 70.7 | **84.8** | 74.4 | 79.9 | 72.6 | 68.9 |
+| MBPP | 73.2 | 70.4 | **79.2** | 72.7 | 67.2 | 69.6 | 74.9 |
+| MultiPL-E | 65.8 | 57.6 | **70.4** | 60.2 | 59.1 | 50.7 | 53.4 |
+| LiveCodeBench2305-2409 | 24.6 | 16.5 | **28.7** | 19.9 | 23.9 | 8.3 | 18.9 |
+
+
+
+Minimum GPU memory requirements
+
+|Model | Precision | 15(s) Video | 30(s) Video | 60(s) Video |
+|--------------|-----------| ------------- | ------------- | ------------------ |
+| Qwen-Omni-3B | FP32 | 89.10 GB | Not Recommend | Not Recommend |
+| Qwen-Omni-3B | BF16 | 18.38 GB | 22.43 GB | 28.22 GB |
+| Qwen-Omni-7B | FP32 | 93.56 GB | Not Recommend | Not Recommend |
+| Qwen-Omni-7B | BF16 | 31.11 GB | 41.85 GB | 60.19 GB |
+
+Note: The table above presents the theoretical minimum memory requirements for inference with `transformers` and `BF16` is test with `attn_implementation="flash_attention_2"`; however, in practice, the actual memory usage is typically at least 1.2 times higher. For more information, see the linked resource [here](https://huggingface.co/docs/accelerate/main/en/usage_guides/model_size_estimator).
+
+
+
+Video URL resource usage
+
+Video URL compatibility largely depends on the third-party library version. The details are in the table below. Change the backend by `FORCE_QWENVL_VIDEO_READER=torchvision` or `FORCE_QWENVL_VIDEO_READER=decord` if you prefer not to use the default one.
+
+| Backend | HTTP | HTTPS |
+|-------------|------|-------|
+| torchvision >= 0.19.0 | ✅ | ✅ |
+| torchvision < 0.19.0 | ❌ | ❌ |
+| decord | ✅ | ❌ |
+
+
+
+### Usage Tips
+
+#### Prompt for audio output
+If users need audio output, the system prompt must be set as "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech.", otherwise the audio output may not work as expected.
+```
+{
+ "role": "system",
+ "content": [
+ {"type": "text", "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."}
+ ],
+}
+```
+#### Use audio in video
+In the process of multimodal interaction, the videos provided by users are often accompanied by audio (such as questions about the content in the video, or sounds generated by certain events in the video). This information is conducive to the model providing a better interactive experience. So we provide the following options for users to decide whether to use audio in video.
+```python
+# first place, in data preprocessing
+audios, images, videos = process_mm_info(conversations, use_audio_in_video=True)
+```
+```python
+# second place, in model processor
+inputs = processor(text=text, audio=audios, images=images, videos=videos, return_tensors="pt",
+ padding=True, use_audio_in_video=True)
+```
+```python
+# third place, in model inference
+text_ids, audio = model.generate(**inputs, use_audio_in_video=True)
+```
+It is worth noting that during a multi-round conversation, the `use_audio_in_video` parameter in these places must be set to the same, otherwise unexpected results will occur.
+
+#### Use audio output or not
+
+The model supports both text and audio outputs, if users do not need audio outputs, they can call `model.disable_talker()` after init the model. This option will save about `~2GB` of GPU memory but the `return_audio` option for `generate` function will only allow to be set at `False`.
+```python
+model = Qwen2_5OmniForConditionalGeneration.from_pretrained(
+ "Qwen/Qwen2.5-Omni-7B",
+ torch_dtype="auto",
+ device_map="auto"
+)
+model.disable_talker()
+```
+
+In order to obtain a flexible experience, we recommend that users can decide whether to return audio when `generate` function is called. If `return_audio` is set to `False`, the model will only return text outputs to get text responses faster.
+
+```python
+model = Qwen2_5OmniForConditionalGeneration.from_pretrained(
+ "Qwen/Qwen2.5-Omni-7B",
+ torch_dtype="auto",
+ device_map="auto"
+)
+...
+text_ids = model.generate(**inputs, return_audio=False)
+```
+
+#### Change voice type of output audio
+Qwen2.5-Omni supports the ability to change the voice of the output audio. The `"Qwen/Qwen2.5-Omni-7B"` checkpoint support two voice types as follow:
+
+| Voice Type | Gender | Description |
+|------------|--------|-------------|
+| Chelsie | Female | A honeyed, velvety voice that carries a gentle warmth and luminous clarity.|
+| Ethan | Male | A bright, upbeat voice with infectious energy and a warm, approachable vibe.|
+
+Users can use the `speaker` parameter of `generate` function to specify the voice type. By default, if `speaker` is not specified, the default voice type is `Chelsie`.
+
+```python
+text_ids, audio = model.generate(**inputs, speaker="Chelsie")
+```
+
+```python
+text_ids, audio = model.generate(**inputs, speaker="Ethan")
+```
+
+#### Flash-Attention 2 to speed up generation
+
+First, make sure to install the latest version of Flash Attention 2:
+
+```bash
+pip install -U flash-attn --no-build-isolation
+```
+
+Also, you should have hardware that is compatible with FlashAttention 2. Read more about it in the official documentation of the [flash attention repository](https://github.com/Dao-AILab/flash-attention). FlashAttention-2 can only be used when a model is loaded in `torch.float16` or `torch.bfloat16`.
+
+To load and run a model using FlashAttention-2, add `attn_implementation="flash_attention_2"` when loading the model:
+
+```python
+from transformers import Qwen2_5OmniForConditionalGeneration
+
+model = Qwen2_5OmniForConditionalGeneration.from_pretrained(
+ "Qwen/Qwen2.5-Omni-7B",
+ device_map="auto",
+ torch_dtype=torch.bfloat16,
+ attn_implementation="flash_attention_2",
+)
+```
+
+
+## Citation
+
+If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil: :)
+
+
+
+```BibTeX
+
+@article{Qwen2.5-Omni,
+ title={Qwen2.5-Omni Technical Report},
+ author={Jin Xu, Zhifang Guo, Jinzheng He, Hangrui Hu, Ting He, Shuai Bai, Keqin Chen, Jialin Wang, Yang Fan, Kai Dang, Bin Zhang, Xiong Wang, Yunfei Chu, Junyang Lin},
+ journal={arXiv preprint arXiv:2503.20215},
+ year={2025}
+}
+```
+
+Batch inference
+
+The model can batch inputs composed of mixed samples of various types such as text, images, audio and videos as input when `return_audio=False` is set. Here is an example.
+
+```python
+# Sample messages for batch inference
+
+# Conversation with video only
+conversation1 = [
+ {
+ "role": "system",
+ "content": [
+ {"type": "text", "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."}
+ ],
+ },
+ {
+ "role": "user",
+ "content": [
+ {"type": "video", "video": "/path/to/video.mp4"},
+ ]
+ }
+]
+
+# Conversation with audio only
+conversation2 = [
+ {
+ "role": "system",
+ "content": [
+ {"type": "text", "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."}
+ ],
+ },
+ {
+ "role": "user",
+ "content": [
+ {"type": "audio", "audio": "/path/to/audio.wav"},
+ ]
+ }
+]
+
+# Conversation with pure text
+conversation3 = [
+ {
+ "role": "system",
+ "content": [
+ {"type": "text", "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."}
+ ],
+ },
+ {
+ "role": "user",
+ "content": "who are you?"
+ }
+]
+
+
+# Conversation with mixed media
+conversation4 = [
+ {
+ "role": "system",
+ "content": [
+ {"type": "text", "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."}
+ ],
+ },
+ {
+ "role": "user",
+ "content": [
+ {"type": "image", "image": "/path/to/image.jpg"},
+ {"type": "video", "video": "/path/to/video.mp4"},
+ {"type": "audio", "audio": "/path/to/audio.wav"},
+ {"type": "text", "text": "What are the elements can you see and hear in these medias?"},
+ ],
+ }
+]
+
+# Combine messages for batch processing
+conversations = [conversation1, conversation2, conversation3, conversation4]
+
+# set use audio in video
+USE_AUDIO_IN_VIDEO = True
+
+# Preparation for batch inference
+text = processor.apply_chat_template(conversations, add_generation_prompt=True, tokenize=False)
+audios, images, videos = process_mm_info(conversations, use_audio_in_video=USE_AUDIO_IN_VIDEO)
+
+inputs = processor(text=text, audio=audios, images=images, videos=videos, return_tensors="pt", padding=True, use_audio_in_video=USE_AUDIO_IN_VIDEO)
+inputs = inputs.to(model.device).to(model.dtype)
+
+# Batch Inference
+text_ids = model.generate(**inputs, use_audio_in_video=USE_AUDIO_IN_VIDEO, return_audio=False)
+text = processor.batch_decode(text_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
+print(text)
+```
+
diff --git a/mllm/models/qwen2_5omni/python_src_code/__init__.py b/mllm/models/qwen2_5omni/python_src_code/__init__.py
new file mode 100644
index 000000000..0d7ddae0d
--- /dev/null
+++ b/mllm/models/qwen2_5omni/python_src_code/__init__.py
@@ -0,0 +1,28 @@
+# Copyright 2025 The Qwen Team and The HuggingFace Inc. 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 TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_qwen2_5_omni import *
+ from .modeling_qwen2_5_omni import *
+ from .processing_qwen2_5_omni import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/mllm/models/qwen2_5omni/python_src_code/added_tokens.json b/mllm/models/qwen2_5omni/python_src_code/added_tokens.json
new file mode 100644
index 000000000..456225635
--- /dev/null
+++ b/mllm/models/qwen2_5omni/python_src_code/added_tokens.json
@@ -0,0 +1,24 @@
+{
+ "": 151658,
+ "
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diff --git a/mllm/models/qwen2_5omni/python_src_code/model.safetensors.index.json b/mllm/models/qwen2_5omni/python_src_code/model.safetensors.index.json
new file mode 100644
index 000000000..e8728f00d
--- /dev/null
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diff --git a/mllm/models/qwen2_5omni/python_src_code/modeling_qwen2_5_omni.py b/mllm/models/qwen2_5omni/python_src_code/modeling_qwen2_5_omni.py
new file mode 100644
index 000000000..ff4ee4d26
--- /dev/null
+++ b/mllm/models/qwen2_5omni/python_src_code/modeling_qwen2_5_omni.py
@@ -0,0 +1,4126 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_qwen2_5_omni.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. 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 math
+from collections.abc import Callable
+from dataclasses import dataclass
+from typing import Any, Optional, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.nn import Parameter
+
+from ... import initialization as init
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
+from ...generation import GenerationMixin
+from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPast, ModelOutput
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, check_torch_load_is_safe, logging
+from ...utils.deprecation import deprecate_kwarg
+from ...utils.generic import maybe_autocast
+from ...utils.hub import cached_file
+from ..qwen2.modeling_qwen2 import Qwen2RMSNorm
+from .configuration_qwen2_5_omni import (
+ Qwen2_5OmniAudioEncoderConfig,
+ Qwen2_5OmniBigVGANConfig,
+ Qwen2_5OmniConfig,
+ Qwen2_5OmniDiTConfig,
+ Qwen2_5OmniTalkerConfig,
+ Qwen2_5OmniTextConfig,
+ Qwen2_5OmniThinkerConfig,
+ Qwen2_5OmniToken2WavConfig,
+ Qwen2_5OmniVisionEncoderConfig,
+)
+
+
+logger = logging.get_logger(__name__)
+
+
+def kaiser_sinc_filter1d(cutoff, half_width, kernel_size):
+ """Generates a 1D Kaiser-windowed sinc filter.
+
+ Args:
+ cutoff (float): Normalized cutoff frequency (0 to 0.5).
+ half_width (float): Transition bandwidth.
+ kernel_size (int): Number of filter taps.
+
+ Returns:
+ torch.Tensor: A tensor of shape (1, 1, kernel_size) representing the filter.
+ """
+ is_even = kernel_size % 2 == 0
+ half_size = kernel_size // 2
+
+ # Compute Kaiser window parameters
+ delta_f = 4 * half_width
+ attenuation = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
+
+ if attenuation > 50.0:
+ beta = 0.1102 * (attenuation - 8.7)
+ elif attenuation >= 21.0:
+ beta = 0.5842 * (attenuation - 21) ** 0.4 + 0.07886 * (attenuation - 21.0)
+ else:
+ beta = 0.0
+
+ kaiser_window = torch.kaiser_window(kernel_size, beta=beta, periodic=False, dtype=torch.float32)
+
+ # Compute time indices
+ if is_even:
+ time_indices = torch.arange(-half_size, half_size) + 0.5
+ else:
+ time_indices = torch.arange(kernel_size) - half_size
+
+ # Compute sinc filter
+ if cutoff == 0:
+ return torch.zeros((1, 1, kernel_size), dtype=torch.float32) # Ensures correct shape
+
+ sinc_filter = torch.sinc(2 * cutoff * time_indices)
+ normalized_filter = 2 * cutoff * kaiser_window * sinc_filter
+
+ # Normalize to ensure sum = 1 (avoid leakage of constant component)
+ normalized_filter /= normalized_filter.sum()
+
+ return normalized_filter.view(1, 1, kernel_size)
+
+
+@auto_docstring
+class Qwen2_5OmniPreTrainedModel(PreTrainedModel):
+ config: Qwen2_5OmniConfig
+ base_model_prefix = "model"
+ input_modalities = ("image", "video", "audio", "text")
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["Qwen2_5OmniDecoderLayer", "Qwen2_5OmniVisionBlock"]
+ _skip_keys_device_placement = "past_key_values"
+ _supports_flash_attn = True
+ _supports_sdpa = True
+ _can_compile_fullgraph = False
+ _supports_attention_backend = True
+
+ def _init_weights(self, module):
+ super()._init_weights(module)
+ if isinstance(module, SinusoidsPositionEmbedding):
+ log_timescale_increment = np.log(module.max_timescale) / (module.channels // 2 - 1)
+ inv_timescales = torch.exp(-log_timescale_increment * torch.arange(module.channels // 2).float())
+ scaled_time = torch.arange(module.length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
+ init.copy_(module.positional_embedding, torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1))
+ elif isinstance(module, UpSample1d):
+ filter_tensor = kaiser_sinc_filter1d(0.5 / module.ratio, 0.6 / module.ratio, module.kernel_size)
+ init.copy_(module.filter, filter_tensor)
+ elif isinstance(module, DownSample1d):
+ filter_tensor = kaiser_sinc_filter1d(module.cutoff, module.half_width, module.kernel_size)
+ init.copy_(module.filter, filter_tensor)
+ elif isinstance(module, Qwen2_5_VisionRotaryEmbedding):
+ inv_freq = 1.0 / (module.theta ** (torch.arange(0, module.dim, 2, dtype=torch.float) / module.dim))
+ init.copy_(module.inv_freq, inv_freq)
+
+
+class Qwen2_5OmniPreTrainedModelForConditionalGeneration(Qwen2_5OmniPreTrainedModel):
+ input_modalities = ("image", "video", "audio", "text")
+
+ def _prepare_4d_causal_attention_mask_with_cache_position(
+ self,
+ attention_mask: torch.Tensor,
+ sequence_length: int,
+ target_length: int,
+ dtype: torch.dtype,
+ device: torch.device,
+ min_dtype: float,
+ cache_position: torch.Tensor,
+ batch_size: int,
+ ):
+ """
+ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+ `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+ Args:
+ attention_mask (`torch.Tensor`):
+ A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
+ sequence_length (`int`):
+ The sequence length being processed.
+ target_length (`int`):
+ The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
+ dtype (`torch.dtype`):
+ The dtype to use for the 4D attention mask.
+ device (`torch.device`):
+ The device to place the 4D attention mask on.
+ min_dtype (`float`):
+ The minimum value representable with the dtype `dtype`.
+ cache_position (`torch.Tensor`):
+ Indices depicting the position of the input sequence tokens in the sequence.
+ batch_size (`torch.Tensor`):
+ Batch size.
+ """
+ if attention_mask is not None and attention_mask.dim() == 4:
+ # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+ causal_mask = attention_mask
+ else:
+ causal_mask = torch.full(
+ (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
+ )
+ if sequence_length != 1:
+ causal_mask = torch.triu(causal_mask, diagonal=1)
+ causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
+ causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+ if attention_mask is not None:
+ causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
+ mask_length = attention_mask.shape[-1]
+ padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+ padding_mask = padding_mask == 0
+ causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+ padding_mask, min_dtype
+ )
+
+ return causal_mask
+
+ def get_llm_pos_ids_for_vision(
+ self,
+ start_idx: int,
+ vision_idx: int,
+ spatial_merge_size: int,
+ t_index: list[int],
+ grid_hs: list[int],
+ grid_ws: list[int],
+ ):
+ llm_pos_ids_list = []
+ llm_grid_h = grid_hs[vision_idx] // spatial_merge_size
+ llm_grid_w = grid_ws[vision_idx] // spatial_merge_size
+ h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(len(t_index), -1, llm_grid_w).flatten()
+ w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(len(t_index), llm_grid_h, -1).flatten()
+ t_index = torch.Tensor(t_index).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w).flatten().long()
+ _llm_pos_ids = torch.stack([t_index, h_index, w_index])
+ llm_pos_ids_list.append(_llm_pos_ids + start_idx) # + 1 ) # 12.09 by malinhan
+ llm_pos_ids = torch.cat(llm_pos_ids_list, dim=1)
+ return llm_pos_ids
+
+ def get_chunked_index(
+ self, token_indices: torch.Tensor, tokens_per_chunk: int, remove_index: int
+ ) -> list[tuple[int, int]]:
+ """
+ Splits token index list into chunks based on token value ranges.
+
+ Given a list of token indices, returns a list of (start, end) index tuples representing
+ slices of the list where the token values fall within successive ranges of `t_ntoken_per_chunk`.
+
+ For example, if `t_ntoken_per_chunk` is 1000, the function will create chunks such that:
+ - the first chunk contains token values < 1000,
+ - the second chunk contains values >= 1000 and < 2000, and so on.
+
+ Parameters:
+ token_indices (`torch.Tensor` of shape `(seq_len, )`): A monotonically increasing list of
+ token index values.
+ t_ntoken_per_chunk (`int`): Number of tokens per chunk (used as the chunk size threshold).
+ remove_index (`int`) An index id to subtract from `token_indices` before chunking
+
+ Returns:
+ `list[tuple[int, int]]`: A list of tuples, each representing the start (inclusive)
+ and end (exclusive) indices of a chunk in `token_indices`.
+ """
+
+ def _iter():
+ i, start_idx = 0, 0 # skip bos token
+ current_chunk = 1
+ while i < len(token_indices): # skip eos token
+ if token_indices[i] - remove_index >= current_chunk * tokens_per_chunk:
+ yield (start_idx, i)
+ start_idx = i
+ current_chunk += 1
+ i += 1
+ yield (start_idx, len(token_indices))
+
+ return list(_iter())
+
+ def get_rope_index(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ image_grid_thw: Optional[torch.LongTensor] = None,
+ video_grid_thw: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ use_audio_in_video: bool = False,
+ audio_seqlens: Optional[torch.LongTensor] = None,
+ second_per_grids: Optional[torch.Tensor] = None,
+ ) -> tuple[torch.Tensor, torch.Tensor]:
+ """
+ Calculate the 3D rope index based on image and video's temporal, height and width in LLM.
+
+ Explanation:
+ Each embedding sequence contains vision embedding and text embedding or just contains text embedding.
+
+ For pure text embedding sequence, the rotary position embedding has no difference with modern LLMs.
+ Examples:
+ input_ids: [T T T T T], here T is for text.
+ temporal position_ids: [0, 1, 2, 3, 4]
+ height position_ids: [0, 1, 2, 3, 4]
+ width position_ids: [0, 1, 2, 3, 4]
+
+ For vision and text embedding sequence, we calculate 3D rotary position embedding for vision part
+ and 1D rotary position embedding for text part.
+ Examples:
+ Temporal (Time): 3 patches, representing different segments of the video in time.
+ Height: 2 patches, dividing each frame vertically.
+ Width: 2 patches, dividing each frame horizontally.
+ We also have some important parameters:
+ fps (Frames Per Second): The video's frame rate, set to 1. This means one frame is processed each second.
+ tokens_per_second: This is a crucial parameter. It dictates how many "time-steps" or "temporal tokens" are conceptually packed into a one-second interval of the video. In this case, we have 25 tokens per second. So each second of the video will be represented with 25 separate time points. It essentially defines the temporal granularity.
+ temporal_patch_size: The number of frames that compose one temporal patch. Here, it's 2 frames.
+ interval: The step size for the temporal position IDs, calculated as tokens_per_second * temporal_patch_size / fps. In this case, 25 * 2 / 1 = 50. This means that each temporal patch will be have a difference of 50 in the temporal position IDs.
+ input_ids: [V V V V V V V V V V V V T T T T T], here V is for vision.
+ vision temporal position_ids: [0, 0, 0, 0, 50, 50, 50, 50, 100, 100, 100, 100]
+ vision height position_ids: [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1]
+ vision width position_ids: [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1]
+ text temporal position_ids: [101, 102, 103, 104, 105]
+ text height position_ids: [101, 102, 103, 104, 105]
+ text width position_ids: [101, 102, 103, 104, 105]
+ Here we calculate the text start position_ids as the max vision position_ids plus 1.
+
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+ it.
+ image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+ The temporal, height and width of feature shape of each image in LLM.
+ video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+ The temporal, height and width of feature shape of each video in LLM.
+ 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**.
+ use_audio_in_video (`bool`, *optional*):
+ If set to `True`, use the audio in video.
+ audio_seqlens (`torch.LongTensor` of shape `(num_audios)`, *optional*):
+ The length of feature shape of each audio in LLM.
+ second_per_grids (`torch.LongTensor` of shape `(num_videos)`, *optional*):
+ The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs.
+
+ Returns:
+ position_ids (`torch.LongTensor` of shape `(3, batch_size, sequence_length)`)
+ mrope_position_deltas (`torch.Tensor` of shape `(batch_size)`)
+ """
+ spatial_merge_size = self.spatial_merge_size
+ image_token_id = self.config.image_token_id
+ video_token_id = self.config.video_token_id
+ audio_token_id = self.config.audio_token_id
+ vision_start_token_id = self.config.vision_start_token_id
+ audio_start_token_id = self.config.audio_start_token_id
+ position_id_per_seconds = self.config.position_id_per_seconds
+ seconds_per_chunk = self.config.seconds_per_chunk
+
+ mrope_position_deltas = []
+ if input_ids is not None and (image_grid_thw is not None or video_grid_thw is not None):
+ total_input_ids = input_ids
+ if attention_mask is not None:
+ attention_mask = attention_mask == 1
+ position_ids = torch.ones(
+ 3,
+ input_ids.shape[0],
+ input_ids.shape[1],
+ dtype=input_ids.dtype,
+ device=input_ids.device,
+ )
+ image_idx, video_idx, audio_idx = 0, 0, 0
+ for i, input_ids in enumerate(total_input_ids):
+ if attention_mask is not None:
+ input_ids = input_ids[attention_mask[i]]
+ image_nums, video_nums, audio_nums = 0, 0, 0
+ vision_start_indices = torch.argwhere(input_ids == vision_start_token_id).squeeze(1)
+ vision_tokens = input_ids[vision_start_indices + 1]
+ audio_nums = torch.sum(input_ids == audio_start_token_id)
+ image_nums = (vision_tokens == image_token_id).sum()
+ video_nums = (
+ (vision_tokens == audio_start_token_id).sum()
+ if use_audio_in_video
+ else (vision_tokens == video_token_id).sum()
+ )
+ input_tokens = input_ids.tolist()
+ llm_pos_ids_list: list = []
+ st = 0
+ remain_images, remain_videos, remain_audios = image_nums, video_nums, audio_nums
+ multimodal_nums = (
+ image_nums + audio_nums if use_audio_in_video else image_nums + video_nums + audio_nums
+ )
+ for _ in range(multimodal_nums):
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ if image_token_id in input_tokens and remain_images > 0:
+ ed_image = input_tokens.index(image_token_id, st)
+ else:
+ ed_image = len(input_tokens) + 1
+ if video_token_id in input_tokens and remain_videos > 0:
+ ed_video = input_tokens.index(video_token_id, st)
+ else:
+ ed_video = len(input_tokens) + 1
+ if audio_token_id in input_tokens and remain_audios > 0:
+ ed_audio = input_tokens.index(audio_token_id, st)
+ else:
+ ed_audio = len(input_tokens) + 1
+ min_ed = min(ed_image, ed_video, ed_audio)
+ if min_ed == ed_audio:
+ text_len = min_ed - st - 1
+ if text_len != 0:
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ bos_len = 1
+ llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ audio_len = ((audio_seqlens[audio_idx] - 1) // 2 + 1 - 2) // 2 + 1
+ llm_pos_ids = torch.arange(audio_len).view(1, -1).expand(3, -1) + st_idx
+ llm_pos_ids_list.append(llm_pos_ids)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ eos_len = 1
+ llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st += text_len + bos_len + audio_len + eos_len
+ audio_idx += 1
+ remain_audios -= 1
+
+ elif min_ed == ed_image:
+ text_len = min_ed - st - 1
+ if text_len != 0:
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ bos_len = 1
+ llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ grid_t = image_grid_thw[image_idx][0]
+ grid_hs = image_grid_thw[:, 1]
+ grid_ws = image_grid_thw[:, 2]
+ t_index = (torch.arange(grid_t) * 1 * position_id_per_seconds).long()
+ llm_pos_ids = self.get_llm_pos_ids_for_vision(
+ st_idx, image_idx, spatial_merge_size, t_index, grid_hs, grid_ws
+ )
+ image_len = image_grid_thw[image_idx].prod() // (spatial_merge_size**2)
+ llm_pos_ids_list.append(llm_pos_ids)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ eos_len = 1
+ llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st += text_len + bos_len + image_len + eos_len
+ image_idx += 1
+ remain_images -= 1
+
+ elif min_ed == ed_video and not use_audio_in_video:
+ text_len = min_ed - st - 1
+ if text_len != 0:
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ bos_len = 1
+ llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ grid_t = video_grid_thw[video_idx][0]
+ grid_hs = video_grid_thw[:, 1]
+ grid_ws = video_grid_thw[:, 2]
+ t_index = (
+ torch.arange(grid_t) * second_per_grids[video_idx].cpu().float() * position_id_per_seconds
+ ).long()
+ llm_pos_ids = self.get_llm_pos_ids_for_vision(
+ st_idx, video_idx, spatial_merge_size, t_index, grid_hs, grid_ws
+ )
+ video_len = video_grid_thw[video_idx].prod() // (spatial_merge_size**2)
+ llm_pos_ids_list.append(llm_pos_ids)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ eos_len = 1
+ llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st += text_len + bos_len + video_len + eos_len
+ video_idx += 1
+ remain_videos -= 1
+
+ elif min_ed == ed_video and use_audio_in_video:
+ text_len = min_ed - st - 2
+ if text_len != 0:
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ bos_len = 1
+ llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx)
+ llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ audio_len = ((audio_seqlens[audio_idx] - 1) // 2 + 1 - 2) // 2 + 1
+ audio_llm_pos_ids = torch.arange(audio_len).view(1, -1).expand(3, -1) + st_idx
+ grid_t = video_grid_thw[video_idx][0]
+ grid_hs = video_grid_thw[:, 1]
+ grid_ws = video_grid_thw[:, 2]
+
+ t_index = (
+ torch.arange(grid_t) * second_per_grids[video_idx].cpu().float() * position_id_per_seconds
+ ).long()
+ video_llm_pos_ids = self.get_llm_pos_ids_for_vision(
+ st_idx, video_idx, spatial_merge_size, t_index, grid_hs, grid_ws
+ )
+
+ t_ntoken_per_chunk = int(position_id_per_seconds * seconds_per_chunk)
+ video_chunk_indexes = self.get_chunked_index(video_llm_pos_ids[0], t_ntoken_per_chunk, st_idx)
+ audio_chunk_indexes = self.get_chunked_index(audio_llm_pos_ids[0], t_ntoken_per_chunk, st_idx)
+ sub_len = 0
+ for j in range(max(len(video_chunk_indexes), len(audio_chunk_indexes))):
+ video_chunk_index = video_chunk_indexes[j] if j < len(video_chunk_indexes) else None
+ audio_chunk_index = audio_chunk_indexes[j] if j < len(audio_chunk_indexes) else None
+ if video_chunk_index is not None:
+ sub_len += video_chunk_index[1] - video_chunk_index[0]
+
+ llm_pos_ids_list.append(
+ video_llm_pos_ids[:, video_chunk_index[0] : video_chunk_index[1]]
+ )
+ if audio_chunk_index is not None:
+ sub_len += audio_chunk_index[1] - audio_chunk_index[0]
+
+ llm_pos_ids_list.append(
+ audio_llm_pos_ids[:, audio_chunk_index[0] : audio_chunk_index[1]]
+ )
+ video_len = video_grid_thw[video_idx].prod() // (spatial_merge_size**2)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ eos_len = 1
+ llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx)
+ llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st += text_len + bos_len * 2 + audio_len + video_len + eos_len * 2
+
+ audio_idx += 1
+ video_idx += 1
+ remain_videos -= 1
+ remain_audios -= 1
+
+ if st < len(input_tokens):
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ text_len = len(input_tokens) - st
+ llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+ llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
+
+ if attention_mask is not None:
+ position_ids[..., i, attention_mask[i]] = llm_positions.to(position_ids.device)
+ else:
+ position_ids[..., i, :] = llm_positions.to(position_ids.device)
+ mrope_position_deltas.append(llm_positions.max() + 1 - len(input_ids))
+ mrope_position_deltas = torch.tensor(mrope_position_deltas).unsqueeze(1).to(device=input_ids.device)
+
+ return position_ids, mrope_position_deltas
+ else:
+ position_ids = attention_mask.long().cumsum(-1) - 1
+ position_ids.masked_fill_(attention_mask == 0, 1)
+ position_ids = position_ids.unsqueeze(0).expand(3, -1, -1).to(attention_mask.device)
+ max_position_ids = position_ids.max(0, keepdim=False)[0].max(-1, keepdim=True)[0]
+ mrope_position_deltas = max_position_ids + 1 - torch.sum(attention_mask, dim=-1, keepdim=True)
+
+ return position_ids, mrope_position_deltas
+
+
+############################
+# Start Thinker #
+############################
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for Qwen2.5OmniThinker causal language model (or autoregressive) outputs.
+ """
+)
+class Qwen2_5OmniThinkerCausalLMOutputWithPast(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Language modeling loss (for next-token prediction).
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`, *optional*):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
+ The rope index difference between sequence length and multimodal rope.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ past_key_values: Optional[Cache] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+ rope_deltas: Optional[torch.LongTensor] = None
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: float,
+ dropout: float = 0.0,
+ **kwargs,
+):
+ key_states = repeat_kv(key, module.num_key_value_groups)
+ value_states = repeat_kv(value, module.num_key_value_groups)
+
+ attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+ if attention_mask is not None:
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+class Qwen2_5OmniAudioAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(
+ self,
+ config: Qwen2_5OmniAudioEncoderConfig,
+ ):
+ super().__init__()
+ self.embed_dim = config.d_model
+ self.num_heads = config.encoder_attention_heads
+ self.dropout = config.attention_dropout
+ self.head_dim = self.embed_dim // self.num_heads
+ self.num_key_value_groups = 1 # needed for eager attention
+ self.config = config
+
+ 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}"
+ f" and `num_heads`: {self.num_heads})."
+ )
+ self.scaling = self.head_dim**-0.5
+ self.attention_dropout = 0.0
+ self.is_decoder = False
+ self.is_causal = False
+
+ self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
+ self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True)
+ self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True)
+ self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ cu_seqlens: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ **kwargs,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ """Input shape: Batch x Time x Channel"""
+
+ seq_length, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states).reshape(seq_length, self.num_heads, -1)
+ key_states = self.k_proj(hidden_states).reshape(seq_length, self.num_heads, -1)
+ value_states = self.v_proj(hidden_states).reshape(seq_length, self.num_heads, -1)
+
+ query_states = query_states.transpose(0, 1).unsqueeze(0)
+ key_states = key_states.transpose(0, 1).unsqueeze(0)
+ value_states = value_states.transpose(0, 1).unsqueeze(0)
+ max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, _ = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask=attention_mask,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=self.scaling,
+ cu_seq_lens_q=cu_seqlens, # pass cu seq lens for FA2
+ cu_seq_lens_k=cu_seqlens,
+ max_length_q=max_seqlen,
+ max_length_k=max_seqlen,
+ is_causal=False,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(seq_length, -1).contiguous()
+ attn_output = self.out_proj(attn_output)
+
+ return attn_output
+
+
+class Qwen2_5OmniAudioEncoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: Qwen2_5OmniAudioEncoderConfig):
+ super().__init__()
+ self.embed_dim = config.d_model
+ self.self_attn = Qwen2_5OmniAudioAttention(config)
+ 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,
+ cu_seqlens: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ **kwargs,
+ ) -> torch.Tensor:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`): attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ output_attentions (`bool`, *optional*):
+ 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.self_attn_layer_norm(hidden_states)
+ hidden_states = self.self_attn(
+ hidden_states=hidden_states,
+ cu_seqlens=cu_seqlens,
+ attention_mask=attention_mask,
+ **kwargs,
+ )
+ hidden_states = residual + hidden_states
+ residual = hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+ hidden_states = self.fc1(hidden_states)
+ hidden_states = self.activation_fn(hidden_states)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = residual + hidden_states
+
+ if hidden_states.dtype == torch.float16:
+ clamp_value = torch.finfo(hidden_states.dtype).max - 1000
+ hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+ outputs = (hidden_states,)
+
+ return outputs
+
+
+class SinusoidsPositionEmbedding(nn.Module):
+ def __init__(self, length, channels, max_timescale=10000):
+ super().__init__()
+ self.length = length
+ self.channels = channels
+ self.max_timescale = max_timescale
+ if channels % 2 != 0:
+ raise ValueError("SinusoidsPositionEmbedding needs even channels input")
+ log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
+ inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2).float())
+ scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
+ self.register_buffer(
+ "positional_embedding",
+ torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1),
+ persistent=False,
+ )
+
+ def forward(self, seqlen: int):
+ return self.positional_embedding[:seqlen, :]
+
+
+@auto_docstring(
+ custom_intro="""
+ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
+ [`Qwen2_5OmniAudioEncoderLayer`].
+ """
+)
+class Qwen2_5OmniAudioEncoder(Qwen2_5OmniPreTrainedModel):
+ config: Qwen2_5OmniAudioEncoderConfig
+ main_input_name = "input_features"
+ input_modalities = "audio"
+ _no_split_modules = ["Qwen2_5OmniAudioEncoderLayer"]
+ _supports_sdpa = True
+
+ def __init__(self, config: Qwen2_5OmniAudioEncoderConfig):
+ super().__init__(config)
+ self.dropout = config.dropout
+
+ embed_dim = config.d_model
+ self.num_mel_bins = config.num_mel_bins
+ self.max_source_positions = config.max_source_positions
+ self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
+ self.n_window = config.n_window
+ self.conv1 = nn.Conv1d(self.num_mel_bins, embed_dim, kernel_size=3, padding=1)
+ self.conv2 = nn.Conv1d(embed_dim, embed_dim, kernel_size=3, stride=2, padding=1)
+ self.positional_embedding = SinusoidsPositionEmbedding(self.max_source_positions, embed_dim)
+ self.audio_bos_eos_token = nn.Embedding(2, config.output_dim)
+ self.layers = nn.ModuleList([Qwen2_5OmniAudioEncoderLayer(config) for _ in range(config.encoder_layers)])
+ self.ln_post = nn.LayerNorm(config.d_model)
+ self.avg_pooler = nn.AvgPool1d(2, stride=2)
+ self.proj = nn.Linear(config.d_model, config.output_dim)
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def _freeze_parameters(self):
+ for param in self.parameters():
+ param.requires_grad = False
+ self._requires_grad = False
+
+ def get_input_embeddings(self) -> nn.Module:
+ return self.conv1
+
+ def set_input_embeddings(self, value: nn.Module):
+ self.conv1 = value
+
+ def _prepare_attention_mask(self, inputs_tensor: torch.Tensor, cu_seqlens: torch.Tensor) -> torch.Tensor:
+ # Flash Attention 2 doesn't need a 4D mask and relies on `cu_seqlens/max_seqlen`
+ # NOTE: the created attention masl only approximates the ragged FA2 attention by
+ # allowing bidirectional attention within `cu_seqlens` blocks, and not attending between
+ # blocks. Though it will not be a 100% match for FA2's `varlen` path
+ if self.config._attn_implementation == "flash_attention_2":
+ return None
+
+ seq_length = inputs_tensor.shape[0]
+ attention_mask = torch.full(
+ [1, 1, seq_length, seq_length],
+ torch.finfo(inputs_tensor.dtype).min,
+ device=inputs_tensor.device,
+ dtype=inputs_tensor.dtype,
+ )
+ for i in range(1, len(cu_seqlens)):
+ attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = 0
+ return attention_mask
+
+ @auto_docstring
+ def forward(
+ self,
+ input_features,
+ feature_lens=None,
+ aftercnn_lens=None,
+ **kwargs,
+ ):
+ r"""
+ feature_lens (`torch.LongTensor` of shape `(batch_size,)`):
+ mel length
+ aftercnn_lens (`torch.LongTensor` of shape `(batch_size,)`):
+ mel length after cnn
+ """
+ chunk_num = torch.ceil(feature_lens / (self.n_window * 2)).long()
+
+ chunk_lengths = torch.tensor(
+ [self.n_window * 2] * chunk_num.sum(),
+ dtype=torch.long,
+ device=feature_lens.device,
+ )
+ tail_chunk_index = F.pad(chunk_num, (1, 0), value=-1).cumsum(0)[1:]
+ chunk_lengths[tail_chunk_index] = feature_lens % (self.n_window * 2)
+ chunk_lengths = torch.where(chunk_lengths == 0, self.n_window * 2, chunk_lengths)
+
+ chunk_list = input_features.split(chunk_lengths.tolist(), dim=1)
+ padded_feature, padded_mask, padded_mask_after_cnn = self.padded_and_mask_function(
+ chunk_list, chunk_lengths, padding_value=0, padding_side="right"
+ )
+ padded_embed = nn.functional.gelu(self.conv1(padded_feature)) * padded_mask
+ padded_embed = nn.functional.gelu(self.conv2(padded_embed)).transpose(1, 2)
+
+ padded_embed = padded_embed + self.positional_embedding.positional_embedding[
+ : padded_embed.shape[1], :
+ ].unsqueeze(0).to(padded_embed.dtype)
+ hidden_states = padded_embed[padded_mask_after_cnn]
+ cu_seqlens = torch.cat(
+ (
+ torch.zeros(1, device=padded_mask_after_cnn.device, dtype=torch.int32),
+ padded_mask_after_cnn.sum(1).cumsum(0),
+ )
+ ).to(torch.int32)
+ attention_mask = self._prepare_attention_mask(hidden_states, cu_seqlens)
+
+ for encoder_layer in self.layers:
+ layer_outputs = encoder_layer(
+ hidden_states,
+ cu_seqlens=cu_seqlens,
+ attention_mask=attention_mask,
+ **kwargs,
+ )
+ hidden_states = layer_outputs[0]
+
+ hidden_states_list = hidden_states.split(aftercnn_lens.tolist(), dim=0)
+ token_audio_list = []
+ for each_audio_states in hidden_states_list:
+ each_audio_states = self.avg_pooler(each_audio_states.transpose(0, 1)).transpose_(0, 1)
+ each_audio_states = self.ln_post(each_audio_states)
+ each_audio_states = self.proj(each_audio_states)
+ token_audio_list.append(each_audio_states)
+ token_audio = torch.cat(token_audio_list, dim=0)
+ return BaseModelOutput(last_hidden_state=token_audio)
+
+ def padded_and_mask_function(self, tensor_list, tensor_len, padding_value=0, padding_side="right"):
+ """
+ Pads a sequence of tensors to their maximum length on indicated `padding_side`.
+ Then prepares a mask so that pad tokens are not attended to.
+ """
+ max_len = tensor_len.max()
+ dim = tensor_list[0].shape[0]
+ padded_tensor = torch.full(
+ size=(len(tensor_list), dim, max_len),
+ fill_value=padding_value,
+ dtype=self.dtype,
+ device=tensor_list[0].device,
+ )
+
+ batch_mask = torch.zeros(
+ (len(tensor_len), max_len),
+ dtype=torch.long,
+ device=padded_tensor.device,
+ )
+ for i, length in enumerate(tensor_len):
+ batch_mask[i, :length] = 1
+ padded_tensor[i, :, :length] = tensor_list[i]
+
+ feature_lens_after_cnn = (tensor_len - 1) // 2 + 1
+ max_len_after_cnn = feature_lens_after_cnn.max()
+ batch_mask_after_cnn = torch.zeros(
+ (len(tensor_len), max_len_after_cnn),
+ dtype=torch.long,
+ device=padded_tensor.device,
+ )
+ for i, length in enumerate(feature_lens_after_cnn):
+ batch_mask_after_cnn[i, :length] = 1
+ return (
+ padded_tensor,
+ batch_mask.unsqueeze(1),
+ batch_mask_after_cnn.bool(),
+ )
+
+ # Ignore copy
+ 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 = (input_lengths - 1) // 2 + 1
+ output_lengths = (input_lengths - 2) // 2 + 1
+ return input_lengths, output_lengths
+
+
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb_vision(tensor: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
+ orig_dtype = tensor.dtype
+ tensor = tensor.float()
+ cos = freqs.cos()
+ sin = freqs.sin()
+ cos = cos.unsqueeze(1).repeat(1, 1, 2).unsqueeze(0).float()
+ sin = sin.unsqueeze(1).repeat(1, 1, 2).unsqueeze(0).float()
+ output = (tensor * cos) + (rotate_half(tensor) * sin)
+ output = output.to(orig_dtype)
+ return output
+
+
+class Qwen2_5OmniVisionAttention(nn.Module):
+ def __init__(self, config: Qwen2_5OmniVisionEncoderConfig = None) -> None:
+ super().__init__()
+ self.dim = config.hidden_size
+ self.num_heads = config.num_heads
+ self.head_dim = self.dim // self.num_heads
+ self.q = nn.Linear(self.dim, self.dim, bias=True)
+ self.k = nn.Linear(self.dim, self.dim, bias=True)
+ self.v = nn.Linear(self.dim, self.dim, bias=True)
+ self.proj = nn.Linear(self.dim, self.dim)
+ self.scaling = self.head_dim**-0.5
+ self.num_key_value_groups = 1 # needed for eager attention
+ self.config = config
+ self.attention_dropout = 0.0
+ self.is_causal = False
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ cu_seqlens: torch.Tensor,
+ rotary_pos_emb: Optional[torch.Tensor] = None,
+ **kwargs,
+ ) -> torch.Tensor:
+ seq_length = hidden_states.shape[0]
+ query_states = self.q(hidden_states).reshape(seq_length, self.num_heads, -1)
+ key_states = self.k(hidden_states).reshape(seq_length, self.num_heads, -1)
+ value_states = self.v(hidden_states).reshape(seq_length, self.num_heads, -1)
+ query_states = apply_rotary_pos_emb_vision(query_states.unsqueeze(0), rotary_pos_emb).squeeze(0)
+ key_states = apply_rotary_pos_emb_vision(key_states.unsqueeze(0), rotary_pos_emb).squeeze(0)
+
+ query_states = query_states.transpose(0, 1).unsqueeze(0)
+ key_states = key_states.transpose(0, 1).unsqueeze(0)
+ value_states = value_states.transpose(0, 1).unsqueeze(0)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ if self.config._attn_implementation == "flash_attention_2":
+ # Flash Attention 2: Use cu_seqlens for variable length attention
+ max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
+ attn_output, _ = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask=None,
+ scaling=self.scaling,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ cu_seq_lens_q=cu_seqlens,
+ cu_seq_lens_k=cu_seqlens,
+ max_length_q=max_seqlen,
+ max_length_k=max_seqlen,
+ is_causal=False,
+ **kwargs,
+ )
+ else:
+ # Other implementations: Process each chunk separately
+ lengths = cu_seqlens[1:] - cu_seqlens[:-1]
+ splits = [
+ torch.split(tensor, lengths.tolist(), dim=2) for tensor in (query_states, key_states, value_states)
+ ]
+
+ attn_outputs = [
+ attention_interface(
+ self,
+ q,
+ k,
+ v,
+ attention_mask=None,
+ scaling=self.scaling,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ is_causal=False,
+ **kwargs,
+ )[0]
+ for q, k, v in zip(*splits)
+ ]
+ attn_output = torch.cat(attn_outputs, dim=1)
+
+ attn_output = attn_output.reshape(seq_length, -1).contiguous()
+ attn_output = self.proj(attn_output)
+ return attn_output
+
+
+class Qwen2_5OmniMLP(nn.Module):
+ def __init__(self, config, bias: bool = False):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+ self.intermediate_size = config.intermediate_size
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias)
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias)
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=bias)
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, hidden_state):
+ return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state))
+
+
+class Qwen2_5OmniVisionBlock(GradientCheckpointingLayer):
+ def __init__(self, config: Qwen2_5OmniVisionEncoderConfig) -> None:
+ super().__init__()
+ self.norm1 = Qwen2RMSNorm(config.hidden_size, eps=1e-6)
+ self.norm2 = Qwen2RMSNorm(config.hidden_size, eps=1e-6)
+ self.attn = Qwen2_5OmniVisionAttention(config=config)
+ self.mlp = Qwen2_5OmniMLP(config, bias=True)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ cu_seqlens: torch.Tensor,
+ rotary_pos_emb: Optional[torch.Tensor] = None,
+ **kwargs,
+ ) -> torch.Tensor:
+ hidden_states = hidden_states + self.attn(
+ self.norm1(hidden_states),
+ cu_seqlens=cu_seqlens,
+ rotary_pos_emb=rotary_pos_emb,
+ **kwargs,
+ )
+ hidden_states = hidden_states + self.mlp(self.norm2(hidden_states))
+ return hidden_states
+
+
+class Qwen2_5_VisionRotaryEmbedding(nn.Module):
+ inv_freq: torch.Tensor # fix linting for `register_buffer`
+
+ def __init__(self, dim: int, theta: float = 10000.0) -> None:
+ super().__init__()
+ self.dim = dim
+ self.theta = theta
+ inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+ def forward(self, seqlen: int) -> torch.Tensor:
+ seq = torch.arange(seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
+ freqs = torch.outer(seq, self.inv_freq)
+ return freqs
+
+
+class Qwen2_5_VisionPatchEmbed(nn.Module):
+ def __init__(
+ self,
+ patch_size: int = 14,
+ temporal_patch_size: int = 2,
+ in_channels: int = 3,
+ embed_dim: int = 1152,
+ ) -> None:
+ super().__init__()
+ self.patch_size = patch_size
+ self.temporal_patch_size = temporal_patch_size
+ self.in_channels = in_channels
+ self.embed_dim = embed_dim
+
+ kernel_size = [temporal_patch_size, patch_size, patch_size]
+ self.proj = nn.Conv3d(in_channels, embed_dim, kernel_size=kernel_size, stride=kernel_size, bias=False)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ target_dtype = self.proj.weight.dtype
+ hidden_states = hidden_states.view(
+ -1, self.in_channels, self.temporal_patch_size, self.patch_size, self.patch_size
+ )
+ hidden_states = self.proj(hidden_states.to(dtype=target_dtype)).view(-1, self.embed_dim)
+ return hidden_states
+
+
+class Qwen2_5OmniPatchMerger(nn.Module):
+ def __init__(self, dim: int, context_dim: int, spatial_merge_size: int = 2) -> None:
+ super().__init__()
+ self.hidden_size = context_dim * (spatial_merge_size**2)
+ self.ln_q = Qwen2RMSNorm(context_dim, eps=1e-6)
+ self.mlp = nn.Sequential(
+ nn.Linear(self.hidden_size, self.hidden_size),
+ nn.GELU(),
+ nn.Linear(self.hidden_size, dim),
+ )
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ x = self.mlp(self.ln_q(x).view(-1, self.hidden_size))
+ return x
+
+
+class Qwen2_5OmniVisionEncoder(Qwen2_5OmniPreTrainedModel):
+ config: Qwen2_5OmniVisionEncoderConfig
+ _no_split_modules = ["Qwen2_5OmniVisionBlock"]
+ _input_embed_layer = "patch_embed"
+ input_modalities = ("image", "video")
+
+ def __init__(self, config: Qwen2_5OmniVisionEncoderConfig, *inputs, **kwargs) -> None:
+ super().__init__(config, *inputs, **kwargs)
+ self.spatial_merge_size = config.spatial_merge_size
+ self.patch_size = config.patch_size
+ self.fullatt_block_indexes = config.fullatt_block_indexes
+ self.window_size = config.window_size
+ self.spatial_merge_unit = self.spatial_merge_size * self.spatial_merge_size
+
+ self.patch_embed = Qwen2_5_VisionPatchEmbed(
+ patch_size=config.patch_size,
+ temporal_patch_size=config.temporal_patch_size,
+ in_channels=config.in_channels,
+ embed_dim=config.hidden_size,
+ )
+
+ head_dim = config.hidden_size // config.num_heads
+ self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2)
+ self.blocks = nn.ModuleList([Qwen2_5OmniVisionBlock(config) for _ in range(config.depth)])
+ self.merger = Qwen2_5OmniPatchMerger(
+ dim=config.out_hidden_size,
+ context_dim=config.hidden_size,
+ spatial_merge_size=config.spatial_merge_size,
+ )
+ self.gradient_checkpointing = False
+
+ self.post_init()
+
+ def rot_pos_emb(self, grid_thw):
+ pos_ids = []
+ for t, h, w in grid_thw:
+ hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
+ hpos_ids = hpos_ids.reshape(
+ h // self.spatial_merge_size,
+ self.spatial_merge_size,
+ w // self.spatial_merge_size,
+ self.spatial_merge_size,
+ )
+ hpos_ids = hpos_ids.permute(0, 2, 1, 3)
+ hpos_ids = hpos_ids.flatten()
+
+ wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
+ wpos_ids = wpos_ids.reshape(
+ h // self.spatial_merge_size,
+ self.spatial_merge_size,
+ w // self.spatial_merge_size,
+ self.spatial_merge_size,
+ )
+ wpos_ids = wpos_ids.permute(0, 2, 1, 3)
+ wpos_ids = wpos_ids.flatten()
+ pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
+ pos_ids = torch.cat(pos_ids, dim=0)
+ max_grid_size = grid_thw[:, 1:].max()
+ rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
+ rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
+ return rotary_pos_emb
+
+ def get_window_index(self, grid_thw):
+ window_index: list = []
+ cu_window_seqlens: list = [0]
+ window_index_id = 0
+ vit_merger_window_size = self.window_size // self.spatial_merge_size // self.patch_size
+
+ for grid_t, grid_h, grid_w in grid_thw:
+ llm_grid_h, llm_grid_w = (
+ grid_h // self.spatial_merge_size,
+ grid_w // self.spatial_merge_size,
+ )
+ index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(grid_t, llm_grid_h, llm_grid_w)
+ pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size
+ pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size
+ num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size
+ num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size
+ index_padded = F.pad(index, (0, pad_w, 0, pad_h), "constant", -100)
+ index_padded = index_padded.reshape(
+ grid_t,
+ num_windows_h,
+ vit_merger_window_size,
+ num_windows_w,
+ vit_merger_window_size,
+ )
+ index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape(
+ grid_t,
+ num_windows_h * num_windows_w,
+ vit_merger_window_size,
+ vit_merger_window_size,
+ )
+ seqlens = (index_padded != -100).sum([2, 3]).reshape(-1)
+ index_padded = index_padded.reshape(-1)
+ index_new = index_padded[index_padded != -100]
+ window_index.append(index_new + window_index_id)
+ cu_seqlens_tmp = seqlens.cumsum(0) * self.spatial_merge_unit + cu_window_seqlens[-1]
+ cu_window_seqlens.extend(cu_seqlens_tmp.tolist())
+ window_index_id += (grid_t * llm_grid_h * llm_grid_w).item()
+ window_index = torch.cat(window_index, dim=0)
+
+ return window_index, cu_window_seqlens
+
+ def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor, **kwargs) -> torch.Tensor:
+ """
+ Args:
+ hidden_states (`torch.Tensor` of shape `(seq_len, hidden_size)`):
+ The final hidden states of the model.
+ grid_thw (`torch.Tensor` of shape `(num_images_or_videos, 3)`):
+ The temporal, height and width of feature shape of each image in LLM.
+
+ Returns:
+ `torch.Tensor`: hidden_states.
+ """
+ hidden_states = self.patch_embed(hidden_states)
+ rotary_pos_emb = self.rot_pos_emb(grid_thw)
+
+ window_index, cu_window_seqlens = self.get_window_index(grid_thw)
+ cu_window_seqlens = torch.tensor(
+ cu_window_seqlens,
+ device=hidden_states.device,
+ dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
+ )
+ cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens)
+
+ seq_len, _ = hidden_states.size()
+ hidden_states = hidden_states.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
+ hidden_states = hidden_states[window_index, :, :]
+ hidden_states = hidden_states.reshape(seq_len, -1)
+ rotary_pos_emb = rotary_pos_emb.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
+ rotary_pos_emb = rotary_pos_emb[window_index, :, :]
+ rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1)
+
+ cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]).cumsum(
+ dim=0,
+ # Select dtype based on the following factors:
+ # - FA2 requires that cu_seqlens_q must have dtype int32
+ # - torch.onnx.export requires that cu_seqlens_q must have same dtype as grid_thw
+ # See https://github.com/huggingface/transformers/pull/34852 for more information
+ dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
+ )
+ cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
+
+ # Modification here
+ for layer_num, blk in enumerate(self.blocks):
+ if layer_num in self.fullatt_block_indexes:
+ cu_seqlens_now = cu_seqlens
+ else:
+ cu_seqlens_now = cu_window_seqlens
+
+ hidden_states = blk(
+ hidden_states,
+ cu_seqlens=cu_seqlens_now,
+ rotary_pos_emb=rotary_pos_emb,
+ **kwargs,
+ )
+ hidden_states = self.merger(hidden_states)
+ reverse_indices = torch.argsort(window_index)
+ hidden_states = hidden_states[reverse_indices, :]
+
+ return hidden_states
+
+
+class Qwen2_5OmniRotaryEmbedding(nn.Module):
+ inv_freq: torch.Tensor # fix linting for `register_buffer`
+
+ def __init__(self, config: Qwen2_5OmniThinkerConfig, device=None):
+ super().__init__()
+ self.max_seq_len_cached = config.max_position_embeddings
+ self.original_max_seq_len = config.max_position_embeddings
+
+ self.config = config
+
+ self.rope_type = self.config.rope_parameters["rope_type"]
+ rope_init_fn: Callable = self.compute_default_rope_parameters
+ if self.rope_type != "default":
+ rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+ inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
+
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+ self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+
+ @staticmethod
+ def compute_default_rope_parameters(
+ config: Optional[Qwen2_5OmniConfig] = None,
+ device: Optional["torch.device"] = None,
+ seq_len: Optional[int] = None,
+ ) -> tuple["torch.Tensor", float]:
+ """
+ Computes the inverse frequencies according to the original RoPE implementation
+ Args:
+ config ([`~transformers.PreTrainedConfig`]):
+ The model configuration.
+ device (`torch.device`):
+ The device to use for initialization of the inverse frequencies.
+ seq_len (`int`, *optional*):
+ The current sequence length. Unused for this type of RoPE.
+ Returns:
+ Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+ post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+ """
+ base = config.rope_parameters["rope_theta"]
+ dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+
+ attention_factor = 1.0 # Unused in this type of RoPE
+
+ # Compute the inverse frequencies
+ inv_freq = 1.0 / (
+ base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
+ )
+ return inv_freq, attention_factor
+
+ # Ignore copy
+ def forward(self, x, position_ids):
+ # In contrast to other models, Qwen2_5Omni has different position ids for the grids
+ # So we expand the inv_freq to shape (3, ...)
+ inv_freq_expanded = self.inv_freq[None, None, :, None].float().expand(3, position_ids.shape[1], -1, 1)
+ position_ids_expanded = position_ids[:, :, None, :].float() # shape (3, bs, 1, positions)
+
+ device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+ with maybe_autocast(device_type=device_type, enabled=False): # Force float32
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(2, 3)
+ emb = torch.cat((freqs, freqs), dim=-1)
+ cos = emb.cos() * self.attention_scaling
+ sin = emb.sin() * self.attention_scaling
+
+ return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+def apply_multimodal_rotary_pos_emb(q, k, cos, sin, mrope_section, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding with Multimodal Sections to the query and key tensors (https://qwenlm.github.io/blog/qwen2-vl/).
+
+ Explanation:
+ Multimodal 3D rotary position embedding is an extension to 1D rotary position embedding. The input embedding
+ sequence contains vision (images / videos) embedding and text embedding or just contains text embedding. For
+ vision embedding part, we apply rotary position embedding on temporal, height and width dimension separately.
+ Here we split the channel dimension to 3 chunks for the temporal, height and width rotary position embedding.
+ For text embedding part, we just apply 1D rotary position embedding. The three rotary position index (temporal,
+ height and width) of text embedding is always the same, so the text embedding rotary position embedding has no
+ difference with modern LLMs.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`):
+ The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+ used to pass offsetted position ids when working with a KV-cache.
+ mrope_section(`List(int)`):
+ Multimodal rope section is for channel dimension of temporal, height and width in rope calculation.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ mrope_section = mrope_section * 2
+ cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze(
+ unsqueeze_dim
+ )
+ sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1).unsqueeze(
+ unsqueeze_dim
+ )
+
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+class Qwen2_5OmniAttention(nn.Module):
+ """
+ Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
+ and "Generating Long Sequences with Sparse Transformers".
+ """
+
+ def __init__(self, config: Qwen2_5OmniConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ if layer_idx is None:
+ logger.warning_once(
+ f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+ "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+ "when creating this class."
+ )
+
+ self.hidden_size = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = getattr(config, "head_dim", self.hidden_size // self.num_heads)
+ self.num_key_value_heads = config.num_key_value_heads
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+ self.is_causal = True
+ self.attention_dropout = config.attention_dropout
+ self.rope_parameters = config.rope_parameters
+ self.scaling = self.head_dim**-0.5
+
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True)
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+ self.layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None
+ self.sliding_window = config.sliding_window if self.layer_type == "sliding_attention" else None
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ 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)
+
+ query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
+
+ cos, sin = position_embeddings
+ query_states, key_states = apply_multimodal_rotary_pos_emb(
+ query_states, key_states, cos, sin, self.config.rope_parameters["mrope_section"]
+ )
+
+ if past_key_values is not None:
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=self.scaling,
+ sliding_window=self.sliding_window,
+ position_ids=position_ids, # pass positions for FA2
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
+ attn_output = self.o_proj(attn_output)
+ return attn_output, attn_weights
+
+
+class Qwen2MLP(nn.Module):
+ def __init__(self, config, bias: bool = False):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+ self.intermediate_size = config.intermediate_size
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias)
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias)
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=bias)
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, hidden_state):
+ return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state))
+
+
+class Qwen2_5OmniDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: Qwen2_5OmniTextConfig, layer_idx: int):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+
+ if config.use_sliding_window and config._attn_implementation != "flash_attention_2":
+ logger.warning_once(
+ f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; "
+ "unexpected results may be encountered."
+ )
+ self.self_attn = Qwen2_5OmniAttention(config, layer_idx)
+
+ self.mlp = Qwen2MLP(config)
+ self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.attention_type = config.layer_types[layer_idx]
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+ `(batch, sequence_length)` where padding elements are indicated by 0.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+ (see `past_key_values`).
+ past_key_values (`Cache`, *optional*): cached past key and value projection states
+ cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+ Indices depicting the position of the input sequence tokens in the sequence.
+ position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
+ Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
+ with `head_dim` being the embedding dimension of each attention head.
+ kwargs (`dict`, *optional*):
+ Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+ into the model
+ """
+
+ residual = hidden_states
+
+ hidden_states = self.input_layernorm(hidden_states)
+
+ # Self Attention
+ hidden_states, self_attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights,)
+
+ return outputs
+
+
+@auto_docstring
+class Qwen2_5OmniThinkerTextModel(Qwen2_5OmniPreTrainedModel):
+ config: Qwen2_5OmniTextConfig
+ input_modalities = ("text",)
+ _no_split_modules = ["Qwen2_5OmniDecoderLayer"]
+
+ def __init__(self, config: Qwen2_5OmniTextConfig):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+ self.layers = nn.ModuleList(
+ [Qwen2_5OmniDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self._attn_implementation = config._attn_implementation
+ self.norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.has_sliding_layers = "sliding_attention" in self.config.layer_types
+ self.rotary_emb = Qwen2_5OmniRotaryEmbedding(config=config)
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> Union[tuple, BaseModelOutputWithPast]:
+ 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
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ # torch.jit.trace() doesn't support cache objects in the output
+ if use_cache and past_key_values is None and not torch.jit.is_tracing():
+ past_key_values = DynamicCache(config=self.config)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+
+ # the hard coded `3` is for temporal, height and width.
+ if position_ids is None:
+ position_ids = cache_position.view(1, 1, -1).expand(3, inputs_embeds.shape[0], -1)
+ elif position_ids.ndim == 2:
+ position_ids = position_ids[None, ...].expand(3, position_ids.shape[0], -1)
+
+ # NOTE: we need to pass text position ids for packing. Qwen2-VL uses 3D positions
+ # where each dim indicates visual spatial positions for temporal/height/width grids.
+ # There are two scenarios when FA2-like packed masking might be activated.
+ # 1. User specifically passed packed `position_ids` and no attention mask.
+ # In this case we expect the useer to create correct position ids for all 3 grids
+ # and prepend text-only position ids to it. The final tensor will be [4, bs, seq-len]
+ # 2. User runs forward with no attention mask and no position ids. In this case, position ids
+ # are prepared by the model (`get_rope_index`) as `[4, bs, seq-len]` tensor. Text-only positions are
+ # prepended by us when creating positions so that the mask is constructed correctly. NOTE: failing to pass
+ # text-only positions will cause incorrect mask construction, do not change `prepare_input_for_generation`
+ if position_ids.ndim == 3 and position_ids.shape[0] == 4:
+ text_position_ids = position_ids[0]
+ position_ids = position_ids[1:]
+ else:
+ # If inputs are not packed (usual 3D positions), do not prepare mask from position_ids
+ text_position_ids = None
+
+ # It may already have been prepared by e.g. `generate`
+ if not isinstance(causal_mask_mapping := attention_mask, dict):
+ # Prepare mask arguments
+ mask_kwargs = {
+ "config": self.config,
+ "input_embeds": inputs_embeds,
+ "attention_mask": attention_mask,
+ "cache_position": cache_position,
+ "past_key_values": past_key_values,
+ "position_ids": text_position_ids,
+ }
+ # Create the masks
+ causal_mask_mapping = {
+ "full_attention": create_causal_mask(**mask_kwargs),
+ }
+ # The sliding window alternating layers are not always activated depending on the config
+ if self.has_sliding_layers:
+ causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs)
+
+ hidden_states = inputs_embeds
+ position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+
+ for decoder_layer in self.layers:
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=causal_mask_mapping[decoder_layer.attention_type],
+ position_embeddings=position_embeddings,
+ position_ids=text_position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ hidden_states = self.norm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns] if v is not None
+ )
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The Qwen2.5OmniThinker model which consists of a audio backbone and a language model.
+ """
+)
+class Qwen2_5OmniThinkerForConditionalGeneration(Qwen2_5OmniPreTrainedModelForConditionalGeneration, GenerationMixin):
+ config: Qwen2_5OmniThinkerConfig
+ base_model_prefix = "thinker"
+ _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
+ _no_split_modules = ["Qwen2_5OmniAudioEncoder", "Qwen2_5OmniVisionEncoder"]
+
+ def __init__(self, config: Qwen2_5OmniThinkerConfig):
+ super().__init__(config)
+ self.audio_tower = Qwen2_5OmniAudioEncoder._from_config(config.audio_config)
+ self.visual = Qwen2_5OmniVisionEncoder._from_config(config.vision_config)
+ self.vocab_size = config.text_config.vocab_size
+ self.model = Qwen2_5OmniThinkerTextModel._from_config(config.text_config)
+ self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
+ self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
+ self.spatial_merge_size = config.vision_config.spatial_merge_size
+ self.rope_deltas = None
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.model.set_input_embeddings(value)
+
+ def get_video_features(
+ self, pixel_values_videos: torch.FloatTensor, video_grid_thw: Optional[torch.LongTensor] = None
+ ):
+ """
+ Encodes videos into continuous embeddings that can be forwarded to the language model.
+
+ Args:
+ pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
+ The tensors corresponding to the input videos.
+ video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+ The temporal, height and width of feature shape of each video in LLM.
+ """
+ pixel_values_videos = pixel_values_videos.type(self.visual.dtype)
+ video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw)
+ return video_embeds
+
+ def get_image_features(self, pixel_values: torch.FloatTensor, image_grid_thw: Optional[torch.LongTensor] = None):
+ """
+ Encodes images into continuous embeddings that can be forwarded to the language model.
+
+ Args:
+ pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
+ The tensors corresponding to the input images.
+ image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+ The temporal, height and width of feature shape of each image in LLM.
+ """
+ pixel_values = pixel_values.type(self.visual.dtype)
+ image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
+ return image_embeds
+
+ def get_audio_features(
+ self,
+ input_features: torch.FloatTensor,
+ feature_attention_mask: Optional[torch.LongTensor] = None,
+ audio_feature_lengths: Optional[torch.LongTensor] = None,
+ ):
+ """
+ Encodes audios into continuous embeddings that can be forwarded to the language model.
+
+ Args:
+ input_features (`torch.FloatTensor`):
+ The tensors corresponding to the input audios.
+ feature_attention_mask (`torch.LongTensor`, *optional*):
+ Mask to avoid performing attention on padding feature indices. Mask values selected in `[0, 1]`:
+ audio_feature_lengths (`torch.LongTensor` of shape `(num_audios)`, *optional*):
+ The length of feature shape of each audio in LLM.
+ """
+ if feature_attention_mask is not None:
+ audio_feature_lengths = torch.sum(feature_attention_mask, dim=1)
+ input_features = input_features.permute(0, 2, 1)[feature_attention_mask.bool()].permute(1, 0)
+ else:
+ audio_feature_lengths = None
+
+ audio_feat_lengths, audio_output_lengths = self.audio_tower._get_feat_extract_output_lengths(
+ audio_feature_lengths if audio_feature_lengths is not None else feature_attention_mask.sum(-1)
+ )
+ feature_lens = audio_feature_lengths if audio_feature_lengths is not None else feature_attention_mask.sum(-1)
+ audio_outputs = self.audio_tower(
+ input_features,
+ feature_lens=feature_lens,
+ aftercnn_lens=audio_feat_lengths,
+ )
+ audio_features = audio_outputs.last_hidden_state
+
+ if audio_features.shape[0] != sum(audio_output_lengths.tolist()):
+ raise ValueError("length of audio_features should match audio_output_lengths")
+
+ return audio_features
+
+ def get_placeholder_mask(
+ self,
+ input_ids: torch.LongTensor,
+ inputs_embeds: torch.FloatTensor,
+ image_features: Optional[torch.FloatTensor] = None,
+ video_features: Optional[torch.FloatTensor] = None,
+ ):
+ """
+ Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+ equal to the length of multimodal features. If the lengths are different, an error is raised.
+ """
+ if input_ids is None:
+ special_image_mask = inputs_embeds == self.get_input_embeddings()(
+ torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
+ )
+ special_image_mask = special_image_mask.all(-1)
+ special_video_mask = inputs_embeds == self.get_input_embeddings()(
+ torch.tensor(self.config.video_token_id, dtype=torch.long, device=inputs_embeds.device)
+ )
+ special_video_mask = special_video_mask.all(-1)
+ special_audio_mask = (
+ inputs_embeds
+ == self.get_input_embeddings()(
+ torch.tensor(self.config.audio_token_id, dtype=torch.long, device=inputs_embeds.device)
+ )
+ ).all(-1)
+ else:
+ special_image_mask = input_ids == self.config.image_token_id
+ special_video_mask = input_ids == self.config.video_token_id
+ special_audio_mask = input_ids == self.config.audio_token_id
+
+ n_image_tokens = special_image_mask.sum()
+ special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+ if image_features is not None and inputs_embeds[special_image_mask].numel() != image_features.numel():
+ raise ValueError(
+ f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {image_features.shape[0]}"
+ )
+
+ n_video_tokens = special_video_mask.sum()
+ special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+ if video_features is not None and inputs_embeds[special_video_mask].numel() != video_features.numel():
+ raise ValueError(
+ f"Videos features and video tokens do not match: tokens: {n_video_tokens}, features {video_features.shape[0]}"
+ )
+
+ special_audio_mask = special_audio_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+ return special_image_mask, special_video_mask, special_audio_mask
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ input_features: Optional[torch.FloatTensor] = None,
+ pixel_values: Optional[torch.FloatTensor] = None,
+ pixel_values_videos: Optional[torch.FloatTensor] = None,
+ image_grid_thw: Optional[torch.LongTensor] = None,
+ video_grid_thw: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ feature_attention_mask: Optional[torch.Tensor] = None,
+ audio_feature_lengths: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ rope_deltas: Optional[torch.LongTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ use_audio_in_video: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ video_second_per_grid: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Union[tuple, Qwen2_5OmniThinkerCausalLMOutputWithPast]:
+ r"""
+ image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+ The temporal, height and width of feature shape of each image in LLM.
+ video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+ The temporal, height and width of feature shape of each video in LLM.
+ feature_attention_mask (`torch.Tensor` of shape `(batch_size, feature_sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding feature indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+ audio_feature_lengths (`torch.LongTensor` of shape `(num_audios)`, *optional*):
+ The length of feature shape of each audio in LLM.
+ rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
+ The rope index difference between sequence length and multimodal rope.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+ use_audio_in_video (`bool`, *optional*):
+ Whether or not use audio track in video, should same as the parameter in `process_audio_info`.
+ video_second_per_grid (`torch.LongTensor` of shape `(num_videos)`, *optional*):
+ Number of seconds per grid for each video, used for temporal feature mapping.
+
+ Example:
+
+ ```python
+ >>> from io import BytesIO
+ >>> from urllib.request import urlopen
+ >>> import librosa
+ >>> from qwen_vl_utils import process_vision_info
+ >>> from transformers import Qwen2_5OmniProcessor, Qwen2_5OmniThinkerForConditionalGeneration
+
+ >>> thinker = Qwen2_5OmniThinkerForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-Omni-7B")
+ >>> processor = Qwen2_5OmniProcessor.from_pretrained("Qwen/Qwen2.5-Omni-7B")
+
+ >>> conversations = [
+ >>> {'role': 'system', 'content': 'You are a helpful voice chat bot, and please respond to me in a casual conversation manner using random voice.'},
+ >>> {"role": "user", "content": [
+ >>> {"type": "image", "image_url": "https://www.ilankelman.org/stopsigns/australia.jpg"},
+ >>> {"type": "audio", "audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/glass-breaking-151256.mp3"},
+ >>> ]},
+ >>> ]
+
+ >>> text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
+ >>> audios = [ librosa.load(BytesIO(urlopen( conversations[1]['content'][1]['audio_url'] ).read()), sr=self.processor.feature_extractor.sampling_rate) ]
+ >>> images, videos = process_vision_info(conversations)
+ >>> inputs = processor(text=text, audio=audios, images=images, videos=videos, return_tensors="pt", padding=True)
+
+ >>> # Generate
+ >>> inputs['use_audio_in_video'] = `True` or `False`
+ >>> generation = thinker.generate(**inputs, max_new_tokens=2048)
+ >>> generate_ids = generation[:, inputs.input_ids.size(1):]
+
+ >>> response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ ```"""
+
+ 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
+
+ if inputs_embeds is None:
+ # 1. Extract the input embeddings
+ inputs_embeds = self.get_input_embeddings()(input_ids)
+
+ # 2. Merge text , audios , image and video
+ if input_features is not None:
+ audio_features = self.get_audio_features(
+ input_features,
+ feature_attention_mask=feature_attention_mask,
+ audio_feature_lengths=audio_feature_lengths,
+ )
+ audio_features = audio_features.to(inputs_embeds.device, inputs_embeds.dtype)
+ _, _, audio_mask = self.get_placeholder_mask(input_ids, inputs_embeds=inputs_embeds)
+ inputs_embeds = inputs_embeds.masked_scatter(audio_mask, audio_features)
+
+ if pixel_values is not None:
+ image_embeds = self.get_image_features(pixel_values, image_grid_thw)
+ image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
+ image_mask, _, _ = self.get_placeholder_mask(
+ input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds
+ )
+ inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
+
+ if pixel_values_videos is not None:
+ video_embeds = self.get_video_features(pixel_values_videos, video_grid_thw)
+ video_embeds = video_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
+ _, video_mask, _ = self.get_placeholder_mask(
+ input_ids, inputs_embeds=inputs_embeds, video_features=video_embeds
+ )
+ inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)
+
+ if feature_attention_mask is not None:
+ audio_feature_lengths = torch.sum(feature_attention_mask, dim=1)
+ else:
+ audio_feature_lengths = None
+
+ if attention_mask is not None and position_ids is None:
+ past_key_values_length = 0 if past_key_values is None else past_key_values.get_seq_length()
+ if past_key_values_length == 0 or self.rope_deltas is None:
+ delta0 = (1 - attention_mask).sum(dim=-1).unsqueeze(1)
+ position_ids, rope_deltas = self.get_rope_index(
+ input_ids,
+ image_grid_thw,
+ video_grid_thw,
+ attention_mask,
+ use_audio_in_video,
+ audio_feature_lengths,
+ video_second_per_grid,
+ )
+ rope_deltas = rope_deltas - delta0
+ self.rope_deltas = rope_deltas
+ else:
+ batch_size, seq_length = input_ids.shape
+ delta = (past_key_values_length + self.rope_deltas).to(input_ids.device)
+ position_ids = torch.arange(seq_length, device=input_ids.device)
+ position_ids = position_ids.view(1, -1).expand(batch_size, -1)
+ position_ids = position_ids.add(delta)
+ position_ids = position_ids.unsqueeze(0).expand(3, -1, -1)
+
+ outputs = self.model(
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = outputs[0]
+ logits = self.lm_head(hidden_states)
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(
+ logits=logits, labels=labels, vocab_size=self.config.get_text_config().vocab_size
+ )
+
+ if not return_dict:
+ output = (logits,) + outputs
+ return (loss,) + output if loss is not None else output
+
+ return Qwen2_5OmniThinkerCausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ rope_deltas=self.rope_deltas,
+ )
+
+ 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,
+ pixel_values=None,
+ pixel_values_videos=None,
+ image_grid_thw=None,
+ video_grid_thw=None,
+ input_features=None,
+ feature_attention_mask=None,
+ use_audio_in_video=False,
+ video_second_per_grid=None,
+ is_first_iteration=False,
+ **kwargs,
+ ):
+ model_inputs = super().prepare_inputs_for_generation(
+ input_ids,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ inputs_embeds=inputs_embeds,
+ cache_position=cache_position,
+ position_ids=position_ids,
+ use_cache=use_cache,
+ pixel_values=pixel_values,
+ pixel_values_videos=pixel_values_videos,
+ image_grid_thw=image_grid_thw,
+ video_grid_thw=video_grid_thw,
+ input_features=input_features,
+ feature_attention_mask=feature_attention_mask,
+ use_audio_in_video=use_audio_in_video,
+ video_second_per_grid=video_second_per_grid,
+ is_first_iteration=is_first_iteration,
+ **kwargs,
+ )
+
+ model_inputs["position_ids"] = None
+
+ if not is_first_iteration and use_cache:
+ model_inputs["pixel_values"] = None
+ model_inputs["pixel_values_videos"] = None
+ model_inputs["input_features"] = None
+
+ return model_inputs
+
+
+############################
+# Start Talker #
+############################
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for Qwen2.5OmniTalker causal language model (or autoregressive) outputs.
+ """
+)
+class Qwen2_5OmniTalkerCausalLMOutputWithPast(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Language modeling loss (for next-token prediction).
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
+ The rope index difference between sequence length and multimodal rope.
+ thinker_reply_part (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Hidden states from the thinker model that are used as input for the talker model. These represent the encoded
+ response that the talker model will use to generate speech tokens.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ past_key_values: Optional[Cache] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+ rope_deltas: Optional[torch.LongTensor] = None
+ thinker_reply_part: Optional[torch.FloatTensor] = None
+
+
+@auto_docstring
+class Qwen2_5OmniTalkerModel(Qwen2_5OmniPreTrainedModel):
+ config: Qwen2_5OmniTalkerConfig
+ input_modalities = ("image", "video", "audio", "text")
+
+ _no_split_modules = ["Qwen2_5OmniTalkerDecoderLayer"]
+
+ def __init__(self, config: Qwen2_5OmniTalkerConfig):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.embedding_size, self.padding_idx)
+ self.layers = nn.ModuleList(
+ [Qwen2_5OmniDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self._attn_implementation = config._attn_implementation
+ self.norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.has_sliding_layers = "sliding_attention" in self.config.layer_types
+ self.rotary_emb = Qwen2_5OmniRotaryEmbedding(config=config)
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> Union[tuple, BaseModelOutputWithPast]:
+ 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
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ # torch.jit.trace() doesn't support cache objects in the output
+ if use_cache and past_key_values is None and not torch.jit.is_tracing():
+ past_key_values = DynamicCache(config=self.config)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+
+ # the hard coded `3` is for temporal, height and width.
+ if position_ids is None:
+ position_ids = cache_position.view(1, 1, -1).expand(3, inputs_embeds.shape[0], -1)
+ elif position_ids.ndim == 2:
+ position_ids = position_ids[None, ...].expand(3, position_ids.shape[0], -1)
+
+ # NOTE: we need to pass text position ids for packing. Qwen2-VL uses 3D positions
+ # where each dim indicates visual spatial positions for temporal/height/width grids.
+ # There are two scenarios when FA2-like packed masking might be activated.
+ # 1. User specifically passed packed `position_ids` and no attention mask.
+ # In this case we expect the useer to create correct position ids for all 3 grids
+ # and prepend text-only position ids to it. The final tensor will be [4, bs, seq-len]
+ # 2. User runs forward with no attention mask and no position ids. In this case, position ids
+ # are prepared by the model (`get_rope_index`) as `[4, bs, seq-len]` tensor. Text-only positions are
+ # prepended by us when creating positions so that the mask is constructed correctly. NOTE: failing to pass
+ # text-only positions will cause incorrect mask construction, do not change `prepare_input_for_generation`
+ if position_ids.ndim == 3 and position_ids.shape[0] == 4:
+ text_position_ids = position_ids[0]
+ position_ids = position_ids[1:]
+ else:
+ # If inputs are not packed (usual 3D positions), do not prepare mask from position_ids
+ text_position_ids = None
+
+ # It may already have been prepared by e.g. `generate`
+ if not isinstance(causal_mask_mapping := attention_mask, dict):
+ # Prepare mask arguments
+ mask_kwargs = {
+ "config": self.config,
+ "input_embeds": inputs_embeds,
+ "attention_mask": attention_mask,
+ "cache_position": cache_position,
+ "past_key_values": past_key_values,
+ "position_ids": text_position_ids,
+ }
+ # Create the masks
+ causal_mask_mapping = {
+ "full_attention": create_causal_mask(**mask_kwargs),
+ }
+ # The sliding window alternating layers are not always activated depending on the config
+ if self.has_sliding_layers:
+ causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs)
+
+ hidden_states = inputs_embeds
+ position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+
+ for decoder_layer in self.layers:
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=causal_mask_mapping[decoder_layer.attention_type],
+ position_embeddings=position_embeddings,
+ position_ids=text_position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ hidden_states = self.norm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns] if v is not None
+ )
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ )
+
+
+class Qwen2_5OmniTalkerForConditionalGeneration(Qwen2_5OmniPreTrainedModelForConditionalGeneration, GenerationMixin):
+ config: Qwen2_5OmniTalkerConfig
+ base_model_prefix = "talker"
+ output_modalities = ("audio",)
+
+ def __init__(self, config: Qwen2_5OmniTalkerConfig):
+ super().__init__(config)
+
+ self.thinker_to_talker_proj = nn.Linear(config.embedding_size, config.hidden_size)
+
+ self.model = Qwen2_5OmniTalkerModel(config)
+ self.codebook_size = config.vocab_size
+ self.codec_head = nn.Linear(config.hidden_size, self.codebook_size, bias=False)
+
+ self.codec_bos_token = config.tts_codec_start_token_id
+ self.codec_eos_token = config.tts_codec_end_token_id
+ self.codec_pad_token = config.tts_codec_pad_token_id
+ self.codec_mask_token = config.tts_codec_mask_token_id
+
+ self.text_bos_token = config.tts_text_start_token_id
+ self.text_eos_token = config.tts_text_end_token_id
+ self.text_pad_token = config.tts_text_pad_token_id
+
+ self.spatial_merge_size = self.config.spatial_merge_size
+ self.rope_deltas = None
+
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.model.set_input_embeddings(value)
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ thinker_reply_part: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ rope_deltas: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ input_text_ids: Optional[torch.LongTensor] = None,
+ image_grid_thw: Optional[torch.LongTensor] = None,
+ video_grid_thw: Optional[torch.LongTensor] = None,
+ use_audio_in_video: Optional[bool] = None,
+ audio_feature_lengths: Optional[torch.LongTensor] = None,
+ video_second_per_grid: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ **kwargs,
+ ) -> Union[tuple, Qwen2_5OmniTalkerCausalLMOutputWithPast]:
+ r"""
+ thinker_reply_part (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Hidden states from the thinker model's output that represent the text reply part to be processed.
+ rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
+ The rope index difference between sequence length and multimodal rope.
+ input_text_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Input token IDs for text-only content, used for position calculation in multimodal contexts.
+ image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+ The temporal, height and width of feature shape of each image in LLM.
+ video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+ The temporal, height and width of feature shape of each video in LLM.
+ use_audio_in_video (`bool`, *optional*):
+ Whether or not use audio track in video, should same as the parameter in `process_audio_info`.
+ audio_feature_lengths (`torch.LongTensor` of shape `(num_audios)`, *optional*):
+ The length of feature shape of each audio in LLM.
+ video_second_per_grid (`torch.LongTensor` of shape `(num_videos)`, *optional*):
+ Number of seconds per grid for each video, used for temporal feature mapping.
+
+ Example:
+
+ ```python
+ >>> from io import BytesIO
+ >>> from urllib.request import urlopen
+ >>> import librosa
+ >>> from transformers import AutoProcessor, Qwen2_5OmniTalkerForConditionalGeneration
+
+ >>> model = Qwen2_5OmniTalkerForConditionalGeneration.from_pretrained("Qwen/Qwen2-Audio-7B")
+ >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2-Audio-7B")
+
+ >>> prompt = "<|audio_bos|><|AUDIO|><|audio_eos|>Generate the caption in English:"
+ >>> url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/glass-breaking-151256.mp3"
+ >>> audio, _ = librosa.load(BytesIO(urlopen(url).read()), sr=self.processor.feature_extractor.sampling_rate)
+
+ >>> inputs = processor(text=prompt, audio=audio, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(**inputs, max_length=30)
+ >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Generate the caption in English: Glass is breaking."
+ ```"""
+
+ 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
+
+ if attention_mask is not None and position_ids is None:
+ past_key_values_length = 0 if past_key_values is None else past_key_values.get_seq_length()
+ if past_key_values_length == 0 or self.rope_deltas is None:
+ position_ids, rope_deltas = self.get_rope_index(
+ input_text_ids,
+ image_grid_thw,
+ video_grid_thw,
+ attention_mask,
+ use_audio_in_video,
+ audio_feature_lengths,
+ video_second_per_grid,
+ )
+
+ inputs_embeds[:, -1, :] += self.get_input_embeddings()(
+ torch.tensor([self.codec_bos_token], dtype=torch.long, device=inputs_embeds.device)
+ )
+ inputs_embeds[:, -2, :] += self.get_input_embeddings()(
+ torch.tensor([self.codec_pad_token], dtype=torch.long, device=inputs_embeds.device)
+ )
+ self.rope_deltas = rope_deltas
+
+ else:
+ batch_size, seq_length, _ = inputs_embeds.shape
+ delta = (past_key_values_length + self.rope_deltas).to(input_ids.device)
+ position_ids = torch.arange(seq_length, device=input_ids.device)
+ position_ids = position_ids.view(1, -1).expand(batch_size, -1)
+ position_ids = position_ids.add(delta)
+ position_ids = position_ids.unsqueeze(0).expand(3, -1, -1)
+
+ if inputs_embeds is None:
+ # 1. Inference tokens after second token
+ codec_embeds = self.get_input_embeddings()(input_ids)
+ inputs_embeds = codec_embeds + thinker_reply_part[:, :1, :]
+ if thinker_reply_part.shape[1] > 1:
+ thinker_reply_part = thinker_reply_part[:, 1:, :]
+
+ talker_lm_input = self.thinker_to_talker_proj(inputs_embeds)
+
+ if attention_mask is not None:
+ attention_mask = attention_mask.to(inputs_embeds.device)
+
+ outputs = self.model(
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=talker_lm_input,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_states = outputs[0]
+ logits = self.codec_head(hidden_states)
+ logits = logits.float()
+
+ loss = None
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return (loss,) + output if loss is not None else output
+
+ return Qwen2_5OmniTalkerCausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=hidden_states,
+ attentions=outputs.attentions,
+ rope_deltas=self.rope_deltas,
+ thinker_reply_part=thinker_reply_part,
+ )
+
+ def _get_initial_cache_position(self, seq_length, device, model_kwargs):
+ # Talker needs to calculate cache_position with input_ids, so pop inputs_embeds temporarily
+ inputs_embeds = model_kwargs.pop("inputs_embeds")
+ model_kwargs = super()._get_initial_cache_position(seq_length, device, model_kwargs)
+ model_kwargs["inputs_embeds"] = inputs_embeds
+ return model_kwargs
+
+ # prepare inputs for talker lm generation
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ input_text_ids,
+ past_key_values=None,
+ attention_mask=None,
+ inputs_embeds=None,
+ thinker_reply_part=None,
+ cache_position=None,
+ position_ids=None,
+ use_cache=True,
+ pixel_values=None,
+ pixel_values_videos=None,
+ image_grid_thw=None,
+ video_grid_thw=None,
+ input_audio_features=None,
+ audio_feature_attention_mask=None,
+ audio_feature_lengths=None,
+ use_audio_in_video=False,
+ video_second_per_grid=None,
+ **kwargs,
+ ):
+ model_inputs = super().prepare_inputs_for_generation(
+ input_ids,
+ past_key_values,
+ attention_mask,
+ inputs_embeds,
+ cache_position,
+ use_cache=use_cache,
+ thinker_reply_part=thinker_reply_part,
+ input_text_ids=input_text_ids,
+ image_grid_thw=image_grid_thw,
+ video_grid_thw=video_grid_thw,
+ use_audio_in_video=use_audio_in_video,
+ audio_feature_lengths=audio_feature_lengths,
+ video_second_per_grid=video_second_per_grid,
+ **kwargs,
+ )
+
+ model_inputs["position_ids"] = None
+
+ return model_inputs
+
+ def _update_model_kwargs_for_generation(
+ self,
+ outputs: ModelOutput,
+ model_kwargs: dict[str, Any],
+ is_encoder_decoder: bool = False,
+ num_new_tokens: int = 1,
+ ) -> dict[str, Any]:
+ model_kwargs = super()._update_model_kwargs_for_generation(
+ outputs, model_kwargs, is_encoder_decoder, num_new_tokens
+ )
+
+ if getattr(outputs, "thinker_reply_part", None) is not None:
+ model_kwargs["thinker_reply_part"] = outputs.thinker_reply_part
+
+ return model_kwargs
+
+
+class Qwen2_5OmniDiTRotaryEmbedding(nn.Module):
+ inv_freq: torch.Tensor # fix linting for `register_buffer`
+
+ def __init__(self, config: Qwen2_5OmniDiTConfig, device=None):
+ super().__init__()
+ self.max_seq_len_cached = config.max_position_embeddings
+ self.original_max_seq_len = config.max_position_embeddings
+
+ self.config = config
+
+ self.rope_type = self.config.rope_parameters["rope_type"]
+ rope_init_fn: Callable = self.compute_default_rope_parameters
+ if self.rope_type != "default":
+ rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+ inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
+
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+ self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+
+ @staticmethod
+ def compute_default_rope_parameters(
+ config: Optional[Qwen2_5OmniDiTConfig] = None,
+ device: Optional["torch.device"] = None,
+ seq_len: Optional[int] = None,
+ ) -> tuple["torch.Tensor", float]:
+ """
+ Computes the inverse frequencies according to the original RoPE implementation
+ Args:
+ config ([`~transformers.PreTrainedConfig`]):
+ The model configuration.
+ device (`torch.device`):
+ The device to use for initialization of the inverse frequencies.
+ seq_len (`int`, *optional*):
+ The current sequence length. Unused for this type of RoPE.
+ Returns:
+ Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+ post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+ """
+ base = config.rope_parameters["rope_theta"]
+ dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+
+ attention_factor = 1.0 # Unused in this type of RoPE
+
+ # Compute the inverse frequencies
+ inv_freq = 1.0 / (
+ base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
+ )
+ return inv_freq, attention_factor
+
+ @torch.no_grad()
+ @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
+ def forward(self, x, position_ids):
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+ position_ids_expanded = position_ids[:, None, :].float()
+
+ device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+ with maybe_autocast(device_type=device_type, enabled=False): # Force float32
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+ emb = torch.cat((freqs, freqs), dim=-1)
+ cos = emb.cos() * self.attention_scaling
+ sin = emb.sin() * self.attention_scaling
+
+ return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+class TimeDelayNetBlock(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ kernel_size,
+ dilation,
+ ):
+ super().__init__()
+ self.conv = nn.Conv1d(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ dilation=dilation,
+ padding="same",
+ padding_mode="reflect",
+ )
+ self.activation = nn.ReLU()
+
+ def forward(self, hidden_states: torch.Tensor):
+ return self.activation(self.conv(hidden_states))
+
+
+class Res2NetBlock(torch.nn.Module):
+ def __init__(self, in_channels, out_channels, scale=8, kernel_size=3, dilation=1):
+ super().__init__()
+
+ in_channel = in_channels // scale
+ hidden_channel = out_channels // scale
+
+ self.blocks = nn.ModuleList(
+ [
+ TimeDelayNetBlock(
+ in_channel,
+ hidden_channel,
+ kernel_size=kernel_size,
+ dilation=dilation,
+ )
+ for i in range(scale - 1)
+ ]
+ )
+ self.scale = scale
+
+ def forward(self, hidden_states):
+ outputs = []
+ for i, hidden_part in enumerate(torch.chunk(hidden_states, self.scale, dim=1)):
+ if i == 0:
+ output_part = hidden_part
+ elif i == 1:
+ output_part = self.blocks[i - 1](hidden_part)
+ else:
+ output_part = self.blocks[i - 1](hidden_part + output_part)
+ outputs.append(output_part)
+ output = torch.cat(outputs, dim=1)
+ return output
+
+
+class SqueezeExcitationBlock(nn.Module):
+ def __init__(self, in_channels, se_channels, out_channels):
+ super().__init__()
+
+ self.conv1 = nn.Conv1d(
+ in_channels=in_channels,
+ out_channels=se_channels,
+ kernel_size=1,
+ padding="same",
+ padding_mode="reflect",
+ )
+ self.relu = nn.ReLU(inplace=True)
+ self.conv2 = nn.Conv1d(
+ in_channels=se_channels,
+ out_channels=out_channels,
+ kernel_size=1,
+ padding="same",
+ padding_mode="reflect",
+ )
+ self.sigmoid = nn.Sigmoid()
+
+ def forward(self, hidden_states):
+ hidden_states_mean = hidden_states.mean(dim=2, keepdim=True)
+
+ hidden_states_mean = self.relu(self.conv1(hidden_states_mean))
+ hidden_states_mean = self.sigmoid(self.conv2(hidden_states_mean))
+
+ return hidden_states * hidden_states_mean
+
+
+class AttentiveStatisticsPooling(nn.Module):
+ """This class implements an attentive statistic pooling layer for each channel.
+ It returns the concatenated mean and std of the input tensor.
+ """
+
+ def __init__(self, channels, attention_channels=128):
+ super().__init__()
+
+ self.eps = 1e-12
+ self.tdnn = TimeDelayNetBlock(channels * 3, attention_channels, 1, 1)
+ self.tanh = nn.Tanh()
+ self.conv = nn.Conv1d(
+ in_channels=attention_channels,
+ out_channels=channels,
+ kernel_size=1,
+ padding="same",
+ padding_mode="reflect",
+ )
+
+ def _length_to_mask(self, length, max_len=None, dtype=None, device=None):
+ """Creates a binary mask for each sequence.
+
+ Reference: https://discuss.pytorch.org/t/how-to-generate-variable-length-mask/23397/3
+
+ Arguments
+ ---------
+ length : torch.LongTensor
+ Containing the length of each sequence in the batch. Must be 1D.
+ max_len : int
+ Max length for the mask, also the size of the second dimension.
+ dtype : torch.dtype, default: None
+ The dtype of the generated mask.
+ device: torch.device, default: None
+ The device to put the mask variable.
+
+ Returns
+ -------
+ mask : tensor
+ The binary mask.
+ """
+
+ if max_len is None:
+ max_len = length.max().long().item() # using arange to generate mask
+ mask = torch.arange(max_len, device=length.device, dtype=length.dtype).expand(
+ len(length), max_len
+ ) < length.unsqueeze(1)
+
+ mask = torch.as_tensor(mask, dtype=dtype, device=device)
+ return mask
+
+ def _compute_statistics(self, x, m, dim=2):
+ mean = (m * x).sum(dim)
+ std = torch.sqrt((m * (x - mean.unsqueeze(dim)).pow(2)).sum(dim).clamp(self.eps))
+ return mean, std
+
+ def forward(self, hidden_states):
+ seq_length = hidden_states.shape[-1]
+ lengths = torch.ones(hidden_states.shape[0], device=hidden_states.device)
+
+ # Make binary mask of shape [N, 1, L]
+ mask = self._length_to_mask(
+ lengths * seq_length, max_len=seq_length, dtype=hidden_states.dtype, device=hidden_states.device
+ )
+ mask = mask.unsqueeze(1)
+
+ # Expand the temporal context of the pooling layer by allowing the
+ # self-attention to look at global properties of the utterance.
+ total = mask.sum(dim=2, keepdim=True)
+
+ mean, std = self._compute_statistics(hidden_states, mask / total)
+ mean = mean.unsqueeze(2).repeat(1, 1, seq_length)
+ std = std.unsqueeze(2).repeat(1, 1, seq_length)
+ attention = torch.cat([hidden_states, mean, std], dim=1)
+
+ # Apply layers
+ attention = self.conv(self.tanh(self.tdnn(attention)))
+
+ # Filter out zero-paddings
+ attention = attention.masked_fill(mask == 0, float("-inf"))
+
+ attention = F.softmax(attention, dim=2)
+ mean, std = self._compute_statistics(hidden_states, attention)
+ # Append mean and std of the batch
+ pooled_stats = torch.cat((mean, std), dim=1)
+ pooled_stats = pooled_stats.unsqueeze(2)
+
+ return pooled_stats
+
+
+class SqueezeExcitationRes2NetBlock(nn.Module):
+ """An implementation of building block in ECAPA-TDNN, i.e.,
+ TDNN-Res2Net-TDNN-SqueezeExcitationBlock.
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ res2net_scale=8,
+ se_channels=128,
+ kernel_size=1,
+ dilation=1,
+ ):
+ super().__init__()
+ self.out_channels = out_channels
+ self.tdnn1 = TimeDelayNetBlock(
+ in_channels,
+ out_channels,
+ kernel_size=1,
+ dilation=1,
+ )
+ self.res2net_block = Res2NetBlock(out_channels, out_channels, res2net_scale, kernel_size, dilation)
+ self.tdnn2 = TimeDelayNetBlock(
+ out_channels,
+ out_channels,
+ kernel_size=1,
+ dilation=1,
+ )
+ self.se_block = SqueezeExcitationBlock(out_channels, se_channels, out_channels)
+
+ def forward(self, hidden_state):
+ residual = hidden_state
+
+ hidden_state = self.tdnn1(hidden_state)
+ hidden_state = self.res2net_block(hidden_state)
+ hidden_state = self.tdnn2(hidden_state)
+ hidden_state = self.se_block(hidden_state)
+
+ return hidden_state + residual
+
+
+class ECAPA_TimeDelayNet(torch.nn.Module):
+ """An implementation of the speaker embedding model in a paper.
+ "ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in
+ TDNN Based Speaker Verification" (https://huggingface.co/papers/2005.07143).
+ """
+
+ def __init__(self, config: Qwen2_5OmniDiTConfig):
+ super().__init__()
+ if len(config.enc_channels) != len(config.enc_kernel_sizes) or len(config.enc_channels) != len(
+ config.enc_dilations
+ ):
+ raise ValueError("enc_channels, enc_kernel_sizes and enc_dilations should have same length")
+ self.channels = config.enc_channels
+ self.blocks = nn.ModuleList()
+
+ # The initial TDNN layer
+ self.blocks.append(
+ TimeDelayNetBlock(
+ config.mel_dim,
+ config.enc_channels[0],
+ config.enc_kernel_sizes[0],
+ config.enc_dilations[0],
+ )
+ )
+
+ # SE-Res2Net layers
+ for i in range(1, len(config.enc_channels) - 1):
+ self.blocks.append(
+ SqueezeExcitationRes2NetBlock(
+ config.enc_channels[i - 1],
+ config.enc_channels[i],
+ res2net_scale=config.enc_res2net_scale,
+ se_channels=config.enc_se_channels,
+ kernel_size=config.enc_kernel_sizes[i],
+ dilation=config.enc_dilations[i],
+ )
+ )
+
+ # Multi-layer feature aggregation
+ self.mfa = TimeDelayNetBlock(
+ config.enc_channels[-1],
+ config.enc_channels[-1],
+ config.enc_kernel_sizes[-1],
+ config.enc_dilations[-1],
+ )
+
+ # Attentive Statistical Pooling
+ self.asp = AttentiveStatisticsPooling(
+ config.enc_channels[-1],
+ attention_channels=config.enc_attention_channels,
+ )
+
+ # Final linear transformation
+ self.fc = nn.Conv1d(
+ in_channels=config.enc_channels[-1] * 2,
+ out_channels=config.enc_dim,
+ kernel_size=1,
+ padding="same",
+ padding_mode="reflect",
+ )
+
+ def forward(self, hidden_states):
+ # Minimize transpose for efficiency
+ hidden_states = hidden_states.transpose(1, 2)
+
+ hidden_states_list = []
+ for layer in self.blocks:
+ hidden_states = layer(hidden_states)
+ hidden_states_list.append(hidden_states)
+
+ # Multi-layer feature aggregation
+ hidden_states = torch.cat(hidden_states_list[1:], dim=1)
+ hidden_states = self.mfa(hidden_states)
+
+ # Attentive Statistical Pooling
+ hidden_states = self.asp(hidden_states)
+
+ # Final linear transformation
+ hidden_states = self.fc(hidden_states)
+
+ hidden_states = hidden_states.squeeze(-1)
+ return hidden_states
+
+
+class DiTInputEmbedding(nn.Module):
+ def __init__(self, config: Qwen2_5OmniDiTConfig):
+ super().__init__()
+ self.proj = nn.Linear(
+ config.mel_dim + config.enc_dim + config.enc_emb_dim + config.emb_dim,
+ config.hidden_size,
+ )
+ self.spk_encoder = ECAPA_TimeDelayNet(config)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ speaker_embedding: torch.Tensor,
+ condition_vector: torch.Tensor,
+ code_embed: torch.Tensor,
+ drop_audio_cond: Optional[bool] = False,
+ code_embed_uncond: Optional[bool] = None,
+ apply_cfg: Optional[bool] = True,
+ ):
+ if apply_cfg:
+ hidden_states = torch.cat([hidden_states, hidden_states], dim=0)
+ speaker_embedding = torch.cat([speaker_embedding, torch.zeros_like(speaker_embedding)], dim=0)
+ condition_vector = torch.cat([condition_vector, torch.zeros_like(condition_vector)], dim=0)
+ code_embed = torch.cat([code_embed, code_embed_uncond], dim=0)
+ elif drop_audio_cond: # cfg for cond audio
+ condition_vector = torch.zeros_like(condition_vector)
+ speaker_embedding = torch.zeros_like(speaker_embedding)
+ condition_vector = self.spk_encoder(condition_vector).unsqueeze(1).repeat(1, hidden_states.size(1), 1)
+ hidden_states = self.proj(torch.cat((hidden_states, condition_vector, code_embed, speaker_embedding), dim=-1))
+
+ return hidden_states
+
+
+# Transformer backbone using DiT blocks
+class DiTCodecEmbedding(nn.Module):
+ def __init__(self, codec_num_embeds, codec_dim, repeats):
+ super().__init__()
+ self.repeats = repeats
+ self.codec_embed = nn.Embedding(codec_num_embeds + 1, codec_dim)
+
+ def forward(self, code, drop_code=False):
+ if drop_code:
+ code = torch.zeros_like(code)
+ code_embed = self.codec_embed(code)
+
+ code_embed = torch.repeat_interleave(code_embed, repeats=self.repeats, dim=1)
+ return code_embed
+
+
+# AdaLayerNormZero
+# return with modulated x for attn input, and params for later mlp modulation
+class Qwen2_5_OmniAdaLayerNormZero(nn.Module):
+ def __init__(self, dim):
+ super().__init__()
+
+ self.silu = nn.SiLU()
+ self.linear = nn.Linear(dim, dim * 6)
+
+ self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+ def forward(self, hidden_states, emb=None):
+ emb = self.linear(self.silu(emb))
+ shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = torch.chunk(emb, 6, dim=1)
+
+ hidden_states = self.norm(hidden_states) * (1 + scale_msa[:, None]) + shift_msa[:, None]
+ return hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp
+
+
+# AdaLayerNormZero for final layer
+# return only with modulated x for attn input, cuz no more mlp modulation
+class Qwen2_5_OmniAdaLayerNormZero_Final(nn.Module):
+ def __init__(self, dim):
+ super().__init__()
+
+ self.silu = nn.SiLU()
+ self.linear = nn.Linear(dim, dim * 2)
+
+ self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+ def forward(self, hidden_states, emb):
+ emb = self.linear(self.silu(emb))
+ scale, shift = torch.chunk(emb, 2, dim=1)
+
+ hidden_states = self.norm(hidden_states) * (1 + scale)[:, None, :] + shift[:, None, :]
+ return hidden_states
+
+
+# FeedForward
+class DiTMLP(nn.Module):
+ def __init__(self, dim, mult=4, dropout=0.0):
+ super().__init__()
+ inner_dim = int(dim * mult)
+
+ self.ff = nn.ModuleList(
+ [
+ nn.Linear(dim, inner_dim),
+ nn.GELU(approximate="tanh"),
+ nn.Dropout(dropout),
+ nn.Linear(inner_dim, dim),
+ ]
+ )
+
+ def forward(self, hidden_states):
+ for layer in self.ff:
+ hidden_states = layer(hidden_states)
+ return hidden_states
+
+
+# Modified from Llama with a different rotate function, will fixed in next release
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`, *optional*):
+ Deprecated and unused.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+
+ def rotate_half_codec(x):
+ # x = rearrange(x, "... (d r) -> ... d r", r=2)
+ x = x.reshape(*x.shape[:-1], -1, 2)
+ x1, x2 = x.unbind(dim=-1)
+ x = torch.stack((-x2, x1), dim=-1)
+ return x.reshape(*x.shape[:-2], -1)
+
+ cos = cos.unsqueeze(unsqueeze_dim)
+ sin = sin.unsqueeze(unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half_codec(q) * sin)
+ k_embed = (k * cos) + (rotate_half_codec(k) * sin)
+ return q_embed, k_embed
+
+
+class DiTAttention(nn.Module):
+ def __init__(self, config: Qwen2_5OmniDiTConfig):
+ super().__init__()
+
+ self.config = config
+ self.dim = config.hidden_size
+ self.heads = config.num_attention_heads
+ self.inner_dim = config.head_dim * config.num_attention_heads
+ self.dropout = config.dropout
+ self.is_causal = False
+
+ self.to_q = nn.Linear(config.hidden_size, self.inner_dim)
+ self.to_k = nn.Linear(config.hidden_size, self.inner_dim)
+ self.to_v = nn.Linear(config.hidden_size, self.inner_dim)
+
+ self.to_out = nn.ModuleList([nn.Linear(self.inner_dim, config.hidden_size), nn.Dropout(config.dropout)])
+
+ def forward(
+ self,
+ hidden_states, # noised input x
+ position_embeddings=None, # rotary position embedding for x
+ attention_mask=None,
+ ) -> torch.Tensor:
+ batch_size = hidden_states.shape[0]
+
+ # `sample` projections.
+ query = self.to_q(hidden_states)
+ key = self.to_k(hidden_states)
+ value = self.to_v(hidden_states)
+
+ # attention
+ inner_dim = key.shape[-1]
+ head_dim = inner_dim // self.heads
+ query = query.view(batch_size, -1, self.heads, head_dim).transpose(1, 2)
+ key = key.view(batch_size, -1, self.heads, head_dim).transpose(1, 2)
+ value = value.view(batch_size, -1, self.heads, head_dim).transpose(1, 2)
+
+ # apply rotary position embedding
+ # Due to training process, only first head is applied with RoPE, will be fixed at next release
+ cos, sin = position_embeddings
+ query[:, :1], key[:, :1] = apply_rotary_pos_emb(query[:, :1], key[:, :1], cos, sin)
+
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+ attention_weights, _ = attention_interface(
+ self,
+ query,
+ key,
+ value,
+ attention_mask=attention_mask,
+ is_causal=False,
+ )
+
+ # mask. e.g. inference got a batch with different target durations, mask out the padding
+ attention_weights = attention_weights.reshape(batch_size, -1, self.heads * head_dim)
+ attention_weights = attention_weights.to(query.dtype)
+
+ # linear proj
+ attention_output = self.to_out[0](attention_weights)
+ attention_output = self.to_out[1](attention_output)
+
+ return attention_output
+
+
+# time step conditioning embedding
+class SinusPositionEmbedding(nn.Module):
+ def __init__(self, dim):
+ super().__init__()
+ self.dim = dim
+
+ def forward(self, hidden_states, scale=1000):
+ device = hidden_states.device
+ half_dim = self.dim // 2
+ emb = math.log(10000) / (half_dim - 1)
+ emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb)
+ emb = scale * hidden_states.unsqueeze(1) * emb.unsqueeze(0)
+ emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
+ return emb.type_as(hidden_states)
+
+
+class DiTTimestepEmbedding(nn.Module):
+ def __init__(self, dim, freq_embed_dim=256):
+ super().__init__()
+ self.time_embed = SinusPositionEmbedding(freq_embed_dim)
+ self.time_mlp = nn.ModuleList([nn.Linear(freq_embed_dim, dim), nn.SiLU(), nn.Linear(dim, dim)])
+
+ def forward(self, timestep):
+ time_hidden = self.time_embed(timestep)
+ time_hidden = time_hidden.to(timestep.dtype)
+ for layer in self.time_mlp:
+ time_hidden = layer(time_hidden) # b d
+ return time_hidden
+
+
+class DiTDecoderLayer(nn.Module):
+ def __init__(self, config: Qwen2_5OmniDiTConfig, look_ahead_block=0, look_backward_block=0):
+ super().__init__()
+ self.attn_norm = Qwen2_5_OmniAdaLayerNormZero(config.hidden_size)
+
+ self.attn = DiTAttention(config)
+ self.look_ahead_block = look_ahead_block
+ self.look_backward_block = look_backward_block
+ self.ff_norm = nn.LayerNorm(config.hidden_size, elementwise_affine=False, eps=1e-6)
+ self.ff = DiTMLP(dim=config.hidden_size, mult=config.ff_mult, dropout=config.dropout)
+
+ def forward(
+ self, hidden_states, timestep, position_embeddings=None, block_diff=None
+ ): # x: noised input, t: time embedding
+ # pre-norm & modulation for attention input
+ norm, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.attn_norm(hidden_states, emb=timestep)
+
+ # attention
+ attn_output = self.attn(
+ hidden_states=norm,
+ position_embeddings=position_embeddings,
+ attention_mask=(block_diff >= -float(self.look_backward_block))
+ & (block_diff <= float(self.look_ahead_block)),
+ )
+
+ # process attention output for input x
+ hidden_states = hidden_states + gate_msa.unsqueeze(1) * attn_output
+
+ norm = self.ff_norm(hidden_states) * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+ ff_output = self.ff(norm)
+ hidden_states = hidden_states + gate_mlp.unsqueeze(1) * ff_output
+
+ return hidden_states
+
+
+class SnakeBeta(nn.Module):
+ """
+ A modified Snake function which uses separate parameters for the magnitude of the periodic components
+ Shape:
+ - Input: (B, C, T)
+ - Output: (B, C, T), same shape as the input
+ Parameters:
+ - alpha - trainable parameter that controls frequency
+ - beta - trainable parameter that controls magnitude
+ References:
+ - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
+ https://huggingface.co/papers/2006.08195
+ """
+
+ def __init__(self, in_features, alpha=1.0):
+ super().__init__()
+ self.in_features = in_features
+
+ # initialize alpha
+ self.alpha = Parameter(torch.zeros(in_features) * alpha)
+ self.beta = Parameter(torch.zeros(in_features) * alpha)
+
+ self.no_div_by_zero = 0.000000001
+
+ def forward(self, hidden_states):
+ """
+ Forward pass of the function.
+ Applies the function to the input elementwise.
+ SnakeBeta ∶= x + 1/b * sin^2 (xa)
+ """
+ alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
+ beta = self.beta.unsqueeze(0).unsqueeze(-1)
+ alpha = torch.exp(alpha)
+ beta = torch.exp(beta)
+ hidden_states = hidden_states + (1.0 / (beta + self.no_div_by_zero)) * torch.pow(
+ torch.sin(hidden_states * alpha), 2
+ )
+
+ return hidden_states
+
+
+class UpSample1d(nn.Module):
+ def __init__(self, ratio=2, kernel_size=None):
+ super().__init__()
+ self.ratio = ratio
+ self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+ self.stride = ratio
+ self.pad = self.kernel_size // ratio - 1
+ self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
+ self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
+
+ filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size)
+ self.register_buffer("filter", filter, persistent=False)
+
+ def forward(self, hidden_states):
+ channels = hidden_states.shape[1]
+
+ hidden_states = F.pad(hidden_states, (self.pad, self.pad), mode="replicate")
+ hidden_states = self.ratio * F.conv_transpose1d(
+ hidden_states, self.filter.expand(channels, -1, -1), stride=self.stride, groups=channels
+ )
+ hidden_states = hidden_states[..., self.pad_left : -self.pad_right]
+
+ return hidden_states
+
+
+class DownSample1d(nn.Module):
+ def __init__(self, ratio=2, kernel_size=None):
+ super().__init__()
+ cutoff = 0.5 / ratio
+ half_width = 0.6 / ratio
+ self.cutoff = cutoff
+ self.half_width = half_width
+ self.kernel_size = kernel_size
+
+ if cutoff < 0.0:
+ raise ValueError("Minimum cutoff must be larger than zero.")
+ if cutoff > 0.5:
+ raise ValueError("A cutoff above 0.5 does not make sense.")
+
+ self.even = kernel_size % 2 == 0
+ self.pad_left = kernel_size // 2 - int(self.even)
+ self.pad_right = kernel_size // 2
+ self.stride = ratio
+ filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size)
+ self.register_buffer("filter", filter, persistent=False)
+
+ def forward(self, hidden_states):
+ channels = hidden_states.shape[1]
+ hidden_states = F.pad(hidden_states, (self.pad_left, self.pad_right), mode="replicate")
+ out = F.conv1d(hidden_states, self.filter.expand(channels, -1, -1), stride=self.stride, groups=channels)
+ return out
+
+
+class TorchActivation1d(nn.Module):
+ def __init__(
+ self,
+ activation,
+ up_ratio: int = 2,
+ down_ratio: int = 2,
+ up_kernel_size: int = 12,
+ down_kernel_size: int = 12,
+ ):
+ super().__init__()
+ if not callable(activation):
+ raise TypeError("Activation function must be callable")
+ self.act = activation
+ self.upsample = UpSample1d(up_ratio, up_kernel_size)
+ self.downsample = DownSample1d(down_ratio, down_kernel_size)
+
+ def forward(self, hidden_states):
+ hidden_states = self.upsample(hidden_states)
+ hidden_states = self.act(hidden_states)
+ hidden_states = self.downsample(hidden_states)
+
+ return hidden_states
+
+
+class AMPBlock(torch.nn.Module):
+ def __init__(
+ self,
+ channels,
+ kernel_size=3,
+ dilation=(1, 3, 5),
+ ):
+ super().__init__()
+
+ self.convs1 = nn.ModuleList(
+ [
+ nn.Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=dilation[0],
+ padding=self._get_padding(kernel_size, dilation[0]),
+ ),
+ nn.Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=dilation[1],
+ padding=self._get_padding(kernel_size, dilation[1]),
+ ),
+ nn.Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=dilation[2],
+ padding=self._get_padding(kernel_size, dilation[2]),
+ ),
+ ]
+ )
+
+ self.convs2 = nn.ModuleList(
+ [
+ nn.Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=1,
+ padding=self._get_padding(kernel_size, 1),
+ ),
+ nn.Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=1,
+ padding=self._get_padding(kernel_size, 1),
+ ),
+ nn.Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=1,
+ padding=self._get_padding(kernel_size, 1),
+ ),
+ ]
+ )
+
+ self.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers
+
+ self.activations = nn.ModuleList(
+ [TorchActivation1d(activation=SnakeBeta(channels)) for _ in range(self.num_layers)]
+ )
+
+ def _get_padding(self, kernel_size, dilation=1):
+ return int((kernel_size * dilation - dilation) / 2)
+
+ def forward(self, hidden_states):
+ acts1, acts2 = self.activations[::2], self.activations[1::2]
+ for conv1, conv2, act1, act2 in zip(self.convs1, self.convs2, acts1, acts2):
+ residual = hidden_states
+ hidden_states = act1(hidden_states)
+ hidden_states = conv1(hidden_states)
+ hidden_states = act2(hidden_states)
+ hidden_states = conv2(hidden_states)
+ hidden_states = residual + hidden_states
+
+ return hidden_states
+
+
+@auto_docstring(
+ custom_intro="""
+ The full Qwen2.5Omni Token2WavBigVGAN model. Which take mel spectrogram as input and predict waveform.
+ """
+)
+class Qwen2_5OmniToken2WavBigVGANModel(Qwen2_5OmniPreTrainedModel):
+ config: Qwen2_5OmniBigVGANConfig
+ input_modalities = "audio"
+
+ def __init__(self, config: Qwen2_5OmniBigVGANConfig):
+ super().__init__(config)
+ self.num_residual_blocks = len(config.resblock_kernel_sizes)
+ self.num_upsample_layers = len(config.upsample_rates)
+
+ self.conv_pre = nn.Conv1d(config.mel_dim, config.upsample_initial_channel, 7, 1, padding=3)
+
+ # Removing extra ModuleList breaks official state dict
+ ups = [
+ nn.ModuleList(
+ [
+ nn.ConvTranspose1d(
+ config.upsample_initial_channel // (2**layer_idx),
+ config.upsample_initial_channel // (2 ** (layer_idx + 1)),
+ kernel_size,
+ stride,
+ padding=(kernel_size - stride) // 2,
+ )
+ ]
+ )
+ for layer_idx, (stride, kernel_size) in enumerate(zip(config.upsample_rates, config.upsample_kernel_sizes))
+ ]
+ self.ups = nn.ModuleList(ups)
+
+ self.resblocks = nn.ModuleList(
+ [
+ AMPBlock(config.upsample_initial_channel // (2 ** (layer_idx + 1)), kernel_size, dilation)
+ for layer_idx in range(self.num_upsample_layers)
+ for kernel_size, dilation in zip(config.resblock_kernel_sizes, config.resblock_dilation_sizes)
+ ]
+ )
+
+ self.activation_post = TorchActivation1d(
+ activation=SnakeBeta(config.upsample_initial_channel // (2**self.num_upsample_layers))
+ )
+ self.conv_post = nn.Conv1d(
+ config.upsample_initial_channel // (2**self.num_upsample_layers), 1, 7, 1, padding=3, bias=False
+ )
+
+ self.post_init()
+
+ def normalize_spectrogram(self, spectrogram, max_value, min_db):
+ return torch.clamp((2 * max_value) * ((spectrogram - min_db) / (-min_db)) - max_value, -max_value, max_value)
+
+ def amplitude_to_db(self, amplitude, min_db_level):
+ min_level = torch.exp(
+ torch.tensor(min_db_level / 20.0 * np.log(10), device=amplitude.device, dtype=amplitude.dtype)
+ )
+ return 20 * torch.log10(torch.clamp(amplitude, min=min_level))
+
+ def process_mel_spectrogram(self, mel_spectrogram):
+ amplitude_spectrum = torch.exp(mel_spectrogram)
+ decibel_spectrum = self.amplitude_to_db(amplitude_spectrum, -115) - 20
+ return self.normalize_spectrogram(decibel_spectrum, 1, -115)
+
+ def forward(self, mel_spectrogram, **kwargs):
+ processed_spectrogram = self.process_mel_spectrogram(mel_spectrogram)
+ hidden_representation = self.conv_pre(processed_spectrogram)
+
+ for layer_index in range(self.num_upsample_layers):
+ hidden_representation = self.ups[layer_index][0](hidden_representation)
+ residual_output = sum(
+ self.resblocks[layer_index * self.num_residual_blocks + block_index](hidden_representation)
+ for block_index in range(self.num_residual_blocks)
+ )
+ residual_output = residual_output / self.num_residual_blocks
+ hidden_representation = residual_output
+
+ hidden_representation = self.activation_post(hidden_representation)
+ output_waveform = self.conv_post(hidden_representation)
+ return torch.clamp(output_waveform, min=-1.0, max=1.0).squeeze().cpu()
+
+
+class RungeKutta4ODESolver:
+ def __init__(self, function, initial_value):
+ self.function = function
+ self.initial_value = initial_value
+
+ self._one_third = 1 / 3
+ self._two_thirds = 2 / 3
+
+ def _rk4_step(self, function, time_start, time_step, time_end, value_start, function_value_start=None):
+ k1 = function_value_start if function_value_start is not None else function(time_start, value_start)
+ k2 = function(time_start + time_step * self._one_third, value_start + time_step * k1 * self._one_third)
+ k3 = function(time_start + time_step * self._two_thirds, value_start + time_step * (k2 - k1 * self._one_third))
+ k4 = function(time_end, value_start + time_step * (k1 - k2 + k3))
+ return (k1 + 3 * (k2 + k3) + k4) * time_step / 8
+
+ def _compute_step(self, function, time_start, time_step, time_end, value_start):
+ function_value_start = function(time_start, value_start)
+ return self._rk4_step(
+ function, time_start, time_step, time_end, value_start, function_value_start=function_value_start
+ ), function_value_start
+
+ def _linear_interpolation(self, time_start, time_end, value_start, value_end, time_point):
+ if time_point == time_start:
+ return value_start
+ if time_point == time_end:
+ return value_end
+ weight = (time_point - time_start) / (time_end - time_start)
+ return value_start + weight * (value_end - value_start)
+
+ def integrate(self, time_points):
+ solution = torch.empty(
+ len(time_points),
+ *self.initial_value.shape,
+ dtype=self.initial_value.dtype,
+ device=self.initial_value.device,
+ )
+ solution[0] = self.initial_value
+
+ current_index = 1
+ current_value = self.initial_value
+ for time_start, time_end in zip(time_points[:-1], time_points[1:]):
+ time_step = time_end - time_start
+ delta_value, _ = self._compute_step(self.function, time_start, time_step, time_end, current_value)
+ next_value = current_value + delta_value
+
+ while current_index < len(time_points) and time_end >= time_points[current_index]:
+ solution[current_index] = self._linear_interpolation(
+ time_start, time_end, current_value, next_value, time_points[current_index]
+ )
+ current_index += 1
+
+ current_value = next_value
+
+ return solution
+
+
+@auto_docstring(
+ custom_intro="""
+ The full Qwen2.5Omni Token2WavDiT model. Which take speech tokens as input and predict mel spectrogram.
+ """
+)
+class Qwen2_5OmniToken2WavDiTModel(Qwen2_5OmniPreTrainedModel):
+ config: Qwen2_5OmniDiTConfig
+ input_modalities = "audio"
+ _no_split_modules = ["DiTDecoderLayer"]
+
+ def __init__(self, config: Qwen2_5OmniDiTConfig):
+ super().__init__(config)
+ self.mel_dim = config.mel_dim
+ self.repeats = config.repeats
+ self.time_embed = DiTTimestepEmbedding(config.hidden_size)
+
+ self.text_embed = DiTCodecEmbedding(config.num_embeds, config.emb_dim, config.repeats)
+ self.input_embed = DiTInputEmbedding(config)
+
+ self.rotary_embed = Qwen2_5OmniDiTRotaryEmbedding(config=config)
+
+ self.hidden_size = config.hidden_size
+ self.layers = config.num_hidden_layers
+ self.block_size = config.block_size
+ self.num_attention_heads = config.num_attention_heads
+
+ self.transformer_blocks = nn.ModuleList()
+ for i in range(config.num_hidden_layers):
+ self.transformer_blocks.append(
+ DiTDecoderLayer(
+ config,
+ look_ahead_block=1 if i in config.look_ahead_layers else 0,
+ look_backward_block=1 if i in config.look_backward_layers else 0,
+ )
+ )
+
+ self.norm_out = Qwen2_5_OmniAdaLayerNormZero_Final(config.hidden_size) # final modulation
+ self.proj_out = nn.Linear(config.hidden_size, config.mel_dim)
+
+ self.post_init()
+
+ def _create_block_diff(self, hidden_states):
+ batch, seq_len = hidden_states.shape[0], hidden_states.shape[1]
+ block_indices = torch.arange(seq_len, device=hidden_states.device) // self.block_size # [seq_length]
+
+ block_i = block_indices.unsqueeze(1) # [seq_length, 1]
+ block_j = block_indices.unsqueeze(0) # [1, seq_length]
+ block_diff = block_j - block_i # (n, n)
+
+ return block_diff.expand(batch, self.num_attention_heads, seq_len, seq_len)
+
+ def forward(
+ self,
+ hidden_states,
+ condition_vector,
+ speaker_embedding,
+ quantized_code,
+ time_step,
+ drop_audio_conditioning=False,
+ drop_code=False,
+ apply_cfg=True,
+ **kwargs,
+ ):
+ batch_size = hidden_states.shape[0]
+ if time_step.ndim == 0:
+ time_step = time_step.repeat(batch_size)
+
+ # Compute embeddings
+ time_embedding = self.time_embed(time_step)
+ text_embedding = self.text_embed(quantized_code, drop_code=False if apply_cfg else drop_code)
+ text_embedding_unconditioned = self.text_embed(quantized_code, drop_code=True) if apply_cfg else None
+
+ hidden_states = self.input_embed(
+ hidden_states,
+ speaker_embedding,
+ condition_vector,
+ text_embedding,
+ drop_audio_cond=drop_audio_conditioning,
+ code_embed_uncond=text_embedding_unconditioned,
+ apply_cfg=apply_cfg,
+ )
+
+ # Compute positional encodings
+ position_ids = torch.arange(hidden_states.shape[1], device=hidden_states.device)
+ position_ids = position_ids[None, :].repeat(batch_size, 1)
+ position_embeddings = self.rotary_embed(hidden_states, position_ids)
+ blockwise_difference = self._create_block_diff(hidden_states)
+
+ # Transformer blocks
+ for transformer_block in self.transformer_blocks:
+ hidden_states = transformer_block(
+ hidden_states,
+ time_embedding,
+ position_embeddings=position_embeddings,
+ block_diff=blockwise_difference,
+ )
+
+ hidden_states = self.norm_out(hidden_states, time_embedding)
+ output = self.proj_out(hidden_states)
+
+ return output
+
+ @torch.no_grad()
+ def sample(
+ self,
+ conditioning_vector,
+ reference_mel_spectrogram,
+ quantized_code,
+ num_steps=10,
+ guidance_scale=0.5,
+ sway_coefficient=-1.0,
+ ):
+ noise_initialization = torch.randn([1, 30000, self.mel_dim], dtype=reference_mel_spectrogram.dtype)
+ maximum_duration = quantized_code.shape[1] * self.repeats
+ initial_state = noise_initialization[:, :maximum_duration].to(quantized_code.device)
+ batch_size = reference_mel_spectrogram.shape[0]
+ conditioning_vector = conditioning_vector.unsqueeze(1).repeat(1, maximum_duration, 1)
+
+ if batch_size != 1:
+ raise ValueError("Only batch size = 1 is currently supported")
+
+ def ode_function(time_step, hidden_states):
+ if guidance_scale < 1e-5:
+ prediction = self(
+ hidden_states=hidden_states,
+ speaker_embedding=conditioning_vector,
+ condition_vector=reference_mel_spectrogram,
+ quantized_code=quantized_code,
+ time_step=time_step,
+ drop_audio_conditioning=False,
+ drop_code=False,
+ )
+ return prediction
+
+ model_output = self(
+ hidden_states=hidden_states,
+ quantized_code=quantized_code,
+ speaker_embedding=conditioning_vector,
+ condition_vector=reference_mel_spectrogram,
+ time_step=time_step,
+ apply_cfg=True,
+ )
+ guided_prediction, null_prediction = torch.chunk(model_output, 2, dim=0)
+ return guided_prediction + (guided_prediction - null_prediction) * guidance_scale
+
+ initial_time = 0
+ time_embedding = torch.linspace(
+ initial_time, 1, num_steps, device=quantized_code.device, dtype=conditioning_vector.dtype
+ )
+
+ if sway_coefficient is not None:
+ time_embedding += sway_coefficient * (torch.cos(torch.pi / 2 * time_embedding) - 1 + time_embedding)
+
+ ode_solver = RungeKutta4ODESolver(function=ode_function, initial_value=initial_state)
+ solution_trajectory = ode_solver.integrate(time_embedding)
+
+ generated_waveform = solution_trajectory[-1]
+ generated_mel_spectrogram = generated_waveform.permute(0, 2, 1)
+ return generated_mel_spectrogram
+
+
+@auto_docstring(
+ custom_intro="""
+ The full Qwen2.5Omni Token2Wav model. Consists a DiT model take speech tokens as input and predict mel spectrogram and a BigVGAN vocoder take mel spectrogram as input and predict waveform.
+ """
+)
+class Qwen2_5OmniToken2WavModel(Qwen2_5OmniPreTrainedModel):
+ config: Qwen2_5OmniToken2WavConfig
+ base_model_prefix = "model"
+ input_modalities = "audio"
+ _no_split_modules = ["Qwen2_5OmniToken2WavDiTModel", "Qwen2_5OmniToken2WavBigVGANModel"]
+
+ def __init__(self, config: Qwen2_5OmniToken2WavConfig):
+ super().__init__(config)
+ attn_impl = config._attn_implementation
+ if config._attn_implementation == "flash_attention_2":
+ logger.warning_once(
+ "Qwen2_5OmniToken2WavModel must inference with fp32, but flash_attention_2 only supports fp16 and bf16, "
+ "attention implementation of Qwen2_5OmniToken2WavModel will fallback to sdpa."
+ )
+ attn_impl = "sdpa"
+ elif config._attn_implementation == "eager":
+ logger.warning_once(
+ "Qwen2_5OmniToken2WavModel does not support eager attention implementation, fall back to sdpa"
+ )
+ attn_impl = "sdpa"
+ self.code2wav_dit_model = Qwen2_5OmniToken2WavDiTModel._from_config(
+ config.dit_config, attn_implementation=attn_impl
+ )
+ self.code2wav_bigvgan_model = Qwen2_5OmniToken2WavBigVGANModel._from_config(
+ config.bigvgan_config, attn_implementation=attn_impl
+ )
+
+ self.post_init()
+
+ def forward(
+ self,
+ code,
+ conditioning,
+ reference_mel,
+ num_steps=10,
+ guidance_scale=0.5,
+ sway_coefficient=-1.0,
+ **kwargs,
+ ):
+ """Generates a waveform from input code and conditioning parameters."""
+
+ mel_spectrogram = self.code2wav_dit_model.sample(
+ conditioning,
+ reference_mel,
+ code,
+ num_steps=num_steps,
+ guidance_scale=guidance_scale,
+ sway_coefficient=sway_coefficient,
+ )
+
+ waveform = self.code2wav_bigvgan_model(mel_spectrogram)
+
+ return waveform
+
+
+############################
+# Start Qwen2.5Omni #
+############################
+
+
+@auto_docstring(
+ custom_intro="""
+ The full Qwen2.5Omni model, a multimodal model composed of 3 sub-models:
+ - [`Qwen2_5OmniThinkerForConditionalGeneration`]:
+ a causal auto-regressive transformer takes text, audio, image, video as input and predict text tokens.
+ - [`Qwen2_5OmniTalkerForConditionalGeneration`]:
+ a causal auto-regressive transformer takes thinker hidden states and response as input and predict speech tokens.
+ - [`Qwen2_5OmniToken2WavModel`]:
+ a DiT model take speech tokens as input and predict mel spectrogram and a BigVGAN vocoder take mel spectrogram as input and predict waveform.
+ """
+)
+class Qwen2_5OmniForConditionalGeneration(Qwen2_5OmniPreTrainedModel, GenerationMixin):
+ config: Qwen2_5OmniConfig
+ output_modalities = ("audio", "text")
+ _no_split_modules = [
+ "Qwen2_5OmniTalkerForConditionalGeneration",
+ "Qwen2_5OmniToken2WavModel",
+ ]
+
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.thinker = Qwen2_5OmniThinkerForConditionalGeneration(config.thinker_config)
+
+ self.has_talker = config.enable_audio_output
+ self.speaker_map = {}
+ if config.enable_audio_output:
+ self.enable_talker()
+ self.post_init()
+
+ def enable_talker(self):
+ self.talker = Qwen2_5OmniTalkerForConditionalGeneration(self.config.talker_config)
+ self.token2wav = Qwen2_5OmniToken2WavModel(self.config.token2wav_config)
+ self.token2wav.float()
+ self.has_talker = True
+
+ def load_speakers(self, path):
+ check_torch_load_is_safe()
+ for key, value in torch.load(path, weights_only=True).items():
+ self.speaker_map[key] = value
+ logger.info(f"Speaker {list(self.speaker_map.keys())} loaded")
+
+ def disable_talker(self):
+ if hasattr(self, "talker"):
+ del self.talker
+ if hasattr(self, "token2wav"):
+ del self.token2wav
+ self.has_talker = False
+
+ @classmethod
+ def from_pretrained(
+ cls,
+ pretrained_model_name_or_path,
+ *model_args,
+ config=None,
+ cache_dir=None,
+ ignore_mismatched_sizes=False,
+ force_download=False,
+ local_files_only=False,
+ token=None,
+ revision="main",
+ use_safetensors=None,
+ weights_only=True,
+ **kwargs,
+ ):
+ model = super().from_pretrained(
+ pretrained_model_name_or_path,
+ *model_args,
+ config=config,
+ cache_dir=cache_dir,
+ ignore_mismatched_sizes=ignore_mismatched_sizes,
+ force_download=force_download,
+ local_files_only=local_files_only,
+ token=token,
+ revision=revision,
+ use_safetensors=use_safetensors,
+ weights_only=weights_only,
+ **kwargs,
+ )
+ spk_path = cached_file(
+ pretrained_model_name_or_path,
+ "spk_dict.pt",
+ subfolder=kwargs.pop("subfolder", None),
+ cache_dir=kwargs.pop("cache_dir", None),
+ force_download=kwargs.pop("force_download", False),
+ proxies=kwargs.pop("proxies", None),
+ local_files_only=kwargs.pop("local_files_only", False),
+ token=token,
+ revision=kwargs.pop("revision", None),
+ )
+ if spk_path is None:
+ raise ValueError(f"""{pretrained_model_name_or_path}/{spk_path} not exists""")
+ model.load_speakers(spk_path)
+
+ return model
+
+ @torch.no_grad()
+ @deprecate_kwarg("return_audio", version="v5", new_name="generation_mode")
+ # TODO: raushan, defaults should be saved in generation config
+ def generate(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ speaker: str = "Chelsie",
+ use_audio_in_video: bool = False,
+ thinker_max_new_tokens: int = 1024,
+ talker_max_new_tokens: int = 4096,
+ talker_do_sample: bool = True,
+ talker_top_k: int = 40,
+ talker_top_p: float = 0.8,
+ talker_temperature: float = 0.9,
+ talker_eos_token_id: list[int] = [8292, 8294],
+ talker_repetition_penalty: float = 1.05,
+ **kwargs,
+ ):
+ r"""
+ Generate text response and audio from input.
+
+ Args:
+ input_ids (`Optional[torch.Tensor]`, *optional*):
+ Input ids, should obtain from processor.
+ speaker (`str` , defaults to "Chelsie"):
+ Which speaker should be used in audio response.
+ use_audio_in_video (`bool`, defaults to False):
+ Whether or not use audio track in video, should same as the parameter in `process_audio_info`.
+ generation_mode (`Optional[str]`, *optional*):
+ Whether or not return response in audio format. When `generation_mode="audio"`, this parameter is same as `config.enable_audio_output`.
+ kwargs (*optional*):
+ - Without a prefix, they will be entered as `**kwargs` for the `generate` method of each sub-model.
+ - With a *thinker_*, *talker_*, *token2wav_* prefix, they will be input for the `generate` method of the
+ thinker, talker and token2wav respectively. It has the priority over the keywords without a prefix.
+ Returns:
+ When `return_audio=False`:
+ - **Text** (`torch.Tensor`): Generated text token sequence.
+ When `return_audio=True`:
+ - **Text** (`torch.Tensor`): Generated text token sequence.
+ - **Audio waveform** (`torch.Tensor`): Generated audio waveform.
+ """
+ # check `False` on purpose because the paramter can be `str/bool`. This is needed for BC
+ generation_mode = kwargs.pop("generation_mode", None)
+ return_audio = generation_mode != "text" and generation_mode is not False
+
+ if speaker not in self.speaker_map:
+ raise ValueError(f"{speaker} is not available, available speakers: {self.speaker_map.keys()}")
+ if return_audio and not self.has_talker:
+ raise ValueError(
+ "Cannot use talker when talker module not initialized. Use `enable_talker` method or set enable_talker in config to enable talker."
+ )
+ if return_audio is None:
+ return_audio = self.has_talker
+ if input_ids.shape[0] != 1 and return_audio:
+ raise NotImplementedError("Qwen2.5-Omni currently does not support batched inference with audio output")
+
+ shared_kwargs = {"use_audio_in_video": use_audio_in_video}
+ thinker_kwargs = {
+ "max_new_tokens": thinker_max_new_tokens,
+ }
+ talker_kwargs = {
+ "max_new_tokens": talker_max_new_tokens,
+ "do_sample": talker_do_sample,
+ "top_k": talker_top_k,
+ "top_p": talker_top_p,
+ "temperature": talker_temperature,
+ "eos_token_id": talker_eos_token_id,
+ "repetition_penalty": talker_repetition_penalty,
+ }
+ token2wav_kwargs = {}
+
+ for key, value in kwargs.items():
+ if key.startswith("thinker_"):
+ thinker_kwargs[key[len("thinker_") :]] = value
+ elif key.startswith("talker_"):
+ talker_kwargs[key[len("talker_") :]] = value
+ elif key.startswith("token2wav_"):
+ token2wav_kwargs[key[len("token2wav_") :]] = value
+ # Process special input values
+ elif key == "feature_attention_mask":
+ thinker_kwargs[key] = value
+ talker_kwargs["audio_feature_lengths"] = torch.sum(value, dim=1)
+ elif key == "input_features" or key == "attention_mask":
+ thinker_kwargs[key] = value
+ # Put other key to shared kwargs
+ else:
+ shared_kwargs[key] = value
+
+ # Merge kwargs
+ for key, value in shared_kwargs.items():
+ if key not in thinker_kwargs:
+ thinker_kwargs[key] = value
+ if key not in talker_kwargs:
+ talker_kwargs[key] = value
+ if key not in token2wav_kwargs:
+ token2wav_kwargs[key] = value
+ speaker_params = self.speaker_map[speaker]
+
+ # 1. Generate from thinker module
+ generate_audio = return_audio and self.has_talker
+ if generate_audio:
+ thinker_kwargs["output_hidden_states"] = True
+ thinker_kwargs["return_dict_in_generate"] = True
+
+ thinker_result = self.thinker.generate(input_ids=input_ids, **thinker_kwargs)
+
+ if not generate_audio:
+ return thinker_result
+
+ # 2. Generate speech tokens from talker module
+ embeds_to_talker = thinker_result.hidden_states[0][0].clone().to(input_ids.device)
+ if thinker_kwargs.get("input_features") is not None:
+ audio_ids_mask = input_ids == self.config.thinker_config.audio_token_index
+ audio_mask = audio_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker)
+ audio_mask_tensor = torch.zeros(
+ [audio_ids_mask.sum(), embeds_to_talker.shape[-1]],
+ dtype=embeds_to_talker.dtype,
+ device=input_ids.device,
+ )
+ embeds_to_talker.masked_scatter_(audio_mask, audio_mask_tensor)
+ if thinker_kwargs.get("pixel_values") is not None:
+ image_ids_mask = input_ids == self.config.thinker_config.image_token_index
+ image_mask = image_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker)
+ image_mask_tensor = torch.zeros(
+ [image_ids_mask.sum(), embeds_to_talker.shape[-1]],
+ dtype=embeds_to_talker.dtype,
+ device=input_ids.device,
+ )
+ embeds_to_talker.masked_scatter_(image_mask, image_mask_tensor)
+ if thinker_kwargs.get("pixel_values_videos") is not None:
+ video_ids_mask = input_ids == self.config.thinker_config.video_token_index
+ video_mask = video_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker)
+ video_mask_tensor = torch.zeros(
+ [video_ids_mask.sum(), embeds_to_talker.shape[-1]],
+ dtype=embeds_to_talker.dtype,
+ device=input_ids.device,
+ )
+ embeds_to_talker.masked_scatter_(video_mask, video_mask_tensor)
+
+ processed_thinker_hidden = (
+ (embeds_to_talker,) + thinker_result.hidden_states[0][1:],
+ ) + thinker_result.hidden_states[1:]
+ thinker_generate_ids = thinker_result.sequences[:, input_ids.size(1) :].to(input_ids.device)
+ thinker_token_embeds = [
+ token_hidden_states[0].to(input_ids.device) for token_hidden_states in processed_thinker_hidden
+ ]
+ thinker_hidden_states = [
+ token_hidden_states[-1].to(input_ids.device) for token_hidden_states in processed_thinker_hidden
+ ]
+
+ talker_text_bos_token = speaker_params["bos_token"]
+ talker_input_text_ids = torch.cat(
+ [
+ input_ids,
+ torch.tensor([[talker_text_bos_token]], dtype=torch.long, device=input_ids.device),
+ thinker_generate_ids[:, :1],
+ ],
+ dim=-1,
+ )
+
+ talker_input_ids = torch.cat(
+ [
+ torch.full_like(input_ids, fill_value=self.talker.codec_mask_token),
+ torch.tensor([[self.talker.codec_pad_token]], dtype=torch.long, device=input_ids.device),
+ torch.tensor([[self.talker.codec_bos_token]], dtype=torch.long, device=input_ids.device),
+ ],
+ dim=1,
+ )
+
+ thinker_embed_tokens = self.thinker.get_input_embeddings()
+ thinker_reply_part = torch.cat(thinker_hidden_states[1:], dim=1) + torch.cat(thinker_token_embeds[1:], dim=1)
+ talker_inputs_embeds = thinker_hidden_states[0] + thinker_token_embeds[0]
+ talker_text_bos_token = torch.tensor([[talker_text_bos_token]], dtype=torch.long, device=input_ids.device)
+ talker_text_bos_embed = thinker_embed_tokens(talker_text_bos_token).to(input_ids.device)
+ talker_inputs_embeds = torch.cat(
+ [
+ talker_inputs_embeds,
+ talker_text_bos_embed,
+ thinker_reply_part[:, :1, :],
+ ],
+ dim=1,
+ )
+
+ eos_token = torch.tensor([[self.talker.text_eos_token]], dtype=torch.long, device=input_ids.device)
+ eos_embedding = thinker_embed_tokens(eos_token).to(input_ids.device)
+
+ pad_token = torch.tensor([[self.talker.text_pad_token]], dtype=torch.long, device=input_ids.device)
+ pad_embedding = thinker_embed_tokens(pad_token).to(input_ids.device)
+
+ thinker_reply_part = torch.cat(
+ [
+ thinker_reply_part[:, 1:, :],
+ eos_embedding,
+ pad_embedding,
+ ],
+ dim=1,
+ )
+
+ talker_attention_mask = None
+ if "attention_mask" in kwargs:
+ talker_attention_mask = torch.cat(
+ [kwargs["attention_mask"], kwargs["attention_mask"].new_ones((1, 2))], dim=1
+ ).to(input_ids.device)
+
+ talker_result = self.talker.generate(
+ input_ids=talker_input_ids,
+ input_text_ids=talker_input_text_ids,
+ thinker_reply_part=thinker_reply_part,
+ inputs_embeds=talker_inputs_embeds,
+ attention_mask=talker_attention_mask,
+ suppress_tokens=[self.talker.codec_bos_token],
+ **{k: (v.to(input_ids.device) if torch.is_tensor(v) else v) for k, v in talker_kwargs.items()},
+ )
+ talker_generate_codes = talker_result[:, talker_input_ids.shape[1] : -1]
+
+ # 3. Generate wavs from code
+ if self.token2wav.dtype != torch.float:
+ self.token2wav.float()
+
+ wav = self.token2wav(
+ talker_generate_codes.to(input_ids.device),
+ conditioning=speaker_params["cond"].to(input_ids.device).float(),
+ reference_mel=speaker_params["ref_mel"].to(input_ids.device).float(),
+ **token2wav_kwargs,
+ )
+
+ return thinker_result.sequences, wav.float()
+
+
+__all__ = [
+ "Qwen2_5OmniForConditionalGeneration",
+ "Qwen2_5OmniThinkerTextModel",
+ "Qwen2_5OmniThinkerForConditionalGeneration",
+ "Qwen2_5OmniTalkerModel",
+ "Qwen2_5OmniTalkerForConditionalGeneration",
+ "Qwen2_5OmniToken2WavDiTModel",
+ "Qwen2_5OmniToken2WavBigVGANModel",
+ "Qwen2_5OmniToken2WavModel",
+ "Qwen2_5OmniPreTrainedModel",
+ "Qwen2_5OmniPreTrainedModelForConditionalGeneration",
+]
diff --git a/mllm/models/qwen2_5omni/python_src_code/modular_qwen2_5_omni.py b/mllm/models/qwen2_5omni/python_src_code/modular_qwen2_5_omni.py
new file mode 100644
index 000000000..6d0c3f9bc
--- /dev/null
+++ b/mllm/models/qwen2_5omni/python_src_code/modular_qwen2_5_omni.py
@@ -0,0 +1,4289 @@
+# coding=utf-8
+# Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. 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 Qwen2.5Omni model (Audio, Image, Video)."""
+
+import math
+from collections.abc import Callable
+from dataclasses import dataclass
+from typing import Any, Optional, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.nn import Parameter
+
+from ... import initialization as init
+from ...cache_utils import Cache
+from ...configuration_utils import PreTrainedConfig, layer_type_validation
+from ...generation import GenerationMixin
+from ...modeling_outputs import BaseModelOutput, ModelOutput
+from ...modeling_rope_utils import RopeParameters
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import (
+ TransformersKwargs,
+ auto_docstring,
+ check_torch_load_is_safe,
+ logging,
+)
+from ...utils.deprecation import deprecate_kwarg
+from ...utils.hub import cached_file
+from ..llama.modeling_llama import LlamaRotaryEmbedding, rotate_half
+from ..qwen2_5_vl.configuration_qwen2_5_vl import Qwen2_5_VLVisionConfig
+from ..qwen2_5_vl.modeling_qwen2_5_vl import (
+ Qwen2_5_VisionRotaryEmbedding,
+ Qwen2_5_VisionTransformerPretrainedModel,
+ Qwen2_5_VLAttention,
+ Qwen2_5_VLMLP,
+ Qwen2_5_VLPreTrainedModel,
+ Qwen2_5_VLTextModel,
+ Qwen2_5_VLVisionBlock,
+ eager_attention_forward,
+)
+from ..qwen2_audio.configuration_qwen2_audio import Qwen2AudioEncoderConfig
+from ..qwen2_audio.modeling_qwen2_audio import Qwen2AudioEncoderLayer
+from ..qwen2_vl.modeling_qwen2_vl import Qwen2VLRotaryEmbedding
+
+
+logger = logging.get_logger(__name__)
+
+
+class Qwen2_5OmniVisionEncoderConfig(Qwen2_5_VLVisionConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`Qwen2_5OmniThinkerVision`]. It is used to instantiate a
+ Qwen2.5-VL 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 audio encoder of the Qwen2.5-VL
+ architecture.
+
+ e.g. [Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B)
+
+ Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PreTrainedConfig`] for more information.
+
+ Args:
+ depth (`int`, *optional*, defaults to 32):
+ Number of layers (depth) in the model.
+ hidden_size (`int`, *optional*, defaults to 3584):
+ The size of the hidden layers.
+ hidden_act (`str`, *optional*, defaults to `"quick_gelu"`):
+ The non-linear activation function used in the model. Supported options include `"quick_gelu"` and others as applicable.
+ mlp_ratio (`float`, *optional*, defaults to 4):
+ The ratio used to determine the size of the MLP (Multi-Layer Perceptron) hidden layer.
+ num_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer.
+ in_channels (`int`, *optional*, defaults to 3):
+ Number of input channels.
+ patch_size (`int`, *optional*, defaults to 14):
+ The size of the patches extracted from the input.
+ spatial_merge_size (`int`, *optional*, defaults to 2):
+ The size used for merging spatial dimensions.
+ temporal_patch_size (`int`, *optional*, defaults to 2):
+ The size used for patches along the temporal dimension.
+
+ Example:
+
+ ```python
+ >>> from transformers import Qwen2_5OmniVisionEncoderConfig, Qwen2_5OmniVisionEncoder
+
+ >>> # Initializing a Qwen2_5OmniVisionEncoderConfig
+ >>> configuration = Qwen2_5OmniVisionEncoderConfig()
+
+ >>> # Initializing a Qwen2_5OmniVisionEncoder (with random weights)
+ >>> model = Qwen2_5OmniVisionEncoder(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "qwen2_5_omni_vision_encoder"
+
+ def __init__(
+ self,
+ depth=32,
+ hidden_size=3584,
+ hidden_act="silu",
+ intermediate_size=3420,
+ num_heads=16,
+ in_channels=3,
+ patch_size=14,
+ spatial_merge_size=2,
+ temporal_patch_size=2,
+ window_size=112,
+ out_hidden_size=3584,
+ fullatt_block_indexes=[7, 15, 23, 31],
+ initializer_range=0.02,
+ **kwargs,
+ ):
+ super().__init__(
+ depth,
+ hidden_size,
+ hidden_act,
+ intermediate_size,
+ num_heads,
+ in_channels,
+ patch_size,
+ spatial_merge_size,
+ temporal_patch_size,
+ window_size,
+ out_hidden_size,
+ fullatt_block_indexes,
+ initializer_range=initializer_range,
+ **kwargs,
+ )
+ del self.tokens_per_second
+
+
+class Qwen2_5OmniAudioEncoderConfig(Qwen2AudioEncoderConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`Qwen2_5OmniAudioEncoder`]. It is used to instantiate a
+ Qwen2.5-Omni-Thinker audio 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 audio encoder of the Qwen2-Audio
+ architecture.
+
+ e.g. [Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B)
+
+ Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PreTrainedConfig`] for more information.
+
+ Args:
+ num_mel_bins (`int`, *optional*, defaults to 128):
+ Number of mel features used per input features. Should correspond to the value used in the
+ `Qwen2_5OmniProcessor` class.
+ encoder_layers (`int`, *optional*, defaults to 32):
+ Number of encoder layers.
+ encoder_attention_heads (`int`, *optional*, defaults to 20):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ encoder_ffn_dim (`int`, *optional*, defaults to 5120):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in encoder.
+ d_model (`int`, *optional*, defaults to 1280):
+ Dimensionality of the layers.
+ dropout (`float`, *optional*, defaults to 0.0):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ activation_function (`str`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ activation_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for activations inside the fully connected layer.
+ scale_embedding (`bool`, *optional*, defaults to `False`):
+ Scale embeddings by diving by sqrt(d_model).
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ max_source_positions (`int`, *optional*, defaults to 1500):
+ The maximum sequence length of log-mel filter-bank features that this model might ever be used with.
+ n_window (`int`, *optional*, defaults to 100):
+ The chunk for conv and flash attn in AudioEncoder.
+ output_dim (`int`, *optional*, defaults to 3584):
+ The output dimension of AudioEncoder.
+
+ Example:
+
+ ```python
+ >>> from transformers import Qwen2_5OmniAudioEncoderConfig, Qwen2_5OmniAudioEncoder
+
+ >>> # Initializing a Qwen2_5OmniAudioEncoderConfig
+ >>> configuration = Qwen2_5OmniAudioEncoderConfig()
+
+ >>> # Initializing a Qwen2_5OmniAudioEncoder (with random weights)
+ >>> model = Qwen2_5OmniAudioEncoder(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "qwen2_5_omni_audio_encoder"
+
+ def __init__(
+ self,
+ num_mel_bins=128,
+ encoder_layers=32,
+ encoder_attention_heads=20,
+ encoder_ffn_dim=5120,
+ d_model=1280,
+ dropout=0,
+ attention_dropout=0,
+ activation_function="gelu",
+ activation_dropout=0,
+ scale_embedding=False,
+ initializer_range=0.02,
+ max_source_positions=1500,
+ n_window=100,
+ output_dim=3584,
+ **kwargs,
+ ):
+ super().__init__(
+ num_mel_bins,
+ encoder_layers,
+ encoder_attention_heads,
+ encoder_ffn_dim,
+ d_model,
+ dropout,
+ attention_dropout,
+ activation_function,
+ activation_dropout,
+ scale_embedding,
+ initializer_range,
+ max_source_positions,
+ **kwargs,
+ )
+ self.n_window = n_window
+ self.output_dim = output_dim
+ del self.encoder_layerdrop
+
+
+class Qwen2_5OmniTextConfig(PreTrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`Qwen2_5OmniThinkerForConditionalGeneration`]. It is used to instantiate an
+ Qwen2.5-Omni-Thinker model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the Qwen2.5-Omni-Thinker.
+
+ e.g. [Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B)
+
+ Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PreTrainedConfig`] for more information.
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 152064):
+ Vocabulary size of the QwenOmni model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`Qwen2VLModel`]
+ hidden_size (`int`, *optional*, defaults to 3584):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 18944):
+ Dimension of the MLP representations.
+ num_hidden_layers (`int`, *optional*, defaults to 28):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 28):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ num_key_value_heads (`int`, *optional*, defaults to 4):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details, check out [this
+ paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the decoder.
+ max_position_embeddings (`int`, *optional*, defaults to 32768):
+ The maximum sequence length that this model might ever be used with.
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+ The epsilon used by the rms normalization layers.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ use_sliding_window (`bool`, *optional*, defaults to `False`):
+ Whether to use sliding window attention.
+ sliding_window (`int`, *optional*, defaults to 32768):
+ Sliding window attention (SWA) window size. If not specified, will default to `4096`.
+ max_window_layers (`int`, *optional*, defaults to 28):
+ The number of layers using full attention. The first `max_window_layers` layers will use full attention, while any
+ additional layer afterwards will use SWA (Sliding Window Attention).
+ layer_types (`list`, *optional*):
+ Attention pattern for each layer.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ rope_parameters (`RopeParameters`, *optional*):
+ Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
+ a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
+ with longer `max_position_embeddings`.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+
+ Example:
+
+ ```python
+ >>> from transformers import Qwen2_5OmniThinkerForConditionalGeneration, Qwen2_5OmniThinkerConfig, Qwen2_5OmniAudioEncoderConfig, Qwen2_5OmniVisionEncoderConfig
+
+ >>> # Initializing a Qwen2_5OmniAudioEncoder config
+ >>> audio_config = Qwen2_5OmniAudioEncoderConfig()
+
+ >>> # Initializing a Qwen2_5OmniVisionEncoder config
+ >>> vision_config = Qwen2_5OmniVisionEncoderConfig()
+
+ >>> # Initializing a Qwen2.5OmniThinker configuration
+ >>> configuration = Qwen2_5OmniThinkerConfig(audio_config, vision_config)
+
+ >>> # Initializing a model from the Qwen-Omni style configuration
+ >>> model = Qwen2_5OmniThinkerForConditionalGeneration(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "qwen2_5_omni_text"
+ keys_to_ignore_at_inference = ["past_key_values"]
+ default_theta = 1000000.0
+
+ # Default tensor parallel plan for base model `Qwen25OmniText`
+ base_model_tp_plan = {
+ "layers.*.self_attn.q_proj": "colwise",
+ "layers.*.self_attn.k_proj": "colwise",
+ "layers.*.self_attn.v_proj": "colwise",
+ "layers.*.self_attn.o_proj": "rowwise",
+ "layers.*.mlp.gate_proj": "colwise",
+ "layers.*.mlp.up_proj": "colwise",
+ "layers.*.mlp.down_proj": "rowwise",
+ }
+ base_model_pp_plan = {
+ "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+ "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+ "norm": (["hidden_states"], ["hidden_states"]),
+ }
+
+ def __init__(
+ self,
+ vocab_size: Optional[int] = 152064,
+ hidden_size: Optional[int] = 3584,
+ intermediate_size: Optional[int] = 18944,
+ num_hidden_layers: Optional[int] = 28,
+ num_attention_heads: Optional[int] = 28,
+ num_key_value_heads: Optional[int] = 4,
+ hidden_act: Optional[str] = "silu",
+ max_position_embeddings: Optional[int] = 32768,
+ initializer_range: Optional[float] = 0.02,
+ rms_norm_eps: Optional[int] = 1e-6,
+ use_cache: Optional[bool] = True,
+ tie_word_embeddings: Optional[bool] = False,
+ rope_parameters: Optional[RopeParameters | dict[str, RopeParameters]] = None,
+ use_sliding_window: Optional[bool] = False,
+ sliding_window: Optional[int] = 32768,
+ max_window_layers: Optional[int] = 28,
+ layer_types: Optional[list[str]] = None,
+ attention_dropout: Optional[float] = 0.0,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ 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.use_sliding_window = use_sliding_window
+ self.sliding_window = sliding_window if self.use_sliding_window else None
+ self.max_window_layers = max_window_layers
+
+ # for backward compatibility
+ if num_key_value_heads is None:
+ num_key_value_heads = num_attention_heads
+
+ self.num_key_value_heads = num_key_value_heads
+ self.hidden_act = hidden_act
+ self.initializer_range = initializer_range
+ self.rms_norm_eps = rms_norm_eps
+ self.use_cache = use_cache
+ self.attention_dropout = attention_dropout
+
+ self.layer_types = layer_types
+ if self.layer_types is None:
+ self.layer_types = [
+ "sliding_attention"
+ if self.sliding_window is not None and i >= self.max_window_layers
+ else "full_attention"
+ for i in range(self.num_hidden_layers)
+ ]
+ layer_type_validation(self.layer_types, self.num_hidden_layers)
+
+ self.rope_parameters = rope_parameters
+ super().__init__(
+ tie_word_embeddings=tie_word_embeddings,
+ ignore_keys_at_rope_validation={"mrope_section"},
+ **kwargs,
+ )
+
+
+class Qwen2_5OmniThinkerConfig(PreTrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`Qwen2_5OmniThinkerForConditionalGeneration`]. It is used to instantiate an
+ Qwen2.5-Omni-Thinker model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the Qwen2.5-Omni-Thinker.
+
+ e.g. [Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B)
+
+ Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PreTrainedConfig`] for more information.
+
+ Args:
+ audio_config (`dict`, *optional*):
+ The config dictionary of the audio backbone.
+ vision_config (`dict`, *optional*):
+ The config dictionary of the vision backbone.
+ text_config (`dict`, *optional*):
+ The config dictionary of the text backbone.
+ audio_token_index (`int`, *optional*, defaults to 151646):
+ The audio token index to encode the audio prompt.
+ image_token_index (`int`, *optional*, defaults to 151655):
+ The image token index to encode the image prompt.
+ video_token_index (`int`, *optional*, defaults to 151656):
+ The video token index to encode the video prompt.
+ position_id_per_seconds (`int`, *optional*, defaults to 25):
+ The increment of position id per second.
+ seconds_per_chunk (`int`, *optional*, defaults to 2):
+ The duration in seconds of the chunk of audio and video data.
+ audio_start_token_id (`int`, *optional*, defaults to 151647):
+ The audio start token index to encode the audio prompt.
+ audio_end_token_id (`int`, *optional*, defaults to 151648):
+ The audio end token index to encode the audio prompt.
+ user_token_id (`int, *optional*, defaults to 872):
+ The user token index to encode the user token.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+
+ Example:
+
+ ```python
+ >>> from transformers import Qwen2_5OmniThinkerForConditionalGeneration, Qwen2_5OmniThinkerConfig, Qwen2_5OmniAudioEncoderConfig, Qwen2_5OmniVisionEncoderConfig
+
+ >>> # Initializing a Qwen2_5OmniAudioEncoder config
+ >>> audio_config = Qwen2_5OmniAudioEncoderConfig()
+
+ >>> # Initializing a Qwen2_5OmniVisionEncoder config
+ >>> vision_config = Qwen2_5OmniVisionEncoderConfig()
+
+ >>> # Initializing a Qwen2_5OmniTextConfig config
+ >>> text_config = Qwen2_5OmniTextConfig()
+
+ >>> # Initializing a Qwen2.5OmniThinker configuration
+ >>> configuration = Qwen2_5OmniThinkerConfig(audio_config, vision_config, text_config)
+
+ >>> # Initializing a model from the Qwen-Omni style configuration
+ >>> model = Qwen2_5OmniThinkerForConditionalGeneration(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "qwen2_5_omni_thinker"
+ attribute_map = {
+ "image_token_id": "image_token_index",
+ "video_token_id": "video_token_index",
+ "audio_token_id": "audio_token_index",
+ }
+ sub_configs = {
+ "audio_config": Qwen2_5OmniAudioEncoderConfig,
+ "vision_config": Qwen2_5OmniVisionEncoderConfig,
+ "text_config": Qwen2_5OmniTextConfig,
+ }
+
+ def __init__(
+ self,
+ audio_config=None,
+ vision_config=None,
+ text_config=None,
+ audio_token_index=151646,
+ image_token_index=151655,
+ video_token_index=151656,
+ position_id_per_seconds=25,
+ seconds_per_chunk=2,
+ audio_start_token_id=151647,
+ audio_end_token_id=151648,
+ user_token_id=872,
+ initializer_range=0.02,
+ **kwargs,
+ ):
+ self.audio_token_index = audio_token_index
+ self.image_token_index = image_token_index
+ self.video_token_index = video_token_index
+ self.user_token_id = user_token_id
+ self.position_id_per_seconds = position_id_per_seconds
+ self.seconds_per_chunk = seconds_per_chunk
+ self.audio_start_token_id = audio_start_token_id
+ self.audio_end_token_id = audio_end_token_id
+ self.initializer_range = initializer_range
+
+ if isinstance(vision_config, dict):
+ vision_config = Qwen2_5OmniVisionEncoderConfig(**vision_config)
+ elif vision_config is None:
+ vision_config = Qwen2_5OmniVisionEncoderConfig()
+ self.vision_config = vision_config
+
+ if isinstance(audio_config, dict):
+ audio_config = Qwen2_5OmniAudioEncoderConfig(**audio_config)
+ elif audio_config is None:
+ audio_config = Qwen2_5OmniAudioEncoderConfig()
+ self.audio_config = audio_config
+
+ if isinstance(text_config, dict):
+ text_config = Qwen2_5OmniTextConfig(**text_config)
+ elif text_config is None:
+ text_config = Qwen2_5OmniTextConfig()
+ self.text_config = text_config
+
+ super().__init__(**kwargs)
+
+
+class Qwen2_5OmniTalkerConfig(PreTrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`Qwen2_5OmniTalkerForConditionalGeneration`]. It is used to instantiate an
+ Qwen2.5-Omni-Talker model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the Qwen2.5-Omni-Thinker.
+
+ e.g. [Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B)
+
+ Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PreTrainedConfig`] for more information.
+
+ Args:
+ audio_token_index (`int`, *optional*, defaults to 151646):
+ The audio token index to encode the audio prompt.
+ image_token_index (`int`, *optional*, defaults to 151655):
+ The image token index to encode the image prompt.
+ video_token_index (`int`, *optional*, defaults to 151656):
+ The video token index to encode the video prompt.
+ vocab_size (`int`, *optional*, defaults to 8448):
+ Vocabulary size of the QwenOmni model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`Qwen2VLModel`]
+ tts_text_start_token_id (`int`, *optional*, defaults to 151860):
+ The tts text start token index to encode the start of tts text.
+ tts_text_end_token_id (`int`, *optional*, defaults to 151861):
+ The tts text end token index to encode the end of tts text.
+ tts_text_pad_token_id (`int`, *optional*, defaults to 151859):
+ The tts text pad token index to encode the pad of tts text.
+ tts_codec_start_token_id (`int`, *optional*, defaults to 8293):
+ The tts codec start token index to encode the start of tts codec.
+ tts_codec_end_token_id (`int`, *optional*, defaults to 8294):
+ The tts codec end token index to encode the end of tts codec.
+ tts_codec_pad_token_id (`int`, *optional*, defaults to 8292):
+ The tts codec pad token index to encode the pad of tts codec.
+ tts_codec_mask_token_id (`int`, *optional*, defaults to 8296):
+ The tts codec mask token index to encode the mask of tts codec.
+ vision_start_token_id (`int`, *optional*, defaults to 151652):
+ The tts vision start token index to encode the start of vision.
+ vision_end_token_id (`int`, *optional*, defaults to 151653):
+ The tts vision end token index to encode the end of vision.
+ embedding_size (`int`, *optional*, defaults to 3584):
+ Dimension of the embedding representations.
+ hidden_size (`int`, *optional*, defaults to 3584):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 18944):
+ Dimension of the MLP representations.
+ num_hidden_layers (`int`, *optional*, defaults to 28):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 28):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ num_key_value_heads (`int`, *optional*, defaults to 4):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details, check out [this
+ paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the decoder.
+ max_position_embeddings (`int`, *optional*, defaults to 32768):
+ The maximum sequence length that this model might ever be used with.
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+ The epsilon used by the rms normalization layers.
+ head_dim (`int`, *optional*, defaults to 128):
+ The dimension of each attention head.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether the model's input and output word embeddings should be tied.
+ use_sliding_window (`bool`, *optional*, defaults to `False`):
+ Whether to use sliding window attention.
+ sliding_window (`int`, *optional*, defaults to 32768):
+ Sliding window attention (SWA) window size. If not specified, will default to `4096`.
+ max_window_layers (`int`, *optional*, defaults to 28):
+ The number of layers using full attention. The first `max_window_layers` layers will use full attention, while any
+ additional layer afterwards will use SWA (Sliding Window Attention).
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ rope_parameters (`RopeParameters`, *optional*):
+ Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
+ a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
+ with longer `max_position_embeddings`.
+ position_id_per_seconds (`int`, *optional*, defaults to 25):
+ The increment of position id per second.
+ seconds_per_chunk (`int`, *optional*, defaults to 2):
+ The duration in seconds of the chunk of audio and video data.
+ audio_start_token_id (`int`, *optional*, defaults to 151647):
+ The audio start token index to encode the audio prompt.
+ audio_end_token_id (`int`, *optional*, defaults to 151648):
+ The audio end token index to encode the audio prompt.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ spatial_merge_size (`int`, *optional*, defaults to 2):
+ The size used for merging spatial dimensions.
+ layer_types (`list`, *optional*):
+ Attention pattern for each layer.
+
+ Example:
+
+ ```python
+ >>> from transformers import Qwen2_5OmniTalkerForConditionalGeneration, Qwen2_5OmniThinkerConfig, Qwen2_5OmniAudioEncoderConfig, Qwen2_5OmniVisionEncoderConfig
+
+ >>> # Initializing a Qwen2_5OmniAudioEncoder config
+ >>> audio_config = Qwen2_5OmniAudioEncoderConfig()
+
+ >>> # Initializing a Qwen2 config
+ >>> text_config = Qwen2Config()
+
+ >>> # Initializing a Qwen2_5Omni configuration
+ >>> configuration = Qwen2_5OmniThinkerConfig(audio_config, text_config)
+
+ >>> # Initializing a model from the qwen2-audio style configuration
+ >>> model = Qwen2_5OmniTalkerForConditionalGeneration(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "qwen2_5_omni_talker"
+ default_theta = 1000000.0
+ attribute_map = {
+ "image_token_id": "image_token_index",
+ "video_token_id": "video_token_index",
+ "audio_token_id": "audio_token_index",
+ }
+
+ def __init__(
+ self,
+ audio_token_index=151646,
+ image_token_index=151655,
+ video_token_index=151656,
+ vocab_size=8448,
+ tts_text_start_token_id=151860,
+ tts_text_end_token_id=151861,
+ tts_text_pad_token_id=151859,
+ tts_codec_start_token_id=8293,
+ tts_codec_end_token_id=8294,
+ tts_codec_pad_token_id=8292,
+ tts_codec_mask_token_id=8296,
+ vision_start_token_id=151652,
+ vision_end_token_id=151653,
+ embedding_size=3584,
+ hidden_size=3584,
+ intermediate_size=18944,
+ num_hidden_layers=28,
+ num_attention_heads=28,
+ num_key_value_heads=4,
+ hidden_act="silu",
+ max_position_embeddings=32768,
+ rms_norm_eps=1e-06,
+ head_dim=128,
+ use_cache=True,
+ tie_word_embeddings=False,
+ use_sliding_window=False,
+ sliding_window=32768,
+ max_window_layers=28,
+ attention_dropout=0.0,
+ rope_parameters: Optional[RopeParameters | dict[str, RopeParameters]] = None,
+ position_id_per_seconds=25,
+ seconds_per_chunk=2,
+ audio_start_token_id=151647,
+ audio_end_token_id=151648,
+ initializer_range=0.02,
+ spatial_merge_size=2,
+ layer_types=None,
+ **kwargs,
+ ):
+ self.audio_token_index = audio_token_index
+ self.image_token_index = image_token_index
+ self.video_token_index = video_token_index
+
+ self.tts_text_start_token_id = tts_text_start_token_id
+ self.tts_text_end_token_id = tts_text_end_token_id
+ self.tts_text_pad_token_id = tts_text_pad_token_id
+ self.tts_codec_start_token_id = tts_codec_start_token_id
+ self.tts_codec_end_token_id = tts_codec_end_token_id
+ self.tts_codec_pad_token_id = tts_codec_pad_token_id
+
+ self.tts_codec_mask_token_id = tts_codec_mask_token_id
+
+ self.vision_start_token_id = vision_start_token_id
+ self.vision_end_token_id = vision_end_token_id
+
+ self.vocab_size = vocab_size
+ self.head_dim = head_dim
+ self.embedding_size = embedding_size
+ self.max_position_embeddings = max_position_embeddings
+ 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.use_sliding_window = use_sliding_window
+ self.sliding_window = sliding_window if self.use_sliding_window else None
+ self.max_window_layers = max_window_layers
+
+ # for backward compatibility
+ if num_key_value_heads is None:
+ num_key_value_heads = num_attention_heads
+
+ self.num_key_value_heads = num_key_value_heads
+ self.hidden_act = hidden_act
+ self.rms_norm_eps = rms_norm_eps
+ self.use_cache = use_cache
+ self.attention_dropout = attention_dropout
+ self.position_id_per_seconds = position_id_per_seconds # zf
+ self.seconds_per_chunk = seconds_per_chunk # zf
+ self.audio_start_token_id = audio_start_token_id # zf
+ self.audio_end_token_id = audio_end_token_id # zf
+
+ self.initializer_range = initializer_range
+ self.spatial_merge_size = spatial_merge_size
+
+ self.layer_types = layer_types
+ if self.layer_types is None:
+ self.layer_types = [
+ "sliding_attention"
+ if self.sliding_window is not None and i >= self.max_window_layers
+ else "full_attention"
+ for i in range(self.num_hidden_layers)
+ ]
+ layer_type_validation(self.layer_types, self.num_hidden_layers)
+
+ self.rope_parameters = rope_parameters
+ super().__init__(
+ tie_word_embeddings=tie_word_embeddings, ignore_keys_at_rope_validation={"mrope_section"}, **kwargs
+ )
+
+
+class Qwen2_5OmniDiTConfig(PreTrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of the Qwen2_5OmniToken2WavDiT used in the Qwen2.5-Omni-Token2Wav model.
+ It defines the architecture of the DiT model, which is used for generating mel-spectrograms from tokens.
+
+ Args:
+ hidden_size (`int`, *optional*, defaults to 1024):
+ The dimension of the model.
+ num_hidden_layers (`int`, *optional*, defaults to 22):
+ The number of transformer blocks in the DiT model.
+ num_attention_heads (`int`, *optional*, defaults to 16):
+ The number of attention heads in each transformer block.
+ ff_mult (`int`, *optional*, defaults to 2):
+ The multiplier for the feedforward layer in each transformer block.
+ emb_dim (`int`, *optional*, defaults to 512):
+ The dimension of the embedding layer.
+ head_dim (`int`, *optional*, defaults to 64):
+ The dimension of each attention head.
+ repeats (`int`, *optional*, defaults to 2):
+ The number of times the codec embeddings are repeated.
+ num_embeds (`int`, *optional*, defaults to 8193):
+ The number of unique embeddings in the codec.
+ mel_dim (`int`, *optional*, defaults to 80):
+ The dimension of the mel-spectrogram.
+ dropout (`float`, *optional*, defaults to 0.1):
+ The dropout rate for the transformer blocks.
+
+ enc_emb_dim (`int`, *optional*, defaults to 192):
+ The dimension of the pre-trained speaker embedding.
+ enc_dim (`int`, *optional*, defaults to 128):
+ The dimension of the encoder output.
+ enc_channels (`list[int]`, *optional*, defaults to `[256, 256, 256, 256, 768]`):
+ A list of output channels for each TDNN/SERes2Net layer in the encoder.
+ enc_kernel_sizes (`list[int]`, *optional*, defaults to `[5, 3, 3, 3, 1]`):
+ A list of kernel sizes for each layer in the encoder.
+ enc_dilations (`list[int]`, *optional*, defaults to `[1, 2, 3, 4, 1]`):
+ A list of dilations for each layer in the encoder.
+ enc_attention_channels (`int`, *optional*, defaults to 64):
+ The number of attention channels in the SqueezeExcitationBlock.
+ enc_res2net_scale (`int`, *optional*, defaults to 2):
+ The scale of the Res2Net block in the encoder.
+ enc_se_channels (`int`, *optional*, defaults to 64):
+ The number of output channels after squeeze in the SqueezeExcitationBlock.
+ """
+
+ model_type = "qwen2_5_omni_dit"
+
+ def __init__(
+ self,
+ hidden_size=1024,
+ num_hidden_layers=22,
+ num_attention_heads=16,
+ ff_mult=2,
+ emb_dim=512,
+ head_dim=64,
+ rope_parameters: Optional[RopeParameters | dict[str, RopeParameters]] = None,
+ max_position_embeddings=32768,
+ block_size=24,
+ look_ahead_layers=[10],
+ look_backward_layers=[0, 20],
+ repeats=2,
+ num_embeds=8193,
+ mel_dim=80,
+ dropout=0.1,
+ enc_emb_dim=192,
+ enc_dim=128,
+ enc_channels=[256, 256, 256, 256, 768],
+ enc_kernel_sizes=[5, 3, 3, 3, 1],
+ enc_dilations=[1, 2, 3, 4, 1],
+ enc_attention_channels=64,
+ enc_res2net_scale=2,
+ enc_se_channels=64,
+ **kwargs,
+ ):
+ self.hidden_size = hidden_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.ff_mult = ff_mult
+ self.emb_dim = emb_dim
+ self.head_dim = head_dim
+ self.max_position_embeddings = max_position_embeddings
+ self.block_size = block_size
+ self.look_ahead_layers = look_ahead_layers
+ self.look_backward_layers = look_backward_layers
+ self.repeats = repeats
+ self.num_embeds = num_embeds
+ self.mel_dim = mel_dim
+ self.dropout = dropout
+ self.enc_emb_dim = enc_emb_dim
+ self.enc_dim = enc_dim
+ self.enc_channels = enc_channels
+ self.enc_kernel_sizes = enc_kernel_sizes
+ self.enc_dilations = enc_dilations
+ self.enc_attention_channels = enc_attention_channels
+ self.enc_res2net_scale = enc_res2net_scale
+ self.enc_se_channels = enc_se_channels
+ self.rope_parameters = rope_parameters
+
+ super().__init__(**kwargs)
+
+
+class Qwen2_5OmniBigVGANConfig(PreTrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of the Qwen2_5OmniToken2WavBigVGAN module used in the Qwen2.5-Omni-Token2Wav model.
+ It defines the architecture of the BigVGAN model, which is used for converting mel-spectrograms to waveforms.
+
+ Args:
+ mel_dim (`int`, *optional*, defaults to 80):
+ The dimension of the mel-spectrogram.
+ upsample_initial_channel (`int`, *optional*, defaults to 1536):
+ The number of channels in the initial upsampling layer.
+ resblock_kernel_sizes (`list[int]`, *optional*, defaults to `[3, 7, 11]`):
+ A list of kernel sizes for each residual block.
+ resblock_dilation_sizes (`list[list[int]]`, *optional*, defaults to `[[1, 3, 5], [1, 3, 5], [1, 3, 5]]`):
+ A list of dilation sizes for each residual block.
+ upsample_rates (`list[int]`, *optional*, defaults to `[5, 3, 2, 2, 2, 2]`):
+ A list of upsampling rates for each upsampling layer.
+ upsample_kernel_sizes (`list[int]`, *optional*, defaults to `[11, 7, 4, 4, 4, 4]`):
+ A list of kernel sizes for each upsampling layer.
+ """
+
+ model_type = "qwen2_5_omni_bigvgan"
+
+ def __init__(
+ self,
+ mel_dim=80,
+ upsample_initial_channel=1536,
+ resblock_kernel_sizes=[3, 7, 11],
+ resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
+ upsample_rates=[5, 3, 2, 2, 2, 2],
+ upsample_kernel_sizes=[11, 7, 4, 4, 4, 4],
+ **kwargs,
+ ):
+ self.mel_dim = mel_dim
+ self.upsample_initial_channel = upsample_initial_channel
+ self.resblock_kernel_sizes = resblock_kernel_sizes
+ self.resblock_dilation_sizes = resblock_dilation_sizes
+ self.upsample_rates = upsample_rates
+ self.upsample_kernel_sizes = upsample_kernel_sizes
+ super().__init__(**kwargs)
+
+
+class Qwen2_5OmniToken2WavConfig(PreTrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`Qwen2_5OmniToken2WavModel`].
+ It is used to instantiate the Qwen2.5-Omni-Token2Wav model which combines a Diffusion Transformer (DiT) for mel-spectrogram generation with a BigVGAN model for waveform synthesis. The configuration contains sub-configurations for both components.
+
+ Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PreTrainedConfig`] for more information.
+
+ Args:
+ dit_config ([`DiT_Args`], *optional*):
+ Configuration class for the Diffusion Transformer (DiT) module responsible for generating mel-spectrograms.
+ bigvgan_config ([`BigVGAN_Args`], *optional*):
+ Configuration class for the BigVGAN module responsible for converting mel-spectrograms to waveforms.
+ Example:
+
+ ```python
+ >>> from transformers import Qwen2_5OmniToken2WavModel, DiT_Args, BigVGAN_Args
+
+ >>> # Initialize DiT configuration
+ >>> dit_config = DiT_Args(
+ ... dim=1024,
+ ... depth=22,
+ ... heads=16,
+ ... ff_mult=2
+ ... )
+
+ >>> # Initialize BigVGAN configuration
+ >>> bigvgan_config = BigVGAN_Args(
+ ... mel_dim=80,
+ ... upsample_rates=[5,3,2,2,2,2]
+ ... )
+
+ >>> # Initialize main configuration
+ >>> config = Qwen2_5OmniToken2WavConfig(dit_config, bigvgan_config)
+
+ >>> # Initialize model with config
+ >>> model = Qwen2_5OmniToken2Wav(config)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```
+ """
+
+ model_type = "qwen2_5_omni_token2wav"
+ sub_configs = {
+ "dit_config": Qwen2_5OmniDiTConfig,
+ "bigvgan_config": Qwen2_5OmniBigVGANConfig,
+ }
+
+ def __init__(self, dit_config=None, bigvgan_config=None, **kwargs):
+ if dit_config is None:
+ dit_config = {}
+ if bigvgan_config is None:
+ bigvgan_config = {}
+ self.dit_config = Qwen2_5OmniDiTConfig(**dit_config)
+ self.bigvgan_config = Qwen2_5OmniBigVGANConfig(**bigvgan_config)
+ super().__init__(**kwargs)
+
+
+class Qwen2_5OmniConfig(PreTrainedConfig):
+ """
+ This is the configuration class to store the configuration of a [`Qwen2_5OmniForConditionalGeneration`]. It is used to instantiate a Qwen2.5Omni
+ model according to the specified sub-models configurations, defining the model architecture.
+
+ Instantiating a configuration with the defaults will yield a similar configuration to that of the
+ [Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B) architecture.
+
+ Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PreTrainedConfig`] for more information.
+
+ Args:
+ thinker_config (`dict`, *optional*): Configuration of the underlying thinker sub-model.
+ talker_config (`dict`, *optional*): Configuration of the underlying talker sub-model.
+ token2wav_config (`dict`, *optional*): Configuration of the underlying codec sub-model.
+ enable_audio_output (`bool`, *optional*, defaults to `True`): Whether enable audio output and load talker and token2wav module.
+
+ Example:
+
+ ```python
+ >>> from transformers import (
+ ... Qwen2_5OmniThinkerConfig,
+ ... Qwen2_5OmniTalkerConfig,
+ ... Qwen2_5OmniToken2WavConfig,
+ ... Qwen2_5OmniForConditionalGeneration,
+ ... Qwen2_5OmniConfig,
+ ... )
+
+ >>> # Initializing sub-modules configurations.
+ >>> thinker_config = Qwen2_5OmniThinkerConfig()
+ >>> talker_config = Qwen2_5OmniTalkerConfig()
+ >>> token2wav_config = Qwen2_5OmniToken2WavConfig()
+
+
+ >>> # Initializing a module style configuration
+ >>> configuration = Qwen2_5OmniConfig(
+ ... thinker_config, talker_config, token2wav_config
+ ... )
+
+ >>> # Initializing a model (with random weights)
+ >>> model = Qwen2_5OmniForConditionalGeneration(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```
+ """
+
+ model_type = "qwen2_5_omni"
+ sub_configs = {
+ "thinker_config": Qwen2_5OmniThinkerConfig,
+ "talker_config": Qwen2_5OmniTalkerConfig,
+ "token2wav_config": Qwen2_5OmniToken2WavConfig,
+ }
+
+ def __init__(
+ self,
+ thinker_config=None,
+ talker_config=None,
+ token2wav_config=None,
+ enable_audio_output: bool = True,
+ **kwargs,
+ ):
+ if thinker_config is None:
+ thinker_config = {}
+ logger.info("thinker_config is None. Initializing thinker model with default values")
+
+ if talker_config is None:
+ talker_config = {}
+ logger.info("talker_config is None. Initializing talker model with default values")
+
+ if token2wav_config is None:
+ token2wav_config = {}
+ logger.info("token2wav_config is None. Initializing token2wav model with default values")
+
+ self.thinker_config = Qwen2_5OmniThinkerConfig(**thinker_config)
+ self.talker_config = Qwen2_5OmniTalkerConfig(**talker_config)
+ self.token2wav_config = Qwen2_5OmniToken2WavConfig(**token2wav_config)
+ self.enable_audio_output = enable_audio_output
+
+ super().__init__(**kwargs)
+
+ def get_text_config(self, *args, **kwargs):
+ """
+ Returns the config that is meant to be used with text IO. On most models, it is the original config instance
+ itself. On specific composite models, it is under a set of valid names.
+
+ Args:
+ decoder (`Optional[bool]`, *optional*, defaults to `False`):
+ If set to `True`, then only search for decoder config names.
+ """
+ # Overridden for deeply nested config like Qwen2-Omni. We don't have any omni model
+ # except for Qwen yet. This has to be generalized if more deeply nested configs are
+ # added. NOTE: currently method used only by vLLM
+ return self.thinker_config.get_text_config(*args, **kwargs)
+
+
+class Qwen2_5OmniPreTrainedModel(Qwen2_5_VLPreTrainedModel):
+ config: Qwen2_5OmniConfig
+ input_modalities = ("image", "video", "audio", "text")
+ _can_compile_fullgraph = False
+
+ def _init_weights(self, module):
+ PreTrainedModel._init_weights(self, module)
+ if isinstance(module, SinusoidsPositionEmbedding):
+ log_timescale_increment = np.log(module.max_timescale) / (module.channels // 2 - 1)
+ inv_timescales = torch.exp(-log_timescale_increment * torch.arange(module.channels // 2).float())
+ scaled_time = torch.arange(module.length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
+ init.copy_(module.positional_embedding, torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1))
+ elif isinstance(module, UpSample1d):
+ filter_tensor = kaiser_sinc_filter1d(0.5 / module.ratio, 0.6 / module.ratio, module.kernel_size)
+ init.copy_(module.filter, filter_tensor)
+ elif isinstance(module, DownSample1d):
+ filter_tensor = kaiser_sinc_filter1d(module.cutoff, module.half_width, module.kernel_size)
+ init.copy_(module.filter, filter_tensor)
+ elif isinstance(module, Qwen2_5_VisionRotaryEmbedding):
+ inv_freq = 1.0 / (module.theta ** (torch.arange(0, module.dim, 2, dtype=torch.float) / module.dim))
+ init.copy_(module.inv_freq, inv_freq)
+
+
+class Qwen2_5OmniPreTrainedModelForConditionalGeneration(Qwen2_5OmniPreTrainedModel):
+ input_modalities = ("image", "video", "audio", "text")
+
+ def _prepare_4d_causal_attention_mask_with_cache_position(
+ self,
+ attention_mask: torch.Tensor,
+ sequence_length: int,
+ target_length: int,
+ dtype: torch.dtype,
+ device: torch.device,
+ min_dtype: float,
+ cache_position: torch.Tensor,
+ batch_size: int,
+ ):
+ """
+ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+ `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+ Args:
+ attention_mask (`torch.Tensor`):
+ A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
+ sequence_length (`int`):
+ The sequence length being processed.
+ target_length (`int`):
+ The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
+ dtype (`torch.dtype`):
+ The dtype to use for the 4D attention mask.
+ device (`torch.device`):
+ The device to place the 4D attention mask on.
+ min_dtype (`float`):
+ The minimum value representable with the dtype `dtype`.
+ cache_position (`torch.Tensor`):
+ Indices depicting the position of the input sequence tokens in the sequence.
+ batch_size (`torch.Tensor`):
+ Batch size.
+ """
+ if attention_mask is not None and attention_mask.dim() == 4:
+ # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+ causal_mask = attention_mask
+ else:
+ causal_mask = torch.full(
+ (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
+ )
+ if sequence_length != 1:
+ causal_mask = torch.triu(causal_mask, diagonal=1)
+ causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
+ causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+ if attention_mask is not None:
+ causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
+ mask_length = attention_mask.shape[-1]
+ padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+ padding_mask = padding_mask == 0
+ causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+ padding_mask, min_dtype
+ )
+
+ return causal_mask
+
+ def get_llm_pos_ids_for_vision(
+ self,
+ start_idx: int,
+ vision_idx: int,
+ spatial_merge_size: int,
+ t_index: list[int],
+ grid_hs: list[int],
+ grid_ws: list[int],
+ ):
+ llm_pos_ids_list = []
+ llm_grid_h = grid_hs[vision_idx] // spatial_merge_size
+ llm_grid_w = grid_ws[vision_idx] // spatial_merge_size
+ h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(len(t_index), -1, llm_grid_w).flatten()
+ w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(len(t_index), llm_grid_h, -1).flatten()
+ t_index = torch.Tensor(t_index).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w).flatten().long()
+ _llm_pos_ids = torch.stack([t_index, h_index, w_index])
+ llm_pos_ids_list.append(_llm_pos_ids + start_idx) # + 1 ) # 12.09 by malinhan
+ llm_pos_ids = torch.cat(llm_pos_ids_list, dim=1)
+ return llm_pos_ids
+
+ def get_chunked_index(
+ self, token_indices: torch.Tensor, tokens_per_chunk: int, remove_index: int
+ ) -> list[tuple[int, int]]:
+ """
+ Splits token index list into chunks based on token value ranges.
+
+ Given a list of token indices, returns a list of (start, end) index tuples representing
+ slices of the list where the token values fall within successive ranges of `t_ntoken_per_chunk`.
+
+ For example, if `t_ntoken_per_chunk` is 1000, the function will create chunks such that:
+ - the first chunk contains token values < 1000,
+ - the second chunk contains values >= 1000 and < 2000, and so on.
+
+ Parameters:
+ token_indices (`torch.Tensor` of shape `(seq_len, )`): A monotonically increasing list of
+ token index values.
+ t_ntoken_per_chunk (`int`): Number of tokens per chunk (used as the chunk size threshold).
+ remove_index (`int`) An index id to subtract from `token_indices` before chunking
+
+ Returns:
+ `list[tuple[int, int]]`: A list of tuples, each representing the start (inclusive)
+ and end (exclusive) indices of a chunk in `token_indices`.
+ """
+
+ def _iter():
+ i, start_idx = 0, 0 # skip bos token
+ current_chunk = 1
+ while i < len(token_indices): # skip eos token
+ if token_indices[i] - remove_index >= current_chunk * tokens_per_chunk:
+ yield (start_idx, i)
+ start_idx = i
+ current_chunk += 1
+ i += 1
+ yield (start_idx, len(token_indices))
+
+ return list(_iter())
+
+ def get_rope_index(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ image_grid_thw: Optional[torch.LongTensor] = None,
+ video_grid_thw: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ use_audio_in_video: bool = False,
+ audio_seqlens: Optional[torch.LongTensor] = None,
+ second_per_grids: Optional[torch.Tensor] = None,
+ ) -> tuple[torch.Tensor, torch.Tensor]:
+ """
+ Calculate the 3D rope index based on image and video's temporal, height and width in LLM.
+
+ Explanation:
+ Each embedding sequence contains vision embedding and text embedding or just contains text embedding.
+
+ For pure text embedding sequence, the rotary position embedding has no difference with modern LLMs.
+ Examples:
+ input_ids: [T T T T T], here T is for text.
+ temporal position_ids: [0, 1, 2, 3, 4]
+ height position_ids: [0, 1, 2, 3, 4]
+ width position_ids: [0, 1, 2, 3, 4]
+
+ For vision and text embedding sequence, we calculate 3D rotary position embedding for vision part
+ and 1D rotary position embedding for text part.
+ Examples:
+ Temporal (Time): 3 patches, representing different segments of the video in time.
+ Height: 2 patches, dividing each frame vertically.
+ Width: 2 patches, dividing each frame horizontally.
+ We also have some important parameters:
+ fps (Frames Per Second): The video's frame rate, set to 1. This means one frame is processed each second.
+ tokens_per_second: This is a crucial parameter. It dictates how many "time-steps" or "temporal tokens" are conceptually packed into a one-second interval of the video. In this case, we have 25 tokens per second. So each second of the video will be represented with 25 separate time points. It essentially defines the temporal granularity.
+ temporal_patch_size: The number of frames that compose one temporal patch. Here, it's 2 frames.
+ interval: The step size for the temporal position IDs, calculated as tokens_per_second * temporal_patch_size / fps. In this case, 25 * 2 / 1 = 50. This means that each temporal patch will be have a difference of 50 in the temporal position IDs.
+ input_ids: [V V V V V V V V V V V V T T T T T], here V is for vision.
+ vision temporal position_ids: [0, 0, 0, 0, 50, 50, 50, 50, 100, 100, 100, 100]
+ vision height position_ids: [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1]
+ vision width position_ids: [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1]
+ text temporal position_ids: [101, 102, 103, 104, 105]
+ text height position_ids: [101, 102, 103, 104, 105]
+ text width position_ids: [101, 102, 103, 104, 105]
+ Here we calculate the text start position_ids as the max vision position_ids plus 1.
+
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+ it.
+ image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+ The temporal, height and width of feature shape of each image in LLM.
+ video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+ The temporal, height and width of feature shape of each video in LLM.
+ 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**.
+ use_audio_in_video (`bool`, *optional*):
+ If set to `True`, use the audio in video.
+ audio_seqlens (`torch.LongTensor` of shape `(num_audios)`, *optional*):
+ The length of feature shape of each audio in LLM.
+ second_per_grids (`torch.LongTensor` of shape `(num_videos)`, *optional*):
+ The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs.
+
+ Returns:
+ position_ids (`torch.LongTensor` of shape `(3, batch_size, sequence_length)`)
+ mrope_position_deltas (`torch.Tensor` of shape `(batch_size)`)
+ """
+ spatial_merge_size = self.spatial_merge_size
+ image_token_id = self.config.image_token_id
+ video_token_id = self.config.video_token_id
+ audio_token_id = self.config.audio_token_id
+ vision_start_token_id = self.config.vision_start_token_id
+ audio_start_token_id = self.config.audio_start_token_id
+ position_id_per_seconds = self.config.position_id_per_seconds
+ seconds_per_chunk = self.config.seconds_per_chunk
+
+ mrope_position_deltas = []
+ if input_ids is not None and (image_grid_thw is not None or video_grid_thw is not None):
+ total_input_ids = input_ids
+ if attention_mask is not None:
+ attention_mask = attention_mask == 1
+ position_ids = torch.ones(
+ 3,
+ input_ids.shape[0],
+ input_ids.shape[1],
+ dtype=input_ids.dtype,
+ device=input_ids.device,
+ )
+ image_idx, video_idx, audio_idx = 0, 0, 0
+ for i, input_ids in enumerate(total_input_ids):
+ if attention_mask is not None:
+ input_ids = input_ids[attention_mask[i]]
+ image_nums, video_nums, audio_nums = 0, 0, 0
+ vision_start_indices = torch.argwhere(input_ids == vision_start_token_id).squeeze(1)
+ vision_tokens = input_ids[vision_start_indices + 1]
+ audio_nums = torch.sum(input_ids == audio_start_token_id)
+ image_nums = (vision_tokens == image_token_id).sum()
+ video_nums = (
+ (vision_tokens == audio_start_token_id).sum()
+ if use_audio_in_video
+ else (vision_tokens == video_token_id).sum()
+ )
+ input_tokens = input_ids.tolist()
+ llm_pos_ids_list: list = []
+ st = 0
+ remain_images, remain_videos, remain_audios = image_nums, video_nums, audio_nums
+ multimodal_nums = (
+ image_nums + audio_nums if use_audio_in_video else image_nums + video_nums + audio_nums
+ )
+ for _ in range(multimodal_nums):
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ if image_token_id in input_tokens and remain_images > 0:
+ ed_image = input_tokens.index(image_token_id, st)
+ else:
+ ed_image = len(input_tokens) + 1
+ if video_token_id in input_tokens and remain_videos > 0:
+ ed_video = input_tokens.index(video_token_id, st)
+ else:
+ ed_video = len(input_tokens) + 1
+ if audio_token_id in input_tokens and remain_audios > 0:
+ ed_audio = input_tokens.index(audio_token_id, st)
+ else:
+ ed_audio = len(input_tokens) + 1
+ min_ed = min(ed_image, ed_video, ed_audio)
+ if min_ed == ed_audio:
+ text_len = min_ed - st - 1
+ if text_len != 0:
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ bos_len = 1
+ llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ audio_len = ((audio_seqlens[audio_idx] - 1) // 2 + 1 - 2) // 2 + 1
+ llm_pos_ids = torch.arange(audio_len).view(1, -1).expand(3, -1) + st_idx
+ llm_pos_ids_list.append(llm_pos_ids)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ eos_len = 1
+ llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st += text_len + bos_len + audio_len + eos_len
+ audio_idx += 1
+ remain_audios -= 1
+
+ elif min_ed == ed_image:
+ text_len = min_ed - st - 1
+ if text_len != 0:
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ bos_len = 1
+ llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ grid_t = image_grid_thw[image_idx][0]
+ grid_hs = image_grid_thw[:, 1]
+ grid_ws = image_grid_thw[:, 2]
+ t_index = (torch.arange(grid_t) * 1 * position_id_per_seconds).long()
+ llm_pos_ids = self.get_llm_pos_ids_for_vision(
+ st_idx, image_idx, spatial_merge_size, t_index, grid_hs, grid_ws
+ )
+ image_len = image_grid_thw[image_idx].prod() // (spatial_merge_size**2)
+ llm_pos_ids_list.append(llm_pos_ids)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ eos_len = 1
+ llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st += text_len + bos_len + image_len + eos_len
+ image_idx += 1
+ remain_images -= 1
+
+ elif min_ed == ed_video and not use_audio_in_video:
+ text_len = min_ed - st - 1
+ if text_len != 0:
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ bos_len = 1
+ llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ grid_t = video_grid_thw[video_idx][0]
+ grid_hs = video_grid_thw[:, 1]
+ grid_ws = video_grid_thw[:, 2]
+ t_index = (
+ torch.arange(grid_t) * second_per_grids[video_idx].cpu().float() * position_id_per_seconds
+ ).long()
+ llm_pos_ids = self.get_llm_pos_ids_for_vision(
+ st_idx, video_idx, spatial_merge_size, t_index, grid_hs, grid_ws
+ )
+ video_len = video_grid_thw[video_idx].prod() // (spatial_merge_size**2)
+ llm_pos_ids_list.append(llm_pos_ids)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ eos_len = 1
+ llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st += text_len + bos_len + video_len + eos_len
+ video_idx += 1
+ remain_videos -= 1
+
+ elif min_ed == ed_video and use_audio_in_video:
+ text_len = min_ed - st - 2
+ if text_len != 0:
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ bos_len = 1
+ llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx)
+ llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ audio_len = ((audio_seqlens[audio_idx] - 1) // 2 + 1 - 2) // 2 + 1
+ audio_llm_pos_ids = torch.arange(audio_len).view(1, -1).expand(3, -1) + st_idx
+ grid_t = video_grid_thw[video_idx][0]
+ grid_hs = video_grid_thw[:, 1]
+ grid_ws = video_grid_thw[:, 2]
+
+ t_index = (
+ torch.arange(grid_t) * second_per_grids[video_idx].cpu().float() * position_id_per_seconds
+ ).long()
+ video_llm_pos_ids = self.get_llm_pos_ids_for_vision(
+ st_idx, video_idx, spatial_merge_size, t_index, grid_hs, grid_ws
+ )
+
+ t_ntoken_per_chunk = int(position_id_per_seconds * seconds_per_chunk)
+ video_chunk_indexes = self.get_chunked_index(video_llm_pos_ids[0], t_ntoken_per_chunk, st_idx)
+ audio_chunk_indexes = self.get_chunked_index(audio_llm_pos_ids[0], t_ntoken_per_chunk, st_idx)
+ sub_len = 0
+ for j in range(max(len(video_chunk_indexes), len(audio_chunk_indexes))):
+ video_chunk_index = video_chunk_indexes[j] if j < len(video_chunk_indexes) else None
+ audio_chunk_index = audio_chunk_indexes[j] if j < len(audio_chunk_indexes) else None
+ if video_chunk_index is not None:
+ sub_len += video_chunk_index[1] - video_chunk_index[0]
+
+ llm_pos_ids_list.append(
+ video_llm_pos_ids[:, video_chunk_index[0] : video_chunk_index[1]]
+ )
+ if audio_chunk_index is not None:
+ sub_len += audio_chunk_index[1] - audio_chunk_index[0]
+
+ llm_pos_ids_list.append(
+ audio_llm_pos_ids[:, audio_chunk_index[0] : audio_chunk_index[1]]
+ )
+ video_len = video_grid_thw[video_idx].prod() // (spatial_merge_size**2)
+
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ eos_len = 1
+ llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx)
+ llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx)
+
+ st += text_len + bos_len * 2 + audio_len + video_len + eos_len * 2
+
+ audio_idx += 1
+ video_idx += 1
+ remain_videos -= 1
+ remain_audios -= 1
+
+ if st < len(input_tokens):
+ st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+ text_len = len(input_tokens) - st
+ llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+ llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
+
+ if attention_mask is not None:
+ position_ids[..., i, attention_mask[i]] = llm_positions.to(position_ids.device)
+ else:
+ position_ids[..., i, :] = llm_positions.to(position_ids.device)
+ mrope_position_deltas.append(llm_positions.max() + 1 - len(input_ids))
+ mrope_position_deltas = torch.tensor(mrope_position_deltas).unsqueeze(1).to(device=input_ids.device)
+
+ return position_ids, mrope_position_deltas
+ else:
+ position_ids = attention_mask.long().cumsum(-1) - 1
+ position_ids.masked_fill_(attention_mask == 0, 1)
+ position_ids = position_ids.unsqueeze(0).expand(3, -1, -1).to(attention_mask.device)
+ max_position_ids = position_ids.max(0, keepdim=False)[0].max(-1, keepdim=True)[0]
+ mrope_position_deltas = max_position_ids + 1 - torch.sum(attention_mask, dim=-1, keepdim=True)
+
+ return position_ids, mrope_position_deltas
+
+
+############################
+# Start Thinker #
+############################
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for Qwen2.5OmniThinker causal language model (or autoregressive) outputs.
+ """
+)
+class Qwen2_5OmniThinkerCausalLMOutputWithPast(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Language modeling loss (for next-token prediction).
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`, *optional*):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
+ The rope index difference between sequence length and multimodal rope.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ past_key_values: Optional[Cache] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+ rope_deltas: Optional[torch.LongTensor] = None
+
+
+class Qwen2_5OmniAudioAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(
+ self,
+ config: Qwen2_5OmniAudioEncoderConfig,
+ ):
+ super().__init__()
+ self.embed_dim = config.d_model
+ self.num_heads = config.encoder_attention_heads
+ self.dropout = config.attention_dropout
+ self.head_dim = self.embed_dim // self.num_heads
+ self.num_key_value_groups = 1 # needed for eager attention
+ self.config = config
+
+ 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}"
+ f" and `num_heads`: {self.num_heads})."
+ )
+ self.scaling = self.head_dim**-0.5
+ self.attention_dropout = 0.0
+ self.is_decoder = False
+ self.is_causal = False
+
+ self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
+ self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True)
+ self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True)
+ self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ cu_seqlens: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ **kwargs,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ """Input shape: Batch x Time x Channel"""
+
+ seq_length, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states).reshape(seq_length, self.num_heads, -1)
+ key_states = self.k_proj(hidden_states).reshape(seq_length, self.num_heads, -1)
+ value_states = self.v_proj(hidden_states).reshape(seq_length, self.num_heads, -1)
+
+ query_states = query_states.transpose(0, 1).unsqueeze(0)
+ key_states = key_states.transpose(0, 1).unsqueeze(0)
+ value_states = value_states.transpose(0, 1).unsqueeze(0)
+ max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, _ = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask=attention_mask,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=self.scaling,
+ cu_seq_lens_q=cu_seqlens, # pass cu seq lens for FA2
+ cu_seq_lens_k=cu_seqlens,
+ max_length_q=max_seqlen,
+ max_length_k=max_seqlen,
+ is_causal=False,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(seq_length, -1).contiguous()
+ attn_output = self.out_proj(attn_output)
+
+ return attn_output
+
+
+class Qwen2_5OmniAudioEncoderLayer(Qwen2AudioEncoderLayer):
+ def __init__(self, config: Qwen2_5OmniAudioEncoderConfig):
+ super().__init__(config)
+ self.self_attn = Qwen2_5OmniAudioAttention(config)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ cu_seqlens: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ **kwargs,
+ ) -> torch.Tensor:
+ residual = hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+ hidden_states = self.self_attn(
+ hidden_states=hidden_states,
+ cu_seqlens=cu_seqlens,
+ attention_mask=attention_mask,
+ **kwargs,
+ )
+ hidden_states = residual + hidden_states
+ residual = hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+ hidden_states = self.fc1(hidden_states)
+ hidden_states = self.activation_fn(hidden_states)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = residual + hidden_states
+
+ if hidden_states.dtype == torch.float16:
+ clamp_value = torch.finfo(hidden_states.dtype).max - 1000
+ hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+ outputs = (hidden_states,)
+
+ return outputs
+
+
+class SinusoidsPositionEmbedding(nn.Module):
+ def __init__(self, length, channels, max_timescale=10000):
+ super().__init__()
+ self.length = length
+ self.channels = channels
+ self.max_timescale = max_timescale
+ if channels % 2 != 0:
+ raise ValueError("SinusoidsPositionEmbedding needs even channels input")
+ log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
+ inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2).float())
+ scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
+ self.register_buffer(
+ "positional_embedding",
+ torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1),
+ persistent=False,
+ )
+
+ def forward(self, seqlen: int):
+ return self.positional_embedding[:seqlen, :]
+
+
+@auto_docstring(
+ custom_intro="""
+ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
+ [`Qwen2_5OmniAudioEncoderLayer`].
+ """
+)
+class Qwen2_5OmniAudioEncoder(Qwen2_5OmniPreTrainedModel):
+ config: Qwen2_5OmniAudioEncoderConfig
+ main_input_name = "input_features"
+ input_modalities = "audio"
+ _no_split_modules = ["Qwen2_5OmniAudioEncoderLayer"]
+ _supports_sdpa = True
+
+ def __init__(self, config: Qwen2_5OmniAudioEncoderConfig):
+ super().__init__(config)
+ self.dropout = config.dropout
+
+ embed_dim = config.d_model
+ self.num_mel_bins = config.num_mel_bins
+ self.max_source_positions = config.max_source_positions
+ self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
+ self.n_window = config.n_window
+ self.conv1 = nn.Conv1d(self.num_mel_bins, embed_dim, kernel_size=3, padding=1)
+ self.conv2 = nn.Conv1d(embed_dim, embed_dim, kernel_size=3, stride=2, padding=1)
+ self.positional_embedding = SinusoidsPositionEmbedding(self.max_source_positions, embed_dim)
+ self.audio_bos_eos_token = nn.Embedding(2, config.output_dim)
+ self.layers = nn.ModuleList([Qwen2_5OmniAudioEncoderLayer(config) for _ in range(config.encoder_layers)])
+ self.ln_post = nn.LayerNorm(config.d_model)
+ self.avg_pooler = nn.AvgPool1d(2, stride=2)
+ self.proj = nn.Linear(config.d_model, config.output_dim)
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def _freeze_parameters(self):
+ for param in self.parameters():
+ param.requires_grad = False
+ self._requires_grad = False
+
+ def get_input_embeddings(self) -> nn.Module:
+ return self.conv1
+
+ def set_input_embeddings(self, value: nn.Module):
+ self.conv1 = value
+
+ def _prepare_attention_mask(self, inputs_tensor: torch.Tensor, cu_seqlens: torch.Tensor) -> torch.Tensor:
+ # Flash Attention 2 doesn't need a 4D mask and relies on `cu_seqlens/max_seqlen`
+ # NOTE: the created attention masl only approximates the ragged FA2 attention by
+ # allowing bidirectional attention within `cu_seqlens` blocks, and not attending between
+ # blocks. Though it will not be a 100% match for FA2's `varlen` path
+ if self.config._attn_implementation == "flash_attention_2":
+ return None
+
+ seq_length = inputs_tensor.shape[0]
+ attention_mask = torch.full(
+ [1, 1, seq_length, seq_length],
+ torch.finfo(inputs_tensor.dtype).min,
+ device=inputs_tensor.device,
+ dtype=inputs_tensor.dtype,
+ )
+ for i in range(1, len(cu_seqlens)):
+ attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = 0
+ return attention_mask
+
+ @auto_docstring
+ def forward(
+ self,
+ input_features,
+ feature_lens=None,
+ aftercnn_lens=None,
+ **kwargs,
+ ):
+ r"""
+ feature_lens (`torch.LongTensor` of shape `(batch_size,)`):
+ mel length
+ aftercnn_lens (`torch.LongTensor` of shape `(batch_size,)`):
+ mel length after cnn
+ """
+ chunk_num = torch.ceil(feature_lens / (self.n_window * 2)).long()
+
+ chunk_lengths = torch.tensor(
+ [self.n_window * 2] * chunk_num.sum(),
+ dtype=torch.long,
+ device=feature_lens.device,
+ )
+ tail_chunk_index = F.pad(chunk_num, (1, 0), value=-1).cumsum(0)[1:]
+ chunk_lengths[tail_chunk_index] = feature_lens % (self.n_window * 2)
+ chunk_lengths = torch.where(chunk_lengths == 0, self.n_window * 2, chunk_lengths)
+
+ chunk_list = input_features.split(chunk_lengths.tolist(), dim=1)
+ padded_feature, padded_mask, padded_mask_after_cnn = self.padded_and_mask_function(
+ chunk_list, chunk_lengths, padding_value=0, padding_side="right"
+ )
+ padded_embed = nn.functional.gelu(self.conv1(padded_feature)) * padded_mask
+ padded_embed = nn.functional.gelu(self.conv2(padded_embed)).transpose(1, 2)
+
+ padded_embed = padded_embed + self.positional_embedding.positional_embedding[
+ : padded_embed.shape[1], :
+ ].unsqueeze(0).to(padded_embed.dtype)
+ hidden_states = padded_embed[padded_mask_after_cnn]
+ cu_seqlens = torch.cat(
+ (
+ torch.zeros(1, device=padded_mask_after_cnn.device, dtype=torch.int32),
+ padded_mask_after_cnn.sum(1).cumsum(0),
+ )
+ ).to(torch.int32)
+ attention_mask = self._prepare_attention_mask(hidden_states, cu_seqlens)
+
+ for encoder_layer in self.layers:
+ layer_outputs = encoder_layer(
+ hidden_states,
+ cu_seqlens=cu_seqlens,
+ attention_mask=attention_mask,
+ **kwargs,
+ )
+ hidden_states = layer_outputs[0]
+
+ hidden_states_list = hidden_states.split(aftercnn_lens.tolist(), dim=0)
+ token_audio_list = []
+ for each_audio_states in hidden_states_list:
+ each_audio_states = self.avg_pooler(each_audio_states.transpose(0, 1)).transpose_(0, 1)
+ each_audio_states = self.ln_post(each_audio_states)
+ each_audio_states = self.proj(each_audio_states)
+ token_audio_list.append(each_audio_states)
+ token_audio = torch.cat(token_audio_list, dim=0)
+ return BaseModelOutput(last_hidden_state=token_audio)
+
+ def padded_and_mask_function(self, tensor_list, tensor_len, padding_value=0, padding_side="right"):
+ """
+ Pads a sequence of tensors to their maximum length on indicated `padding_side`.
+ Then prepares a mask so that pad tokens are not attended to.
+ """
+ max_len = tensor_len.max()
+ dim = tensor_list[0].shape[0]
+ padded_tensor = torch.full(
+ size=(len(tensor_list), dim, max_len),
+ fill_value=padding_value,
+ dtype=self.dtype,
+ device=tensor_list[0].device,
+ )
+
+ batch_mask = torch.zeros(
+ (len(tensor_len), max_len),
+ dtype=torch.long,
+ device=padded_tensor.device,
+ )
+ for i, length in enumerate(tensor_len):
+ batch_mask[i, :length] = 1
+ padded_tensor[i, :, :length] = tensor_list[i]
+
+ feature_lens_after_cnn = (tensor_len - 1) // 2 + 1
+ max_len_after_cnn = feature_lens_after_cnn.max()
+ batch_mask_after_cnn = torch.zeros(
+ (len(tensor_len), max_len_after_cnn),
+ dtype=torch.long,
+ device=padded_tensor.device,
+ )
+ for i, length in enumerate(feature_lens_after_cnn):
+ batch_mask_after_cnn[i, :length] = 1
+ return (
+ padded_tensor,
+ batch_mask.unsqueeze(1),
+ batch_mask_after_cnn.bool(),
+ )
+
+ # Ignore copy
+ 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 = (input_lengths - 1) // 2 + 1
+ output_lengths = (input_lengths - 2) // 2 + 1
+ return input_lengths, output_lengths
+
+
+def apply_rotary_pos_emb_vision(tensor: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
+ orig_dtype = tensor.dtype
+ tensor = tensor.float()
+ cos = freqs.cos()
+ sin = freqs.sin()
+ cos = cos.unsqueeze(1).repeat(1, 1, 2).unsqueeze(0).float()
+ sin = sin.unsqueeze(1).repeat(1, 1, 2).unsqueeze(0).float()
+ output = (tensor * cos) + (rotate_half(tensor) * sin)
+ output = output.to(orig_dtype)
+ return output
+
+
+class Qwen2_5OmniVisionAttention(nn.Module):
+ def __init__(self, config: Qwen2_5OmniVisionEncoderConfig = None) -> None:
+ super().__init__()
+ self.dim = config.hidden_size
+ self.num_heads = config.num_heads
+ self.head_dim = self.dim // self.num_heads
+ self.q = nn.Linear(self.dim, self.dim, bias=True)
+ self.k = nn.Linear(self.dim, self.dim, bias=True)
+ self.v = nn.Linear(self.dim, self.dim, bias=True)
+ self.proj = nn.Linear(self.dim, self.dim)
+ self.scaling = self.head_dim**-0.5
+ self.num_key_value_groups = 1 # needed for eager attention
+ self.config = config
+ self.attention_dropout = 0.0
+ self.is_causal = False
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ cu_seqlens: torch.Tensor,
+ rotary_pos_emb: Optional[torch.Tensor] = None,
+ **kwargs,
+ ) -> torch.Tensor:
+ seq_length = hidden_states.shape[0]
+ query_states = self.q(hidden_states).reshape(seq_length, self.num_heads, -1)
+ key_states = self.k(hidden_states).reshape(seq_length, self.num_heads, -1)
+ value_states = self.v(hidden_states).reshape(seq_length, self.num_heads, -1)
+ query_states = apply_rotary_pos_emb_vision(query_states.unsqueeze(0), rotary_pos_emb).squeeze(0)
+ key_states = apply_rotary_pos_emb_vision(key_states.unsqueeze(0), rotary_pos_emb).squeeze(0)
+
+ query_states = query_states.transpose(0, 1).unsqueeze(0)
+ key_states = key_states.transpose(0, 1).unsqueeze(0)
+ value_states = value_states.transpose(0, 1).unsqueeze(0)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ if self.config._attn_implementation == "flash_attention_2":
+ # Flash Attention 2: Use cu_seqlens for variable length attention
+ max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
+ attn_output, _ = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask=None,
+ scaling=self.scaling,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ cu_seq_lens_q=cu_seqlens,
+ cu_seq_lens_k=cu_seqlens,
+ max_length_q=max_seqlen,
+ max_length_k=max_seqlen,
+ is_causal=False,
+ **kwargs,
+ )
+ else:
+ # Other implementations: Process each chunk separately
+ lengths = cu_seqlens[1:] - cu_seqlens[:-1]
+ splits = [
+ torch.split(tensor, lengths.tolist(), dim=2) for tensor in (query_states, key_states, value_states)
+ ]
+
+ attn_outputs = [
+ attention_interface(
+ self,
+ q,
+ k,
+ v,
+ attention_mask=None,
+ scaling=self.scaling,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ is_causal=False,
+ **kwargs,
+ )[0]
+ for q, k, v in zip(*splits)
+ ]
+ attn_output = torch.cat(attn_outputs, dim=1)
+
+ attn_output = attn_output.reshape(seq_length, -1).contiguous()
+ attn_output = self.proj(attn_output)
+ return attn_output
+
+
+class Qwen2_5OmniVisionBlock(Qwen2_5_VLVisionBlock):
+ def __init__(self, config: Qwen2_5OmniVisionEncoderConfig) -> None:
+ super().__init__(config, config._attn_implementation)
+ self.attn = Qwen2_5OmniVisionAttention(config=config)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ cu_seqlens: torch.Tensor,
+ rotary_pos_emb: Optional[torch.Tensor] = None,
+ **kwargs,
+ ) -> torch.Tensor:
+ hidden_states = hidden_states + self.attn(
+ self.norm1(hidden_states),
+ cu_seqlens=cu_seqlens,
+ rotary_pos_emb=rotary_pos_emb,
+ **kwargs,
+ )
+ hidden_states = hidden_states + self.mlp(self.norm2(hidden_states))
+ return hidden_states
+
+
+class Qwen2_5_VisionRotaryEmbedding(Qwen2_5_VisionRotaryEmbedding):
+ pass
+
+
+class Qwen2_5OmniVisionEncoder(Qwen2_5_VisionTransformerPretrainedModel):
+ config: Qwen2_5OmniVisionEncoderConfig
+ input_modalities = ("image", "video")
+ _no_split_modules = ["Qwen2_5OmniVisionBlock"]
+ _input_embed_layer = "patch_embed"
+
+ def __init__(self, config: Qwen2_5OmniVisionEncoderConfig, *inputs, **kwargs) -> None:
+ super().__init__(config, *inputs, **kwargs)
+ self.blocks = nn.ModuleList([Qwen2_5OmniVisionBlock(config) for _ in range(config.depth)])
+
+ def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor, **kwargs) -> torch.Tensor:
+ """
+ Args:
+ hidden_states (`torch.Tensor` of shape `(seq_len, hidden_size)`):
+ The final hidden states of the model.
+ grid_thw (`torch.Tensor` of shape `(num_images_or_videos, 3)`):
+ The temporal, height and width of feature shape of each image in LLM.
+
+ Returns:
+ `torch.Tensor`: hidden_states.
+ """
+ hidden_states = self.patch_embed(hidden_states)
+ rotary_pos_emb = self.rot_pos_emb(grid_thw)
+
+ window_index, cu_window_seqlens = self.get_window_index(grid_thw)
+ cu_window_seqlens = torch.tensor(
+ cu_window_seqlens,
+ device=hidden_states.device,
+ dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
+ )
+ cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens)
+
+ seq_len, _ = hidden_states.size()
+ hidden_states = hidden_states.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
+ hidden_states = hidden_states[window_index, :, :]
+ hidden_states = hidden_states.reshape(seq_len, -1)
+ rotary_pos_emb = rotary_pos_emb.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
+ rotary_pos_emb = rotary_pos_emb[window_index, :, :]
+ rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1)
+
+ cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]).cumsum(
+ dim=0,
+ # Select dtype based on the following factors:
+ # - FA2 requires that cu_seqlens_q must have dtype int32
+ # - torch.onnx.export requires that cu_seqlens_q must have same dtype as grid_thw
+ # See https://github.com/huggingface/transformers/pull/34852 for more information
+ dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
+ )
+ cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
+
+ # Modification here
+ for layer_num, blk in enumerate(self.blocks):
+ if layer_num in self.fullatt_block_indexes:
+ cu_seqlens_now = cu_seqlens
+ else:
+ cu_seqlens_now = cu_window_seqlens
+
+ hidden_states = blk(
+ hidden_states,
+ cu_seqlens=cu_seqlens_now,
+ rotary_pos_emb=rotary_pos_emb,
+ **kwargs,
+ )
+ hidden_states = self.merger(hidden_states)
+ reverse_indices = torch.argsort(window_index)
+ hidden_states = hidden_states[reverse_indices, :]
+
+ return hidden_states
+
+
+class Qwen2_5OmniRotaryEmbedding(Qwen2VLRotaryEmbedding):
+ def __init__(self, config: Qwen2_5OmniThinkerConfig, device=None):
+ super().__init__(config, device)
+
+
+# It's same as `Qwen2_5_VLAttention`, but talker model's hidden_size isn't divisible by num_heads.
+# Removes the value error as a workaround.
+class Qwen2_5OmniAttention(Qwen2_5_VLAttention):
+ def __init__(self, config: Qwen2_5OmniConfig, layer_idx: Optional[int] = None):
+ nn.Module.__init__(self)
+ self.config = config
+ self.layer_idx = layer_idx
+ if layer_idx is None:
+ logger.warning_once(
+ f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+ "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+ "when creating this class."
+ )
+
+ self.hidden_size = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = getattr(config, "head_dim", self.hidden_size // self.num_heads)
+ self.num_key_value_heads = config.num_key_value_heads
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+ self.is_causal = True
+ self.attention_dropout = config.attention_dropout
+ self.rope_parameters = config.rope_parameters
+ self.scaling = self.head_dim**-0.5
+
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True)
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+ self.layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None
+ self.sliding_window = config.sliding_window if self.layer_type == "sliding_attention" else None
+
+
+class Qwen2MLP(Qwen2_5_VLMLP):
+ pass
+
+
+class Qwen2_5OmniThinkerTextModel(Qwen2_5_VLTextModel):
+ config: Qwen2_5OmniTextConfig
+ _no_split_modules = ["Qwen2_5OmniDecoderLayer"]
+
+ def __init__(self, config: Qwen2_5OmniTextConfig):
+ super().__init__(config)
+
+
+@auto_docstring(
+ custom_intro="""
+ The Qwen2.5OmniThinker model which consists of a audio backbone and a language model.
+ """
+)
+class Qwen2_5OmniThinkerForConditionalGeneration(Qwen2_5OmniPreTrainedModelForConditionalGeneration, GenerationMixin):
+ config: Qwen2_5OmniThinkerConfig
+ base_model_prefix = "thinker"
+ _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
+ _no_split_modules = ["Qwen2_5OmniAudioEncoder", "Qwen2_5OmniVisionEncoder"]
+
+ def __init__(self, config: Qwen2_5OmniThinkerConfig):
+ super().__init__(config)
+ self.audio_tower = Qwen2_5OmniAudioEncoder._from_config(config.audio_config)
+ self.visual = Qwen2_5OmniVisionEncoder._from_config(config.vision_config)
+ self.vocab_size = config.text_config.vocab_size
+ self.model = Qwen2_5OmniThinkerTextModel._from_config(config.text_config)
+ self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
+ self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
+ self.spatial_merge_size = config.vision_config.spatial_merge_size
+ self.rope_deltas = None
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.model.set_input_embeddings(value)
+
+ def get_video_features(
+ self, pixel_values_videos: torch.FloatTensor, video_grid_thw: Optional[torch.LongTensor] = None
+ ):
+ """
+ Encodes videos into continuous embeddings that can be forwarded to the language model.
+
+ Args:
+ pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
+ The tensors corresponding to the input videos.
+ video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+ The temporal, height and width of feature shape of each video in LLM.
+ """
+ pixel_values_videos = pixel_values_videos.type(self.visual.dtype)
+ video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw)
+ return video_embeds
+
+ def get_image_features(self, pixel_values: torch.FloatTensor, image_grid_thw: Optional[torch.LongTensor] = None):
+ """
+ Encodes images into continuous embeddings that can be forwarded to the language model.
+
+ Args:
+ pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
+ The tensors corresponding to the input images.
+ image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+ The temporal, height and width of feature shape of each image in LLM.
+ """
+ pixel_values = pixel_values.type(self.visual.dtype)
+ image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
+ return image_embeds
+
+ def get_audio_features(
+ self,
+ input_features: torch.FloatTensor,
+ feature_attention_mask: Optional[torch.LongTensor] = None,
+ audio_feature_lengths: Optional[torch.LongTensor] = None,
+ ):
+ """
+ Encodes audios into continuous embeddings that can be forwarded to the language model.
+
+ Args:
+ input_features (`torch.FloatTensor`):
+ The tensors corresponding to the input audios.
+ feature_attention_mask (`torch.LongTensor`, *optional*):
+ Mask to avoid performing attention on padding feature indices. Mask values selected in `[0, 1]`:
+ audio_feature_lengths (`torch.LongTensor` of shape `(num_audios)`, *optional*):
+ The length of feature shape of each audio in LLM.
+ """
+ if feature_attention_mask is not None:
+ audio_feature_lengths = torch.sum(feature_attention_mask, dim=1)
+ input_features = input_features.permute(0, 2, 1)[feature_attention_mask.bool()].permute(1, 0)
+ else:
+ audio_feature_lengths = None
+
+ audio_feat_lengths, audio_output_lengths = self.audio_tower._get_feat_extract_output_lengths(
+ audio_feature_lengths if audio_feature_lengths is not None else feature_attention_mask.sum(-1)
+ )
+ feature_lens = audio_feature_lengths if audio_feature_lengths is not None else feature_attention_mask.sum(-1)
+ audio_outputs = self.audio_tower(
+ input_features,
+ feature_lens=feature_lens,
+ aftercnn_lens=audio_feat_lengths,
+ )
+ audio_features = audio_outputs.last_hidden_state
+
+ if audio_features.shape[0] != sum(audio_output_lengths.tolist()):
+ raise ValueError("length of audio_features should match audio_output_lengths")
+
+ return audio_features
+
+ def get_placeholder_mask(
+ self,
+ input_ids: torch.LongTensor,
+ inputs_embeds: torch.FloatTensor,
+ image_features: Optional[torch.FloatTensor] = None,
+ video_features: Optional[torch.FloatTensor] = None,
+ ):
+ """
+ Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+ equal to the length of multimodal features. If the lengths are different, an error is raised.
+ """
+ if input_ids is None:
+ special_image_mask = inputs_embeds == self.get_input_embeddings()(
+ torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
+ )
+ special_image_mask = special_image_mask.all(-1)
+ special_video_mask = inputs_embeds == self.get_input_embeddings()(
+ torch.tensor(self.config.video_token_id, dtype=torch.long, device=inputs_embeds.device)
+ )
+ special_video_mask = special_video_mask.all(-1)
+ special_audio_mask = (
+ inputs_embeds
+ == self.get_input_embeddings()(
+ torch.tensor(self.config.audio_token_id, dtype=torch.long, device=inputs_embeds.device)
+ )
+ ).all(-1)
+ else:
+ special_image_mask = input_ids == self.config.image_token_id
+ special_video_mask = input_ids == self.config.video_token_id
+ special_audio_mask = input_ids == self.config.audio_token_id
+
+ n_image_tokens = special_image_mask.sum()
+ special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+ if image_features is not None and inputs_embeds[special_image_mask].numel() != image_features.numel():
+ raise ValueError(
+ f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {image_features.shape[0]}"
+ )
+
+ n_video_tokens = special_video_mask.sum()
+ special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+ if video_features is not None and inputs_embeds[special_video_mask].numel() != video_features.numel():
+ raise ValueError(
+ f"Videos features and video tokens do not match: tokens: {n_video_tokens}, features {video_features.shape[0]}"
+ )
+
+ special_audio_mask = special_audio_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+ return special_image_mask, special_video_mask, special_audio_mask
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ input_features: Optional[torch.FloatTensor] = None,
+ pixel_values: Optional[torch.FloatTensor] = None,
+ pixel_values_videos: Optional[torch.FloatTensor] = None,
+ image_grid_thw: Optional[torch.LongTensor] = None,
+ video_grid_thw: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ feature_attention_mask: Optional[torch.Tensor] = None,
+ audio_feature_lengths: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ rope_deltas: Optional[torch.LongTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ use_audio_in_video: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ video_second_per_grid: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Union[tuple, Qwen2_5OmniThinkerCausalLMOutputWithPast]:
+ r"""
+ image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+ The temporal, height and width of feature shape of each image in LLM.
+ video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+ The temporal, height and width of feature shape of each video in LLM.
+ feature_attention_mask (`torch.Tensor` of shape `(batch_size, feature_sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding feature indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+ audio_feature_lengths (`torch.LongTensor` of shape `(num_audios)`, *optional*):
+ The length of feature shape of each audio in LLM.
+ rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
+ The rope index difference between sequence length and multimodal rope.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+ use_audio_in_video (`bool`, *optional*):
+ Whether or not use audio track in video, should same as the parameter in `process_audio_info`.
+ video_second_per_grid (`torch.LongTensor` of shape `(num_videos)`, *optional*):
+ Number of seconds per grid for each video, used for temporal feature mapping.
+
+ Example:
+
+ ```python
+ >>> from io import BytesIO
+ >>> from urllib.request import urlopen
+ >>> import librosa
+ >>> from qwen_vl_utils import process_vision_info
+ >>> from transformers import Qwen2_5OmniProcessor, Qwen2_5OmniThinkerForConditionalGeneration
+
+ >>> thinker = Qwen2_5OmniThinkerForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-Omni-7B")
+ >>> processor = Qwen2_5OmniProcessor.from_pretrained("Qwen/Qwen2.5-Omni-7B")
+
+ >>> conversations = [
+ >>> {'role': 'system', 'content': 'You are a helpful voice chat bot, and please respond to me in a casual conversation manner using random voice.'},
+ >>> {"role": "user", "content": [
+ >>> {"type": "image", "image_url": "https://www.ilankelman.org/stopsigns/australia.jpg"},
+ >>> {"type": "audio", "audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/glass-breaking-151256.mp3"},
+ >>> ]},
+ >>> ]
+
+ >>> text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
+ >>> audios = [ librosa.load(BytesIO(urlopen( conversations[1]['content'][1]['audio_url'] ).read()), sr=self.processor.feature_extractor.sampling_rate) ]
+ >>> images, videos = process_vision_info(conversations)
+ >>> inputs = processor(text=text, audio=audios, images=images, videos=videos, return_tensors="pt", padding=True)
+
+ >>> # Generate
+ >>> inputs['use_audio_in_video'] = `True` or `False`
+ >>> generation = thinker.generate(**inputs, max_new_tokens=2048)
+ >>> generate_ids = generation[:, inputs.input_ids.size(1):]
+
+ >>> response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ ```"""
+
+ 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
+
+ if inputs_embeds is None:
+ # 1. Extract the input embeddings
+ inputs_embeds = self.get_input_embeddings()(input_ids)
+
+ # 2. Merge text , audios , image and video
+ if input_features is not None:
+ audio_features = self.get_audio_features(
+ input_features,
+ feature_attention_mask=feature_attention_mask,
+ audio_feature_lengths=audio_feature_lengths,
+ )
+ audio_features = audio_features.to(inputs_embeds.device, inputs_embeds.dtype)
+ _, _, audio_mask = self.get_placeholder_mask(input_ids, inputs_embeds=inputs_embeds)
+ inputs_embeds = inputs_embeds.masked_scatter(audio_mask, audio_features)
+
+ if pixel_values is not None:
+ image_embeds = self.get_image_features(pixel_values, image_grid_thw)
+ image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
+ image_mask, _, _ = self.get_placeholder_mask(
+ input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds
+ )
+ inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
+
+ if pixel_values_videos is not None:
+ video_embeds = self.get_video_features(pixel_values_videos, video_grid_thw)
+ video_embeds = video_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
+ _, video_mask, _ = self.get_placeholder_mask(
+ input_ids, inputs_embeds=inputs_embeds, video_features=video_embeds
+ )
+ inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)
+
+ if feature_attention_mask is not None:
+ audio_feature_lengths = torch.sum(feature_attention_mask, dim=1)
+ else:
+ audio_feature_lengths = None
+
+ if attention_mask is not None and position_ids is None:
+ past_key_values_length = 0 if past_key_values is None else past_key_values.get_seq_length()
+ if past_key_values_length == 0 or self.rope_deltas is None:
+ delta0 = (1 - attention_mask).sum(dim=-1).unsqueeze(1)
+ position_ids, rope_deltas = self.get_rope_index(
+ input_ids,
+ image_grid_thw,
+ video_grid_thw,
+ attention_mask,
+ use_audio_in_video,
+ audio_feature_lengths,
+ video_second_per_grid,
+ )
+ rope_deltas = rope_deltas - delta0
+ self.rope_deltas = rope_deltas
+ else:
+ batch_size, seq_length = input_ids.shape
+ delta = (past_key_values_length + self.rope_deltas).to(input_ids.device)
+ position_ids = torch.arange(seq_length, device=input_ids.device)
+ position_ids = position_ids.view(1, -1).expand(batch_size, -1)
+ position_ids = position_ids.add(delta)
+ position_ids = position_ids.unsqueeze(0).expand(3, -1, -1)
+
+ outputs = self.model(
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = outputs[0]
+ logits = self.lm_head(hidden_states)
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(
+ logits=logits, labels=labels, vocab_size=self.config.get_text_config().vocab_size
+ )
+
+ if not return_dict:
+ output = (logits,) + outputs
+ return (loss,) + output if loss is not None else output
+
+ return Qwen2_5OmniThinkerCausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ rope_deltas=self.rope_deltas,
+ )
+
+ 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,
+ pixel_values=None,
+ pixel_values_videos=None,
+ image_grid_thw=None,
+ video_grid_thw=None,
+ input_features=None,
+ feature_attention_mask=None,
+ use_audio_in_video=False,
+ video_second_per_grid=None,
+ is_first_iteration=False,
+ **kwargs,
+ ):
+ model_inputs = super().prepare_inputs_for_generation(
+ input_ids,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ inputs_embeds=inputs_embeds,
+ cache_position=cache_position,
+ position_ids=position_ids,
+ use_cache=use_cache,
+ pixel_values=pixel_values,
+ pixel_values_videos=pixel_values_videos,
+ image_grid_thw=image_grid_thw,
+ video_grid_thw=video_grid_thw,
+ input_features=input_features,
+ feature_attention_mask=feature_attention_mask,
+ use_audio_in_video=use_audio_in_video,
+ video_second_per_grid=video_second_per_grid,
+ is_first_iteration=is_first_iteration,
+ **kwargs,
+ )
+
+ model_inputs["position_ids"] = None
+
+ if not is_first_iteration and use_cache:
+ model_inputs["pixel_values"] = None
+ model_inputs["pixel_values_videos"] = None
+ model_inputs["input_features"] = None
+
+ return model_inputs
+
+
+############################
+# Start Talker #
+############################
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for Qwen2.5OmniTalker causal language model (or autoregressive) outputs.
+ """
+)
+class Qwen2_5OmniTalkerCausalLMOutputWithPast(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Language modeling loss (for next-token prediction).
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
+ The rope index difference between sequence length and multimodal rope.
+ thinker_reply_part (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Hidden states from the thinker model that are used as input for the talker model. These represent the encoded
+ response that the talker model will use to generate speech tokens.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ past_key_values: Optional[Cache] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+ rope_deltas: Optional[torch.LongTensor] = None
+ thinker_reply_part: Optional[torch.FloatTensor] = None
+
+
+class Qwen2_5OmniTalkerModel(Qwen2_5_VLTextModel):
+ config: Qwen2_5OmniTalkerConfig
+ input_modalities = ("image", "video", "audio", "text")
+
+ _no_split_modules = ["Qwen2_5OmniTalkerDecoderLayer"]
+
+ def __init__(self, config: Qwen2_5OmniTalkerConfig):
+ super().__init__(config)
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.embedding_size, self.padding_idx)
+
+
+class Qwen2_5OmniTalkerForConditionalGeneration(Qwen2_5OmniPreTrainedModelForConditionalGeneration, GenerationMixin):
+ config: Qwen2_5OmniTalkerConfig
+ base_model_prefix = "talker"
+ output_modalities = ("audio",)
+
+ def __init__(self, config: Qwen2_5OmniTalkerConfig):
+ super().__init__(config)
+
+ self.thinker_to_talker_proj = nn.Linear(config.embedding_size, config.hidden_size)
+
+ self.model = Qwen2_5OmniTalkerModel(config)
+ self.codebook_size = config.vocab_size
+ self.codec_head = nn.Linear(config.hidden_size, self.codebook_size, bias=False)
+
+ self.codec_bos_token = config.tts_codec_start_token_id
+ self.codec_eos_token = config.tts_codec_end_token_id
+ self.codec_pad_token = config.tts_codec_pad_token_id
+ self.codec_mask_token = config.tts_codec_mask_token_id
+
+ self.text_bos_token = config.tts_text_start_token_id
+ self.text_eos_token = config.tts_text_end_token_id
+ self.text_pad_token = config.tts_text_pad_token_id
+
+ self.spatial_merge_size = self.config.spatial_merge_size
+ self.rope_deltas = None
+
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.model.set_input_embeddings(value)
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ thinker_reply_part: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ rope_deltas: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ input_text_ids: Optional[torch.LongTensor] = None,
+ image_grid_thw: Optional[torch.LongTensor] = None,
+ video_grid_thw: Optional[torch.LongTensor] = None,
+ use_audio_in_video: Optional[bool] = None,
+ audio_feature_lengths: Optional[torch.LongTensor] = None,
+ video_second_per_grid: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ **kwargs,
+ ) -> Union[tuple, Qwen2_5OmniTalkerCausalLMOutputWithPast]:
+ r"""
+ thinker_reply_part (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Hidden states from the thinker model's output that represent the text reply part to be processed.
+ rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
+ The rope index difference between sequence length and multimodal rope.
+ input_text_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Input token IDs for text-only content, used for position calculation in multimodal contexts.
+ image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+ The temporal, height and width of feature shape of each image in LLM.
+ video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+ The temporal, height and width of feature shape of each video in LLM.
+ use_audio_in_video (`bool`, *optional*):
+ Whether or not use audio track in video, should same as the parameter in `process_audio_info`.
+ audio_feature_lengths (`torch.LongTensor` of shape `(num_audios)`, *optional*):
+ The length of feature shape of each audio in LLM.
+ video_second_per_grid (`torch.LongTensor` of shape `(num_videos)`, *optional*):
+ Number of seconds per grid for each video, used for temporal feature mapping.
+
+ Example:
+
+ ```python
+ >>> from io import BytesIO
+ >>> from urllib.request import urlopen
+ >>> import librosa
+ >>> from transformers import AutoProcessor, Qwen2_5OmniTalkerForConditionalGeneration
+
+ >>> model = Qwen2_5OmniTalkerForConditionalGeneration.from_pretrained("Qwen/Qwen2-Audio-7B")
+ >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2-Audio-7B")
+
+ >>> prompt = "<|audio_bos|><|AUDIO|><|audio_eos|>Generate the caption in English:"
+ >>> url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/glass-breaking-151256.mp3"
+ >>> audio, _ = librosa.load(BytesIO(urlopen(url).read()), sr=self.processor.feature_extractor.sampling_rate)
+
+ >>> inputs = processor(text=prompt, audio=audio, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(**inputs, max_length=30)
+ >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Generate the caption in English: Glass is breaking."
+ ```"""
+
+ 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
+
+ if attention_mask is not None and position_ids is None:
+ past_key_values_length = 0 if past_key_values is None else past_key_values.get_seq_length()
+ if past_key_values_length == 0 or self.rope_deltas is None:
+ position_ids, rope_deltas = self.get_rope_index(
+ input_text_ids,
+ image_grid_thw,
+ video_grid_thw,
+ attention_mask,
+ use_audio_in_video,
+ audio_feature_lengths,
+ video_second_per_grid,
+ )
+
+ inputs_embeds[:, -1, :] += self.get_input_embeddings()(
+ torch.tensor([self.codec_bos_token], dtype=torch.long, device=inputs_embeds.device)
+ )
+ inputs_embeds[:, -2, :] += self.get_input_embeddings()(
+ torch.tensor([self.codec_pad_token], dtype=torch.long, device=inputs_embeds.device)
+ )
+ self.rope_deltas = rope_deltas
+
+ else:
+ batch_size, seq_length, _ = inputs_embeds.shape
+ delta = (past_key_values_length + self.rope_deltas).to(input_ids.device)
+ position_ids = torch.arange(seq_length, device=input_ids.device)
+ position_ids = position_ids.view(1, -1).expand(batch_size, -1)
+ position_ids = position_ids.add(delta)
+ position_ids = position_ids.unsqueeze(0).expand(3, -1, -1)
+
+ if inputs_embeds is None:
+ # 1. Inference tokens after second token
+ codec_embeds = self.get_input_embeddings()(input_ids)
+ inputs_embeds = codec_embeds + thinker_reply_part[:, :1, :]
+ if thinker_reply_part.shape[1] > 1:
+ thinker_reply_part = thinker_reply_part[:, 1:, :]
+
+ talker_lm_input = self.thinker_to_talker_proj(inputs_embeds)
+
+ if attention_mask is not None:
+ attention_mask = attention_mask.to(inputs_embeds.device)
+
+ outputs = self.model(
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=talker_lm_input,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_states = outputs[0]
+ logits = self.codec_head(hidden_states)
+ logits = logits.float()
+
+ loss = None
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return (loss,) + output if loss is not None else output
+
+ return Qwen2_5OmniTalkerCausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=hidden_states,
+ attentions=outputs.attentions,
+ rope_deltas=self.rope_deltas,
+ thinker_reply_part=thinker_reply_part,
+ )
+
+ def _get_initial_cache_position(self, seq_length, device, model_kwargs):
+ # Talker needs to calculate cache_position with input_ids, so pop inputs_embeds temporarily
+ inputs_embeds = model_kwargs.pop("inputs_embeds")
+ model_kwargs = super()._get_initial_cache_position(seq_length, device, model_kwargs)
+ model_kwargs["inputs_embeds"] = inputs_embeds
+ return model_kwargs
+
+ # prepare inputs for talker lm generation
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ input_text_ids,
+ past_key_values=None,
+ attention_mask=None,
+ inputs_embeds=None,
+ thinker_reply_part=None,
+ cache_position=None,
+ position_ids=None,
+ use_cache=True,
+ pixel_values=None,
+ pixel_values_videos=None,
+ image_grid_thw=None,
+ video_grid_thw=None,
+ input_audio_features=None,
+ audio_feature_attention_mask=None,
+ audio_feature_lengths=None,
+ use_audio_in_video=False,
+ video_second_per_grid=None,
+ **kwargs,
+ ):
+ model_inputs = super().prepare_inputs_for_generation(
+ input_ids,
+ past_key_values,
+ attention_mask,
+ inputs_embeds,
+ cache_position,
+ use_cache=use_cache,
+ thinker_reply_part=thinker_reply_part,
+ input_text_ids=input_text_ids,
+ image_grid_thw=image_grid_thw,
+ video_grid_thw=video_grid_thw,
+ use_audio_in_video=use_audio_in_video,
+ audio_feature_lengths=audio_feature_lengths,
+ video_second_per_grid=video_second_per_grid,
+ **kwargs,
+ )
+
+ model_inputs["position_ids"] = None
+
+ return model_inputs
+
+ def _update_model_kwargs_for_generation(
+ self,
+ outputs: ModelOutput,
+ model_kwargs: dict[str, Any],
+ is_encoder_decoder: bool = False,
+ num_new_tokens: int = 1,
+ ) -> dict[str, Any]:
+ model_kwargs = super()._update_model_kwargs_for_generation(
+ outputs, model_kwargs, is_encoder_decoder, num_new_tokens
+ )
+
+ if getattr(outputs, "thinker_reply_part", None) is not None:
+ model_kwargs["thinker_reply_part"] = outputs.thinker_reply_part
+
+ return model_kwargs
+
+
+############################
+# Start Token2Wav #
+############################
+
+
+class Qwen2_5OmniDiTRotaryEmbedding(LlamaRotaryEmbedding):
+ def __init__(self, config: Qwen2_5OmniDiTConfig, device=None):
+ super().__init__(config, device=device)
+
+ @staticmethod
+ def compute_default_rope_parameters(
+ config: Optional[Qwen2_5OmniDiTConfig] = None,
+ device: Optional["torch.device"] = None,
+ seq_len: Optional[int] = None,
+ ) -> tuple["torch.Tensor", float]:
+ return super().compute_default_rope_parameters(
+ config,
+ device=device,
+ seq_len=seq_len,
+ )
+
+
+# Modified from Llama with a different rotate function, will fixed in next release
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`, *optional*):
+ Deprecated and unused.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+
+ def rotate_half_codec(x):
+ # x = rearrange(x, "... (d r) -> ... d r", r=2)
+ x = x.reshape(*x.shape[:-1], -1, 2)
+ x1, x2 = x.unbind(dim=-1)
+ x = torch.stack((-x2, x1), dim=-1)
+ return x.reshape(*x.shape[:-2], -1)
+
+ cos = cos.unsqueeze(unsqueeze_dim)
+ sin = sin.unsqueeze(unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half_codec(q) * sin)
+ k_embed = (k * cos) + (rotate_half_codec(k) * sin)
+ return q_embed, k_embed
+
+
+class TimeDelayNetBlock(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ kernel_size,
+ dilation,
+ ):
+ super().__init__()
+ self.conv = nn.Conv1d(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ dilation=dilation,
+ padding="same",
+ padding_mode="reflect",
+ )
+ self.activation = nn.ReLU()
+
+ def forward(self, hidden_states: torch.Tensor):
+ return self.activation(self.conv(hidden_states))
+
+
+class Res2NetBlock(torch.nn.Module):
+ def __init__(self, in_channels, out_channels, scale=8, kernel_size=3, dilation=1):
+ super().__init__()
+
+ in_channel = in_channels // scale
+ hidden_channel = out_channels // scale
+
+ self.blocks = nn.ModuleList(
+ [
+ TimeDelayNetBlock(
+ in_channel,
+ hidden_channel,
+ kernel_size=kernel_size,
+ dilation=dilation,
+ )
+ for i in range(scale - 1)
+ ]
+ )
+ self.scale = scale
+
+ def forward(self, hidden_states):
+ outputs = []
+ for i, hidden_part in enumerate(torch.chunk(hidden_states, self.scale, dim=1)):
+ if i == 0:
+ output_part = hidden_part
+ elif i == 1:
+ output_part = self.blocks[i - 1](hidden_part)
+ else:
+ output_part = self.blocks[i - 1](hidden_part + output_part)
+ outputs.append(output_part)
+ output = torch.cat(outputs, dim=1)
+ return output
+
+
+class SqueezeExcitationBlock(nn.Module):
+ def __init__(self, in_channels, se_channels, out_channels):
+ super().__init__()
+
+ self.conv1 = nn.Conv1d(
+ in_channels=in_channels,
+ out_channels=se_channels,
+ kernel_size=1,
+ padding="same",
+ padding_mode="reflect",
+ )
+ self.relu = nn.ReLU(inplace=True)
+ self.conv2 = nn.Conv1d(
+ in_channels=se_channels,
+ out_channels=out_channels,
+ kernel_size=1,
+ padding="same",
+ padding_mode="reflect",
+ )
+ self.sigmoid = nn.Sigmoid()
+
+ def forward(self, hidden_states):
+ hidden_states_mean = hidden_states.mean(dim=2, keepdim=True)
+
+ hidden_states_mean = self.relu(self.conv1(hidden_states_mean))
+ hidden_states_mean = self.sigmoid(self.conv2(hidden_states_mean))
+
+ return hidden_states * hidden_states_mean
+
+
+class AttentiveStatisticsPooling(nn.Module):
+ """This class implements an attentive statistic pooling layer for each channel.
+ It returns the concatenated mean and std of the input tensor.
+ """
+
+ def __init__(self, channels, attention_channels=128):
+ super().__init__()
+
+ self.eps = 1e-12
+ self.tdnn = TimeDelayNetBlock(channels * 3, attention_channels, 1, 1)
+ self.tanh = nn.Tanh()
+ self.conv = nn.Conv1d(
+ in_channels=attention_channels,
+ out_channels=channels,
+ kernel_size=1,
+ padding="same",
+ padding_mode="reflect",
+ )
+
+ def _length_to_mask(self, length, max_len=None, dtype=None, device=None):
+ """Creates a binary mask for each sequence.
+
+ Reference: https://discuss.pytorch.org/t/how-to-generate-variable-length-mask/23397/3
+
+ Arguments
+ ---------
+ length : torch.LongTensor
+ Containing the length of each sequence in the batch. Must be 1D.
+ max_len : int
+ Max length for the mask, also the size of the second dimension.
+ dtype : torch.dtype, default: None
+ The dtype of the generated mask.
+ device: torch.device, default: None
+ The device to put the mask variable.
+
+ Returns
+ -------
+ mask : tensor
+ The binary mask.
+ """
+
+ if max_len is None:
+ max_len = length.max().long().item() # using arange to generate mask
+ mask = torch.arange(max_len, device=length.device, dtype=length.dtype).expand(
+ len(length), max_len
+ ) < length.unsqueeze(1)
+
+ mask = torch.as_tensor(mask, dtype=dtype, device=device)
+ return mask
+
+ def _compute_statistics(self, x, m, dim=2):
+ mean = (m * x).sum(dim)
+ std = torch.sqrt((m * (x - mean.unsqueeze(dim)).pow(2)).sum(dim).clamp(self.eps))
+ return mean, std
+
+ def forward(self, hidden_states):
+ seq_length = hidden_states.shape[-1]
+ lengths = torch.ones(hidden_states.shape[0], device=hidden_states.device)
+
+ # Make binary mask of shape [N, 1, L]
+ mask = self._length_to_mask(
+ lengths * seq_length, max_len=seq_length, dtype=hidden_states.dtype, device=hidden_states.device
+ )
+ mask = mask.unsqueeze(1)
+
+ # Expand the temporal context of the pooling layer by allowing the
+ # self-attention to look at global properties of the utterance.
+ total = mask.sum(dim=2, keepdim=True)
+
+ mean, std = self._compute_statistics(hidden_states, mask / total)
+ mean = mean.unsqueeze(2).repeat(1, 1, seq_length)
+ std = std.unsqueeze(2).repeat(1, 1, seq_length)
+ attention = torch.cat([hidden_states, mean, std], dim=1)
+
+ # Apply layers
+ attention = self.conv(self.tanh(self.tdnn(attention)))
+
+ # Filter out zero-paddings
+ attention = attention.masked_fill(mask == 0, float("-inf"))
+
+ attention = F.softmax(attention, dim=2)
+ mean, std = self._compute_statistics(hidden_states, attention)
+ # Append mean and std of the batch
+ pooled_stats = torch.cat((mean, std), dim=1)
+ pooled_stats = pooled_stats.unsqueeze(2)
+
+ return pooled_stats
+
+
+class SqueezeExcitationRes2NetBlock(nn.Module):
+ """An implementation of building block in ECAPA-TDNN, i.e.,
+ TDNN-Res2Net-TDNN-SqueezeExcitationBlock.
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ res2net_scale=8,
+ se_channels=128,
+ kernel_size=1,
+ dilation=1,
+ ):
+ super().__init__()
+ self.out_channels = out_channels
+ self.tdnn1 = TimeDelayNetBlock(
+ in_channels,
+ out_channels,
+ kernel_size=1,
+ dilation=1,
+ )
+ self.res2net_block = Res2NetBlock(out_channels, out_channels, res2net_scale, kernel_size, dilation)
+ self.tdnn2 = TimeDelayNetBlock(
+ out_channels,
+ out_channels,
+ kernel_size=1,
+ dilation=1,
+ )
+ self.se_block = SqueezeExcitationBlock(out_channels, se_channels, out_channels)
+
+ def forward(self, hidden_state):
+ residual = hidden_state
+
+ hidden_state = self.tdnn1(hidden_state)
+ hidden_state = self.res2net_block(hidden_state)
+ hidden_state = self.tdnn2(hidden_state)
+ hidden_state = self.se_block(hidden_state)
+
+ return hidden_state + residual
+
+
+class ECAPA_TimeDelayNet(torch.nn.Module):
+ """An implementation of the speaker embedding model in a paper.
+ "ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in
+ TDNN Based Speaker Verification" (https://huggingface.co/papers/2005.07143).
+ """
+
+ def __init__(self, config: Qwen2_5OmniDiTConfig):
+ super().__init__()
+ if len(config.enc_channels) != len(config.enc_kernel_sizes) or len(config.enc_channels) != len(
+ config.enc_dilations
+ ):
+ raise ValueError("enc_channels, enc_kernel_sizes and enc_dilations should have same length")
+ self.channels = config.enc_channels
+ self.blocks = nn.ModuleList()
+
+ # The initial TDNN layer
+ self.blocks.append(
+ TimeDelayNetBlock(
+ config.mel_dim,
+ config.enc_channels[0],
+ config.enc_kernel_sizes[0],
+ config.enc_dilations[0],
+ )
+ )
+
+ # SE-Res2Net layers
+ for i in range(1, len(config.enc_channels) - 1):
+ self.blocks.append(
+ SqueezeExcitationRes2NetBlock(
+ config.enc_channels[i - 1],
+ config.enc_channels[i],
+ res2net_scale=config.enc_res2net_scale,
+ se_channels=config.enc_se_channels,
+ kernel_size=config.enc_kernel_sizes[i],
+ dilation=config.enc_dilations[i],
+ )
+ )
+
+ # Multi-layer feature aggregation
+ self.mfa = TimeDelayNetBlock(
+ config.enc_channels[-1],
+ config.enc_channels[-1],
+ config.enc_kernel_sizes[-1],
+ config.enc_dilations[-1],
+ )
+
+ # Attentive Statistical Pooling
+ self.asp = AttentiveStatisticsPooling(
+ config.enc_channels[-1],
+ attention_channels=config.enc_attention_channels,
+ )
+
+ # Final linear transformation
+ self.fc = nn.Conv1d(
+ in_channels=config.enc_channels[-1] * 2,
+ out_channels=config.enc_dim,
+ kernel_size=1,
+ padding="same",
+ padding_mode="reflect",
+ )
+
+ def forward(self, hidden_states):
+ # Minimize transpose for efficiency
+ hidden_states = hidden_states.transpose(1, 2)
+
+ hidden_states_list = []
+ for layer in self.blocks:
+ hidden_states = layer(hidden_states)
+ hidden_states_list.append(hidden_states)
+
+ # Multi-layer feature aggregation
+ hidden_states = torch.cat(hidden_states_list[1:], dim=1)
+ hidden_states = self.mfa(hidden_states)
+
+ # Attentive Statistical Pooling
+ hidden_states = self.asp(hidden_states)
+
+ # Final linear transformation
+ hidden_states = self.fc(hidden_states)
+
+ hidden_states = hidden_states.squeeze(-1)
+ return hidden_states
+
+
+class DiTInputEmbedding(nn.Module):
+ def __init__(self, config: Qwen2_5OmniDiTConfig):
+ super().__init__()
+ self.proj = nn.Linear(
+ config.mel_dim + config.enc_dim + config.enc_emb_dim + config.emb_dim,
+ config.hidden_size,
+ )
+ self.spk_encoder = ECAPA_TimeDelayNet(config)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ speaker_embedding: torch.Tensor,
+ condition_vector: torch.Tensor,
+ code_embed: torch.Tensor,
+ drop_audio_cond: Optional[bool] = False,
+ code_embed_uncond: Optional[bool] = None,
+ apply_cfg: Optional[bool] = True,
+ ):
+ if apply_cfg:
+ hidden_states = torch.cat([hidden_states, hidden_states], dim=0)
+ speaker_embedding = torch.cat([speaker_embedding, torch.zeros_like(speaker_embedding)], dim=0)
+ condition_vector = torch.cat([condition_vector, torch.zeros_like(condition_vector)], dim=0)
+ code_embed = torch.cat([code_embed, code_embed_uncond], dim=0)
+ elif drop_audio_cond: # cfg for cond audio
+ condition_vector = torch.zeros_like(condition_vector)
+ speaker_embedding = torch.zeros_like(speaker_embedding)
+ condition_vector = self.spk_encoder(condition_vector).unsqueeze(1).repeat(1, hidden_states.size(1), 1)
+ hidden_states = self.proj(torch.cat((hidden_states, condition_vector, code_embed, speaker_embedding), dim=-1))
+
+ return hidden_states
+
+
+# Transformer backbone using DiT blocks
+class DiTCodecEmbedding(nn.Module):
+ def __init__(self, codec_num_embeds, codec_dim, repeats):
+ super().__init__()
+ self.repeats = repeats
+ self.codec_embed = nn.Embedding(codec_num_embeds + 1, codec_dim)
+
+ def forward(self, code, drop_code=False):
+ if drop_code:
+ code = torch.zeros_like(code)
+ code_embed = self.codec_embed(code)
+
+ code_embed = torch.repeat_interleave(code_embed, repeats=self.repeats, dim=1)
+ return code_embed
+
+
+# AdaLayerNormZero
+# return with modulated x for attn input, and params for later mlp modulation
+class Qwen2_5_OmniAdaLayerNormZero(nn.Module):
+ def __init__(self, dim):
+ super().__init__()
+
+ self.silu = nn.SiLU()
+ self.linear = nn.Linear(dim, dim * 6)
+
+ self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+ def forward(self, hidden_states, emb=None):
+ emb = self.linear(self.silu(emb))
+ shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = torch.chunk(emb, 6, dim=1)
+
+ hidden_states = self.norm(hidden_states) * (1 + scale_msa[:, None]) + shift_msa[:, None]
+ return hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp
+
+
+# AdaLayerNormZero for final layer
+# return only with modulated x for attn input, cuz no more mlp modulation
+class Qwen2_5_OmniAdaLayerNormZero_Final(nn.Module):
+ def __init__(self, dim):
+ super().__init__()
+
+ self.silu = nn.SiLU()
+ self.linear = nn.Linear(dim, dim * 2)
+
+ self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+ def forward(self, hidden_states, emb):
+ emb = self.linear(self.silu(emb))
+ scale, shift = torch.chunk(emb, 2, dim=1)
+
+ hidden_states = self.norm(hidden_states) * (1 + scale)[:, None, :] + shift[:, None, :]
+ return hidden_states
+
+
+# FeedForward
+class DiTMLP(nn.Module):
+ def __init__(self, dim, mult=4, dropout=0.0):
+ super().__init__()
+ inner_dim = int(dim * mult)
+
+ self.ff = nn.ModuleList(
+ [
+ nn.Linear(dim, inner_dim),
+ nn.GELU(approximate="tanh"),
+ nn.Dropout(dropout),
+ nn.Linear(inner_dim, dim),
+ ]
+ )
+
+ def forward(self, hidden_states):
+ for layer in self.ff:
+ hidden_states = layer(hidden_states)
+ return hidden_states
+
+
+class DiTAttention(nn.Module):
+ def __init__(self, config: Qwen2_5OmniDiTConfig):
+ super().__init__()
+
+ self.config = config
+ self.dim = config.hidden_size
+ self.heads = config.num_attention_heads
+ self.inner_dim = config.head_dim * config.num_attention_heads
+ self.dropout = config.dropout
+ self.is_causal = False
+
+ self.to_q = nn.Linear(config.hidden_size, self.inner_dim)
+ self.to_k = nn.Linear(config.hidden_size, self.inner_dim)
+ self.to_v = nn.Linear(config.hidden_size, self.inner_dim)
+
+ self.to_out = nn.ModuleList([nn.Linear(self.inner_dim, config.hidden_size), nn.Dropout(config.dropout)])
+
+ def forward(
+ self,
+ hidden_states, # noised input x
+ position_embeddings=None, # rotary position embedding for x
+ attention_mask=None,
+ ) -> torch.Tensor:
+ batch_size = hidden_states.shape[0]
+
+ # `sample` projections.
+ query = self.to_q(hidden_states)
+ key = self.to_k(hidden_states)
+ value = self.to_v(hidden_states)
+
+ # attention
+ inner_dim = key.shape[-1]
+ head_dim = inner_dim // self.heads
+ query = query.view(batch_size, -1, self.heads, head_dim).transpose(1, 2)
+ key = key.view(batch_size, -1, self.heads, head_dim).transpose(1, 2)
+ value = value.view(batch_size, -1, self.heads, head_dim).transpose(1, 2)
+
+ # apply rotary position embedding
+ # Due to training process, only first head is applied with RoPE, will be fixed at next release
+ cos, sin = position_embeddings
+ query[:, :1], key[:, :1] = apply_rotary_pos_emb(query[:, :1], key[:, :1], cos, sin)
+
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+ attention_weights, _ = attention_interface(
+ self,
+ query,
+ key,
+ value,
+ attention_mask=attention_mask,
+ is_causal=False,
+ )
+
+ # mask. e.g. inference got a batch with different target durations, mask out the padding
+ attention_weights = attention_weights.reshape(batch_size, -1, self.heads * head_dim)
+ attention_weights = attention_weights.to(query.dtype)
+
+ # linear proj
+ attention_output = self.to_out[0](attention_weights)
+ attention_output = self.to_out[1](attention_output)
+
+ return attention_output
+
+
+# time step conditioning embedding
+class SinusPositionEmbedding(nn.Module):
+ def __init__(self, dim):
+ super().__init__()
+ self.dim = dim
+
+ def forward(self, hidden_states, scale=1000):
+ device = hidden_states.device
+ half_dim = self.dim // 2
+ emb = math.log(10000) / (half_dim - 1)
+ emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb)
+ emb = scale * hidden_states.unsqueeze(1) * emb.unsqueeze(0)
+ emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
+ return emb.type_as(hidden_states)
+
+
+class DiTTimestepEmbedding(nn.Module):
+ def __init__(self, dim, freq_embed_dim=256):
+ super().__init__()
+ self.time_embed = SinusPositionEmbedding(freq_embed_dim)
+ self.time_mlp = nn.ModuleList([nn.Linear(freq_embed_dim, dim), nn.SiLU(), nn.Linear(dim, dim)])
+
+ def forward(self, timestep):
+ time_hidden = self.time_embed(timestep)
+ time_hidden = time_hidden.to(timestep.dtype)
+ for layer in self.time_mlp:
+ time_hidden = layer(time_hidden) # b d
+ return time_hidden
+
+
+class DiTDecoderLayer(nn.Module):
+ def __init__(self, config: Qwen2_5OmniDiTConfig, look_ahead_block=0, look_backward_block=0):
+ super().__init__()
+ self.attn_norm = Qwen2_5_OmniAdaLayerNormZero(config.hidden_size)
+
+ self.attn = DiTAttention(config)
+ self.look_ahead_block = look_ahead_block
+ self.look_backward_block = look_backward_block
+ self.ff_norm = nn.LayerNorm(config.hidden_size, elementwise_affine=False, eps=1e-6)
+ self.ff = DiTMLP(dim=config.hidden_size, mult=config.ff_mult, dropout=config.dropout)
+
+ def forward(
+ self, hidden_states, timestep, position_embeddings=None, block_diff=None
+ ): # x: noised input, t: time embedding
+ # pre-norm & modulation for attention input
+ norm, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.attn_norm(hidden_states, emb=timestep)
+
+ # attention
+ attn_output = self.attn(
+ hidden_states=norm,
+ position_embeddings=position_embeddings,
+ attention_mask=(block_diff >= -float(self.look_backward_block))
+ & (block_diff <= float(self.look_ahead_block)),
+ )
+
+ # process attention output for input x
+ hidden_states = hidden_states + gate_msa.unsqueeze(1) * attn_output
+
+ norm = self.ff_norm(hidden_states) * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+ ff_output = self.ff(norm)
+ hidden_states = hidden_states + gate_mlp.unsqueeze(1) * ff_output
+
+ return hidden_states
+
+
+class SnakeBeta(nn.Module):
+ """
+ A modified Snake function which uses separate parameters for the magnitude of the periodic components
+ Shape:
+ - Input: (B, C, T)
+ - Output: (B, C, T), same shape as the input
+ Parameters:
+ - alpha - trainable parameter that controls frequency
+ - beta - trainable parameter that controls magnitude
+ References:
+ - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
+ https://huggingface.co/papers/2006.08195
+ """
+
+ def __init__(self, in_features, alpha=1.0):
+ super().__init__()
+ self.in_features = in_features
+
+ # initialize alpha
+ self.alpha = Parameter(torch.zeros(in_features) * alpha)
+ self.beta = Parameter(torch.zeros(in_features) * alpha)
+
+ self.no_div_by_zero = 0.000000001
+
+ def forward(self, hidden_states):
+ """
+ Forward pass of the function.
+ Applies the function to the input elementwise.
+ SnakeBeta ∶= x + 1/b * sin^2 (xa)
+ """
+ alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
+ beta = self.beta.unsqueeze(0).unsqueeze(-1)
+ alpha = torch.exp(alpha)
+ beta = torch.exp(beta)
+ hidden_states = hidden_states + (1.0 / (beta + self.no_div_by_zero)) * torch.pow(
+ torch.sin(hidden_states * alpha), 2
+ )
+
+ return hidden_states
+
+
+def kaiser_sinc_filter1d(cutoff, half_width, kernel_size):
+ """Generates a 1D Kaiser-windowed sinc filter.
+
+ Args:
+ cutoff (float): Normalized cutoff frequency (0 to 0.5).
+ half_width (float): Transition bandwidth.
+ kernel_size (int): Number of filter taps.
+
+ Returns:
+ torch.Tensor: A tensor of shape (1, 1, kernel_size) representing the filter.
+ """
+ is_even = kernel_size % 2 == 0
+ half_size = kernel_size // 2
+
+ # Compute Kaiser window parameters
+ delta_f = 4 * half_width
+ attenuation = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
+
+ if attenuation > 50.0:
+ beta = 0.1102 * (attenuation - 8.7)
+ elif attenuation >= 21.0:
+ beta = 0.5842 * (attenuation - 21) ** 0.4 + 0.07886 * (attenuation - 21.0)
+ else:
+ beta = 0.0
+
+ kaiser_window = torch.kaiser_window(kernel_size, beta=beta, periodic=False, dtype=torch.float32)
+
+ # Compute time indices
+ if is_even:
+ time_indices = torch.arange(-half_size, half_size) + 0.5
+ else:
+ time_indices = torch.arange(kernel_size) - half_size
+
+ # Compute sinc filter
+ if cutoff == 0:
+ return torch.zeros((1, 1, kernel_size), dtype=torch.float32) # Ensures correct shape
+
+ sinc_filter = torch.sinc(2 * cutoff * time_indices)
+ normalized_filter = 2 * cutoff * kaiser_window * sinc_filter
+
+ # Normalize to ensure sum = 1 (avoid leakage of constant component)
+ normalized_filter /= normalized_filter.sum()
+
+ return normalized_filter.view(1, 1, kernel_size)
+
+
+class UpSample1d(nn.Module):
+ def __init__(self, ratio=2, kernel_size=None):
+ super().__init__()
+ self.ratio = ratio
+ self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+ self.stride = ratio
+ self.pad = self.kernel_size // ratio - 1
+ self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
+ self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
+
+ filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size)
+ self.register_buffer("filter", filter, persistent=False)
+
+ def forward(self, hidden_states):
+ channels = hidden_states.shape[1]
+
+ hidden_states = F.pad(hidden_states, (self.pad, self.pad), mode="replicate")
+ hidden_states = self.ratio * F.conv_transpose1d(
+ hidden_states, self.filter.expand(channels, -1, -1), stride=self.stride, groups=channels
+ )
+ hidden_states = hidden_states[..., self.pad_left : -self.pad_right]
+
+ return hidden_states
+
+
+class DownSample1d(nn.Module):
+ def __init__(self, ratio=2, kernel_size=None):
+ super().__init__()
+ cutoff = 0.5 / ratio
+ half_width = 0.6 / ratio
+ self.cutoff = cutoff
+ self.half_width = half_width
+ self.kernel_size = kernel_size
+
+ if cutoff < 0.0:
+ raise ValueError("Minimum cutoff must be larger than zero.")
+ if cutoff > 0.5:
+ raise ValueError("A cutoff above 0.5 does not make sense.")
+
+ self.even = kernel_size % 2 == 0
+ self.pad_left = kernel_size // 2 - int(self.even)
+ self.pad_right = kernel_size // 2
+ self.stride = ratio
+ filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size)
+ self.register_buffer("filter", filter, persistent=False)
+
+ def forward(self, hidden_states):
+ channels = hidden_states.shape[1]
+ hidden_states = F.pad(hidden_states, (self.pad_left, self.pad_right), mode="replicate")
+ out = F.conv1d(hidden_states, self.filter.expand(channels, -1, -1), stride=self.stride, groups=channels)
+ return out
+
+
+class TorchActivation1d(nn.Module):
+ def __init__(
+ self,
+ activation,
+ up_ratio: int = 2,
+ down_ratio: int = 2,
+ up_kernel_size: int = 12,
+ down_kernel_size: int = 12,
+ ):
+ super().__init__()
+ if not callable(activation):
+ raise TypeError("Activation function must be callable")
+ self.act = activation
+ self.upsample = UpSample1d(up_ratio, up_kernel_size)
+ self.downsample = DownSample1d(down_ratio, down_kernel_size)
+
+ def forward(self, hidden_states):
+ hidden_states = self.upsample(hidden_states)
+ hidden_states = self.act(hidden_states)
+ hidden_states = self.downsample(hidden_states)
+
+ return hidden_states
+
+
+class AMPBlock(torch.nn.Module):
+ def __init__(
+ self,
+ channels,
+ kernel_size=3,
+ dilation=(1, 3, 5),
+ ):
+ super().__init__()
+
+ self.convs1 = nn.ModuleList(
+ [
+ nn.Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=dilation[0],
+ padding=self._get_padding(kernel_size, dilation[0]),
+ ),
+ nn.Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=dilation[1],
+ padding=self._get_padding(kernel_size, dilation[1]),
+ ),
+ nn.Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=dilation[2],
+ padding=self._get_padding(kernel_size, dilation[2]),
+ ),
+ ]
+ )
+
+ self.convs2 = nn.ModuleList(
+ [
+ nn.Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=1,
+ padding=self._get_padding(kernel_size, 1),
+ ),
+ nn.Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=1,
+ padding=self._get_padding(kernel_size, 1),
+ ),
+ nn.Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=1,
+ padding=self._get_padding(kernel_size, 1),
+ ),
+ ]
+ )
+
+ self.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers
+
+ self.activations = nn.ModuleList(
+ [TorchActivation1d(activation=SnakeBeta(channels)) for _ in range(self.num_layers)]
+ )
+
+ def _get_padding(self, kernel_size, dilation=1):
+ return int((kernel_size * dilation - dilation) / 2)
+
+ def forward(self, hidden_states):
+ acts1, acts2 = self.activations[::2], self.activations[1::2]
+ for conv1, conv2, act1, act2 in zip(self.convs1, self.convs2, acts1, acts2):
+ residual = hidden_states
+ hidden_states = act1(hidden_states)
+ hidden_states = conv1(hidden_states)
+ hidden_states = act2(hidden_states)
+ hidden_states = conv2(hidden_states)
+ hidden_states = residual + hidden_states
+
+ return hidden_states
+
+
+@auto_docstring(
+ custom_intro="""
+ The full Qwen2.5Omni Token2WavBigVGAN model. Which take mel spectrogram as input and predict waveform.
+ """
+)
+class Qwen2_5OmniToken2WavBigVGANModel(Qwen2_5OmniPreTrainedModel):
+ config: Qwen2_5OmniBigVGANConfig
+ input_modalities = "audio"
+
+ def __init__(self, config: Qwen2_5OmniBigVGANConfig):
+ super().__init__(config)
+ self.num_residual_blocks = len(config.resblock_kernel_sizes)
+ self.num_upsample_layers = len(config.upsample_rates)
+
+ self.conv_pre = nn.Conv1d(config.mel_dim, config.upsample_initial_channel, 7, 1, padding=3)
+
+ # Removing extra ModuleList breaks official state dict
+ ups = [
+ nn.ModuleList(
+ [
+ nn.ConvTranspose1d(
+ config.upsample_initial_channel // (2**layer_idx),
+ config.upsample_initial_channel // (2 ** (layer_idx + 1)),
+ kernel_size,
+ stride,
+ padding=(kernel_size - stride) // 2,
+ )
+ ]
+ )
+ for layer_idx, (stride, kernel_size) in enumerate(zip(config.upsample_rates, config.upsample_kernel_sizes))
+ ]
+ self.ups = nn.ModuleList(ups)
+
+ self.resblocks = nn.ModuleList(
+ [
+ AMPBlock(config.upsample_initial_channel // (2 ** (layer_idx + 1)), kernel_size, dilation)
+ for layer_idx in range(self.num_upsample_layers)
+ for kernel_size, dilation in zip(config.resblock_kernel_sizes, config.resblock_dilation_sizes)
+ ]
+ )
+
+ self.activation_post = TorchActivation1d(
+ activation=SnakeBeta(config.upsample_initial_channel // (2**self.num_upsample_layers))
+ )
+ self.conv_post = nn.Conv1d(
+ config.upsample_initial_channel // (2**self.num_upsample_layers), 1, 7, 1, padding=3, bias=False
+ )
+
+ self.post_init()
+
+ def normalize_spectrogram(self, spectrogram, max_value, min_db):
+ return torch.clamp((2 * max_value) * ((spectrogram - min_db) / (-min_db)) - max_value, -max_value, max_value)
+
+ def amplitude_to_db(self, amplitude, min_db_level):
+ min_level = torch.exp(
+ torch.tensor(min_db_level / 20.0 * np.log(10), device=amplitude.device, dtype=amplitude.dtype)
+ )
+ return 20 * torch.log10(torch.clamp(amplitude, min=min_level))
+
+ def process_mel_spectrogram(self, mel_spectrogram):
+ amplitude_spectrum = torch.exp(mel_spectrogram)
+ decibel_spectrum = self.amplitude_to_db(amplitude_spectrum, -115) - 20
+ return self.normalize_spectrogram(decibel_spectrum, 1, -115)
+
+ def forward(self, mel_spectrogram, **kwargs):
+ processed_spectrogram = self.process_mel_spectrogram(mel_spectrogram)
+ hidden_representation = self.conv_pre(processed_spectrogram)
+
+ for layer_index in range(self.num_upsample_layers):
+ hidden_representation = self.ups[layer_index][0](hidden_representation)
+ residual_output = sum(
+ self.resblocks[layer_index * self.num_residual_blocks + block_index](hidden_representation)
+ for block_index in range(self.num_residual_blocks)
+ )
+ residual_output = residual_output / self.num_residual_blocks
+ hidden_representation = residual_output
+
+ hidden_representation = self.activation_post(hidden_representation)
+ output_waveform = self.conv_post(hidden_representation)
+ return torch.clamp(output_waveform, min=-1.0, max=1.0).squeeze().cpu()
+
+
+class RungeKutta4ODESolver:
+ def __init__(self, function, initial_value):
+ self.function = function
+ self.initial_value = initial_value
+
+ self._one_third = 1 / 3
+ self._two_thirds = 2 / 3
+
+ def _rk4_step(self, function, time_start, time_step, time_end, value_start, function_value_start=None):
+ k1 = function_value_start if function_value_start is not None else function(time_start, value_start)
+ k2 = function(time_start + time_step * self._one_third, value_start + time_step * k1 * self._one_third)
+ k3 = function(time_start + time_step * self._two_thirds, value_start + time_step * (k2 - k1 * self._one_third))
+ k4 = function(time_end, value_start + time_step * (k1 - k2 + k3))
+ return (k1 + 3 * (k2 + k3) + k4) * time_step / 8
+
+ def _compute_step(self, function, time_start, time_step, time_end, value_start):
+ function_value_start = function(time_start, value_start)
+ return self._rk4_step(
+ function, time_start, time_step, time_end, value_start, function_value_start=function_value_start
+ ), function_value_start
+
+ def _linear_interpolation(self, time_start, time_end, value_start, value_end, time_point):
+ if time_point == time_start:
+ return value_start
+ if time_point == time_end:
+ return value_end
+ weight = (time_point - time_start) / (time_end - time_start)
+ return value_start + weight * (value_end - value_start)
+
+ def integrate(self, time_points):
+ solution = torch.empty(
+ len(time_points),
+ *self.initial_value.shape,
+ dtype=self.initial_value.dtype,
+ device=self.initial_value.device,
+ )
+ solution[0] = self.initial_value
+
+ current_index = 1
+ current_value = self.initial_value
+ for time_start, time_end in zip(time_points[:-1], time_points[1:]):
+ time_step = time_end - time_start
+ delta_value, _ = self._compute_step(self.function, time_start, time_step, time_end, current_value)
+ next_value = current_value + delta_value
+
+ while current_index < len(time_points) and time_end >= time_points[current_index]:
+ solution[current_index] = self._linear_interpolation(
+ time_start, time_end, current_value, next_value, time_points[current_index]
+ )
+ current_index += 1
+
+ current_value = next_value
+
+ return solution
+
+
+@auto_docstring(
+ custom_intro="""
+ The full Qwen2.5Omni Token2WavDiT model. Which take speech tokens as input and predict mel spectrogram.
+ """
+)
+class Qwen2_5OmniToken2WavDiTModel(Qwen2_5OmniPreTrainedModel):
+ config: Qwen2_5OmniDiTConfig
+ input_modalities = "audio"
+ _no_split_modules = ["DiTDecoderLayer"]
+
+ def __init__(self, config: Qwen2_5OmniDiTConfig):
+ super().__init__(config)
+ self.mel_dim = config.mel_dim
+ self.repeats = config.repeats
+ self.time_embed = DiTTimestepEmbedding(config.hidden_size)
+
+ self.text_embed = DiTCodecEmbedding(config.num_embeds, config.emb_dim, config.repeats)
+ self.input_embed = DiTInputEmbedding(config)
+
+ self.rotary_embed = Qwen2_5OmniDiTRotaryEmbedding(config=config)
+
+ self.hidden_size = config.hidden_size
+ self.layers = config.num_hidden_layers
+ self.block_size = config.block_size
+ self.num_attention_heads = config.num_attention_heads
+
+ self.transformer_blocks = nn.ModuleList()
+ for i in range(config.num_hidden_layers):
+ self.transformer_blocks.append(
+ DiTDecoderLayer(
+ config,
+ look_ahead_block=1 if i in config.look_ahead_layers else 0,
+ look_backward_block=1 if i in config.look_backward_layers else 0,
+ )
+ )
+
+ self.norm_out = Qwen2_5_OmniAdaLayerNormZero_Final(config.hidden_size) # final modulation
+ self.proj_out = nn.Linear(config.hidden_size, config.mel_dim)
+
+ self.post_init()
+
+ def _create_block_diff(self, hidden_states):
+ batch, seq_len = hidden_states.shape[0], hidden_states.shape[1]
+ block_indices = torch.arange(seq_len, device=hidden_states.device) // self.block_size # [seq_length]
+
+ block_i = block_indices.unsqueeze(1) # [seq_length, 1]
+ block_j = block_indices.unsqueeze(0) # [1, seq_length]
+ block_diff = block_j - block_i # (n, n)
+
+ return block_diff.expand(batch, self.num_attention_heads, seq_len, seq_len)
+
+ def forward(
+ self,
+ hidden_states,
+ condition_vector,
+ speaker_embedding,
+ quantized_code,
+ time_step,
+ drop_audio_conditioning=False,
+ drop_code=False,
+ apply_cfg=True,
+ **kwargs,
+ ):
+ batch_size = hidden_states.shape[0]
+ if time_step.ndim == 0:
+ time_step = time_step.repeat(batch_size)
+
+ # Compute embeddings
+ time_embedding = self.time_embed(time_step)
+ text_embedding = self.text_embed(quantized_code, drop_code=False if apply_cfg else drop_code)
+ text_embedding_unconditioned = self.text_embed(quantized_code, drop_code=True) if apply_cfg else None
+
+ hidden_states = self.input_embed(
+ hidden_states,
+ speaker_embedding,
+ condition_vector,
+ text_embedding,
+ drop_audio_cond=drop_audio_conditioning,
+ code_embed_uncond=text_embedding_unconditioned,
+ apply_cfg=apply_cfg,
+ )
+
+ # Compute positional encodings
+ position_ids = torch.arange(hidden_states.shape[1], device=hidden_states.device)
+ position_ids = position_ids[None, :].repeat(batch_size, 1)
+ position_embeddings = self.rotary_embed(hidden_states, position_ids)
+ blockwise_difference = self._create_block_diff(hidden_states)
+
+ # Transformer blocks
+ for transformer_block in self.transformer_blocks:
+ hidden_states = transformer_block(
+ hidden_states,
+ time_embedding,
+ position_embeddings=position_embeddings,
+ block_diff=blockwise_difference,
+ )
+
+ hidden_states = self.norm_out(hidden_states, time_embedding)
+ output = self.proj_out(hidden_states)
+
+ return output
+
+ @torch.no_grad()
+ def sample(
+ self,
+ conditioning_vector,
+ reference_mel_spectrogram,
+ quantized_code,
+ num_steps=10,
+ guidance_scale=0.5,
+ sway_coefficient=-1.0,
+ ):
+ noise_initialization = torch.randn([1, 30000, self.mel_dim], dtype=reference_mel_spectrogram.dtype)
+ maximum_duration = quantized_code.shape[1] * self.repeats
+ initial_state = noise_initialization[:, :maximum_duration].to(quantized_code.device)
+ batch_size = reference_mel_spectrogram.shape[0]
+ conditioning_vector = conditioning_vector.unsqueeze(1).repeat(1, maximum_duration, 1)
+
+ if batch_size != 1:
+ raise ValueError("Only batch size = 1 is currently supported")
+
+ def ode_function(time_step, hidden_states):
+ if guidance_scale < 1e-5:
+ prediction = self(
+ hidden_states=hidden_states,
+ speaker_embedding=conditioning_vector,
+ condition_vector=reference_mel_spectrogram,
+ quantized_code=quantized_code,
+ time_step=time_step,
+ drop_audio_conditioning=False,
+ drop_code=False,
+ )
+ return prediction
+
+ model_output = self(
+ hidden_states=hidden_states,
+ quantized_code=quantized_code,
+ speaker_embedding=conditioning_vector,
+ condition_vector=reference_mel_spectrogram,
+ time_step=time_step,
+ apply_cfg=True,
+ )
+ guided_prediction, null_prediction = torch.chunk(model_output, 2, dim=0)
+ return guided_prediction + (guided_prediction - null_prediction) * guidance_scale
+
+ initial_time = 0
+ time_embedding = torch.linspace(
+ initial_time, 1, num_steps, device=quantized_code.device, dtype=conditioning_vector.dtype
+ )
+
+ if sway_coefficient is not None:
+ time_embedding += sway_coefficient * (torch.cos(torch.pi / 2 * time_embedding) - 1 + time_embedding)
+
+ ode_solver = RungeKutta4ODESolver(function=ode_function, initial_value=initial_state)
+ solution_trajectory = ode_solver.integrate(time_embedding)
+
+ generated_waveform = solution_trajectory[-1]
+ generated_mel_spectrogram = generated_waveform.permute(0, 2, 1)
+ return generated_mel_spectrogram
+
+
+@auto_docstring(
+ custom_intro="""
+ The full Qwen2.5Omni Token2Wav model. Consists a DiT model take speech tokens as input and predict mel spectrogram and a BigVGAN vocoder take mel spectrogram as input and predict waveform.
+ """
+)
+class Qwen2_5OmniToken2WavModel(Qwen2_5OmniPreTrainedModel):
+ config: Qwen2_5OmniToken2WavConfig
+ base_model_prefix = "model"
+ input_modalities = "audio"
+ _no_split_modules = ["Qwen2_5OmniToken2WavDiTModel", "Qwen2_5OmniToken2WavBigVGANModel"]
+
+ def __init__(self, config: Qwen2_5OmniToken2WavConfig):
+ super().__init__(config)
+ attn_impl = config._attn_implementation
+ if config._attn_implementation == "flash_attention_2":
+ logger.warning_once(
+ "Qwen2_5OmniToken2WavModel must inference with fp32, but flash_attention_2 only supports fp16 and bf16, "
+ "attention implementation of Qwen2_5OmniToken2WavModel will fallback to sdpa."
+ )
+ attn_impl = "sdpa"
+ elif config._attn_implementation == "eager":
+ logger.warning_once(
+ "Qwen2_5OmniToken2WavModel does not support eager attention implementation, fall back to sdpa"
+ )
+ attn_impl = "sdpa"
+ self.code2wav_dit_model = Qwen2_5OmniToken2WavDiTModel._from_config(
+ config.dit_config, attn_implementation=attn_impl
+ )
+ self.code2wav_bigvgan_model = Qwen2_5OmniToken2WavBigVGANModel._from_config(
+ config.bigvgan_config, attn_implementation=attn_impl
+ )
+
+ self.post_init()
+
+ def forward(
+ self,
+ code,
+ conditioning,
+ reference_mel,
+ num_steps=10,
+ guidance_scale=0.5,
+ sway_coefficient=-1.0,
+ **kwargs,
+ ):
+ """Generates a waveform from input code and conditioning parameters."""
+
+ mel_spectrogram = self.code2wav_dit_model.sample(
+ conditioning,
+ reference_mel,
+ code,
+ num_steps=num_steps,
+ guidance_scale=guidance_scale,
+ sway_coefficient=sway_coefficient,
+ )
+
+ waveform = self.code2wav_bigvgan_model(mel_spectrogram)
+
+ return waveform
+
+
+############################
+# Start Qwen2.5Omni #
+############################
+
+
+@auto_docstring(
+ custom_intro="""
+ The full Qwen2.5Omni model, a multimodal model composed of 3 sub-models:
+ - [`Qwen2_5OmniThinkerForConditionalGeneration`]:
+ a causal auto-regressive transformer takes text, audio, image, video as input and predict text tokens.
+ - [`Qwen2_5OmniTalkerForConditionalGeneration`]:
+ a causal auto-regressive transformer takes thinker hidden states and response as input and predict speech tokens.
+ - [`Qwen2_5OmniToken2WavModel`]:
+ a DiT model take speech tokens as input and predict mel spectrogram and a BigVGAN vocoder take mel spectrogram as input and predict waveform.
+ """
+)
+class Qwen2_5OmniForConditionalGeneration(Qwen2_5OmniPreTrainedModel, GenerationMixin):
+ config: Qwen2_5OmniConfig
+ output_modalities = ("audio", "text")
+ _no_split_modules = [
+ "Qwen2_5OmniTalkerForConditionalGeneration",
+ "Qwen2_5OmniToken2WavModel",
+ ]
+
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.thinker = Qwen2_5OmniThinkerForConditionalGeneration(config.thinker_config)
+
+ self.has_talker = config.enable_audio_output
+ self.speaker_map = {}
+ if config.enable_audio_output:
+ self.enable_talker()
+ self.post_init()
+
+ def enable_talker(self):
+ self.talker = Qwen2_5OmniTalkerForConditionalGeneration(self.config.talker_config)
+ self.token2wav = Qwen2_5OmniToken2WavModel(self.config.token2wav_config)
+ self.token2wav.float()
+ self.has_talker = True
+
+ def load_speakers(self, path):
+ check_torch_load_is_safe()
+ for key, value in torch.load(path, weights_only=True).items():
+ self.speaker_map[key] = value
+ logger.info(f"Speaker {list(self.speaker_map.keys())} loaded")
+
+ def disable_talker(self):
+ if hasattr(self, "talker"):
+ del self.talker
+ if hasattr(self, "token2wav"):
+ del self.token2wav
+ self.has_talker = False
+
+ @classmethod
+ def from_pretrained(
+ cls,
+ pretrained_model_name_or_path,
+ *model_args,
+ config=None,
+ cache_dir=None,
+ ignore_mismatched_sizes=False,
+ force_download=False,
+ local_files_only=False,
+ token=None,
+ revision="main",
+ use_safetensors=None,
+ weights_only=True,
+ **kwargs,
+ ):
+ model = super().from_pretrained(
+ pretrained_model_name_or_path,
+ *model_args,
+ config=config,
+ cache_dir=cache_dir,
+ ignore_mismatched_sizes=ignore_mismatched_sizes,
+ force_download=force_download,
+ local_files_only=local_files_only,
+ token=token,
+ revision=revision,
+ use_safetensors=use_safetensors,
+ weights_only=weights_only,
+ **kwargs,
+ )
+ spk_path = cached_file(
+ pretrained_model_name_or_path,
+ "spk_dict.pt",
+ subfolder=kwargs.pop("subfolder", None),
+ cache_dir=kwargs.pop("cache_dir", None),
+ force_download=kwargs.pop("force_download", False),
+ proxies=kwargs.pop("proxies", None),
+ local_files_only=kwargs.pop("local_files_only", False),
+ token=token,
+ revision=kwargs.pop("revision", None),
+ )
+ if spk_path is None:
+ raise ValueError(f"""{pretrained_model_name_or_path}/{spk_path} not exists""")
+ model.load_speakers(spk_path)
+
+ return model
+
+ @torch.no_grad()
+ @deprecate_kwarg("return_audio", version="v5", new_name="generation_mode")
+ # TODO: raushan, defaults should be saved in generation config
+ def generate(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ speaker: str = "Chelsie",
+ use_audio_in_video: bool = False,
+ thinker_max_new_tokens: int = 1024,
+ talker_max_new_tokens: int = 4096,
+ talker_do_sample: bool = True,
+ talker_top_k: int = 40,
+ talker_top_p: float = 0.8,
+ talker_temperature: float = 0.9,
+ talker_eos_token_id: list[int] = [8292, 8294],
+ talker_repetition_penalty: float = 1.05,
+ **kwargs,
+ ):
+ r"""
+ Generate text response and audio from input.
+
+ Args:
+ input_ids (`Optional[torch.Tensor]`, *optional*):
+ Input ids, should obtain from processor.
+ speaker (`str` , defaults to "Chelsie"):
+ Which speaker should be used in audio response.
+ use_audio_in_video (`bool`, defaults to False):
+ Whether or not use audio track in video, should same as the parameter in `process_audio_info`.
+ generation_mode (`Optional[str]`, *optional*):
+ Whether or not return response in audio format. When `generation_mode="audio"`, this parameter is same as `config.enable_audio_output`.
+ kwargs (*optional*):
+ - Without a prefix, they will be entered as `**kwargs` for the `generate` method of each sub-model.
+ - With a *thinker_*, *talker_*, *token2wav_* prefix, they will be input for the `generate` method of the
+ thinker, talker and token2wav respectively. It has the priority over the keywords without a prefix.
+ Returns:
+ When `return_audio=False`:
+ - **Text** (`torch.Tensor`): Generated text token sequence.
+ When `return_audio=True`:
+ - **Text** (`torch.Tensor`): Generated text token sequence.
+ - **Audio waveform** (`torch.Tensor`): Generated audio waveform.
+ """
+ # check `False` on purpose because the paramter can be `str/bool`. This is needed for BC
+ generation_mode = kwargs.pop("generation_mode", None)
+ return_audio = generation_mode != "text" and generation_mode is not False
+
+ if speaker not in self.speaker_map:
+ raise ValueError(f"{speaker} is not available, available speakers: {self.speaker_map.keys()}")
+ if return_audio and not self.has_talker:
+ raise ValueError(
+ "Cannot use talker when talker module not initialized. Use `enable_talker` method or set enable_talker in config to enable talker."
+ )
+ if return_audio is None:
+ return_audio = self.has_talker
+ if input_ids.shape[0] != 1 and return_audio:
+ raise NotImplementedError("Qwen2.5-Omni currently does not support batched inference with audio output")
+
+ shared_kwargs = {"use_audio_in_video": use_audio_in_video}
+ thinker_kwargs = {
+ "max_new_tokens": thinker_max_new_tokens,
+ }
+ talker_kwargs = {
+ "max_new_tokens": talker_max_new_tokens,
+ "do_sample": talker_do_sample,
+ "top_k": talker_top_k,
+ "top_p": talker_top_p,
+ "temperature": talker_temperature,
+ "eos_token_id": talker_eos_token_id,
+ "repetition_penalty": talker_repetition_penalty,
+ }
+ token2wav_kwargs = {}
+
+ for key, value in kwargs.items():
+ if key.startswith("thinker_"):
+ thinker_kwargs[key[len("thinker_") :]] = value
+ elif key.startswith("talker_"):
+ talker_kwargs[key[len("talker_") :]] = value
+ elif key.startswith("token2wav_"):
+ token2wav_kwargs[key[len("token2wav_") :]] = value
+ # Process special input values
+ elif key == "feature_attention_mask":
+ thinker_kwargs[key] = value
+ talker_kwargs["audio_feature_lengths"] = torch.sum(value, dim=1)
+ elif key == "input_features" or key == "attention_mask":
+ thinker_kwargs[key] = value
+ # Put other key to shared kwargs
+ else:
+ shared_kwargs[key] = value
+
+ # Merge kwargs
+ for key, value in shared_kwargs.items():
+ if key not in thinker_kwargs:
+ thinker_kwargs[key] = value
+ if key not in talker_kwargs:
+ talker_kwargs[key] = value
+ if key not in token2wav_kwargs:
+ token2wav_kwargs[key] = value
+ speaker_params = self.speaker_map[speaker]
+
+ # 1. Generate from thinker module
+ generate_audio = return_audio and self.has_talker
+ if generate_audio:
+ thinker_kwargs["output_hidden_states"] = True
+ thinker_kwargs["return_dict_in_generate"] = True
+
+ thinker_result = self.thinker.generate(input_ids=input_ids, **thinker_kwargs)
+
+ if not generate_audio:
+ return thinker_result
+
+ # 2. Generate speech tokens from talker module
+ embeds_to_talker = thinker_result.hidden_states[0][0].clone().to(input_ids.device)
+ if thinker_kwargs.get("input_features") is not None:
+ audio_ids_mask = input_ids == self.config.thinker_config.audio_token_index
+ audio_mask = audio_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker)
+ audio_mask_tensor = torch.zeros(
+ [audio_ids_mask.sum(), embeds_to_talker.shape[-1]],
+ dtype=embeds_to_talker.dtype,
+ device=input_ids.device,
+ )
+ embeds_to_talker.masked_scatter_(audio_mask, audio_mask_tensor)
+ if thinker_kwargs.get("pixel_values") is not None:
+ image_ids_mask = input_ids == self.config.thinker_config.image_token_index
+ image_mask = image_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker)
+ image_mask_tensor = torch.zeros(
+ [image_ids_mask.sum(), embeds_to_talker.shape[-1]],
+ dtype=embeds_to_talker.dtype,
+ device=input_ids.device,
+ )
+ embeds_to_talker.masked_scatter_(image_mask, image_mask_tensor)
+ if thinker_kwargs.get("pixel_values_videos") is not None:
+ video_ids_mask = input_ids == self.config.thinker_config.video_token_index
+ video_mask = video_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker)
+ video_mask_tensor = torch.zeros(
+ [video_ids_mask.sum(), embeds_to_talker.shape[-1]],
+ dtype=embeds_to_talker.dtype,
+ device=input_ids.device,
+ )
+ embeds_to_talker.masked_scatter_(video_mask, video_mask_tensor)
+
+ processed_thinker_hidden = (
+ (embeds_to_talker,) + thinker_result.hidden_states[0][1:],
+ ) + thinker_result.hidden_states[1:]
+ thinker_generate_ids = thinker_result.sequences[:, input_ids.size(1) :].to(input_ids.device)
+ thinker_token_embeds = [
+ token_hidden_states[0].to(input_ids.device) for token_hidden_states in processed_thinker_hidden
+ ]
+ thinker_hidden_states = [
+ token_hidden_states[-1].to(input_ids.device) for token_hidden_states in processed_thinker_hidden
+ ]
+
+ talker_text_bos_token = speaker_params["bos_token"]
+ talker_input_text_ids = torch.cat(
+ [
+ input_ids,
+ torch.tensor([[talker_text_bos_token]], dtype=torch.long, device=input_ids.device),
+ thinker_generate_ids[:, :1],
+ ],
+ dim=-1,
+ )
+
+ talker_input_ids = torch.cat(
+ [
+ torch.full_like(input_ids, fill_value=self.talker.codec_mask_token),
+ torch.tensor([[self.talker.codec_pad_token]], dtype=torch.long, device=input_ids.device),
+ torch.tensor([[self.talker.codec_bos_token]], dtype=torch.long, device=input_ids.device),
+ ],
+ dim=1,
+ )
+
+ thinker_embed_tokens = self.thinker.get_input_embeddings()
+ thinker_reply_part = torch.cat(thinker_hidden_states[1:], dim=1) + torch.cat(thinker_token_embeds[1:], dim=1)
+ talker_inputs_embeds = thinker_hidden_states[0] + thinker_token_embeds[0]
+ talker_text_bos_token = torch.tensor([[talker_text_bos_token]], dtype=torch.long, device=input_ids.device)
+ talker_text_bos_embed = thinker_embed_tokens(talker_text_bos_token).to(input_ids.device)
+ talker_inputs_embeds = torch.cat(
+ [
+ talker_inputs_embeds,
+ talker_text_bos_embed,
+ thinker_reply_part[:, :1, :],
+ ],
+ dim=1,
+ )
+
+ eos_token = torch.tensor([[self.talker.text_eos_token]], dtype=torch.long, device=input_ids.device)
+ eos_embedding = thinker_embed_tokens(eos_token).to(input_ids.device)
+
+ pad_token = torch.tensor([[self.talker.text_pad_token]], dtype=torch.long, device=input_ids.device)
+ pad_embedding = thinker_embed_tokens(pad_token).to(input_ids.device)
+
+ thinker_reply_part = torch.cat(
+ [
+ thinker_reply_part[:, 1:, :],
+ eos_embedding,
+ pad_embedding,
+ ],
+ dim=1,
+ )
+
+ talker_attention_mask = None
+ if "attention_mask" in kwargs:
+ talker_attention_mask = torch.cat(
+ [kwargs["attention_mask"], kwargs["attention_mask"].new_ones((1, 2))], dim=1
+ ).to(input_ids.device)
+
+ talker_result = self.talker.generate(
+ input_ids=talker_input_ids,
+ input_text_ids=talker_input_text_ids,
+ thinker_reply_part=thinker_reply_part,
+ inputs_embeds=talker_inputs_embeds,
+ attention_mask=talker_attention_mask,
+ suppress_tokens=[self.talker.codec_bos_token],
+ **{k: (v.to(input_ids.device) if torch.is_tensor(v) else v) for k, v in talker_kwargs.items()},
+ )
+ talker_generate_codes = talker_result[:, talker_input_ids.shape[1] : -1]
+
+ # 3. Generate wavs from code
+ if self.token2wav.dtype != torch.float:
+ self.token2wav.float()
+
+ wav = self.token2wav(
+ talker_generate_codes.to(input_ids.device),
+ conditioning=speaker_params["cond"].to(input_ids.device).float(),
+ reference_mel=speaker_params["ref_mel"].to(input_ids.device).float(),
+ **token2wav_kwargs,
+ )
+
+ return thinker_result.sequences, wav.float()
+
+
+__all__ = [
+ "Qwen2_5OmniConfig",
+ "Qwen2_5OmniThinkerConfig",
+ "Qwen2_5OmniTalkerConfig",
+ "Qwen2_5OmniToken2WavConfig",
+ "Qwen2_5OmniForConditionalGeneration",
+ "Qwen2_5OmniThinkerTextModel",
+ "Qwen2_5OmniThinkerForConditionalGeneration",
+ "Qwen2_5OmniTalkerModel",
+ "Qwen2_5OmniTalkerForConditionalGeneration",
+ "Qwen2_5OmniToken2WavDiTModel",
+ "Qwen2_5OmniToken2WavBigVGANModel",
+ "Qwen2_5OmniToken2WavModel",
+ "Qwen2_5OmniPreTrainedModel",
+ "Qwen2_5OmniPreTrainedModelForConditionalGeneration",
+]
diff --git a/mllm/models/qwen2_5omni/python_src_code/name.py b/mllm/models/qwen2_5omni/python_src_code/name.py
new file mode 100644
index 000000000..87e194c17
--- /dev/null
+++ b/mllm/models/qwen2_5omni/python_src_code/name.py
@@ -0,0 +1,75 @@
+#!/usr/bin/env python3
+"""
+打印 safetensors 模型文件的所有键名
+用法: python print_safetensors_keys.py <文件路径或目录>
+"""
+
+import sys
+from pathlib import Path
+
+try:
+ from safetensors import safe_open
+except ImportError:
+ print("请先安装 safetensors 库:")
+ print(" pip install safetensors")
+ sys.exit(1)
+
+
+def print_keys_from_file(filepath: Path):
+ """打印单个 safetensors 文件的所有键名"""
+ print(f"\n{'='*60}")
+ print(f"文件: {filepath.name}")
+ print(f"{'='*60}")
+
+ with safe_open(filepath, framework="pt") as f:
+ keys = f.keys()
+ print(f"共 {len(keys)} 个键:\n")
+ for i, key in enumerate(keys, 1):
+ # 获取张量的形状信息
+ tensor = f.get_tensor(key)
+ shape = tuple(tensor.shape)
+ dtype = tensor.dtype
+ print(f"{key}")
+
+
+def main():
+ if len(sys.argv) < 2:
+ print("用法: python print_safetensors_keys.py <文件路径或目录>")
+ print("示例:")
+ print(" python print_safetensors_keys.py model.safetensors")
+ print(" python print_safetensors_keys.py ./models/")
+ sys.exit(1)
+
+ path = Path(sys.argv[1])
+
+ if path.is_file():
+ # 单个文件
+ if path.suffix == ".safetensors":
+ print_keys_from_file(path)
+ else:
+ print(f"错误: {path} 不是 .safetensors 文件")
+ sys.exit(1)
+ elif path.is_dir():
+ # 目录，查找所有 safetensors 文件
+ files = sorted(path.glob("*.safetensors"))
+ if not files:
+ print(f"错误: 在 {path} 中未找到 .safetensors 文件")
+ sys.exit(1)
+
+ print(f"找到 {len(files)} 个 safetensors 文件")
+
+ total_keys = 0
+ for filepath in files:
+ print_keys_from_file(filepath)
+ with safe_open(filepath, framework="pt") as f:
+ total_keys += len(f.keys())
+
+ print(f"\n{'='*60}")
+ print(f"总计: {len(files)} 个文件, {total_keys} 个键")
+ else:
+ print(f"错误: {path} 不存在")
+ sys.exit(1)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/mllm/models/qwen2_5omni/python_src_code/preprocessor_config.json b/mllm/models/qwen2_5omni/python_src_code/preprocessor_config.json
new file mode 100644
index 000000000..63c79f1c4
--- /dev/null
+++ b/mllm/models/qwen2_5omni/python_src_code/preprocessor_config.json
@@ -0,0 +1,31 @@
+{
+ "chunk_length": 300,
+ "dither": 0.0,
+ "feature_extractor_type": "WhisperFeatureExtractor",
+ "feature_size": 128,
+ "hop_length": 160,
+ "image_mean": [
+ 0.48145466,
+ 0.4578275,
+ 0.40821073
+ ],
+ "image_processor_type": "Qwen2VLImageProcessor",
+ "image_std": [
+ 0.26862954,
+ 0.26130258,
+ 0.27577711
+ ],
+ "max_pixels": 12845056,
+ "merge_size": 2,
+ "min_pixels": 3136,
+ "n_fft": 400,
+ "n_samples": 4800000,
+ "nb_max_frames": 30000,
+ "padding_side": "right",
+ "padding_value": 0.0,
+ "patch_size": 14,
+ "processor_class": "Qwen2_5OmniProcessor",
+ "return_attention_mask": true,
+ "sampling_rate": 16000,
+ "temporal_patch_size": 2
+}
diff --git a/mllm/models/qwen2_5omni/python_src_code/processing_qwen2_5_omni.py b/mllm/models/qwen2_5omni/python_src_code/processing_qwen2_5_omni.py
new file mode 100644
index 000000000..55906c8f8
--- /dev/null
+++ b/mllm/models/qwen2_5omni/python_src_code/processing_qwen2_5_omni.py
@@ -0,0 +1,405 @@
+# coding=utf-8
+# Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. 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.
+"""
+Processor class for Qwen2.5Omni.
+"""
+
+import logging
+import re
+from typing import Optional, Union
+
+import numpy as np
+
+from ...feature_extraction_utils import BatchFeature
+from ...image_utils import ImageInput
+from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack, VideosKwargs
+from ...tokenization_utils_base import AudioInput, PreTokenizedInput, TextInput
+from ...video_utils import VideoInput
+
+
+# Redefine kwargs for videos because Qwen-Omni uses some kwargs for processing omni
+# and does not use them in video processor class
+class Qwen2_5_OmniVideosKwargs(VideosKwargs, total=False):
+ min_pixels: int
+ max_pixels: int
+ patch_size: int
+ temporal_patch_size: int
+ merge_size: int
+ min_frames: int
+ max_frames: int
+ use_audio_in_video: bool
+ seconds_per_chunk: float
+ position_id_per_seconds: Union[int, float]
+
+
+class Qwen2_5OmniProcessorKwargs(ProcessingKwargs, total=False):
+ videos_kwargs: Qwen2_5_OmniVideosKwargs
+
+ _defaults = {
+ "text_kwargs": {
+ "padding": False,
+ "padding_side": "left",
+ },
+ "videos_kwargs": {
+ "seconds_per_chunk": 2.0,
+ "position_id_per_seconds": 25,
+ "use_audio_in_video": False,
+ "size": {
+ "shortest_edge": 128 * 28 * 28,
+ "longest_edge": 768 * 28 * 28,
+ },
+ },
+ "audio_kwargs": {
+ "sampling_rate": 16000,
+ "padding": "max_length",
+ "return_attention_mask": True,
+ },
+ }
+
+
+class Qwen2_5OmniProcessor(ProcessorMixin):
+ r"""
+ Constructs a Qwen2.5Omni processor.
+ [`Qwen2_5OmniProcessor`] offers all the functionalities of [`Qwen2VLImageProcessor`], [`WhisperFeatureExtractor`], and [`Qwen2TokenizerFast`]. See the
+ [`~Qwen2_5OmniProcessor.__call__`] and [`~Qwen2_5OmniProcessor.decode`] for more information.
+
+ Args:
+ image_processor ([`Qwen2VLImageProcessor`], *optional*):
+ The image processor.
+ video_processor ([`Qwen2VLVideoProcessor`], *optional*):
+ The video processor.
+ feature_extractor ([`WhisperFeatureExtractor`], *optional*):
+ The audio feature extractor.
+ tokenizer ([`Qwen2TokenizerFast`], *optional*):
+ The text tokenizer.
+ chat_template (`Optional[str]`, *optional*):
+ The Jinja template to use for formatting the conversation. If not provided, the default chat template is used.
+ """
+
+ def __init__(
+ self, image_processor=None, video_processor=None, feature_extractor=None, tokenizer=None, chat_template=None
+ ):
+ super().__init__(image_processor, video_processor, feature_extractor, tokenizer, chat_template=chat_template)
+ self.image_token = self.tokenizer.image_token
+ self.audio_token = self.tokenizer.audio_token
+ self.video_token = self.tokenizer.video_token
+ self.vision_bos_token = self.tokenizer.vision_bos_token
+ self.vision_eos_token = self.tokenizer.vision_eos_token
+ self.audio_bos_token = self.tokenizer.audio_bos_token
+ self.audio_eos_token = self.tokenizer.audio_eos_token
+
+ def __call__(
+ self,
+ text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
+ images: Optional[ImageInput] = None,
+ videos: Optional[VideoInput] = None,
+ audio: Optional[AudioInput] = None,
+ **kwargs: Unpack[Qwen2_5OmniProcessorKwargs],
+ ) -> BatchFeature:
+ """
+ Main method to prepare for the model one or several sequences(s) and audio(s). This method forwards the `text`
+ and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode
+ the text. To prepare the audio(s), this method forwards the `audio` and `kwargs` arguments to
+ WhisperFeatureExtractor's [`~WhisperFeatureExtractor.__call__`] if `audio` is not `None`. To prepare the vision inputs,
+ this method forwards the `vision_infos` and `kwargs` arguments to Qwen2VLImageProcessor's [`~Qwen2VLImageProcessor.__call__`]
+ if `vision_infos` is not `None`. Please refer to the doctsring
+ of the above two methods for more information.
+
+ Args:
+ text (`str`, `list[str]`, `list[list[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+ (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+ `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+ images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
+ The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+ tensor. Both channels-first and channels-last formats are supported.
+ videos (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`, `list[torch.Tensor]`):
+ The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
+ tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported.
+ audio (`np.ndarray`, `list[np.ndarray]`):
+ The audio or batch of audio to be prepared. Each audio can be a NumPy array.
+ """
+
+ if text is None:
+ raise ValueError("You need to specify either a `text` input to process.")
+
+ output_kwargs = self._merge_kwargs(
+ Qwen2_5OmniProcessorKwargs,
+ tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+ **kwargs,
+ )
+
+ seconds_per_chunk = output_kwargs["videos_kwargs"].pop("seconds_per_chunk")
+ position_id_per_seconds = output_kwargs["videos_kwargs"].pop("position_id_per_seconds")
+ use_audio_in_video = output_kwargs["videos_kwargs"].pop("use_audio_in_video")
+
+ if audio is not None:
+ output_kwargs["audio_kwargs"]["padding"] = "max_length" # Support "max_length" padding only here
+ audio_inputs = self.feature_extractor(audio, **output_kwargs["audio_kwargs"])
+ audio_inputs["feature_attention_mask"] = audio_inputs.pop(
+ "attention_mask"
+ ) # rename feature_attention_mask to prevent conflicts later on
+ audio_inputs["input_features"] = audio_inputs.pop(
+ "input_features"
+ ) # rename input_features to prevent conflicts later on
+ input_lengths = (audio_inputs["feature_attention_mask"].sum(-1) - 1) // 2 + 1
+ audio_lengths = iter((input_lengths - 2) // 2 + 1)
+ else:
+ audio_inputs = {}
+ audio_lengths = iter([])
+
+ if images is not None:
+ images_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
+ image_grid_thw = iter(images_inputs["image_grid_thw"])
+ else:
+ images_inputs = {}
+ image_grid_thw = iter([])
+
+ if videos is not None:
+ videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
+
+ fps = output_kwargs["videos_kwargs"].get("fps", 2.0)
+ video_grid_thw = videos_inputs["video_grid_thw"]
+ second_per_grid_ts = [self.video_processor.temporal_patch_size / fps] * len(video_grid_thw)
+ videos_inputs["video_second_per_grid"] = second_per_grid_ts
+
+ video_grid_thw = iter(video_grid_thw)
+ video_second_per_grid = iter(second_per_grid_ts)
+ else:
+ videos_inputs = {}
+ video_grid_thw = iter([])
+ video_second_per_grid = iter([])
+
+ if not isinstance(text, list):
+ text = [text]
+
+ if images is not None or videos is not None or audio is not None:
+ text = self.replace_multimodal_special_tokens(
+ text,
+ audio_lengths,
+ image_grid_thw,
+ video_grid_thw,
+ video_second_per_grid=video_second_per_grid,
+ use_audio_in_video=use_audio_in_video,
+ position_id_per_seconds=position_id_per_seconds,
+ seconds_per_chunk=seconds_per_chunk,
+ )
+
+ texts_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+
+ return BatchFeature(
+ data={**texts_inputs, **images_inputs, **videos_inputs, **audio_inputs},
+ tensor_type=kwargs.get("return_tensors"),
+ )
+
+ def replace_multimodal_special_tokens(
+ self,
+ text,
+ audio_lengths,
+ image_grid_thw,
+ video_grid_thw,
+ video_second_per_grid,
+ use_audio_in_video,
+ position_id_per_seconds,
+ seconds_per_chunk,
+ ):
+ # Extend mm token length
+ merge_length_image = self.image_processor.merge_size**2
+ merge_length_video = self.video_processor.merge_size**2
+
+ processed_text = []
+ for sample in text:
+ positions = []
+ special_tokens = [re.escape(tok) for tok in [self.audio_token, self.image_token, self.video_token]]
+ pattern = "|".join(special_tokens)
+ positions = sorted([(match.start(), match.group()) for match in re.finditer(pattern, sample)])
+ positions.sort(key=lambda x: x[0])
+
+ for _, special_token in positions:
+ if special_token == self.audio_token:
+ sample = sample.replace(self.audio_token, "<|audio_placeholder|>" * next(audio_lengths), 1)
+ elif special_token == self.image_token:
+ image_seq_length = next(image_grid_thw).prod() // merge_length_image
+ sample = sample.replace(self.image_token, "<|image_placeholder|>" * image_seq_length, 1)
+ elif special_token == self.video_token:
+ if not use_audio_in_video:
+ video_seq_length = next(video_grid_thw).prod() // merge_length_video
+ sample = sample.replace(self.video_token, "<|video_placeholder|>" * video_seq_length, 1)
+ else:
+ audio_token_indices = np.arange(next(audio_lengths))
+ curr_video_grid_thw = next(video_grid_thw)
+ height = curr_video_grid_thw[1] // self.video_processor.merge_size
+ width = curr_video_grid_thw[2] // self.video_processor.merge_size
+ video_token_indices = np.arange(curr_video_grid_thw[0]).reshape(-1, 1, 1)
+ video_token_indices = np.broadcast_to(
+ video_token_indices, (video_token_indices.shape[0], height, width)
+ ).reshape(-1)
+ video_token_indices = (
+ video_token_indices * next(video_second_per_grid) * position_id_per_seconds
+ )
+
+ tokens_per_chunk = int(position_id_per_seconds * seconds_per_chunk)
+ video_chunk_indexes = self.get_chunked_index(video_token_indices, tokens_per_chunk)
+ audio_chunk_indexes = self.get_chunked_index(audio_token_indices, tokens_per_chunk)
+
+ placeholder_string = self.vision_bos_token + self.audio_bos_token
+ for j in range(max(len(video_chunk_indexes), len(audio_chunk_indexes))):
+ if j < len(video_chunk_indexes):
+ video_seq_length = video_chunk_indexes[j][1] - video_chunk_indexes[j][0]
+ placeholder_string += "<|video_placeholder|>" * video_seq_length
+ if j < len(audio_chunk_indexes):
+ audio_seq_length = audio_chunk_indexes[j][1] - audio_chunk_indexes[j][0]
+ placeholder_string += "<|audio_placeholder|>" * audio_seq_length
+ placeholder_string += self.audio_eos_token + self.vision_eos_token
+ sample = sample.replace(
+ self.vision_bos_token + self.video_token + self.vision_eos_token,
+ placeholder_string,
+ 1,
+ )
+
+ sample = sample.replace("<|audio_placeholder|>", self.audio_token)
+ sample = sample.replace("<|image_placeholder|>", self.image_token)
+ sample = sample.replace("<|video_placeholder|>", self.video_token)
+ processed_text.append(sample)
+ return processed_text
+
+ def get_chunked_index(self, token_indices: np.ndarray, tokens_per_chunk: int) -> list[tuple[int, int]]:
+ """
+ Splits token index list into chunks based on token value ranges.
+
+ Given a list of token indices, returns a list of (start, end) index tuples representing
+ slices of the list where the token values fall within successive ranges of `t_ntoken_per_chunk`.
+
+ For example, if `t_ntoken_per_chunk` is 1000, the function will create chunks such that:
+ - the first chunk contains token values < 1000,
+ - the second chunk contains values >= 1000 and < 2000, and so on.
+
+ Parameters:
+ token_indices (`np.ndarray`): A monotonically increasing list of token index values.
+ t_ntoken_per_chunk (`int`): Number of tokens per chunk (used as the chunk size threshold).
+
+ Returns:
+ `list[tuple[int, int]]`: A list of tuples, each representing the start (inclusive)
+ and end (exclusive) indices of a chunk in `token_indices`.
+ """
+
+ def _iter():
+ i, start_idx = 0, 0 # skip bos token
+ current_chunk = 1
+ while i < len(token_indices): # skip eos token
+ if token_indices[i] >= current_chunk * tokens_per_chunk:
+ yield (start_idx, i)
+ start_idx = i
+ current_chunk += 1
+ i += 1
+ yield (start_idx, len(token_indices))
+
+ return list(_iter())
+
+ def apply_chat_template(self, conversations, chat_template=None, **kwargs):
+ is_batched = False
+ if isinstance(conversations[0], dict):
+ conversations = [conversations]
+ is_batched = True
+
+ for conversation in conversations:
+ if (
+ conversation[0]["role"] != "system"
+ or conversation[0]["content"][0]["text"]
+ != "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."
+ ):
+ logging.warning(
+ "System prompt modified, audio output may not work as expected. "
+ + "Audio output mode only works when using default system prompt 'You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech.'"
+ )
+ if is_batched:
+ conversations = conversations[0]
+
+ return super().apply_chat_template(conversations, chat_template, **kwargs)
+
+ def post_process_image_text_to_text(self, generated_outputs, skip_special_tokens=True, **kwargs):
+ """
+ Post-process the output of a vlm to decode the text.
+
+ Args:
+ generated_outputs (`torch.Tensor` or `np.ndarray`):
+ The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
+ or `(sequence_length,)`.
+ skip_special_tokens (`bool`, *optional*, defaults to `True`):
+ Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
+ **kwargs:
+ Additional arguments to be passed to the tokenizer's `batch_decode method`.
+
+ Returns:
+ `list[str]`: The decoded text.
+ """
+ return self.tokenizer.batch_decode(generated_outputs[0], skip_special_tokens=skip_special_tokens, **kwargs)
+
+ def post_process_multimodal_output(
+ self, generated_outputs, skip_special_tokens=True, generation_mode=None, **kwargs
+ ):
+ """
+ Post-process the output of a multimodal model to return the requested modality output.
+ If the model cannot generated the requested modality, an error will be raised.
+
+ Args:
+ generated_outputs (`torch.Tensor` or `np.ndarray`):
+ The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
+ or `(sequence_length,)`.
+ skip_special_tokens (`bool`, *optional*, defaults to `True`):
+ Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
+ generation_mode (`str`, *optional*):
+ Generation mode indicated which modality to output and can be one of `["text", "image", "audio"]`.
+ **kwargs:
+ Additional arguments to be passed to the tokenizer's `batch_decode method`.
+
+ Returns:
+ `list[Inion[str, np.ndarray]]`: The decoded text or generated audio.
+ """
+ if generation_mode is None or generation_mode == "text":
+ return self.post_process_image_text_to_text(
+ generated_outputs, skip_special_tokens=skip_special_tokens, **kwargs
+ )
+
+ elif generation_mode == "audio":
+ # model supports only bs=1, so we will never get several audio outputs
+ audio = generated_outputs[1].reshape(-1).detach().cpu().numpy()
+ return [audio]
+
+ else:
+ raise ValueError(
+ f"{self.__class__.__name__} got an unexpected generation_mode={generation_mode}. Supported options are only `text` and `audio"
+ )
+
+ @property
+ def model_input_names(self):
+ tokenizer_input_names = self.tokenizer.model_input_names
+ feature_extractor_input_names = self.feature_extractor.model_input_names
+ image_processor_input_names = self.image_processor.model_input_names
+ video_processor_input_names = self.video_processor.model_input_names
+ return list(
+ dict.fromkeys(
+ tokenizer_input_names
+ + feature_extractor_input_names
+ + image_processor_input_names
+ + video_processor_input_names
+ + ["feature_attention_mask"]
+ + ["video_second_per_grid"]
+ )
+ )
+
+
+__all__ = ["Qwen2_5OmniProcessor"]
diff --git a/mllm/models/qwen2_5omni/python_src_code/special_tokens_map.json b/mllm/models/qwen2_5omni/python_src_code/special_tokens_map.json
new file mode 100644
index 000000000..8c2a71eaa
--- /dev/null
+++ b/mllm/models/qwen2_5omni/python_src_code/special_tokens_map.json
@@ -0,0 +1,38 @@
+{
+ "additional_special_tokens": [
+ "<|im_start|>",
+ "<|im_end|>",
+ "<|AUDIO|>",
+ "<|audio_bos|>",
+ "<|audio_eos|>",
+ "<|box_end|>",
+ "<|quad_start|>",
+ "<|quad_end|>",
+ "<|vision_bos|>",
+ "<|vision_eos|>",
+ "<|vision_pad|>",
+ "<|IMAGE|>",
+ "<|VIDEO|>"
+ ],
+ "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
+ },
+ "image_token": "<|IMAGE|>",
+ "audio_token": "<|AUDIO|>",
+ "video_token": "<|VIDEO|>",
+ "vision_bos_token": "<|vision_bos|>",
+ "vision_eos_token": "<|vision_eos|>",
+ "audio_bos_token": "<|audio_bos|>",
+ "audio_eos_token": "<|audio_eos|>"
+}
\ No newline at end of file
diff --git a/mllm/models/qwen2_5omni/python_src_code/spk_dict.pt b/mllm/models/qwen2_5omni/python_src_code/spk_dict.pt
new file mode 100644
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