mirror of
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synced 2025-07-31 10:42:50 +08:00
[model] support audio (#6701)
* support qwen2_audio * improve code * lint * fix * fix * fix --------- Co-authored-by: hiyouga <hiyouga@buaa.edu.cn> Former-commit-id: 24c78429489809873a1269a735ea5421340b32a2
This commit is contained in:
Zhangchi Feng
GitHub
parent
e665e1fed5
commit
01915eaf40
@ -76,8 +76,9 @@ Choose your path:
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- **Various models**: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Qwen2-VL, DeepSeek, Yi, Gemma, ChatGLM, Phi, etc.
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- **Integrated methods**: (Continuous) pre-training, (multimodal) supervised fine-tuning, reward modeling, PPO, DPO, KTO, ORPO, etc.
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- **Scalable resources**: 16-bit full-tuning, freeze-tuning, LoRA and 2/3/4/5/6/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ.
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- **Advanced algorithms**: [GaLore](https://github.com/jiaweizzhao/GaLore), [BAdam](https://github.com/Ledzy/BAdam), [APOLLO](https://github.com/zhuhanqing/APOLLO), [Adam-mini](https://github.com/zyushun/Adam-mini), DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ, PiSSA and Agent tuning.
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- **Advanced algorithms**: [GaLore](https://github.com/jiaweizzhao/GaLore), [BAdam](https://github.com/Ledzy/BAdam), [APOLLO](https://github.com/zhuhanqing/APOLLO), [Adam-mini](https://github.com/zyushun/Adam-mini), DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ and PiSSA.
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- **Practical tricks**: [FlashAttention-2](https://github.com/Dao-AILab/flash-attention), [Unsloth](https://github.com/unslothai/unsloth), [Liger Kernel](https://github.com/linkedin/Liger-Kernel), RoPE scaling, NEFTune and rsLoRA.
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- **Wide tasks**: Multi-turn dialogue, tool using, image understanding, visual grounding, video recognition, audio understanding, etc.
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- **Experiment monitors**: LlamaBoard, TensorBoard, Wandb, MLflow, SwanLab, etc.
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- **Faster inference**: OpenAI-style API, Gradio UI and CLI with vLLM worker.
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@ -105,6 +106,8 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
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## Changelog
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[25/02/05] We supported fine-tuning the **[Qwen2-Audio](Qwen/Qwen2-Audio-7B-Instruct)** and **[MiniCPM-o-2.6](https://huggingface.co/openbmb/MiniCPM-o-2_6)** on audio understanding tasks.
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[25/01/31] We supported fine-tuning the **[DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1)** and **[Qwen2.5-VL](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)** model.
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[25/01/15] We supported **[APOLLO](https://arxiv.org/abs/2412.05270)** optimizer. See [examples](examples/README.md) for usage.
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@ -247,6 +250,7 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
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| [Phi-4](https://huggingface.co/microsoft) | 14B | phi4 |
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| [Pixtral](https://huggingface.co/mistralai) | 12B | pixtral |
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| [Qwen/QwQ (1-2.5) (Code/Math/MoE)](https://huggingface.co/Qwen) | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen |
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| [Qwen2-Audio](https://huggingface.co/Qwen) | 7B | qwen2_audio |
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| [Qwen2-VL/Qwen2.5-VL/QVQ](https://huggingface.co/Qwen) | 2B/3B/7B/72B | qwen2_vl |
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| [Skywork o1](https://huggingface.co/Skywork) | 8B | skywork_o1 |
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| [StarCoder 2](https://huggingface.co/bigcode) | 3B/7B/15B | - |
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@ -78,8 +78,9 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
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- **多种模型**：LLaMA、LLaVA、Mistral、Mixtral-MoE、Qwen、Qwen2-VL、DeepSeek、Yi、Gemma、ChatGLM、Phi 等等。
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- **集成方法**：（增量）预训练、（多模态）指令监督微调、奖励模型训练、PPO 训练、DPO 训练、KTO 训练、ORPO 训练等等。
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- **多种精度**：16 比特全参数微调、冻结微调、LoRA 微调和基于 AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ 的 2/3/4/5/6/8 比特 QLoRA 微调。
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- **先进算法**：[GaLore](https://github.com/jiaweizzhao/GaLore)、[BAdam](https://github.com/Ledzy/BAdam)、[APOLLO](https://github.com/zhuhanqing/APOLLO)、[Adam-mini](https://github.com/zyushun/Adam-mini)、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ、PiSSA 和 Agent 微调。
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- **先进算法**：[GaLore](https://github.com/jiaweizzhao/GaLore)、[BAdam](https://github.com/Ledzy/BAdam)、[APOLLO](https://github.com/zhuhanqing/APOLLO)、[Adam-mini](https://github.com/zyushun/Adam-mini)、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ 和 PiSSA。
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- **实用技巧**：[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)、[Unsloth](https://github.com/unslothai/unsloth)、[Liger Kernel](https://github.com/linkedin/Liger-Kernel)、RoPE scaling、NEFTune 和 rsLoRA。
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- **广泛任务**：多轮对话、工具调用、图像理解、视觉定位、视频识别和语音理解等等。
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- **实验监控**：LlamaBoard、TensorBoard、Wandb、MLflow、SwanLab 等等。
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- **极速推理**：基于 vLLM 的 OpenAI 风格 API、浏览器界面和命令行接口。
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@ -115,6 +116,8 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
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<details><summary>展开日志</summary>
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[25/02/05] 我们支持了在语音理解任务上微调 **[Qwen2-Audio](Qwen/Qwen2-Audio-7B-Instruct)** 和 **[MiniCPM-o-2.6](https://huggingface.co/openbmb/MiniCPM-o-2_6)** 模型。
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[25/01/14] 我们支持了 **[InternLM3](https://huggingface.co/collections/internlm/)** 模型的微调。感谢 [@hhaAndroid](https://github.com/hhaAndroid) 的 PR。
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[25/01/10] 我们支持了 **[Phi-4](https://huggingface.co/microsoft/phi-4)** 模型的微调。
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@ -249,6 +252,7 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
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| [Phi-4](https://huggingface.co/microsoft) | 14B | phi4 |
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| [Pixtral](https://huggingface.co/mistralai) | 12B | pixtral |
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| [Qwen/QwQ (1-2.5) (Code/Math/MoE)](https://huggingface.co/Qwen) | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen |
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| [Qwen2-Audio](https://huggingface.co/Qwen) | 7B | qwen2_audio |
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| [Qwen2-VL/Qwen2.5-VL/QVQ](https://huggingface.co/Qwen) | 2B/3B/7B/72B | qwen2_vl |
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| [Skywork o1](https://huggingface.co/Skywork) | 8B | skywork_o1 |
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| [StarCoder 2](https://huggingface.co/bigcode) | 3B/7B/15B | - |
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@ -24,6 +24,7 @@ Currently we support datasets in **alpaca** and **sharegpt** format.
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"tools": "the column name in the dataset containing the tool description. (default: None)",
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"images": "the column name in the dataset containing the image inputs. (default: None)",
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"videos": "the column name in the dataset containing the videos inputs. (default: None)",
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"audios": "the column name in the dataset containing the audios inputs. (default: None)",
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"chosen": "the column name in the dataset containing the chosen answers. (default: None)",
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"rejected": "the column name in the dataset containing the rejected answers. (default: None)",
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"kto_tag": "the column name in the dataset containing the kto tags. (default: None)"
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@ -150,6 +151,10 @@ An additional column `images` is required. Please refer to the [sharegpt](#share
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An additional column `videos` is required. Please refer to the [sharegpt](#sharegpt-format) format for details.
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### Multimodal Audio Dataset
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An additional column `audios` is required. Please refer to the [sharegpt](#sharegpt-format) format for details.
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## Sharegpt Format
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### Supervised Fine-Tuning Dataset
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@ -296,7 +301,7 @@ Regarding the above dataset, the *dataset description* in `dataset_info.json` sh
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- [Example dataset](mllm_demo.json)
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Multimodal image datasets require a `images` column containing the paths to the input images.
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Multimodal image datasets require an `images` column containing the paths to the input images.
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The number of images should be identical to the `<image>` tokens in the conversations.
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@ -374,6 +379,47 @@ Regarding the above dataset, the *dataset description* in `dataset_info.json` sh
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}
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```
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### Multimodal Audio Dataset
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- [Example dataset](mllm_audio_demo.json)
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Multimodal audio datasets require an `audios` column containing the paths to the input audios.
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The number of audios should be identical to the `<audio>` tokens in the conversations.
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```json
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[
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{
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"conversations": [
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{
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"from": "human",
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"value": "<audio>human instruction"
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},
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{
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"from": "gpt",
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"value": "model response"
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}
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],
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"audios": [
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"audio path (required)"
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]
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}
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]
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```
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Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
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```json
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"dataset_name": {
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"file_name": "data.json",
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"formatting": "sharegpt",
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"columns": {
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"messages": "conversations",
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"audios": "audios"
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}
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}
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```
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### OpenAI Format
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The openai format is simply a special case of the sharegpt format, where the first message may be a system prompt.
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@ -24,6 +24,7 @@
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"tools": "数据集代表工具描述的表头名称（默认：None）",
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"images": "数据集代表图像输入的表头名称（默认：None）",
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"videos": "数据集代表视频输入的表头名称（默认：None）",
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"audios": "数据集代表音频输入的表头名称（默认：None）",
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"chosen": "数据集代表更优回答的表头名称（默认：None）",
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"rejected": "数据集代表更差回答的表头名称（默认：None）",
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"kto_tag": "数据集代表 KTO 标签的表头名称（默认：None）"
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@ -150,6 +151,10 @@ KTO 数据集需要提供额外的 `kto_tag` 列。详情请参阅 [sharegpt](#s
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多模态视频数据集需要提供额外的 `videos` 列。详情请参阅 [sharegpt](#sharegpt-格式)。
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### 多模态音频数据集
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多模态音频数据集需要提供额外的 `audios` 列。详情请参阅 [sharegpt](#sharegpt-格式)。
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## Sharegpt 格式
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### 指令监督微调数据集
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@ -374,6 +379,48 @@ KTO 数据集需要额外添加一个 `kto_tag` 列，包含 bool 类型的人
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}
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```
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### 多模态音频数据集
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- [样例数据集](mllm_audio_demo.json)
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多模态音频数据集需要额外添加一个 `audios` 列，包含输入音频的路径。
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注意音频的数量必须与文本中所有 `<audio>` 标记的数量严格一致。
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```json
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[
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{
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"conversations": [
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{
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"from": "human",
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"value": "<audio>人类指令"
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},
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{
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"from": "gpt",
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"value": "模型回答"
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}
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],
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"audios": [
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"音频路径（必填）"
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]
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}
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]
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```
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对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
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```json
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"数据集名称": {
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"file_name": "data.json",
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"formatting": "sharegpt",
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"columns": {
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"messages": "conversations",
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"audios": "audios"
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}
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}
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```
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### OpenAI 格式
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OpenAI 格式仅仅是 sharegpt 格式的一种特殊情况，其中第一条消息可能是系统提示词。
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@ -38,6 +38,20 @@
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"assistant_tag": "assistant"
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}
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},
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"mllm_audio_demo": {
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"file_name": "mllm_audio_demo.json",
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"formatting": "sharegpt",
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"columns": {
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"messages": "messages",
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"audios": "audios"
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},
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"tags": {
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"role_tag": "role",
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"content_tag": "content",
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"user_tag": "user",
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"assistant_tag": "assistant"
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}
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},
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"mllm_video_demo": {
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"file_name": "mllm_video_demo.json",
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"formatting": "sharegpt",
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47
data/mllm_audio_demo.json
Normal file
47
data/mllm_audio_demo.json
Normal file
@ -0,0 +1,47 @@
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[
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{
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"messages": [
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{
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"content": "<audio>What's that sound?",
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"role": "user"
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},
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{
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"content": "It is the sound of glass shattering.",
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"role": "assistant"
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}
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],
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"audios": [
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"mllm_demo_data/1.mp3"
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]
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},
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{
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"messages": [
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{
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"content": "<audio>What can you hear?",
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"role": "user"
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},
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{
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"content": "A woman is coughing.",
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"role": "assistant"
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}
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],
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"audios": [
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"mllm_demo_data/2.wav"
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]
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},
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{
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"messages": [
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{
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"content": "<audio>What does the person say?",
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"role": "user"
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},
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{
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"content": "Mister Quiller is the apostle of the middle classes and we are glad to welcome his gospel.",
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"role": "assistant"
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}
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],
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"audios": [
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"mllm_demo_data/3.flac"
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]
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}
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]
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BIN
data/mllm_demo_data/1.mp3
Normal file
BIN
data/mllm_demo_data/1.mp3
Normal file
Binary file not shown.
BIN
data/mllm_demo_data/2.wav
Normal file
BIN
data/mllm_demo_data/2.wav
Normal file
Binary file not shown.
BIN
data/mllm_demo_data/3.flac
Normal file
BIN
data/mllm_demo_data/3.flac
Normal file
Binary file not shown.
@ -22,4 +22,5 @@ packaging
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pyyaml
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numpy<2.0.0
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av
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librosa
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tyro<0.9.0
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@ -49,6 +49,7 @@ class PairwiseDataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
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"labels": feature["chosen_input_ids"] if self.train_on_prompt else feature["chosen_labels"],
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"images": feature["images"],
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"videos": feature["videos"],
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"audios": feature["audios"],
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}
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)
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1
setup.py
1
setup.py
@ -69,7 +69,6 @@ extra_require = {
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"msgpack",
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"referencing",
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"jsonschema_specifications",
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"librosa",
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],
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"modelscope": ["modelscope"],
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"openmind": ["openmind"],
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@ -22,7 +22,7 @@ if TYPE_CHECKING:
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from vllm import AsyncLLMEngine
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from ..data import Template
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from ..data.mm_plugin import ImageInput, VideoInput
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from ..data.mm_plugin import AudioInput, ImageInput, VideoInput
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from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
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@ -68,6 +68,7 @@ class BaseEngine(ABC):
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tools: Optional[str] = None,
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images: Optional[Sequence["ImageInput"]] = None,
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videos: Optional[Sequence["VideoInput"]] = None,
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audios: Optional[Sequence["AudioInput"]] = None,
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**input_kwargs,
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) -> List["Response"]:
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r"""
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@ -83,6 +84,7 @@ class BaseEngine(ABC):
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tools: Optional[str] = None,
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images: Optional[Sequence["ImageInput"]] = None,
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videos: Optional[Sequence["VideoInput"]] = None,
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audios: Optional[Sequence["AudioInput"]] = None,
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**input_kwargs,
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) -> AsyncGenerator[str, None]:
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r"""
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@ -27,7 +27,7 @@ from .vllm_engine import VllmEngine
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if TYPE_CHECKING:
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from ..data.mm_plugin import ImageInput, VideoInput
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from ..data.mm_plugin import AudioInput, ImageInput, VideoInput
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from .base_engine import BaseEngine, Response
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@ -66,13 +66,14 @@ class ChatModel:
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tools: Optional[str] = None,
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images: Optional[Sequence["ImageInput"]] = None,
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videos: Optional[Sequence["VideoInput"]] = None,
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audios: Optional[Sequence["AudioInput"]] = None,
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**input_kwargs,
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) -> List["Response"]:
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r"""
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Gets a list of responses of the chat model.
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"""
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task = asyncio.run_coroutine_threadsafe(
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self.achat(messages, system, tools, images, videos, **input_kwargs), self._loop
|
||||
self.achat(messages, system, tools, images, videos, audios, **input_kwargs), self._loop
|
||||
)
|
||||
return task.result()
|
||||
|
||||
@ -83,12 +84,13 @@ class ChatModel:
|
||||
tools: Optional[str] = None,
|
||||
images: Optional[Sequence["ImageInput"]] = None,
|
||||
videos: Optional[Sequence["VideoInput"]] = None,
|
||||
audios: Optional[Sequence["AudioInput"]] = None,
|
||||
**input_kwargs,
|
||||
) -> List["Response"]:
|
||||
r"""
|
||||
Asynchronously gets a list of responses of the chat model.
|
||||
"""
|
||||
return await self.engine.chat(messages, system, tools, images, videos, **input_kwargs)
|
||||
return await self.engine.chat(messages, system, tools, images, videos, audios, **input_kwargs)
|
||||
|
||||
def stream_chat(
|
||||
self,
|
||||
@ -97,12 +99,13 @@ class ChatModel:
|
||||
tools: Optional[str] = None,
|
||||
images: Optional[Sequence["ImageInput"]] = None,
|
||||
videos: Optional[Sequence["VideoInput"]] = None,
|
||||
audios: Optional[Sequence["AudioInput"]] = None,
|
||||
**input_kwargs,
|
||||
) -> Generator[str, None, None]:
|
||||
r"""
|
||||
Gets the response token-by-token of the chat model.
|
||||
"""
|
||||
generator = self.astream_chat(messages, system, tools, images, videos, **input_kwargs)
|
||||
generator = self.astream_chat(messages, system, tools, images, videos, audios, **input_kwargs)
|
||||
while True:
|
||||
try:
|
||||
task = asyncio.run_coroutine_threadsafe(generator.__anext__(), self._loop)
|
||||
@ -117,12 +120,15 @@ class ChatModel:
|
||||
tools: Optional[str] = None,
|
||||
images: Optional[Sequence["ImageInput"]] = None,
|
||||
videos: Optional[Sequence["VideoInput"]] = None,
|
||||
audios: Optional[Sequence["AudioInput"]] = None,
|
||||
**input_kwargs,
|
||||
) -> AsyncGenerator[str, None]:
|
||||
r"""
|
||||
Asynchronously gets the response token-by-token of the chat model.
|
||||
"""
|
||||
async for new_token in self.engine.stream_chat(messages, system, tools, images, videos, **input_kwargs):
|
||||
async for new_token in self.engine.stream_chat(
|
||||
messages, system, tools, images, videos, audios, **input_kwargs
|
||||
):
|
||||
yield new_token
|
||||
|
||||
def get_scores(
|
||||
|
||||
@ -24,7 +24,7 @@ from typing_extensions import override
|
||||
|
||||
from ..data import get_template_and_fix_tokenizer
|
||||
from ..extras import logging
|
||||
from ..extras.constants import IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
|
||||
from ..extras.constants import AUDIO_PLACEHOLDER, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
|
||||
from ..extras.misc import get_logits_processor
|
||||
from ..model import load_model, load_tokenizer
|
||||
from .base_engine import BaseEngine, Response
|
||||
@ -35,7 +35,7 @@ if TYPE_CHECKING:
|
||||
from trl import PreTrainedModelWrapper
|
||||
|
||||
from ..data import Template
|
||||
from ..data.mm_plugin import ImageInput, VideoInput
|
||||
from ..data.mm_plugin import AudioInput, ImageInput, VideoInput
|
||||
from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
|
||||
|
||||
|
||||
@ -81,9 +81,10 @@ class HuggingfaceEngine(BaseEngine):
|
||||
tools: Optional[str] = None,
|
||||
images: Optional[Sequence["ImageInput"]] = None,
|
||||
videos: Optional[Sequence["VideoInput"]] = None,
|
||||
audios: Optional[Sequence["AudioInput"]] = None,
|
||||
input_kwargs: Optional[Dict[str, Any]] = {},
|
||||
) -> Tuple[Dict[str, Any], int]:
|
||||
mm_input_dict = {"images": [], "videos": [], "imglens": [0], "vidlens": [0]}
|
||||
mm_input_dict = {"images": [], "videos": [], "audios": [], "imglens": [0], "vidlens": [0], "audlens": [0]}
|
||||
if images is not None:
|
||||
mm_input_dict.update({"images": images, "imglens": [len(images)]})
|
||||
if not any(IMAGE_PLACEHOLDER in message["content"] for message in messages):
|
||||
@ -94,14 +95,25 @@ class HuggingfaceEngine(BaseEngine):
|
||||
if not any(VIDEO_PLACEHOLDER in message["content"] for message in messages):
|
||||
messages[0]["content"] = VIDEO_PLACEHOLDER * len(videos) + messages[0]["content"]
|
||||
|
||||
if audios is not None:
|
||||
mm_input_dict.update({"audios": audios, "audlens": [len(audios)]})
|
||||
if not any(AUDIO_PLACEHOLDER in message["content"] for message in messages):
|
||||
messages[0]["content"] = AUDIO_PLACEHOLDER * len(audios) + messages[0]["content"]
|
||||
|
||||
messages = template.mm_plugin.process_messages(
|
||||
messages, mm_input_dict["images"], mm_input_dict["videos"], processor
|
||||
messages, mm_input_dict["images"], mm_input_dict["videos"], mm_input_dict["audios"], processor
|
||||
)
|
||||
paired_messages = messages + [{"role": "assistant", "content": ""}]
|
||||
system = system or generating_args["default_system"]
|
||||
prompt_ids, _ = template.encode_oneturn(tokenizer, paired_messages, system, tools)
|
||||
prompt_ids, _ = template.mm_plugin.process_token_ids(
|
||||
prompt_ids, None, mm_input_dict["images"], mm_input_dict["videos"], tokenizer, processor
|
||||
prompt_ids,
|
||||
None,
|
||||
mm_input_dict["images"],
|
||||
mm_input_dict["videos"],
|
||||
mm_input_dict["audios"],
|
||||
tokenizer,
|
||||
processor,
|
||||
)
|
||||
prompt_length = len(prompt_ids)
|
||||
inputs = torch.tensor([prompt_ids], device=model.device)
|
||||
@ -184,6 +196,9 @@ class HuggingfaceEngine(BaseEngine):
|
||||
if getattr(model.config, "model_type", None) in ["minicpmv", "minicpmo"]:
|
||||
gen_kwargs["input_ids"] = inputs
|
||||
gen_kwargs["tokenizer"] = tokenizer
|
||||
if "audio_feature_lens" in mm_inputs:
|
||||
gen_kwargs["audio_feature_lens"] = mm_inputs["audio_feature_lens"]
|
||||
|
||||
gen_kwargs.pop("image_sizes", None)
|
||||
|
||||
return gen_kwargs, prompt_length
|
||||
@ -201,6 +216,7 @@ class HuggingfaceEngine(BaseEngine):
|
||||
tools: Optional[str] = None,
|
||||
images: Optional[Sequence["ImageInput"]] = None,
|
||||
videos: Optional[Sequence["VideoInput"]] = None,
|
||||
audios: Optional[Sequence["AudioInput"]] = None,
|
||||
input_kwargs: Optional[Dict[str, Any]] = {},
|
||||
) -> List["Response"]:
|
||||
gen_kwargs, prompt_length = HuggingfaceEngine._process_args(
|
||||
@ -214,6 +230,7 @@ class HuggingfaceEngine(BaseEngine):
|
||||
tools,
|
||||
images,
|
||||
videos,
|
||||
audios,
|
||||
input_kwargs,
|
||||
)
|
||||
generate_output = model.generate(**gen_kwargs)
|
||||
@ -252,6 +269,7 @@ class HuggingfaceEngine(BaseEngine):
|
||||
tools: Optional[str] = None,
|
||||
images: Optional[Sequence["ImageInput"]] = None,
|
||||
videos: Optional[Sequence["VideoInput"]] = None,
|
||||
audios: Optional[Sequence["AudioInput"]] = None,
|
||||
input_kwargs: Optional[Dict[str, Any]] = {},
|
||||
) -> Callable[[], str]:
|
||||
gen_kwargs, _ = HuggingfaceEngine._process_args(
|
||||
@ -265,6 +283,7 @@ class HuggingfaceEngine(BaseEngine):
|
||||
tools,
|
||||
images,
|
||||
videos,
|
||||
audios,
|
||||
input_kwargs,
|
||||
)
|
||||
streamer = TextIteratorStreamer(
|
||||
@ -312,6 +331,7 @@ class HuggingfaceEngine(BaseEngine):
|
||||
tools: Optional[str] = None,
|
||||
images: Optional[Sequence["ImageInput"]] = None,
|
||||
videos: Optional[Sequence["VideoInput"]] = None,
|
||||
audios: Optional[Sequence["AudioInput"]] = None,
|
||||
**input_kwargs,
|
||||
) -> List["Response"]:
|
||||
if not self.can_generate:
|
||||
@ -329,6 +349,7 @@ class HuggingfaceEngine(BaseEngine):
|
||||
tools,
|
||||
images,
|
||||
videos,
|
||||
audios,
|
||||
input_kwargs,
|
||||
)
|
||||
async with self.semaphore:
|
||||
@ -343,6 +364,7 @@ class HuggingfaceEngine(BaseEngine):
|
||||
tools: Optional[str] = None,
|
||||
images: Optional[Sequence["ImageInput"]] = None,
|
||||
videos: Optional[Sequence["VideoInput"]] = None,
|
||||
audios: Optional[Sequence["AudioInput"]] = None,
|
||||
**input_kwargs,
|
||||
) -> AsyncGenerator[str, None]:
|
||||
if not self.can_generate:
|
||||
@ -360,6 +382,7 @@ class HuggingfaceEngine(BaseEngine):
|
||||
tools,
|
||||
images,
|
||||
videos,
|
||||
audios,
|
||||
input_kwargs,
|
||||
)
|
||||
async with self.semaphore:
|
||||
|
||||
@ -19,7 +19,7 @@ from typing_extensions import override
|
||||
|
||||
from ..data import get_template_and_fix_tokenizer
|
||||
from ..extras import logging
|
||||
from ..extras.constants import IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
|
||||
from ..extras.constants import AUDIO_PLACEHOLDER, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
|
||||
from ..extras.misc import get_device_count
|
||||
from ..extras.packages import is_pillow_available, is_vllm_available
|
||||
from ..model import load_config, load_tokenizer
|
||||
@ -39,7 +39,7 @@ if is_vllm_available():
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from ..data.mm_plugin import ImageInput, VideoInput
|
||||
from ..data.mm_plugin import AudioInput, ImageInput, VideoInput
|
||||
from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
|
||||
|
||||
|
||||
@ -109,10 +109,11 @@ class VllmEngine(BaseEngine):
|
||||
tools: Optional[str] = None,
|
||||
images: Optional[Sequence["ImageInput"]] = None,
|
||||
videos: Optional[Sequence["VideoInput"]] = None,
|
||||
audios: Optional[Sequence["AudioInput"]] = None,
|
||||
**input_kwargs,
|
||||
) -> AsyncIterator["RequestOutput"]:
|
||||
request_id = f"chatcmpl-{uuid.uuid4().hex}"
|
||||
mm_input_dict = {"images": [], "videos": [], "imglens": [0], "vidlens": [0]}
|
||||
mm_input_dict = {"images": [], "videos": [], "audios": [], "imglens": [0], "vidlens": [0], "audlens": [0]}
|
||||
if images is not None:
|
||||
mm_input_dict.update({"images": images, "imglens": [len(images)]})
|
||||
if not any(IMAGE_PLACEHOLDER in message["content"] for message in messages):
|
||||
@ -123,8 +124,13 @@ class VllmEngine(BaseEngine):
|
||||
if not any(VIDEO_PLACEHOLDER in message["content"] for message in messages):
|
||||
messages[0]["content"] = VIDEO_PLACEHOLDER * len(videos) + messages[0]["content"]
|
||||
|
||||
if audios is not None:
|
||||
mm_input_dict.update({"audios": audios, "audlens": [len(audios)]})
|
||||
if not any(AUDIO_PLACEHOLDER in message["content"] for message in messages):
|
||||
messages[0]["content"] = AUDIO_PLACEHOLDER * len(audios) + messages[0]["content"]
|
||||
|
||||
messages = self.template.mm_plugin.process_messages(
|
||||
messages, mm_input_dict["images"], mm_input_dict["videos"], self.processor
|
||||
messages, mm_input_dict["images"], mm_input_dict["videos"], mm_input_dict["audios"], self.processor
|
||||
)
|
||||
paired_messages = messages + [{"role": "assistant", "content": ""}]
|
||||
system = system or self.generating_args["default_system"]
|
||||
@ -202,10 +208,11 @@ class VllmEngine(BaseEngine):
|
||||
tools: Optional[str] = None,
|
||||
images: Optional[Sequence["ImageInput"]] = None,
|
||||
videos: Optional[Sequence["VideoInput"]] = None,
|
||||
audios: Optional[Sequence["AudioInput"]] = None,
|
||||
**input_kwargs,
|
||||
) -> List["Response"]:
|
||||
final_output = None
|
||||
generator = await self._generate(messages, system, tools, images, videos, **input_kwargs)
|
||||
generator = await self._generate(messages, system, tools, images, videos, audios, **input_kwargs)
|
||||
async for request_output in generator:
|
||||
final_output = request_output
|
||||
|
||||
@ -230,10 +237,11 @@ class VllmEngine(BaseEngine):
|
||||
tools: Optional[str] = None,
|
||||
images: Optional[Sequence["ImageInput"]] = None,
|
||||
videos: Optional[Sequence["VideoInput"]] = None,
|
||||
audios: Optional[Sequence["AudioInput"]] = None,
|
||||
**input_kwargs,
|
||||
) -> AsyncGenerator[str, None]:
|
||||
generated_text = ""
|
||||
generator = await self._generate(messages, system, tools, images, videos, **input_kwargs)
|
||||
generator = await self._generate(messages, system, tools, images, videos, audios, **input_kwargs)
|
||||
async for result in generator:
|
||||
delta_text = result.outputs[0].text[len(generated_text) :]
|
||||
generated_text = result.outputs[0].text
|
||||
|
||||
@ -25,57 +25,33 @@ if TYPE_CHECKING:
|
||||
from transformers import Seq2SeqTrainingArguments
|
||||
|
||||
from ..hparams import DataArguments
|
||||
from .mm_plugin import ImageInput, VideoInput
|
||||
from .parser import DatasetAttr
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
def _convert_images(
|
||||
images: Union["ImageInput", Sequence["ImageInput"]],
|
||||
def _regularize_medias(
|
||||
inputs: Union[Any, Sequence[Any]],
|
||||
dataset_attr: "DatasetAttr",
|
||||
data_args: "DataArguments",
|
||||
) -> Optional[List["ImageInput"]]:
|
||||
) -> Optional[List[Any]]:
|
||||
r"""
|
||||
Optionally concatenates image path to dataset dir when loading from local disk.
|
||||
Optionally concatenates media path to media dir when loading from local disk.
|
||||
"""
|
||||
if not isinstance(images, list):
|
||||
images = [images]
|
||||
elif len(images) == 0:
|
||||
if not isinstance(inputs, list):
|
||||
inputs = [inputs]
|
||||
elif len(inputs) == 0:
|
||||
return None
|
||||
else:
|
||||
images = images[:]
|
||||
inputs = inputs[:]
|
||||
|
||||
if dataset_attr.load_from in ["script", "file"]:
|
||||
for i in range(len(images)):
|
||||
if isinstance(images[i], str) and os.path.isfile(os.path.join(data_args.image_dir, images[i])):
|
||||
images[i] = os.path.join(data_args.image_dir, images[i])
|
||||
for i in range(len(inputs)):
|
||||
if isinstance(inputs[i], str) and os.path.isfile(os.path.join(data_args.media_dir, inputs[i])):
|
||||
inputs[i] = os.path.join(data_args.media_dir, inputs[i])
|
||||
|
||||
return images
|
||||
|
||||
|
||||
def _convert_videos(
|
||||
videos: Union["VideoInput", Sequence["VideoInput"]],
|
||||
dataset_attr: "DatasetAttr",
|
||||
data_args: "DataArguments",
|
||||
) -> Optional[List["VideoInput"]]:
|
||||
r"""
|
||||
Optionally concatenates video path to dataset dir when loading from local disk.
|
||||
"""
|
||||
if not isinstance(videos, list):
|
||||
videos = [videos]
|
||||
elif len(videos) == 0:
|
||||
return None
|
||||
else:
|
||||
videos = videos[:]
|
||||
|
||||
if dataset_attr.load_from in ["script", "file"]:
|
||||
for i in range(len(videos)):
|
||||
if isinstance(videos[i], str) and os.path.isfile(os.path.join(data_args.image_dir, videos[i])):
|
||||
videos[i] = os.path.join(data_args.image_dir, videos[i])
|
||||
|
||||
return videos
|
||||
return inputs
|
||||
|
||||
|
||||
def convert_alpaca(
|
||||
@ -121,15 +97,15 @@ def convert_alpaca(
|
||||
else: # unsupervised
|
||||
response = []
|
||||
|
||||
convert_images = partial(_convert_images, dataset_attr=dataset_attr, data_args=data_args)
|
||||
convert_videos = partial(_convert_videos, dataset_attr=dataset_attr, data_args=data_args)
|
||||
regularize_medias = partial(_regularize_medias, dataset_attr=dataset_attr, data_args=data_args)
|
||||
output = {
|
||||
"_prompt": prompt,
|
||||
"_response": response,
|
||||
"_system": example[dataset_attr.system] if dataset_attr.system else "",
|
||||
"_tools": example[dataset_attr.tools] if dataset_attr.tools else "",
|
||||
"_images": convert_images(example[dataset_attr.images]) if dataset_attr.images else None,
|
||||
"_videos": convert_videos(example[dataset_attr.videos]) if dataset_attr.videos else None,
|
||||
"_images": regularize_medias(example[dataset_attr.images]) if dataset_attr.images else None,
|
||||
"_videos": regularize_medias(example[dataset_attr.videos]) if dataset_attr.videos else None,
|
||||
"_audios": regularize_medias(example[dataset_attr.audios]) if dataset_attr.audios else None,
|
||||
}
|
||||
return output
|
||||
|
||||
@ -214,15 +190,15 @@ def convert_sharegpt(
|
||||
logger.warning_rank0("Skipping this abnormal example.")
|
||||
prompt, response = [], []
|
||||
|
||||
convert_images = partial(_convert_images, dataset_attr=dataset_attr, data_args=data_args)
|
||||
convert_videos = partial(_convert_videos, dataset_attr=dataset_attr, data_args=data_args)
|
||||
regularize_medias = partial(_regularize_medias, dataset_attr=dataset_attr, data_args=data_args)
|
||||
output = {
|
||||
"_prompt": prompt,
|
||||
"_response": response,
|
||||
"_system": system,
|
||||
"_tools": example[dataset_attr.tools] if dataset_attr.tools else "",
|
||||
"_images": convert_images(example[dataset_attr.images]) if dataset_attr.images else None,
|
||||
"_videos": convert_videos(example[dataset_attr.videos]) if dataset_attr.videos else None,
|
||||
"_images": regularize_medias(example[dataset_attr.images]) if dataset_attr.images else None,
|
||||
"_videos": regularize_medias(example[dataset_attr.videos]) if dataset_attr.videos else None,
|
||||
"_audios": regularize_medias(example[dataset_attr.audios]) if dataset_attr.audios else None,
|
||||
}
|
||||
return output
|
||||
|
||||
@ -241,6 +217,7 @@ def align_dataset(
|
||||
_tools: "...",
|
||||
_images: [],
|
||||
_videos: [],
|
||||
_audios: [],
|
||||
"""
|
||||
if dataset_attr.formatting == "alpaca":
|
||||
convert_func = partial(convert_alpaca, dataset_attr=dataset_attr, data_args=data_args)
|
||||
|
||||
@ -18,11 +18,12 @@
|
||||
from dataclasses import dataclass
|
||||
from typing import TYPE_CHECKING, Any, Dict, Literal, Optional, Sequence
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from transformers import DataCollatorForSeq2Seq
|
||||
|
||||
from ..extras.constants import IGNORE_INDEX, IMAGE_PLACEHOLDER
|
||||
from ..extras.constants import AUDIO_PLACEHOLDER, IGNORE_INDEX, IMAGE_PLACEHOLDER
|
||||
from ..extras.packages import is_pillow_available
|
||||
|
||||
|
||||
@ -80,7 +81,7 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
|
||||
r"""
|
||||
Data collator that supports VLMs.
|
||||
|
||||
Features should contain input_ids, attention_mask, labels, and optionally contain images and videos.
|
||||
Features should contain input_ids, attention_mask, labels, and optionally contain images, videos and audios.
|
||||
"""
|
||||
|
||||
template: Optional["Template"] = None
|
||||
@ -91,26 +92,54 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
|
||||
raise ValueError("Template is required for MultiModalDataCollator.")
|
||||
|
||||
def __call__(self, features: Sequence[Dict[str, Any]]) -> Dict[str, "torch.Tensor"]:
|
||||
batch_images, batch_videos, batch_imglens, batch_vidlens, batch_input_ids = [], [], [], [], []
|
||||
batch_images, batch_videos, batch_audios = [], [], []
|
||||
batch_imglens, batch_vidlens, batch_audlens, batch_input_ids = [], [], [], []
|
||||
for feature in features:
|
||||
images = feature.pop("images", None) or []
|
||||
videos = feature.pop("videos", None) or []
|
||||
audios = feature.pop("audios", None) or []
|
||||
batch_images.extend(images)
|
||||
batch_videos.extend(videos)
|
||||
batch_audios.extend(audios)
|
||||
batch_imglens.append(len(images))
|
||||
batch_vidlens.append(len(videos))
|
||||
batch_audlens.append(len(audios))
|
||||
batch_input_ids.append(feature["input_ids"])
|
||||
|
||||
fake_input_ids = None
|
||||
if (
|
||||
self.processor is not None and sum(batch_imglens) == 0 and sum(batch_vidlens) == 0
|
||||
self.template.mm_plugin.image_token is not None and sum(batch_imglens) == 0 and sum(batch_vidlens) == 0
|
||||
): # avoid process hanging in zero3/fsdp case
|
||||
fake_messages = [{"role": "user", "content": IMAGE_PLACEHOLDER}]
|
||||
fake_images = [Image.new("RGB", (64, 64), (255, 255, 255))]
|
||||
fake_messages = self.template.mm_plugin.process_messages(fake_messages, fake_images, [], self.processor)
|
||||
fake_messages = self.template.mm_plugin.process_messages(
|
||||
fake_messages, fake_images, [], [], self.processor
|
||||
)
|
||||
fake_input_ids = self.tokenizer.encode(fake_messages[0]["content"], add_special_tokens=False)
|
||||
fake_input_ids, _ = self.template.mm_plugin.process_token_ids(
|
||||
fake_input_ids, None, fake_images, [], self.tokenizer, self.processor
|
||||
fake_input_ids, None, fake_images, [], [], self.tokenizer, self.processor
|
||||
)
|
||||
batch_images = fake_images
|
||||
batch_imglens[0] = 1
|
||||
batch_input_ids[0] = features[0]["input_ids"]
|
||||
|
||||
if (
|
||||
self.template.mm_plugin.audio_token is not None and sum(batch_audlens) == 0
|
||||
): # avoid process hanging in zero3/fsdp case
|
||||
fake_messages = [{"role": "user", "content": AUDIO_PLACEHOLDER}]
|
||||
fake_audios = [np.zeros(1600)]
|
||||
fake_messages = self.template.mm_plugin.process_messages(
|
||||
fake_messages, [], [], fake_audios, self.processor
|
||||
)
|
||||
fake_input_ids = self.tokenizer.encode(fake_messages[0]["content"], add_special_tokens=False)
|
||||
fake_input_ids, _ = self.template.mm_plugin.process_token_ids(
|
||||
fake_input_ids, None, [], [], fake_audios, self.tokenizer, self.processor
|
||||
)
|
||||
batch_audios = fake_audios
|
||||
batch_audlens[0] = 1
|
||||
batch_input_ids[0] = features[0]["input_ids"]
|
||||
|
||||
if fake_input_ids is not None:
|
||||
if self.tokenizer.padding_side == "right":
|
||||
features[0]["input_ids"] = features[0]["input_ids"] + fake_input_ids
|
||||
features[0]["attention_mask"] = features[0]["attention_mask"] + [0] * len(fake_input_ids)
|
||||
@ -120,12 +149,15 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
|
||||
features[0]["attention_mask"] = [0] * len(fake_input_ids) + features[0]["attention_mask"]
|
||||
features[0]["labels"] = [IGNORE_INDEX] * len(fake_input_ids) + features[0]["labels"]
|
||||
|
||||
batch_images = fake_images
|
||||
batch_imglens[0] = 1
|
||||
batch_input_ids[0] = features[0]["input_ids"]
|
||||
|
||||
mm_inputs = self.template.mm_plugin.get_mm_inputs(
|
||||
batch_images, batch_videos, batch_imglens, batch_vidlens, batch_input_ids, self.processor
|
||||
batch_images,
|
||||
batch_videos,
|
||||
batch_audios,
|
||||
batch_imglens,
|
||||
batch_vidlens,
|
||||
batch_audlens,
|
||||
batch_input_ids,
|
||||
self.processor,
|
||||
)
|
||||
if "token_type_ids" in mm_inputs:
|
||||
token_type_ids = mm_inputs.pop("token_type_ids")
|
||||
@ -208,6 +240,7 @@ class PairwiseDataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
|
||||
"labels": feature[f"{key}_labels"],
|
||||
"images": feature["images"],
|
||||
"videos": feature["videos"],
|
||||
"audios": feature["audios"],
|
||||
}
|
||||
concatenated_features.append(target_feature)
|
||||
|
||||
@ -231,6 +264,7 @@ class KTODataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
|
||||
"labels": feature["labels"],
|
||||
"images": feature["images"],
|
||||
"videos": feature["videos"],
|
||||
"audios": feature["audios"],
|
||||
}
|
||||
kl_feature = {
|
||||
"input_ids": feature["kl_input_ids"],
|
||||
@ -238,6 +272,7 @@ class KTODataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
|
||||
"labels": feature["kl_labels"],
|
||||
"images": feature["images"],
|
||||
"videos": feature["videos"],
|
||||
"audios": feature["audios"],
|
||||
}
|
||||
target_features.append(target_feature)
|
||||
kl_features.append(kl_feature)
|
||||
|
||||
@ -9,8 +9,17 @@ import torch
|
||||
from transformers.image_utils import get_image_size, to_numpy_array
|
||||
from typing_extensions import override
|
||||
|
||||
from ..extras.constants import IGNORE_INDEX, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
|
||||
from ..extras.packages import is_pillow_available, is_pyav_available, is_transformers_version_greater_than
|
||||
from ..extras.constants import AUDIO_PLACEHOLDER, IGNORE_INDEX, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
|
||||
from ..extras.packages import (
|
||||
is_librosa_available,
|
||||
is_pillow_available,
|
||||
is_pyav_available,
|
||||
is_transformers_version_greater_than,
|
||||
)
|
||||
|
||||
|
||||
if is_librosa_available():
|
||||
import librosa
|
||||
|
||||
|
||||
if is_pillow_available():
|
||||
@ -31,7 +40,9 @@ if is_transformers_version_greater_than("4.45.0"):
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from av.stream import Stream
|
||||
from numpy.typing import NDArray
|
||||
from transformers import PreTrainedTokenizer, ProcessorMixin
|
||||
from transformers.feature_extraction_sequence_utils import SequenceFeatureExtractor
|
||||
from transformers.image_processing_utils import BaseImageProcessor
|
||||
|
||||
class EncodedImage(TypedDict):
|
||||
@ -40,6 +51,7 @@ if TYPE_CHECKING:
|
||||
|
||||
ImageInput = Union[str, bytes, EncodedImage, ImageObject]
|
||||
VideoInput = str
|
||||
AudioInput = Union[str, NDArray]
|
||||
|
||||
|
||||
def _get_paligemma_token_type_ids(
|
||||
@ -60,15 +72,17 @@ def _get_paligemma_token_type_ids(
|
||||
|
||||
|
||||
class BasePlugin:
|
||||
def __init__(self, image_token: Optional[str], video_token: Optional[str]) -> None:
|
||||
def __init__(self, image_token: Optional[str], video_token: Optional[str], audio_token: Optional[str]) -> None:
|
||||
self.image_token = image_token
|
||||
self.video_token = video_token
|
||||
self.audio_token = audio_token
|
||||
self.expand_mm_tokens = True
|
||||
|
||||
def _validate_input(
|
||||
self,
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
) -> None:
|
||||
r"""
|
||||
Validates if this model accepts the input modalities.
|
||||
@ -83,11 +97,16 @@ class BasePlugin:
|
||||
"This model does not support video input. Please check whether the correct `template` is used."
|
||||
)
|
||||
|
||||
if len(audios) != 0 and self.audio_token is None:
|
||||
raise ValueError(
|
||||
"This model does not support audio input. Please check whether the correct `template` is used."
|
||||
)
|
||||
|
||||
def _preprocess_image(self, image: "ImageObject", **kwargs) -> "ImageObject":
|
||||
r"""
|
||||
Pre-processes a single image.
|
||||
"""
|
||||
image_resolution: int = kwargs.get("image_resolution")
|
||||
image_resolution: int = kwargs["image_resolution"]
|
||||
if (image.width * image.height) > image_resolution:
|
||||
resize_factor = math.sqrt(image_resolution / (image.width * image.height))
|
||||
width, height = int(image.width * resize_factor), int(image.height * resize_factor)
|
||||
@ -102,8 +121,8 @@ class BasePlugin:
|
||||
r"""
|
||||
Computes video sample frames according to fps.
|
||||
"""
|
||||
video_fps: float = kwargs.get("video_fps")
|
||||
video_maxlen: int = kwargs.get("video_maxlen")
|
||||
video_fps: float = kwargs["video_fps"]
|
||||
video_maxlen: int = kwargs["video_maxlen"]
|
||||
total_frames = video_stream.frames
|
||||
sample_frames = float(video_stream.duration * video_stream.time_base) * video_fps
|
||||
sample_frames = min(total_frames, video_maxlen, sample_frames)
|
||||
@ -126,7 +145,7 @@ class BasePlugin:
|
||||
image = Image.open(image["path"])
|
||||
|
||||
if not isinstance(image, ImageObject):
|
||||
raise ValueError(f"Expect input is a list of Images, but got {type(image)}.")
|
||||
raise ValueError(f"Expect input is a list of images, but got {type(image)}.")
|
||||
|
||||
results.append(self._preprocess_image(image, **kwargs))
|
||||
|
||||
@ -154,10 +173,28 @@ class BasePlugin:
|
||||
|
||||
return results
|
||||
|
||||
def _regularize_audios(self, audios: Sequence["AudioInput"], **kwargs) -> List["NDArray"]:
|
||||
r"""
|
||||
Regularizes audios to avoid error. Including reading and resampling.
|
||||
"""
|
||||
results = []
|
||||
sampling_rate = kwargs["sampling_rate"]
|
||||
for audio in audios:
|
||||
if isinstance(audio, str):
|
||||
audio = librosa.load(audio, sr=sampling_rate)[0]
|
||||
|
||||
if not isinstance(audio, np.ndarray):
|
||||
raise ValueError(f"Expect input is a list of audios, but got {type(audio)}.")
|
||||
|
||||
results.append(audio)
|
||||
|
||||
return results
|
||||
|
||||
def _get_mm_inputs(
|
||||
self,
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: "ProcessorMixin",
|
||||
) -> Dict[str, "torch.Tensor"]:
|
||||
r"""
|
||||
@ -172,15 +209,17 @@ class BasePlugin:
|
||||
|
||||
It holds num_patches == torch.prod(image_grid_thw)
|
||||
"""
|
||||
image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
|
||||
image_processor: "BaseImageProcessor" = getattr(processor, "image_processor", None)
|
||||
video_processor: "BaseImageProcessor" = getattr(processor, "video_processor", image_processor)
|
||||
input_dict = {"images": None} # default key
|
||||
feature_extractor: "SequenceFeatureExtractor" = getattr(processor, "feature_extractor", None)
|
||||
mm_inputs = {}
|
||||
|
||||
if len(images) != 0:
|
||||
images = self._regularize_images(
|
||||
images,
|
||||
image_resolution=getattr(processor, "image_resolution", 768 * 768),
|
||||
)
|
||||
input_dict["images"] = images
|
||||
mm_inputs.update(image_processor(images, return_tensors="pt"))
|
||||
|
||||
if len(videos) != 0:
|
||||
videos = self._regularize_videos(
|
||||
@ -189,16 +228,23 @@ class BasePlugin:
|
||||
video_fps=getattr(processor, "video_fps", 2.0),
|
||||
video_maxlen=getattr(processor, "video_maxlen", 128),
|
||||
)
|
||||
input_dict["videos"] = videos
|
||||
mm_inputs.update(video_processor(videos, return_tensors="pt"))
|
||||
|
||||
mm_inputs = {}
|
||||
if image_processor != video_processor:
|
||||
if input_dict.get("images") is not None:
|
||||
mm_inputs.update(image_processor(input_dict["images"], return_tensors="pt"))
|
||||
if input_dict.get("videos") is not None:
|
||||
mm_inputs.update(video_processor(input_dict["videos"], return_tensors="pt"))
|
||||
elif input_dict.get("images") is not None or input_dict.get("videos") is not None: # same processor (qwen2-vl)
|
||||
mm_inputs.update(image_processor(**input_dict, return_tensors="pt"))
|
||||
if len(audios) != 0:
|
||||
audios = self._regularize_audios(
|
||||
audios,
|
||||
sampling_rate=getattr(feature_extractor, "sampling_rate", 16000),
|
||||
)
|
||||
mm_inputs.update(
|
||||
feature_extractor(
|
||||
audios,
|
||||
sampling_rate=getattr(feature_extractor, "sampling_rate", 16000),
|
||||
return_attention_mask=True,
|
||||
padding="max_length",
|
||||
return_tensors="pt",
|
||||
)
|
||||
)
|
||||
mm_inputs["feature_attention_mask"] = mm_inputs.pop("attention_mask") # prevent conflicts
|
||||
|
||||
return mm_inputs
|
||||
|
||||
@ -207,12 +253,13 @@ class BasePlugin:
|
||||
messages: Sequence[Dict[str, str]],
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> List[Dict[str, str]]:
|
||||
r"""
|
||||
Pre-processes input messages before tokenization for VLMs.
|
||||
"""
|
||||
self._validate_input(images, videos)
|
||||
self._validate_input(images, videos, audios)
|
||||
return messages
|
||||
|
||||
def process_token_ids(
|
||||
@ -221,21 +268,24 @@ class BasePlugin:
|
||||
labels: Optional[List[int]],
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
tokenizer: "PreTrainedTokenizer",
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> Tuple[List[int], Optional[List[int]]]:
|
||||
r"""
|
||||
Pre-processes token ids after tokenization for VLMs.
|
||||
"""
|
||||
self._validate_input(images, videos)
|
||||
self._validate_input(images, videos, audios)
|
||||
return input_ids, labels
|
||||
|
||||
def get_mm_inputs(
|
||||
self,
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
imglens: Sequence[int],
|
||||
vidlens: Sequence[int],
|
||||
audlens: Sequence[int],
|
||||
batch_ids: Sequence[List[int]],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
|
||||
@ -247,10 +297,11 @@ class BasePlugin:
|
||||
videos: a list of video inputs, shape (num_videos,)
|
||||
imglens: number of images in each sample, shape (batch_size,)
|
||||
vidlens: number of videos in each sample, shape (batch_size,)
|
||||
audlens: number of audios in each sample, shape (batch_size,)
|
||||
batch_ids: token ids of input samples, shape (batch_size, seq_len)
|
||||
processor: a processor for pre-processing images and videos
|
||||
"""
|
||||
self._validate_input(images, videos)
|
||||
self._validate_input(images, videos, audios)
|
||||
return {}
|
||||
|
||||
|
||||
@ -261,9 +312,10 @@ class LlavaPlugin(BasePlugin):
|
||||
messages: Sequence[Dict[str, str]],
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> List[Dict[str, str]]:
|
||||
self._validate_input(images, videos)
|
||||
self._validate_input(images, videos, audios)
|
||||
num_image_tokens = 0
|
||||
image_seqlen = getattr(processor, "image_seqlen") if self.expand_mm_tokens else 1
|
||||
messages = deepcopy(messages)
|
||||
@ -285,13 +337,15 @@ class LlavaPlugin(BasePlugin):
|
||||
self,
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
imglens: Sequence[int],
|
||||
vidlens: Sequence[int],
|
||||
audlens: Sequence[int],
|
||||
batch_ids: Sequence[List[int]],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
|
||||
self._validate_input(images, videos)
|
||||
return self._get_mm_inputs(images, videos, processor)
|
||||
self._validate_input(images, videos, audios)
|
||||
return self._get_mm_inputs(images, videos, audios, processor)
|
||||
|
||||
|
||||
class LlavaNextPlugin(BasePlugin):
|
||||
@ -301,12 +355,13 @@ class LlavaNextPlugin(BasePlugin):
|
||||
messages: Sequence[Dict[str, str]],
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> List[Dict[str, str]]:
|
||||
self._validate_input(images, videos)
|
||||
self._validate_input(images, videos, audios)
|
||||
num_image_tokens = 0
|
||||
messages = deepcopy(messages)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, processor)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
|
||||
if "image_sizes" in mm_inputs:
|
||||
image_sizes = iter(mm_inputs["image_sizes"])
|
||||
|
||||
@ -339,13 +394,15 @@ class LlavaNextPlugin(BasePlugin):
|
||||
self,
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
imglens: Sequence[int],
|
||||
vidlens: Sequence[int],
|
||||
audlens: Sequence[int],
|
||||
batch_ids: Sequence[List[int]],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
|
||||
self._validate_input(images, videos)
|
||||
return self._get_mm_inputs(images, videos, processor)
|
||||
self._validate_input(images, videos, audios)
|
||||
return self._get_mm_inputs(images, videos, audios, processor)
|
||||
|
||||
|
||||
class LlavaNextVideoPlugin(BasePlugin):
|
||||
@ -355,12 +412,13 @@ class LlavaNextVideoPlugin(BasePlugin):
|
||||
messages: Sequence[Dict[str, str]],
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> List[Dict[str, str]]:
|
||||
self._validate_input(images, videos)
|
||||
self._validate_input(images, videos, audios)
|
||||
num_image_tokens, num_video_tokens = 0, 0
|
||||
messages = deepcopy(messages)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, processor)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
|
||||
if "pixel_values" in mm_inputs:
|
||||
image_sizes = iter(mm_inputs["image_sizes"])
|
||||
height, width = get_image_size(to_numpy_array(mm_inputs["pixel_values"][0][0]))
|
||||
@ -408,13 +466,15 @@ class LlavaNextVideoPlugin(BasePlugin):
|
||||
self,
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
imglens: Sequence[int],
|
||||
vidlens: Sequence[int],
|
||||
audlens: Sequence[int],
|
||||
batch_ids: Sequence[List[int]],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
|
||||
self._validate_input(images, videos)
|
||||
return self._get_mm_inputs(images, videos, processor)
|
||||
self._validate_input(images, videos, audios)
|
||||
return self._get_mm_inputs(images, videos, audios, processor)
|
||||
|
||||
|
||||
class MiniCPMVPlugin(BasePlugin):
|
||||
@ -424,26 +484,30 @@ class MiniCPMVPlugin(BasePlugin):
|
||||
messages: Sequence[Dict[str, str]],
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> List[Dict[str, str]]:
|
||||
self._validate_input(images, videos)
|
||||
self._validate_input(images, videos, audios)
|
||||
num_image_tokens = 0
|
||||
num_video_tokens = 0
|
||||
num_audio_tokens = 0
|
||||
messages = deepcopy(messages)
|
||||
image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
|
||||
mm_inputs = {}
|
||||
audio_inputs = {}
|
||||
audio_parts = []
|
||||
if len(images) != 0 and len(videos) != 0:
|
||||
raise ValueError("MiniCPM-V model does not support input images and videos at the same time.")
|
||||
|
||||
if len(videos) != 0:
|
||||
max_slice_nums = 2
|
||||
use_image_id = False
|
||||
mm_inputs = self._get_mm_inputs([], videos, processor)
|
||||
mm_inputs = self._get_mm_inputs([], videos, [], processor)
|
||||
else:
|
||||
max_slice_nums = image_processor.max_slice_nums
|
||||
use_image_id = image_processor.use_image_id
|
||||
|
||||
for message in messages:
|
||||
for i, message in enumerate(messages):
|
||||
content = message["content"]
|
||||
while IMAGE_PLACEHOLDER in content:
|
||||
content = content.replace(IMAGE_PLACEHOLDER, "{{image}}", 1)
|
||||
@ -454,15 +518,25 @@ class MiniCPMVPlugin(BasePlugin):
|
||||
content = content.replace(VIDEO_PLACEHOLDER, "{{image}}" * video_seqlen, 1)
|
||||
num_video_tokens += 1
|
||||
|
||||
message["content"] = content.replace("{{image}}", "(<image>./</image>)")
|
||||
while AUDIO_PLACEHOLDER in content:
|
||||
audio_parts.append(i)
|
||||
content = content.replace(AUDIO_PLACEHOLDER, "{{audio}}", 1)
|
||||
num_audio_tokens += 1
|
||||
|
||||
message["content"] = content.replace("{{image}}", "(<image>./</image>)").replace(
|
||||
"{{audio}}", "(<audio>./</audio>)"
|
||||
)
|
||||
|
||||
if num_image_tokens > 0:
|
||||
mm_inputs = self._get_mm_inputs(images, [], processor)
|
||||
mm_inputs = self._get_mm_inputs(images, [], [], processor)
|
||||
|
||||
if num_audio_tokens > 0:
|
||||
audio_parts_ls = [audio_parts]
|
||||
audio_inputs = self._get_mm_inputs([], [], audios, processor, audio_parts_ls=audio_parts_ls, ret_phs=True)
|
||||
|
||||
if mm_inputs:
|
||||
pattern = "(<image>./</image>)"
|
||||
image_sizes = mm_inputs["image_sizes"]
|
||||
|
||||
for index, message in enumerate(messages):
|
||||
text = message["content"]
|
||||
image_tags = re.findall(pattern, text)
|
||||
@ -480,12 +554,29 @@ class MiniCPMVPlugin(BasePlugin):
|
||||
final_text += text_chunks[-1]
|
||||
messages[index]["content"] = final_text
|
||||
|
||||
if audio_inputs:
|
||||
pattern = "(<audio>./</audio>)"
|
||||
for index, message in enumerate(messages):
|
||||
text = message["content"]
|
||||
audio_tags = re.findall(pattern, text)
|
||||
text_chunks = text.split(pattern)
|
||||
final_text = ""
|
||||
for i in range(len(audio_tags)):
|
||||
audio_placeholder = audio_inputs["audio_phs"][0][i]
|
||||
final_text = final_text + text_chunks[i] + audio_placeholder
|
||||
|
||||
final_text += text_chunks[-1]
|
||||
messages[index]["content"] = final_text
|
||||
|
||||
if len(images) != num_image_tokens:
|
||||
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens.")
|
||||
|
||||
if len(videos) != num_video_tokens:
|
||||
raise ValueError(f"The number of videos does not match the number of {VIDEO_PLACEHOLDER} tokens.")
|
||||
|
||||
if len(audios) != num_audio_tokens:
|
||||
raise ValueError(f"The number of audios does not match the number of {AUDIO_PLACEHOLDER} tokens.")
|
||||
|
||||
return messages
|
||||
|
||||
@override
|
||||
@ -493,6 +584,7 @@ class MiniCPMVPlugin(BasePlugin):
|
||||
self,
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: "ProcessorMixin",
|
||||
**kwargs,
|
||||
) -> Dict[str, "torch.Tensor"]:
|
||||
@ -528,6 +620,30 @@ class MiniCPMVPlugin(BasePlugin):
|
||||
video_inputs = image_processor(videos, do_pad=True, max_slice_nums=2, return_tensors="pt")
|
||||
mm_inputs.update(video_inputs)
|
||||
|
||||
if len(audios) != 0:
|
||||
audio_parts_ls = kwargs.get("audio_parts_ls", None)
|
||||
new_audios = []
|
||||
for audio in audios:
|
||||
if not isinstance(audio, np.ndarray):
|
||||
audio = librosa.load(audio, sr=processor.feature_extractor.sampling_rate)[0]
|
||||
new_audios.append(audio)
|
||||
|
||||
audios_ls = []
|
||||
idx = 0
|
||||
for audio_parts in audio_parts_ls:
|
||||
audios_ls.append(new_audios[idx : idx + len(audio_parts)])
|
||||
idx += len(audio_parts)
|
||||
|
||||
audio_features, audio_feature_lens, audio_phs = processor.audio_feature_extract(
|
||||
audios_ls,
|
||||
audio_parts_ls,
|
||||
chunk_input=True,
|
||||
sampling_rate=16000,
|
||||
)
|
||||
mm_inputs.update({"audio_features": audio_features, "audio_feature_lens": audio_feature_lens})
|
||||
if kwargs.get("ret_phs", False):
|
||||
mm_inputs.update({"audio_phs": audio_phs})
|
||||
|
||||
return mm_inputs
|
||||
|
||||
@override
|
||||
@ -535,12 +651,16 @@ class MiniCPMVPlugin(BasePlugin):
|
||||
self,
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
imglens: Sequence[int],
|
||||
vidlens: Sequence[int],
|
||||
audlens: Sequence[int],
|
||||
batch_ids: Sequence[List[int]],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
|
||||
self._validate_input(images, videos)
|
||||
self._validate_input(images, videos, audios)
|
||||
|
||||
# image bound
|
||||
image_bounds_list = []
|
||||
valid_image_nums_ls = []
|
||||
for i, input_ids in enumerate(batch_ids):
|
||||
@ -561,8 +681,38 @@ class MiniCPMVPlugin(BasePlugin):
|
||||
)
|
||||
image_bounds_list.append(image_bounds)
|
||||
|
||||
mm_inputs = self._get_mm_inputs(images, videos, processor, valid_image_nums_ls=valid_image_nums_ls)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, [], processor, valid_image_nums_ls=valid_image_nums_ls)
|
||||
if "tgt_sizes" not in mm_inputs:
|
||||
dummy_data = [torch.empty(0) for _ in range(len(batch_ids))]
|
||||
mm_inputs.update({"tgt_sizes": dummy_data, "pixel_values": dummy_data, "image_sizes": dummy_data})
|
||||
|
||||
mm_inputs.update({"image_bound": image_bounds_list})
|
||||
|
||||
if len(audios) > 0:
|
||||
# audio bound
|
||||
audio_bounds_ls = []
|
||||
spk_bounds_ls = []
|
||||
audio_parts_ls = []
|
||||
|
||||
for input_ids, audiolen in zip(batch_ids, audlens):
|
||||
input_ids_ = torch.tensor(input_ids)
|
||||
audio_start_idx = torch.where(input_ids_ == processor.tokenizer.audio_start_id)[0]
|
||||
audio_end_idx = torch.where(input_ids_ == processor.tokenizer.audio_end_id)[0]
|
||||
assert len(audio_start_idx) == len(audio_end_idx)
|
||||
audio_bounds = torch.hstack([(audio_start_idx + 1).unsqueeze(-1), audio_end_idx.unsqueeze(-1)])
|
||||
audio_bounds_ls.append(audio_bounds)
|
||||
audio_parts_ls.append(list(range(audiolen)))
|
||||
|
||||
spk_start_idx = torch.where(input_ids_ == processor.tokenizer.spk_start_id)[0]
|
||||
spk_end_idx = torch.where(input_ids_ == processor.tokenizer.spk_end_id)[0]
|
||||
assert len(spk_start_idx) == len(spk_end_idx)
|
||||
spk_bounds = torch.hstack([(spk_start_idx + 1).unsqueeze(-1), spk_end_idx.unsqueeze(-1)])
|
||||
spk_bounds_ls.append(spk_bounds)
|
||||
|
||||
audio_inputs = self._get_mm_inputs([], [], audios, processor, audio_parts_ls=audio_parts_ls)
|
||||
mm_inputs.update(audio_inputs)
|
||||
mm_inputs.update({"audio_bounds": audio_bounds_ls, "spk_bounds": spk_bounds_ls})
|
||||
|
||||
return mm_inputs
|
||||
|
||||
|
||||
@ -573,9 +723,10 @@ class MllamaPlugin(BasePlugin):
|
||||
messages: Sequence[Dict[str, str]],
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> List[Dict[str, str]]:
|
||||
self._validate_input(images, videos)
|
||||
self._validate_input(images, videos, audios)
|
||||
num_image_tokens = 0
|
||||
messages = deepcopy(messages)
|
||||
for message in messages:
|
||||
@ -593,6 +744,7 @@ class MllamaPlugin(BasePlugin):
|
||||
self,
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: "ProcessorMixin",
|
||||
**kwargs,
|
||||
) -> Dict[str, "torch.Tensor"]:
|
||||
@ -617,17 +769,20 @@ class MllamaPlugin(BasePlugin):
|
||||
|
||||
return image_processor(batch_images, return_tensors="pt")
|
||||
|
||||
@override
|
||||
def get_mm_inputs(
|
||||
self,
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
imglens: Sequence[int],
|
||||
vidlens: Sequence[int],
|
||||
audlens: Sequence[int],
|
||||
batch_ids: Sequence[List[int]],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
|
||||
self._validate_input(images, videos)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, processor, imglens=imglens)
|
||||
self._validate_input(images, videos, audios)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, audios, processor, imglens=imglens)
|
||||
num_tiles = mm_inputs.pop("num_tiles")
|
||||
image_token_id = getattr(processor, "image_token_id")
|
||||
max_image_tiles = getattr(processor.image_processor, "max_image_tiles")
|
||||
@ -652,9 +807,10 @@ class PaliGemmaPlugin(BasePlugin):
|
||||
messages: Sequence[Dict[str, str]],
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> List[Dict[str, str]]:
|
||||
self._validate_input(images, videos)
|
||||
self._validate_input(images, videos, audios)
|
||||
num_image_tokens = 0
|
||||
messages = deepcopy(messages)
|
||||
for message in messages:
|
||||
@ -677,10 +833,11 @@ class PaliGemmaPlugin(BasePlugin):
|
||||
labels: Optional[List[int]],
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
tokenizer: "PreTrainedTokenizer",
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> Tuple[List[int], Optional[List[int]]]:
|
||||
self._validate_input(images, videos)
|
||||
self._validate_input(images, videos, audios)
|
||||
num_images = len(images)
|
||||
image_seqlen = num_images * getattr(processor, "image_seqlen") if self.expand_mm_tokens else 0 # skip mm token
|
||||
image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
|
||||
@ -695,14 +852,16 @@ class PaliGemmaPlugin(BasePlugin):
|
||||
self,
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
imglens: Sequence[int],
|
||||
vidlens: Sequence[int],
|
||||
audlens: Sequence[int],
|
||||
batch_ids: Sequence[List[int]],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
|
||||
self._validate_input(images, videos)
|
||||
self._validate_input(images, videos, audios)
|
||||
seqlens = [len(input_ids) for input_ids in batch_ids]
|
||||
mm_inputs = self._get_mm_inputs(images, videos, processor)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
|
||||
mm_inputs["token_type_ids"] = _get_paligemma_token_type_ids(imglens, seqlens, processor)
|
||||
return mm_inputs
|
||||
|
||||
@ -714,9 +873,10 @@ class PixtralPlugin(BasePlugin):
|
||||
messages: Sequence[Dict[str, str]],
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> List[Dict[str, str]]:
|
||||
self._validate_input(images, videos)
|
||||
self._validate_input(images, videos, audios)
|
||||
patch_size = getattr(processor, "patch_size")
|
||||
image_token = getattr(processor, "image_token")
|
||||
image_break_token = getattr(processor, "image_break_token")
|
||||
@ -724,7 +884,7 @@ class PixtralPlugin(BasePlugin):
|
||||
|
||||
num_image_tokens = 0
|
||||
messages = deepcopy(messages)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, processor)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
|
||||
image_input_sizes = mm_inputs.get("image_sizes", None)
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
@ -759,13 +919,15 @@ class PixtralPlugin(BasePlugin):
|
||||
self,
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
imglens: Sequence[int],
|
||||
vidlens: Sequence[int],
|
||||
audlens: Sequence[int],
|
||||
batch_ids: Sequence[List[int]],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
|
||||
self._validate_input(images, videos)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, processor)
|
||||
self._validate_input(images, videos, audios)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
|
||||
if mm_inputs.get("pixel_values"):
|
||||
mm_inputs["pixel_values"] = mm_inputs["pixel_values"][0]
|
||||
|
||||
@ -773,6 +935,58 @@ class PixtralPlugin(BasePlugin):
|
||||
return mm_inputs
|
||||
|
||||
|
||||
class Qwen2AudioPlugin(BasePlugin):
|
||||
@override
|
||||
def process_messages(
|
||||
self,
|
||||
messages: Sequence[Dict[str, str]],
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> List[Dict[str, str]]:
|
||||
self._validate_input(images, videos, audios)
|
||||
bos_token: str = getattr(processor, "audio_bos_token")
|
||||
eos_token: str = getattr(processor, "audio_eos_token")
|
||||
mm_inputs = self._get_mm_inputs([], [], audios, processor)
|
||||
if "feature_attention_mask" in mm_inputs:
|
||||
audio_lengths = mm_inputs["feature_attention_mask"].sum(-1).tolist()
|
||||
|
||||
num_audio_tokens = 0
|
||||
for message in messages:
|
||||
content = message["content"]
|
||||
while AUDIO_PLACEHOLDER in content:
|
||||
audio_length = audio_lengths.pop(0)
|
||||
input_length = (audio_length - 1) // 2 + 1
|
||||
audio_seqlen = (input_length - 2) // 2 + 1 if self.expand_mm_tokens else 1
|
||||
content = content.replace(
|
||||
AUDIO_PLACEHOLDER, f"{bos_token}{self.audio_token * audio_seqlen}{eos_token}", 1
|
||||
)
|
||||
num_audio_tokens += 1
|
||||
|
||||
message["content"] = content
|
||||
|
||||
if len(audios) != num_audio_tokens:
|
||||
raise ValueError(f"The number of audios does not match the number of {AUDIO_PLACEHOLDER} tokens.")
|
||||
|
||||
return messages
|
||||
|
||||
@override
|
||||
def get_mm_inputs(
|
||||
self,
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
imglens: Sequence[int],
|
||||
vidlens: Sequence[int],
|
||||
audlens: Sequence[int],
|
||||
batch_ids: Sequence[List[int]],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
|
||||
self._validate_input(images, videos, audios)
|
||||
return self._get_mm_inputs(images, videos, audios, processor)
|
||||
|
||||
|
||||
class Qwen2vlPlugin(BasePlugin):
|
||||
@override
|
||||
def _preprocess_image(self, image: "ImageObject", **kwargs) -> "ImageObject":
|
||||
@ -820,12 +1034,13 @@ class Qwen2vlPlugin(BasePlugin):
|
||||
messages: Sequence[Dict[str, str]],
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> List[Dict[str, str]]:
|
||||
self._validate_input(images, videos)
|
||||
self._validate_input(images, videos, audios)
|
||||
image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
|
||||
merge_length: int = getattr(image_processor, "merge_size") ** 2
|
||||
mm_inputs = self._get_mm_inputs(images, videos, processor)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
|
||||
image_grid_thw = mm_inputs.get("image_grid_thw", [])
|
||||
video_grid_thw = mm_inputs.get("video_grid_thw", [])
|
||||
|
||||
@ -868,13 +1083,15 @@ class Qwen2vlPlugin(BasePlugin):
|
||||
self,
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
imglens: Sequence[int],
|
||||
vidlens: Sequence[int],
|
||||
audlens: Sequence[int],
|
||||
batch_ids: Sequence[List[int]],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
|
||||
self._validate_input(images, videos)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, processor)
|
||||
self._validate_input(images, videos, audios)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
|
||||
image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
|
||||
if "second_per_grid_ts" in getattr(image_processor, "model_input_names", []) and "video_grid_thw" in mm_inputs:
|
||||
video_fps = getattr(processor, "video_fps", 2.0)
|
||||
@ -892,12 +1109,13 @@ class VideoLlavaPlugin(BasePlugin):
|
||||
messages: Sequence[Dict[str, str]],
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> List[Dict[str, str]]:
|
||||
self._validate_input(images, videos)
|
||||
self._validate_input(images, videos, audios)
|
||||
num_image_tokens, num_video_tokens = 0, 0
|
||||
messages = deepcopy(messages)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, processor)
|
||||
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
|
||||
num_frames = 0
|
||||
has_images = "pixel_values_images" in mm_inputs
|
||||
has_videos = "pixel_values_videos" in mm_inputs
|
||||
@ -945,13 +1163,15 @@ class VideoLlavaPlugin(BasePlugin):
|
||||
self,
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
imglens: Sequence[int],
|
||||
vidlens: Sequence[int],
|
||||
audlens: Sequence[int],
|
||||
batch_ids: Sequence[List[int]],
|
||||
processor: Optional["ProcessorMixin"],
|
||||
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
|
||||
self._validate_input(images, videos)
|
||||
return self._get_mm_inputs(images, videos, processor)
|
||||
self._validate_input(images, videos, audios)
|
||||
return self._get_mm_inputs(images, videos, audios, processor)
|
||||
|
||||
|
||||
PLUGINS = {
|
||||
@ -963,6 +1183,7 @@ PLUGINS = {
|
||||
"mllama": MllamaPlugin,
|
||||
"paligemma": PaliGemmaPlugin,
|
||||
"pixtral": PixtralPlugin,
|
||||
"qwen2_audio": Qwen2AudioPlugin,
|
||||
"qwen2_vl": Qwen2vlPlugin,
|
||||
"video_llava": VideoLlavaPlugin,
|
||||
}
|
||||
@ -972,9 +1193,10 @@ def get_mm_plugin(
|
||||
name: str,
|
||||
image_token: Optional[str] = None,
|
||||
video_token: Optional[str] = None,
|
||||
audio_token: Optional[str] = None,
|
||||
) -> "BasePlugin":
|
||||
plugin_class = PLUGINS.get(name, None)
|
||||
if plugin_class is None:
|
||||
raise ValueError(f"Multimodal plugin `{name}` not found.")
|
||||
|
||||
return plugin_class(image_token, video_token)
|
||||
return plugin_class(image_token, video_token, audio_token)
|
||||
|
||||
@ -44,6 +44,7 @@ class DatasetAttr:
|
||||
tools: Optional[str] = None
|
||||
images: Optional[str] = None
|
||||
videos: Optional[str] = None
|
||||
audios: Optional[str] = None
|
||||
# rlhf columns
|
||||
chosen: Optional[str] = None
|
||||
rejected: Optional[str] = None
|
||||
@ -135,7 +136,7 @@ def get_dataset_list(dataset_names: Optional[Sequence[str]], dataset_dir: str) -
|
||||
dataset_attr.set_attr("num_samples", dataset_info[name])
|
||||
|
||||
if "columns" in dataset_info[name]:
|
||||
column_names = ["system", "tools", "images", "videos", "chosen", "rejected", "kto_tag"]
|
||||
column_names = ["system", "tools", "images", "videos", "audios", "chosen", "rejected", "kto_tag"]
|
||||
if dataset_attr.formatting == "alpaca":
|
||||
column_names.extend(["prompt", "query", "response", "history"])
|
||||
else:
|
||||
|
||||
@ -24,7 +24,7 @@ if TYPE_CHECKING:
|
||||
from transformers import PreTrainedTokenizer, ProcessorMixin
|
||||
|
||||
from ...hparams import DataArguments
|
||||
from ..mm_plugin import ImageInput, VideoInput
|
||||
from ..mm_plugin import AudioInput, ImageInput, VideoInput
|
||||
from ..template import Template
|
||||
|
||||
|
||||
@ -39,6 +39,7 @@ def _encode_feedback_example(
|
||||
tools: Optional[str],
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
template: "Template",
|
||||
tokenizer: "PreTrainedTokenizer",
|
||||
processor: Optional["ProcessorMixin"],
|
||||
@ -56,8 +57,8 @@ def _encode_feedback_example(
|
||||
else:
|
||||
kl_messages = prompt + [kl_response[1]]
|
||||
|
||||
messages = template.mm_plugin.process_messages(messages, images, videos, processor)
|
||||
kl_messages = template.mm_plugin.process_messages(kl_messages, images, videos, processor)
|
||||
messages = template.mm_plugin.process_messages(messages, images, videos, audios, processor)
|
||||
kl_messages = template.mm_plugin.process_messages(kl_messages, images, videos, audios, processor)
|
||||
prompt_ids, response_ids = template.encode_oneturn(tokenizer, messages, system, tools)
|
||||
kl_prompt_ids, kl_response_ids = template.encode_oneturn(tokenizer, kl_messages, system, tools)
|
||||
|
||||
@ -65,8 +66,12 @@ def _encode_feedback_example(
|
||||
response_ids += [tokenizer.eos_token_id]
|
||||
kl_response_ids += [tokenizer.eos_token_id]
|
||||
|
||||
prompt_ids, _ = template.mm_plugin.process_token_ids(prompt_ids, None, images, videos, tokenizer, processor)
|
||||
kl_prompt_ids, _ = template.mm_plugin.process_token_ids(kl_prompt_ids, None, images, videos, tokenizer, processor)
|
||||
prompt_ids, _ = template.mm_plugin.process_token_ids(
|
||||
prompt_ids, None, images, videos, audios, tokenizer, processor
|
||||
)
|
||||
kl_prompt_ids, _ = template.mm_plugin.process_token_ids(
|
||||
kl_prompt_ids, None, images, videos, audios, tokenizer, processor
|
||||
)
|
||||
|
||||
source_len, target_len = infer_seqlen(len(prompt_ids), len(response_ids), cutoff_len)
|
||||
prompt_ids = prompt_ids[:source_len]
|
||||
@ -107,6 +112,7 @@ def preprocess_feedback_dataset(
|
||||
tools=examples["_tools"][i],
|
||||
images=examples["_images"][i] or [],
|
||||
videos=examples["_videos"][i] or [],
|
||||
audios=examples["_audios"][i] or [],
|
||||
template=template,
|
||||
tokenizer=tokenizer,
|
||||
processor=processor,
|
||||
@ -121,6 +127,7 @@ def preprocess_feedback_dataset(
|
||||
model_inputs["kto_tags"].append(kto_tag)
|
||||
model_inputs["images"].append(examples["_images"][i])
|
||||
model_inputs["videos"].append(examples["_videos"][i])
|
||||
model_inputs["audios"].append(examples["_audios"][i])
|
||||
|
||||
desirable_num = sum([1 for tag in model_inputs["kto_tags"] if tag])
|
||||
undesirable_num = len(model_inputs["kto_tags"]) - desirable_num
|
||||
|
||||
@ -24,7 +24,7 @@ if TYPE_CHECKING:
|
||||
from transformers import PreTrainedTokenizer, ProcessorMixin
|
||||
|
||||
from ...hparams import DataArguments
|
||||
from ..mm_plugin import ImageInput, VideoInput
|
||||
from ..mm_plugin import AudioInput, ImageInput, VideoInput
|
||||
from ..template import Template
|
||||
|
||||
|
||||
@ -38,13 +38,14 @@ def _encode_pairwise_example(
|
||||
tools: Optional[str],
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
template: "Template",
|
||||
tokenizer: "PreTrainedTokenizer",
|
||||
processor: Optional["ProcessorMixin"],
|
||||
cutoff_len: int,
|
||||
) -> Tuple[List[int], List[int], List[int], List[int]]:
|
||||
chosen_messages = template.mm_plugin.process_messages(prompt + [response[0]], images, videos, processor)
|
||||
rejected_messages = template.mm_plugin.process_messages(prompt + [response[1]], images, videos, processor)
|
||||
chosen_messages = template.mm_plugin.process_messages(prompt + [response[0]], images, videos, audios, processor)
|
||||
rejected_messages = template.mm_plugin.process_messages(prompt + [response[1]], images, videos, audios, processor)
|
||||
prompt_ids, chosen_ids = template.encode_oneturn(tokenizer, chosen_messages, system, tools)
|
||||
_, rejected_ids = template.encode_oneturn(tokenizer, rejected_messages, system, tools)
|
||||
|
||||
@ -52,7 +53,9 @@ def _encode_pairwise_example(
|
||||
chosen_ids += [tokenizer.eos_token_id]
|
||||
rejected_ids += [tokenizer.eos_token_id]
|
||||
|
||||
prompt_ids, _ = template.mm_plugin.process_token_ids(prompt_ids, None, images, videos, tokenizer, processor)
|
||||
prompt_ids, _ = template.mm_plugin.process_token_ids(
|
||||
prompt_ids, None, images, videos, audios, tokenizer, processor
|
||||
)
|
||||
# consider the response is more important
|
||||
source_len, target_len = infer_seqlen(len(prompt_ids), max(len(chosen_ids), len(rejected_ids)), cutoff_len)
|
||||
prompt_ids = prompt_ids[:source_len]
|
||||
@ -89,6 +92,7 @@ def preprocess_pairwise_dataset(
|
||||
tools=examples["_tools"][i],
|
||||
images=examples["_images"][i] or [],
|
||||
videos=examples["_videos"][i] or [],
|
||||
audios=examples["_audios"][i] or [],
|
||||
template=template,
|
||||
tokenizer=tokenizer,
|
||||
processor=processor,
|
||||
@ -102,6 +106,7 @@ def preprocess_pairwise_dataset(
|
||||
model_inputs["rejected_labels"].append(rejected_labels)
|
||||
model_inputs["images"].append(examples["_images"][i])
|
||||
model_inputs["videos"].append(examples["_videos"][i])
|
||||
model_inputs["audios"].append(examples["_audios"][i])
|
||||
|
||||
return model_inputs
|
||||
|
||||
|
||||
@ -24,7 +24,7 @@ if TYPE_CHECKING:
|
||||
from transformers import PreTrainedTokenizer, ProcessorMixin
|
||||
|
||||
from ...hparams import DataArguments
|
||||
from ..mm_plugin import ImageInput, VideoInput
|
||||
from ..mm_plugin import AudioInput, ImageInput, VideoInput
|
||||
from ..template import Template
|
||||
|
||||
|
||||
@ -38,6 +38,7 @@ def _encode_supervised_example(
|
||||
tools: Optional[str],
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
template: "Template",
|
||||
tokenizer: "PreTrainedTokenizer",
|
||||
processor: Optional["ProcessorMixin"],
|
||||
@ -45,8 +46,8 @@ def _encode_supervised_example(
|
||||
train_on_prompt: bool,
|
||||
mask_history: bool,
|
||||
) -> Tuple[List[int], List[int]]:
|
||||
messages = template.mm_plugin.process_messages(prompt + response, images, videos, processor)
|
||||
input_ids, labels = template.mm_plugin.process_token_ids([], [], images, videos, tokenizer, processor)
|
||||
messages = template.mm_plugin.process_messages(prompt + response, images, videos, audios, processor)
|
||||
input_ids, labels = template.mm_plugin.process_token_ids([], [], images, videos, audios, tokenizer, processor)
|
||||
encoded_pairs = template.encode_multiturn(tokenizer, messages, system, tools)
|
||||
total_length = len(input_ids) + (1 if template.efficient_eos else 0)
|
||||
if mask_history:
|
||||
@ -111,6 +112,7 @@ def preprocess_supervised_dataset(
|
||||
tools=examples["_tools"][i],
|
||||
images=examples["_images"][i] or [],
|
||||
videos=examples["_videos"][i] or [],
|
||||
audios=examples["_audios"][i] or [],
|
||||
template=template,
|
||||
tokenizer=tokenizer,
|
||||
processor=processor,
|
||||
@ -123,6 +125,7 @@ def preprocess_supervised_dataset(
|
||||
model_inputs["labels"].append(labels)
|
||||
model_inputs["images"].append(examples["_images"][i])
|
||||
model_inputs["videos"].append(examples["_videos"][i])
|
||||
model_inputs["audios"].append(examples["_audios"][i])
|
||||
|
||||
return model_inputs
|
||||
|
||||
@ -138,7 +141,7 @@ def preprocess_packed_supervised_dataset(
|
||||
# build inputs with format `<bos> X1 Y1 <eos> <bos> X2 Y2 <eos>`
|
||||
# and labels with format `<ignore> ... <ignore> Y1 <eos> <ignore> ... <ignore> Y2 <eos>`
|
||||
valid_num = 0
|
||||
batch_input_ids, batch_labels, batch_images, batch_videos = [], [], [], []
|
||||
batch_input_ids, batch_labels, batch_images, batch_videos, batch_audios = [], [], [], [], []
|
||||
lengths = []
|
||||
length2indexes = defaultdict(list)
|
||||
for i in range(len(examples["_prompt"])):
|
||||
@ -155,6 +158,7 @@ def preprocess_packed_supervised_dataset(
|
||||
tools=examples["_tools"][i],
|
||||
images=examples["_images"][i] or [],
|
||||
videos=examples["_videos"][i] or [],
|
||||
audios=examples["_audios"][i] or [],
|
||||
template=template,
|
||||
tokenizer=tokenizer,
|
||||
processor=processor,
|
||||
@ -172,19 +176,21 @@ def preprocess_packed_supervised_dataset(
|
||||
batch_labels.append(labels)
|
||||
batch_images.append(examples["_images"][i] or [])
|
||||
batch_videos.append(examples["_videos"][i] or [])
|
||||
batch_audios.append(examples["_audios"][i] or [])
|
||||
valid_num += 1
|
||||
|
||||
model_inputs = defaultdict(list)
|
||||
knapsacks = greedy_knapsack(lengths, data_args.cutoff_len - 1) # reserved for the padding token
|
||||
for knapsack in knapsacks:
|
||||
packed_input_ids, packed_attention_masks, packed_labels = [], [], []
|
||||
packed_images, packed_videos = [], []
|
||||
packed_images, packed_videos, packed_audios = [], [], []
|
||||
for i, length in enumerate(knapsack):
|
||||
index = length2indexes[length].pop()
|
||||
packed_input_ids += batch_input_ids[index]
|
||||
packed_labels += batch_labels[index]
|
||||
packed_images += batch_images[index]
|
||||
packed_videos += batch_videos[index]
|
||||
packed_audios += batch_audios[index]
|
||||
if data_args.neat_packing:
|
||||
packed_attention_masks += [i + 1] * len(batch_input_ids[index]) # start from 1
|
||||
else:
|
||||
@ -207,6 +213,7 @@ def preprocess_packed_supervised_dataset(
|
||||
model_inputs["labels"].append(packed_labels)
|
||||
model_inputs["images"].append(packed_images or None)
|
||||
model_inputs["videos"].append(packed_videos or None)
|
||||
model_inputs["audios"].append(packed_audios or None)
|
||||
|
||||
return model_inputs
|
||||
|
||||
|
||||
@ -24,7 +24,7 @@ if TYPE_CHECKING:
|
||||
from transformers import PreTrainedTokenizer, ProcessorMixin
|
||||
|
||||
from ...hparams import DataArguments
|
||||
from ..mm_plugin import ImageInput, VideoInput
|
||||
from ..mm_plugin import AudioInput, ImageInput, VideoInput
|
||||
from ..template import Template
|
||||
|
||||
|
||||
@ -38,6 +38,7 @@ def _encode_unsupervised_example(
|
||||
tools: Optional[str],
|
||||
images: Sequence["ImageInput"],
|
||||
videos: Sequence["VideoInput"],
|
||||
audios: Sequence["AudioInput"],
|
||||
template: "Template",
|
||||
tokenizer: "PreTrainedTokenizer",
|
||||
processor: Optional["ProcessorMixin"],
|
||||
@ -48,12 +49,12 @@ def _encode_unsupervised_example(
|
||||
else:
|
||||
messages = prompt + [{"role": Role.ASSISTANT.value, "content": ""}]
|
||||
|
||||
messages = template.mm_plugin.process_messages(messages, images, videos, processor)
|
||||
messages = template.mm_plugin.process_messages(messages, images, videos, audios, processor)
|
||||
input_ids, labels = template.encode_oneturn(tokenizer, messages, system, tools)
|
||||
if template.efficient_eos:
|
||||
labels += [tokenizer.eos_token_id]
|
||||
|
||||
input_ids, _ = template.mm_plugin.process_token_ids(input_ids, None, images, videos, tokenizer, processor)
|
||||
input_ids, _ = template.mm_plugin.process_token_ids(input_ids, None, images, videos, audios, tokenizer, processor)
|
||||
source_len, target_len = infer_seqlen(len(input_ids), len(labels), cutoff_len)
|
||||
input_ids = input_ids[:source_len]
|
||||
labels = labels[:target_len]
|
||||
@ -83,6 +84,7 @@ def preprocess_unsupervised_dataset(
|
||||
tools=examples["_tools"][i],
|
||||
images=examples["_images"][i] or [],
|
||||
videos=examples["_videos"][i] or [],
|
||||
audios=examples["_audios"][i] or [],
|
||||
template=template,
|
||||
tokenizer=tokenizer,
|
||||
processor=processor,
|
||||
@ -93,6 +95,7 @@ def preprocess_unsupervised_dataset(
|
||||
model_inputs["labels"].append(labels)
|
||||
model_inputs["images"].append(examples["_images"][i])
|
||||
model_inputs["videos"].append(examples["_videos"][i])
|
||||
model_inputs["audios"].append(examples["_audios"][i])
|
||||
|
||||
return model_inputs
|
||||
|
||||
|
||||
@ -890,7 +890,7 @@ _register_template(
|
||||
)
|
||||
|
||||
|
||||
# copied from chatml template
|
||||
# copied from qwen template
|
||||
_register_template(
|
||||
name="llava_next_qwen",
|
||||
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
|
||||
@ -979,7 +979,7 @@ _register_template(
|
||||
format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
|
||||
format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
|
||||
stop_words=["<|im_end|>"],
|
||||
mm_plugin=get_mm_plugin(name="minicpm_v", image_token="<image>", video_token="<video>"),
|
||||
mm_plugin=get_mm_plugin(name="minicpm_v", image_token="<image>", video_token="<video>", audio_token="<audio>"),
|
||||
)
|
||||
|
||||
|
||||
@ -1144,6 +1144,18 @@ _register_template(
|
||||
|
||||
|
||||
# copied from chatml template
|
||||
_register_template(
|
||||
name="qwen2_audio",
|
||||
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
|
||||
format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
|
||||
format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
|
||||
default_system="You are a helpful assistant.",
|
||||
stop_words=["<|im_end|>"],
|
||||
mm_plugin=get_mm_plugin(name="qwen2_audio", audio_token="<|AUDIO|>"),
|
||||
)
|
||||
|
||||
|
||||
# copied from qwen template
|
||||
_register_template(
|
||||
name="qwen2_vl",
|
||||
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
|
||||
|
||||
@ -22,6 +22,8 @@ from peft.utils import WEIGHTS_NAME as ADAPTER_WEIGHTS_NAME
|
||||
from transformers.utils import SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, WEIGHTS_INDEX_NAME, WEIGHTS_NAME
|
||||
|
||||
|
||||
AUDIO_PLACEHOLDER = os.environ.get("AUDIO_PLACEHOLDER", "<audio>")
|
||||
|
||||
CHECKPOINT_NAMES = {
|
||||
SAFE_ADAPTER_WEIGHTS_NAME,
|
||||
ADAPTER_WEIGHTS_NAME,
|
||||
@ -58,6 +60,8 @@ METHODS = ["full", "freeze", "lora"]
|
||||
|
||||
MOD_SUPPORTED_MODELS = {"bloom", "falcon", "gemma", "llama", "mistral", "mixtral", "phi", "starcoder2"}
|
||||
|
||||
MULTIMODAL_SUPPORTED_MODELS = set()
|
||||
|
||||
PEFT_METHODS = {"lora"}
|
||||
|
||||
RUNNING_LOG = "running_log.txt"
|
||||
@ -89,8 +93,6 @@ V_HEAD_WEIGHTS_NAME = "value_head.bin"
|
||||
|
||||
V_HEAD_SAFE_WEIGHTS_NAME = "value_head.safetensors"
|
||||
|
||||
VISION_MODELS = set()
|
||||
|
||||
|
||||
class DownloadSource(str, Enum):
|
||||
DEFAULT = "hf"
|
||||
@ -101,14 +103,16 @@ class DownloadSource(str, Enum):
|
||||
def register_model_group(
|
||||
models: Dict[str, Dict[DownloadSource, str]],
|
||||
template: Optional[str] = None,
|
||||
vision: bool = False,
|
||||
multimodal: bool = False,
|
||||
) -> None:
|
||||
for name, path in models.items():
|
||||
SUPPORTED_MODELS[name] = path
|
||||
if template is not None and (any(suffix in name for suffix in ("-Chat", "-Distill", "-Instruct")) or vision):
|
||||
if template is not None and (
|
||||
any(suffix in name for suffix in ("-Chat", "-Distill", "-Instruct")) or multimodal
|
||||
):
|
||||
DEFAULT_TEMPLATE[name] = template
|
||||
if vision:
|
||||
VISION_MODELS.add(name)
|
||||
if multimodal:
|
||||
MULTIMODAL_SUPPORTED_MODELS.add(name)
|
||||
|
||||
|
||||
register_model_group(
|
||||
@ -1030,7 +1034,7 @@ register_model_group(
|
||||
},
|
||||
},
|
||||
template="mllama",
|
||||
vision=True,
|
||||
multimodal=True,
|
||||
)
|
||||
|
||||
|
||||
@ -1046,7 +1050,7 @@ register_model_group(
|
||||
},
|
||||
},
|
||||
template="llava",
|
||||
vision=True,
|
||||
multimodal=True,
|
||||
)
|
||||
|
||||
|
||||
@ -1062,7 +1066,7 @@ register_model_group(
|
||||
},
|
||||
},
|
||||
template="llava_next",
|
||||
vision=True,
|
||||
multimodal=True,
|
||||
)
|
||||
|
||||
|
||||
@ -1074,7 +1078,7 @@ register_model_group(
|
||||
},
|
||||
},
|
||||
template="llava_next_mistral",
|
||||
vision=True,
|
||||
multimodal=True,
|
||||
)
|
||||
|
||||
|
||||
@ -1086,7 +1090,7 @@ register_model_group(
|
||||
},
|
||||
},
|
||||
template="llava_next_llama3",
|
||||
vision=True,
|
||||
multimodal=True,
|
||||
)
|
||||
|
||||
|
||||
@ -1098,7 +1102,7 @@ register_model_group(
|
||||
},
|
||||
},
|
||||
template="llava_next_yi",
|
||||
vision=True,
|
||||
multimodal=True,
|
||||
)
|
||||
|
||||
|
||||
@ -1114,7 +1118,7 @@ register_model_group(
|
||||
},
|
||||
},
|
||||
template="llava_next_qwen",
|
||||
vision=True,
|
||||
multimodal=True,
|
||||
)
|
||||
|
||||
|
||||
@ -1130,7 +1134,7 @@ register_model_group(
|
||||
},
|
||||
},
|
||||
template="llava_next_video",
|
||||
vision=True,
|
||||
multimodal=True,
|
||||
)
|
||||
|
||||
|
||||
@ -1142,7 +1146,7 @@ register_model_group(
|
||||
},
|
||||
},
|
||||
template="llava_next_video_mistral",
|
||||
vision=True,
|
||||
multimodal=True,
|
||||
)
|
||||
|
||||
|
||||
@ -1157,7 +1161,7 @@ register_model_group(
|
||||
},
|
||||
},
|
||||
template="llava_next_video_yi",
|
||||
vision=True,
|
||||
multimodal=True,
|
||||
)
|
||||
|
||||
|
||||
@ -1207,7 +1211,7 @@ register_model_group(
|
||||
},
|
||||
},
|
||||
template="minicpm_v",
|
||||
vision=True,
|
||||
multimodal=True,
|
||||
)
|
||||
|
||||
|
||||
@ -1219,7 +1223,7 @@ register_model_group(
|
||||
},
|
||||
},
|
||||
template="minicpm_v",
|
||||
vision=True,
|
||||
multimodal=True,
|
||||
)
|
||||
|
||||
|
||||
@ -1424,7 +1428,7 @@ register_model_group(
|
||||
},
|
||||
},
|
||||
template="paligemma",
|
||||
vision=True,
|
||||
multimodal=True,
|
||||
)
|
||||
|
||||
|
||||
@ -1468,7 +1472,7 @@ register_model_group(
|
||||
},
|
||||
},
|
||||
template="paligemma",
|
||||
vision=True,
|
||||
multimodal=True,
|
||||
)
|
||||
|
||||
|
||||
@ -1551,7 +1555,7 @@ register_model_group(
|
||||
}
|
||||
},
|
||||
template="pixtral",
|
||||
vision=True,
|
||||
multimodal=True,
|
||||
)
|
||||
|
||||
|
||||
@ -2134,6 +2138,22 @@ register_model_group(
|
||||
)
|
||||
|
||||
|
||||
register_model_group(
|
||||
models={
|
||||
"Qwen2-Audio-7B": {
|
||||
DownloadSource.DEFAULT: "Qwen/Qwen2-Audio-7B",
|
||||
DownloadSource.MODELSCOPE: "Qwen/Qwen2-Audio-7B",
|
||||
},
|
||||
"Qwen2-Audio-7B-Instruct": {
|
||||
DownloadSource.DEFAULT: "Qwen/Qwen2-Audio-7B-Instruct",
|
||||
DownloadSource.MODELSCOPE: "Qwen/Qwen2-Audio-7B-Instruct",
|
||||
},
|
||||
},
|
||||
template="qwen2_audio",
|
||||
multimodal=True,
|
||||
)
|
||||
|
||||
|
||||
register_model_group(
|
||||
models={
|
||||
"Qwen2-VL-2B-Instruct": {
|
||||
@ -2204,7 +2224,7 @@ register_model_group(
|
||||
},
|
||||
},
|
||||
template="qwen2_vl",
|
||||
vision=True,
|
||||
multimodal=True,
|
||||
)
|
||||
|
||||
|
||||
@ -2329,7 +2349,7 @@ register_model_group(
|
||||
},
|
||||
},
|
||||
template="video_llava",
|
||||
vision=True,
|
||||
multimodal=True,
|
||||
)
|
||||
|
||||
|
||||
@ -2556,7 +2576,7 @@ register_model_group(
|
||||
},
|
||||
},
|
||||
template="yi_vl",
|
||||
vision=True,
|
||||
multimodal=True,
|
||||
)
|
||||
|
||||
|
||||
|
||||
@ -42,6 +42,10 @@ def is_pyav_available():
|
||||
return _is_package_available("av")
|
||||
|
||||
|
||||
def is_librosa_available():
|
||||
return _is_package_available("librosa")
|
||||
|
||||
|
||||
def is_fastapi_available():
|
||||
return _is_package_available("fastapi")
|
||||
|
||||
|
||||
@ -41,9 +41,9 @@ class DataArguments:
|
||||
default="data",
|
||||
metadata={"help": "Path to the folder containing the datasets."},
|
||||
)
|
||||
image_dir: Optional[str] = field(
|
||||
media_dir: Optional[str] = field(
|
||||
default=None,
|
||||
metadata={"help": "Path to the folder containing the images or videos. Defaults to `dataset_dir`."},
|
||||
metadata={"help": "Path to the folder containing the images, videos or audios. Defaults to `dataset_dir`."},
|
||||
)
|
||||
cutoff_len: int = field(
|
||||
default=2048,
|
||||
@ -133,8 +133,8 @@ class DataArguments:
|
||||
self.dataset = split_arg(self.dataset)
|
||||
self.eval_dataset = split_arg(self.eval_dataset)
|
||||
|
||||
if self.image_dir is None:
|
||||
self.image_dir = self.dataset_dir
|
||||
if self.media_dir is None:
|
||||
self.media_dir = self.dataset_dir
|
||||
|
||||
if self.dataset is None and self.val_size > 1e-6:
|
||||
raise ValueError("Cannot specify `val_size` if `dataset` is None.")
|
||||
|
||||
@ -16,7 +16,14 @@ import os
|
||||
from typing import TYPE_CHECKING, Any, Dict, Optional, TypedDict
|
||||
|
||||
import torch
|
||||
from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForVision2Seq, AutoProcessor, AutoTokenizer
|
||||
from transformers import (
|
||||
AutoConfig,
|
||||
AutoModelForCausalLM,
|
||||
AutoModelForSeq2SeqLM,
|
||||
AutoModelForVision2Seq,
|
||||
AutoProcessor,
|
||||
AutoTokenizer,
|
||||
)
|
||||
from trl import AutoModelForCausalLMWithValueHead
|
||||
|
||||
from ..extras import logging
|
||||
@ -142,6 +149,8 @@ def load_model(
|
||||
else:
|
||||
if type(config) in AutoModelForVision2Seq._model_mapping.keys(): # assume built-in models
|
||||
load_class = AutoModelForVision2Seq
|
||||
elif type(config) in AutoModelForSeq2SeqLM._model_mapping.keys():
|
||||
load_class = AutoModelForSeq2SeqLM
|
||||
else:
|
||||
load_class = AutoModelForCausalLM
|
||||
|
||||
|
||||
@ -280,6 +280,12 @@ _register_composite_model(
|
||||
)
|
||||
|
||||
|
||||
_register_composite_model(
|
||||
model_type="qwen2_audio",
|
||||
vision_model_keys=["audio_tower"],
|
||||
)
|
||||
|
||||
|
||||
_register_composite_model(
|
||||
model_type="qwen2_vl",
|
||||
projector_key="visual.merger",
|
||||
|
||||
@ -78,13 +78,14 @@ def patch_processor(
|
||||
model_args: "ModelArguments",
|
||||
) -> None:
|
||||
setattr(processor, "tokenizer", tokenizer)
|
||||
setattr(processor, "image_seqlen", get_image_seqlen(config))
|
||||
setattr(processor, "image_resolution", model_args.image_resolution)
|
||||
setattr(processor, "patch_size", get_patch_size(config, processor))
|
||||
setattr(processor, "video_resolution", model_args.video_resolution)
|
||||
setattr(processor, "video_fps", model_args.video_fps)
|
||||
setattr(processor, "video_maxlen", model_args.video_maxlen)
|
||||
setattr(processor, "vision_feature_select_strategy", get_vision_feature_select_strategy(config, processor))
|
||||
if getattr(config, "vision_config", None) is not None: # visual models
|
||||
setattr(processor, "image_seqlen", get_image_seqlen(config))
|
||||
setattr(processor, "image_resolution", model_args.image_resolution)
|
||||
setattr(processor, "patch_size", get_patch_size(config, processor))
|
||||
setattr(processor, "video_resolution", model_args.video_resolution)
|
||||
setattr(processor, "video_fps", model_args.video_fps)
|
||||
setattr(processor, "video_maxlen", model_args.video_maxlen)
|
||||
setattr(processor, "vision_feature_select_strategy", get_vision_feature_select_strategy(config, processor))
|
||||
|
||||
|
||||
def patch_config(
|
||||
|
||||
@ -172,6 +172,7 @@ class WebChatModel(ChatModel):
|
||||
tools: str,
|
||||
image: Optional[Any],
|
||||
video: Optional[Any],
|
||||
audio: Optional[Any],
|
||||
max_new_tokens: int,
|
||||
top_p: float,
|
||||
temperature: float,
|
||||
@ -190,6 +191,7 @@ class WebChatModel(ChatModel):
|
||||
tools,
|
||||
images=[image] if image else None,
|
||||
videos=[video] if video else None,
|
||||
audios=[audio] if audio else None,
|
||||
max_new_tokens=max_new_tokens,
|
||||
top_p=top_p,
|
||||
temperature=temperature,
|
||||
|
||||
@ -26,9 +26,9 @@ from ..extras import logging
|
||||
from ..extras.constants import (
|
||||
DATA_CONFIG,
|
||||
DEFAULT_TEMPLATE,
|
||||
MULTIMODAL_SUPPORTED_MODELS,
|
||||
SUPPORTED_MODELS,
|
||||
TRAINING_ARGS,
|
||||
VISION_MODELS,
|
||||
DownloadSource,
|
||||
)
|
||||
from ..extras.misc import use_modelscope, use_openmind
|
||||
@ -136,13 +136,6 @@ def get_template(model_name: str) -> str:
|
||||
return DEFAULT_TEMPLATE.get(model_name, "default")
|
||||
|
||||
|
||||
def get_visual(model_name: str) -> bool:
|
||||
r"""
|
||||
Judges if the model is a vision language model.
|
||||
"""
|
||||
return model_name in VISION_MODELS
|
||||
|
||||
|
||||
def get_time() -> str:
|
||||
r"""
|
||||
Gets current date and time.
|
||||
@ -150,6 +143,13 @@ def get_time() -> str:
|
||||
return datetime.now().strftime(r"%Y-%m-%d-%H-%M-%S")
|
||||
|
||||
|
||||
def is_multimodal(model_name: str) -> bool:
|
||||
r"""
|
||||
Judges if the model is a vision language model.
|
||||
"""
|
||||
return model_name in MULTIMODAL_SUPPORTED_MODELS
|
||||
|
||||
|
||||
def load_dataset_info(dataset_dir: str) -> Dict[str, Dict[str, Any]]:
|
||||
r"""
|
||||
Loads dataset_info.json.
|
||||
|
||||
@ -64,10 +64,13 @@ def create_chat_box(
|
||||
|
||||
with gr.Column() as mm_box:
|
||||
with gr.Tab("Image"):
|
||||
image = gr.Image(sources=["upload"], type="pil")
|
||||
image = gr.Image(type="pil")
|
||||
|
||||
with gr.Tab("Video"):
|
||||
video = gr.Video(sources=["upload"])
|
||||
video = gr.Video()
|
||||
|
||||
with gr.Tab("Audio"):
|
||||
audio = gr.Audio(type="filepath")
|
||||
|
||||
query = gr.Textbox(show_label=False, lines=8)
|
||||
submit_btn = gr.Button(variant="primary")
|
||||
@ -86,7 +89,7 @@ def create_chat_box(
|
||||
[chatbot, messages, query],
|
||||
).then(
|
||||
engine.chatter.stream,
|
||||
[chatbot, messages, lang, system, tools, image, video, max_new_tokens, top_p, temperature],
|
||||
[chatbot, messages, lang, system, tools, image, video, audio, max_new_tokens, top_p, temperature],
|
||||
[chatbot, messages],
|
||||
)
|
||||
clear_btn.click(lambda: ([], []), outputs=[chatbot, messages])
|
||||
@ -102,6 +105,7 @@ def create_chat_box(
|
||||
mm_box=mm_box,
|
||||
image=image,
|
||||
video=video,
|
||||
audio=audio,
|
||||
query=query,
|
||||
submit_btn=submit_btn,
|
||||
max_new_tokens=max_new_tokens,
|
||||
|
||||
@ -15,7 +15,7 @@
|
||||
from typing import TYPE_CHECKING, Dict
|
||||
|
||||
from ...extras.packages import is_gradio_available
|
||||
from ..common import get_visual
|
||||
from ..common import is_multimodal
|
||||
from .chatbot import create_chat_box
|
||||
|
||||
|
||||
@ -66,7 +66,7 @@ def create_infer_tab(engine: "Engine") -> Dict[str, "Component"]:
|
||||
).then(lambda: gr.Column(visible=engine.chatter.loaded), outputs=[chat_elems["chat_box"]])
|
||||
|
||||
engine.manager.get_elem_by_id("top.model_name").change(
|
||||
lambda model_name: gr.Column(visible=get_visual(model_name)),
|
||||
lambda model_name: gr.Column(visible=is_multimodal(model_name)),
|
||||
[engine.manager.get_elem_by_id("top.model_name")],
|
||||
[chat_elems["mm_box"]],
|
||||
)
|
||||
|
||||
@ -52,12 +52,16 @@ NO_IMAGES = []
|
||||
|
||||
NO_VIDEOS = []
|
||||
|
||||
NO_AUDIOS = []
|
||||
|
||||
IMGLENS = [1]
|
||||
|
||||
NO_IMGLENS = [0]
|
||||
|
||||
NO_VIDLENS = [0]
|
||||
|
||||
NO_AUDLENS = [0]
|
||||
|
||||
INPUT_IDS = [0, 1, 2, 3, 4]
|
||||
|
||||
LABELS = [0, 1, 2, 3, 4]
|
||||
@ -99,23 +103,25 @@ def _check_plugin(
|
||||
expected_no_mm_inputs: Dict[str, Any] = {},
|
||||
) -> None:
|
||||
# test mm_messages
|
||||
assert plugin.process_messages(MM_MESSAGES, IMAGES, NO_VIDEOS, processor) == expected_mm_messages
|
||||
assert plugin.process_token_ids(INPUT_IDS, LABELS, IMAGES, NO_VIDEOS, tokenizer, processor) == (
|
||||
assert plugin.process_messages(MM_MESSAGES, IMAGES, NO_VIDEOS, NO_AUDIOS, processor) == expected_mm_messages
|
||||
assert plugin.process_token_ids(INPUT_IDS, LABELS, IMAGES, NO_VIDEOS, NO_AUDIOS, tokenizer, processor) == (
|
||||
expected_input_ids,
|
||||
expected_labels,
|
||||
)
|
||||
_is_close(
|
||||
plugin.get_mm_inputs(IMAGES, NO_VIDEOS, IMGLENS, NO_VIDLENS, BATCH_IDS, processor),
|
||||
plugin.get_mm_inputs(IMAGES, NO_VIDEOS, NO_AUDIOS, IMGLENS, NO_VIDLENS, NO_AUDLENS, BATCH_IDS, processor),
|
||||
expected_mm_inputs,
|
||||
)
|
||||
# test text_messages
|
||||
assert plugin.process_messages(TEXT_MESSAGES, NO_IMAGES, NO_VIDEOS, processor) == TEXT_MESSAGES
|
||||
assert plugin.process_token_ids(INPUT_IDS, LABELS, NO_IMAGES, NO_VIDEOS, tokenizer, processor) == (
|
||||
assert plugin.process_messages(TEXT_MESSAGES, NO_IMAGES, NO_VIDEOS, NO_AUDIOS, processor) == TEXT_MESSAGES
|
||||
assert plugin.process_token_ids(INPUT_IDS, LABELS, NO_IMAGES, NO_VIDEOS, NO_AUDIOS, tokenizer, processor) == (
|
||||
INPUT_IDS,
|
||||
LABELS,
|
||||
)
|
||||
_is_close(
|
||||
plugin.get_mm_inputs(NO_IMAGES, NO_VIDEOS, NO_IMGLENS, NO_VIDLENS, BATCH_IDS, processor),
|
||||
plugin.get_mm_inputs(
|
||||
NO_IMAGES, NO_VIDEOS, NO_AUDIOS, NO_IMGLENS, NO_VIDLENS, NO_AUDLENS, BATCH_IDS, processor
|
||||
),
|
||||
expected_no_mm_inputs,
|
||||
)
|
||||
|
||||
|
||||
@ -167,7 +167,7 @@ def test_phi4_template(use_fast: bool):
|
||||
_check_template("microsoft/phi-4", "phi4", prompt_str, answer_str, use_fast)
|
||||
|
||||
|
||||
@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
|
||||
@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.") # TODO: why it is gated?
|
||||
@pytest.mark.parametrize("use_fast", [True, False])
|
||||
def test_qwen_template(use_fast: bool):
|
||||
prompt_str = (
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user