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README.md
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# MiniCPM-V-2_6_a13646723549884416169583
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---
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frameworks:
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- Pytorch
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license: other
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tasks:
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- visual-question-answering
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---
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MiniCPM-V-2_6
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<h1>A GPT-4V Level MLLM for Single Image, Multi Image and Video on Your Phone</h1>
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[GitHub](https://github.com/OpenBMB/MiniCPM-V) | [Demo](http://120.92.209.146:8887/)</a>
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## MiniCPM-V 2.6
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**MiniCPM-V 2.6** 是 MiniCPM-V 系列中最新、性能最佳的模型。该模型基于 SigLip-400M 和 Qwen2-7B 构建，共 8B 参数。与 MiniCPM-Llama3-V 2.5 相比，MiniCPM-V 2.6 性能提升显著，并引入了多图和视频理解的新功能。MiniCPM-V 2.6 的主要特点包括：
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- 🔥 **领先的性能。**
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MiniCPM-V 2.6 在最新版本 OpenCompass 榜单上（综合 8 个主流多模态评测基准）平均得分 65.2，**以8B量级的大小在单图理解方面超越了 GPT-4o mini、GPT-4V、Gemini 1.5 Pro 和 Claude 3.5 Sonnet 等主流商用闭源多模态大模型**。
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- 🖼️ **多图理解和上下文学习。**
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MiniCPM-V 2.6 还支持**多图对话和推理**。它在 Mantis-Eval、BLINK、Mathverse mv 和 Sciverse mv 等主流多图评测基准中取得了**最佳水平**，并展现出了优秀的上下文学习能力。
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- 🎬 **视频理解。**
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MiniCPM-V 2.6 还可以**接受视频输入**，进行对话和提供涵盖时序和空间信息的详细视频描述。模型在 有/无字幕 评测场景下的 Video-MME 表现均超过了 **GPT-4V、Claude 3.5 Sonnet 和 LLaVA-NeXT-Video-34B**等商用闭源模型。
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- 💪 **强大的 OCR 能力及其他功能。**
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MiniCPM-V 2.6 可以处理任意长宽比的图像，像素数可达 180 万（如 1344x1344）。在 OCRBench 上取得**最佳水平，超过 GPT-4o、GPT-4V 和 Gemini 1.5 Pro 等商用闭源模型**。基于最新的 [RLAIF-V](https://github.com/RLHF-V/RLAIF-V/) 和 [VisCPM](https://github.com/OpenBMB/VisCPM) 技术，其具备了**可信的多模态行为**，在 Object HalBench 上的幻觉率显著低于 GPT-4o 和 GPT-4V，并支持英语、中文、德语、法语、意大利语、韩语等**多种语言**。
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- 🚀 **卓越的效率。**
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除了对个人用户友好的模型大小，MiniCPM-V 2.6 还表现出**最先进的视觉 token 密度**（即每个视觉 token 编码的像素数量）。它**仅需 640 个 token 即可处理 180 万像素图像，比大多数模型少 75%**。这一特性优化了模型的推理速度、首 token 延迟、内存占用和功耗。因此，MiniCPM-V 2.6 可以支持 iPad 等终端设备上的高效**实时视频理解**。
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- 💫 **易于使用。**
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MiniCPM-V 2.6 可以通过多种方式轻松使用：(1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpmv-main/examples/llava/README-minicpmv2.6.md) 和 [ollama](https://github.com/OpenBMB/ollama/blob/minicpm-v2.6/examples/minicpm-v2.6/README.md) 支持在本地设备上进行高效的 CPU 推理，(2) [int4](https://huggingface.co/openbmb/MiniCPM-V-2_6-int4) 和 [GGUF](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) 格式的量化模型，有 16 种尺寸，(3) [vLLM](https://github.com/OpenBMB/MiniCPM-V/blob/main/README_zh.md#vllm-%E9%83%A8%E7%BD%B2-) 支持高吞吐量和内存高效的推理，(4) 针对新领域和任务进行微调，(5) 使用 [Gradio](https://github.com/OpenBMB/MiniCPM-V/blob/main/README_zh.md#%E6%9C%AC%E5%9C%B0-webui-demo-) 快速设置本地 WebUI 演示，(6) 在线[demo](http://120.92.209.146:8887/)即可体验。
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### 性能评估 <!-- omit in toc -->
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<div align="center">
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<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/radar_final.png" width=66% />
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</div>
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##### 单图评测结果
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OpenCompass, MME, MMVet, OCRBench, MMMU, MathVista, MMB, AI2D, TextVQA, DocVQA, HallusionBench, Object HalBench：
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<div align="center">
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<table style="margin: 0px auto;">
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<thead>
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<tr>
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<th align="left">Model</th>
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<th>Size</th>
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<th>Token Density<sup>+</sup></th>
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<th>OpenCompass</th>
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<th>MME</th>
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<th>MMVet</th>
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<th>OCRBench</th>
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<th>MMMU val</th>
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<th>MathVista mini</th>
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<th>MMB1.1 test</th>
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<th>AI2D</th>
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<th>TextVQA val</th>
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<th>DocVQA test</th>
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<th>HallusionBench</th>
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<th>Object HalBench</th>
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</tr>
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</thead>
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<tbody align="center">
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<tr>
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<td colspan="15" align="left"><strong>Proprietary</strong></td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left">GPT-4o</td>
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<td>-</td>
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<td>1088</td>
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<td>69.9</td>
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<td>2328.7</td>
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<td>69.1</td>
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<td>736</td>
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<td>69.2</td>
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<td>61.3</td>
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<td>82.2</td>
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<td>84.6</td>
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<td>-</td>
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<td>92.8</td>
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<td>55.0</td>
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<td>17.6</td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left">Claude 3.5 Sonnet</td>
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<td>-</td>
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<td>750</td>
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<td>67.9</td>
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<td>1920.0</td>
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<td>66.0</td>
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<td>788</td>
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<td>65.9</td>
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<td>61.6</td>
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<td>78.5</td>
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<td>80.2</td>
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<td>-</td>
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<td>95.2</td>
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<td>49.9</td>
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<td>13.8</td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left">Gemini 1.5 Pro</td>
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<td>-</td>
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<td>-</td>
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<td>64.4</td>
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<td>2110.6</td>
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<td>64.0</td>
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<td>754</td>
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<td>60.6</td>
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<td>57.7</td>
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<td>73.9</td>
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<td>79.1</td>
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<td>73.5</td>
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<td>86.5</td>
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<td>45.6</td>
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<td>-</td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left">GPT-4o mini</td>
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<td>-</td>
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<td>1088</td>
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<td>64.1</td>
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<td>2003.4</td>
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<td>66.9</td>
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<td>785</td>
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<td>60.0</td>
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<td>52.4</td>
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<td>76.0</td>
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<td>77.8</td>
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<td>-</td>
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<td>-</td>
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<td>46.1</td>
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<td>12.4</td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left">GPT-4V</td>
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<td>-</td>
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<td>1088</td>
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<td>63.5</td>
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<td>2070.2</td>
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<td>67.5</td>
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<td>656</td>
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<td>61.7</td>
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<td>54.7</td>
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<td>79.8</td>
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<td>78.6</td>
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<td>78.0</td>
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<td>87.2</td>
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<td>43.9</td>
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<td>14.2</td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left">Step-1V</td>
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<td>-</td>
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<td>-</td>
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<td>59.5</td>
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<td>2206.4</td>
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<td>63.3</td>
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<td>625</td>
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<td>49.9</td>
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<td>44.8</td>
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<td>78.0</td>
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<td>79.2</td>
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<td>71.6</td>
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<td>-</td>
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<td>48.4</td>
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<td>-</td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left">Qwen-VL-Max</td>
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<td>-</td>
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<td>784</td>
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<td>58.3</td>
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<td>2281.7</td>
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<td>61.8</td>
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<td>684</td>
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<td>52.0</td>
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<td>43.4</td>
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<td>74.6</td>
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<td>75.7</td>
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<td>79.5</td>
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<td>93.1</td>
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<td>41.2</td>
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<td>13.4</td>
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</tr>
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<tr>
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<td colspan="15" align="left"><strong>Open-source</strong></td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left">LLaVA-NeXT-Yi-34B</td>
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<td>34B</td>
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<td>157</td>
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<td>55.0</td>
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<td>2006.5</td>
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<td>50.7</td>
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<td>574</td>
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<td>48.8</td>
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<td>40.4</td>
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<td>77.8</td>
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<td>78.9</td>
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<td>69.3</td>
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<td>-</td>
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<td>34.8</td>
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<td>12.6</td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left">Mini-Gemini-HD-34B</td>
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<td>34B</td>
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<td>157</td>
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<td>-</td>
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<td>2141</td>
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<td>59.3</td>
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<td>518</td>
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<td>48.0</td>
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<td>43.3</td>
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<td>-</td>
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<td>80.5</td>
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<td>74.1</td>
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<td>78.9</td>
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<td>-</td>
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<td>-</td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left">Cambrian-34B</td>
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<td>34B</td>
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<td>1820</td>
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<td>58.3</td>
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<td>2049.9</td>
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<td>53.2</td>
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<td>591</td>
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<td>50.4</td>
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<td>50.3</td>
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<td>77.8</td>
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<td>79.5</td>
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<td>76.7</td>
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<td>75.5</td>
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<td>41.6</td>
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<td>14.7</td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left">GLM-4V-9B</td>
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<td>13B</td>
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<td>784</td>
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<td>59.1</td>
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<td>2018.8</td>
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<td>58.0</td>
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<td>776</td>
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<td>46.9</td>
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<td>51.1</td>
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<td>67.9</td>
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<td>71.2</td>
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<td>-</td>
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<td>-</td>
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<td>45.0</td>
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<td>-</td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left">InternVL2-8B</td>
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<td>8B</td>
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<td>706</td>
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<td>64.1</td>
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<td>2215.1</td>
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<td>54.3</td>
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<td>794</td>
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<td><strong>51.2</strong></td>
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<td>58.3</td>
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<td><strong>79.4</strong></td>
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<td><strong>83.6</strong></td>
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<td>77.4</td>
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<td><strong>91.6</strong></td>
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<td>45.0</td>
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<td>21.3</td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left">MiniCPM-Llama-V 2.5</td>
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<td>8B</td>
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<td>1882</td>
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<td>58.8</td>
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<td>2024.6</td>
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<td>52.8</td>
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<td>725</td>
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<td>45.8</td>
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<td>54.3</td>
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<td>72.0</td>
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<td>78.4</td>
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<td>76.6</td>
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<td>84.8</td>
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<td>42.4</td>
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<td>10.3</td>
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</tr>
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<tr style="background-color: #e6f2ff;">
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<td nowrap="nowrap" align="left">MiniCPM-V 2.6</td>
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<td>8B</td>
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<td><strong>2822</strong></td>
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<td><strong>65.2</strong></td>
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<td><strong>2348.4</strong>*</td>
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<td><strong>60.0</strong></td>
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<td><strong>852</strong>*</td>
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<td>49.8*</td>
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<td><strong>60.6</strong></td>
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<td>78.0</td>
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<td>82.1</td>
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<td><strong>80.1<strong></td>
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<td>90.8</td>
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<td><strong>48.1</strong>*</td>
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<td><strong>8.2</strong></td>
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</tr>
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</tbody>
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</table>
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</div>
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* 我们使用思维链提示词来评估这些基准。
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<sup>+</sup> Token Density：每个视觉 token 在最大分辨率下编码的像素数，即最大分辨率下的像素数 / 视觉 token 数。
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注意：闭源模型的 Token Density 由 API 收费方式估算得到。
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##### 多图评测结果
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Mantis Eval, BLINK, Mathverse mv, Sciverse mv, MIRB：
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<div align="center">
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<table style="margin: 0px auto;">
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<thead>
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<tr>
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<th align="left">Model</th>
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<th>Size</th>
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<th>Mantis Eval</th>
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<th>BLINK val</th>
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<th>Mathverse mv</th>
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<th>Sciverse mv</th>
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<th>MIRB</th>
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</tr>
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</thead>
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<tbody align="center">
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<tr>
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<td colspan="7" align="left"><strong>Proprietary</strong></td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left">GPT-4V</td>
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<td>-</td>
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<td>62.7</td>
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<td>54.6</td>
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<td>60.3</td>
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<td>66.9</td>
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<td>53.1</td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left">LLaVA-NeXT-Interleave-14B</td>
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<td>14B</td>
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<td>66.4</td>
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<td>52.6</td>
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<td>32.7</td>
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<td>30.2</td>
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<td>-</td>
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</tr>
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<tr>
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<td colspan="7" align="left"><strong>Open-source</strong></td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left">Emu2-Chat</td>
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<td>37B</td>
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<td>37.8</td>
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<td>36.2</td>
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<td>-</td>
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<td>27.2</td>
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<td>-</td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left">CogVLM</td>
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<td>17B</td>
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<td>45.2</td>
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<td>41.1</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="left">VPG-C</td>
|
||||
<td>7B</td>
|
||||
<td>52.4</td>
|
||||
<td>43.1</td>
|
||||
<td>24.3</td>
|
||||
<td>23.1</td>
|
||||
<td>-</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left">VILA 8B</td>
|
||||
<td>8B</td>
|
||||
<td>51.2</td>
|
||||
<td>39.3</td>
|
||||
<td>-</td>
|
||||
<td>36.5</td>
|
||||
<td>-</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left">InternLM-XComposer-2.5</td>
|
||||
<td>8B</td>
|
||||
<td>53.1*</td>
|
||||
<td>48.9</td>
|
||||
<td>32.1*</td>
|
||||
<td>-</td>
|
||||
<td>42.5</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left">InternVL2-8B</td>
|
||||
<td>8B</td>
|
||||
<td>59.0*</td>
|
||||
<td>50.9</td>
|
||||
<td>30.5*</td>
|
||||
<td>34.4*</td>
|
||||
<td><strong>56.9*</strong></td>
|
||||
</tr>
|
||||
<tr style="background-color: #e6f2ff;">
|
||||
<td nowrap="nowrap" align="left">MiniCPM-V 2.6</td>
|
||||
<td>8B</td>
|
||||
<td><strong>69.1</strong></td>
|
||||
<td><strong>53.0</strong></td>
|
||||
<td><strong>84.9</strong></td>
|
||||
<td><strong>74.9</strong></td>
|
||||
<td>53.8</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
|
||||
</div>
|
||||
* 正式开源模型权重的评测结果。
|
||||
|
||||
|
||||
##### 视频评测结果
|
||||
Video-MME 和 Video-ChatGPT：
|
||||
<div align="center">
|
||||
|
||||
<table style="margin: 0px auto;">
|
||||
<thead>
|
||||
<tr>
|
||||
<th align="left">Model</th>
|
||||
<th>Size</th>
|
||||
<th colspan="2">Video-MME</th>
|
||||
<th colspan="5">Video-ChatGPT</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<th align="left"></th>
|
||||
<th></th>
|
||||
<th>w/o subs</th>
|
||||
<th>w subs</th>
|
||||
<th>Correctness</th>
|
||||
<th>Detail</th>
|
||||
<th>Context</th>
|
||||
<th>Temporal</th>
|
||||
<th>Consistency</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody align="center">
|
||||
<tr>
|
||||
<td colspan="9" align="left"><strong>Proprietary</strong></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left">Claude 3.5 Sonnet</td>
|
||||
<td>-</td>
|
||||
<td>60.0</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left">GPT-4V</td>
|
||||
<td>-</td>
|
||||
<td>59.9</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td colspan="9" align="left"><strong>Open-source</strong></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left">LLaVA-NeXT-7B</td>
|
||||
<td>7B</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
<td>3.39</td>
|
||||
<td>3.29</td>
|
||||
<td>3.92</td>
|
||||
<td>2.60</td>
|
||||
<td>3.12</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left">LLaVA-NeXT-34B</td>
|
||||
<td>34B</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
<td>3.29</td>
|
||||
<td>3.23</td>
|
||||
<td>3.83</td>
|
||||
<td>2.51</td>
|
||||
<td>3.47</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left">CogVLM2-Video</td>
|
||||
<td>12B</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
<td>3.49</td>
|
||||
<td><strong>3.46</strong></td>
|
||||
<td>3.23</td>
|
||||
<td><strong>2.98</strong></td>
|
||||
<td><strong>3.64</strong></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left">LongVA</td>
|
||||
<td>7B</td>
|
||||
<td>52.4</td>
|
||||
<td>54.3</td>
|
||||
<td>3.05</td>
|
||||
<td>3.09</td>
|
||||
<td>3.77</td>
|
||||
<td>2.44</td>
|
||||
<td><strong>3.64</strong></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left">InternVL2-8B</td>
|
||||
<td>8B</td>
|
||||
<td>54.0</td>
|
||||
<td>56.9</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left">InternLM-XComposer-2.5</td>
|
||||
<td>8B</td>
|
||||
<td>55.8</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
<td>-</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td nowrap="nowrap" align="left">LLaVA-NeXT-Video</td>
|
||||
<td>32B</td>
|
||||
<td>60.2</td>
|
||||
<td>63.0</td>
|
||||
<td>3.48</td>
|
||||
<td>3.37</td>
|
||||
<td><strong>3.95</strong></td>
|
||||
<td>2.64</td>
|
||||
<td>3.28</td>
|
||||
</tr>
|
||||
<tr style="background-color: #e6f2ff;">
|
||||
<td nowrap="nowrap" align="left">MiniCPM-V 2.6</td>
|
||||
<td>8B</td>
|
||||
<td><strong>60.9</strong></td>
|
||||
<td><strong>63.6</strong></td>
|
||||
<td><strong>3.59</strong></td>
|
||||
<td>3.28</td>
|
||||
<td>3.93</td>
|
||||
<td>2.73</td>
|
||||
<td>3.62</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
|
||||
|
||||
##### 少样本评测结果
|
||||
TextVQA, VizWiz, VQAv2, OK-VQA：
|
||||
<div align="center">
|
||||
|
||||
<table style="margin: 0px auto;">
|
||||
<thead>
|
||||
<tr>
|
||||
<th align="left">Model</th>
|
||||
<th>Size</th>
|
||||
<th>Shot</th>
|
||||
<th>TextVQA val</th>
|
||||
<th>VizWiz test-dev</th>
|
||||
<th>VQAv2 test-dev</th>
|
||||
<th>OK-VQA val</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody align="center">
|
||||
<tr>
|
||||
<td align="left" nowrap="nowrap" rowspan="3">Flamingo</td>
|
||||
<td rowspan="3">80B</td>
|
||||
<td>0*</td>
|
||||
<td>35.0</td>
|
||||
<td>31.6</td>
|
||||
<td>56.3</td>
|
||||
<td>40.6</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>4</td>
|
||||
<td>36.5</td>
|
||||
<td>39.6</td>
|
||||
<td>63.1</td>
|
||||
<td><strong>57.4</strong></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>8</td>
|
||||
<td>37.3</td>
|
||||
<td>44.8</td>
|
||||
<td>65.6</td>
|
||||
<td>57.5</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="left" nowrap="nowrap" rowspan="3">IDEFICS</td>
|
||||
<td rowspan="3">80B</td>
|
||||
<td>0*</td>
|
||||
<td>30.9</td>
|
||||
<td>36.0</td>
|
||||
<td>60.0</td>
|
||||
<td>45.2</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>4</td>
|
||||
<td>34.3</td>
|
||||
<td>40.4</td>
|
||||
<td>63.6</td>
|
||||
<td>52.4</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>8</td>
|
||||
<td>35.7</td>
|
||||
<td>46.1</td>
|
||||
<td>64.8</td>
|
||||
<td>55.1</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="left" nowrap="nowrap" rowspan="3">OmniCorpus</td>
|
||||
<td rowspan="3">7B</td>
|
||||
<td>0*</td>
|
||||
<td>43.0</td>
|
||||
<td>49.8</td>
|
||||
<td>63.2</td>
|
||||
<td>45.5</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>4</td>
|
||||
<td>45.4</td>
|
||||
<td>51.3</td>
|
||||
<td>64.5</td>
|
||||
<td>46.5</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>8</td>
|
||||
<td>45.6</td>
|
||||
<td>52.2</td>
|
||||
<td>64.7</td>
|
||||
<td>46.6</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="left" nowrap="nowrap" rowspan="3">Emu2</td>
|
||||
<td rowspan="3">37B</td>
|
||||
<td>0</td>
|
||||
<td>26.4</td>
|
||||
<td>40.4</td>
|
||||
<td>33.5</td>
|
||||
<td>26.7</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>4</td>
|
||||
<td>48.2</td>
|
||||
<td>54.6</td>
|
||||
<td>67.0</td>
|
||||
<td>53.2</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>8</td>
|
||||
<td>49.3</td>
|
||||
<td>54.7</td>
|
||||
<td>67.8</td>
|
||||
<td>54.1</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="left" nowrap="nowrap" rowspan="2">MM1</td>
|
||||
<td rowspan="2">30B</td>
|
||||
<td>0</td>
|
||||
<td>26.2</td>
|
||||
<td>40.4</td>
|
||||
<td>48.9</td>
|
||||
<td>26.7</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>8</td>
|
||||
<td>49.3</td>
|
||||
<td>54.7</td>
|
||||
<td><strong>70.9</strong></td>
|
||||
<td>54.1</td>
|
||||
</tr>
|
||||
<tr style="background-color: #e6f2ff;">
|
||||
<td align="left" nowrap="nowrap" rowspan="3">MiniCPM-V 2.6<sup>+</sup></td>
|
||||
<td rowspan="3">8B</td>
|
||||
<td>0</td>
|
||||
<td>43.9</td>
|
||||
<td>33.8</td>
|
||||
<td>45.4</td>
|
||||
<td>23.9</td>
|
||||
</tr>
|
||||
<tr style="background-color: #e6f2ff;">
|
||||
<td>4</td>
|
||||
<td>63.6</td>
|
||||
<td>60.5</td>
|
||||
<td>65.5</td>
|
||||
<td>50.1</td>
|
||||
</tr>
|
||||
<tr style="background-color: #e6f2ff;">
|
||||
<td>8</td>
|
||||
<td><strong>64.6</strong></td>
|
||||
<td><strong>63.4</strong></td>
|
||||
<td>68.2</td>
|
||||
<td>51.4</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
|
||||
</div>
|
||||
* 使用 Flamingo 方式 zero image shot 和 two additional text shots 评估零样本性能。
|
||||
|
||||
<sup>+</sup> 我们在没有进行监督微调 (SFT) 的情况下评估预训练的模型权重 (ckpt)。
|
||||
|
||||
|
||||
|
||||
### 典型示例 <!-- omit in toc -->
|
||||
|
||||
<div style="display: flex; flex-direction: column; align-items: center;">
|
||||
<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/multi_img-bike.png" alt="Bike" style="margin-bottom: 5px;">
|
||||
<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/multi_img-menu.png" alt="Menu" style="margin-bottom: 5px;">
|
||||
<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/multi_img-code.png" alt="Code" style="margin-bottom: 5px;">
|
||||
<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/ICL-Mem.png" alt="Mem" style="margin-bottom: 5px;">
|
||||
<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/multiling-medal.png" alt="medal" style="margin-bottom: 10px;">
|
||||
</div>
|
||||
<details>
|
||||
<summary>点击查看更多示例.</summary>
|
||||
<div style="display: flex; flex-direction: column; align-items: center;">
|
||||
<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/ICL-elec.png" alt="elec" style="margin-bottom: 5px;">
|
||||
<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/multiling-olympic.png" alt="Menu" style="margin-bottom: 10px;">
|
||||
</div>
|
||||
</details>
|
||||
|
||||
我们将 MiniCPM-V 2.6 部署在iPad Pro上，并录制了以下演示视频。
|
||||
|
||||
<div style="display: flex; justify-content: center;">
|
||||
<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/gif_cases/ai.gif" width="48%" style="margin: 0 10px;"/>
|
||||
<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/gif_cases/beer.gif" width="48%" style="margin: 0 10px;"/>
|
||||
</div>
|
||||
<div style="display: flex; justify-content: center; margin-top: 20px;">
|
||||
<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/gif_cases/ticket.gif" width="48%" style="margin: 0 10px;"/>
|
||||
<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/gif_cases/wfh.gif" width="48%" style="margin: 0 10px;"/>
|
||||
</div>
|
||||
|
||||
<div style="text-align: center;">
|
||||
<video controls autoplay src="https://modelscope.cn/models/OpenBMB/MiniCPM-V-2_6/resolve/master/assets/case_draw.mp4" width="50%" /> </video>
|
||||
<video controls autoplay src="https://modelscope.cn/models/OpenBMB/MiniCPM-V-2_6/resolve/master/assets/case_mb.mp4" width="50%" /> </video>
|
||||
</div>
|
||||
|
||||
|
||||
## Demo
|
||||
Click here to try out the Demo of [MiniCPM-V 2.6](http://120.92.209.146:8887/).
|
||||
|
||||
|
||||
## 使用方法
|
||||
使用Huggingface transformers 在NVIDIA GPUs推理。Requirements如下：(python 3.10)
|
||||
```
|
||||
Pillow==10.1.0
|
||||
torch==2.1.2
|
||||
torchvision==0.16.2
|
||||
transformers==4.40.0
|
||||
sentencepiece==0.1.99
|
||||
decord
|
||||
```
|
||||
|
||||
```python
|
||||
# test.py
|
||||
# test.py
|
||||
import torch
|
||||
from PIL import Image
|
||||
from modelscope import AutoModel, AutoTokenizer
|
||||
|
||||
model = AutoModel.from_pretrained('OpenBMB/MiniCPM-V-2_6', trust_remote_code=True,
|
||||
attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager
|
||||
model = model.eval().cuda()
|
||||
tokenizer = AutoTokenizer.from_pretrained('OpenBMB/MiniCPM-V-2_6', trust_remote_code=True)
|
||||
|
||||
image = Image.open('image.png').convert('RGB')
|
||||
question = 'What is in the image?'
|
||||
msgs = [{'role': 'user', 'content': [image, question]}]
|
||||
|
||||
res = model.chat(
|
||||
image=None,
|
||||
msgs=msgs,
|
||||
tokenizer=tokenizer
|
||||
)
|
||||
print(res)
|
||||
|
||||
## if you want to use streaming, please make sure sampling=True and stream=True
|
||||
## the model.chat will return a generator
|
||||
res = model.chat(
|
||||
image=None,
|
||||
msgs=msgs,
|
||||
tokenizer=tokenizer,
|
||||
sampling=True,
|
||||
stream=True
|
||||
)
|
||||
|
||||
generated_text = ""
|
||||
for new_text in res:
|
||||
generated_text += new_text
|
||||
print(new_text, flush=True, end='')
|
||||
```
|
||||
|
||||
### 多图理解
|
||||
<details>
|
||||
<summary> 点击查看使用 MiniCPM-V 2.6 进行多图理解的Python示例 </summary>
|
||||
|
||||
```python
|
||||
import torch
|
||||
from PIL import Image
|
||||
from transformers import AutoModel, AutoTokenizer
|
||||
|
||||
model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True,
|
||||
attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager
|
||||
model = model.eval().cuda()
|
||||
tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True)
|
||||
|
||||
image1 = Image.open('image1.jpg').convert('RGB')
|
||||
image2 = Image.open('image2.jpg').convert('RGB')
|
||||
question = 'Compare image 1 and image 2, tell me about the differences between image 1 and image 2.'
|
||||
|
||||
msgs = [{'role': 'user', 'content': [image1, image2, question]}]
|
||||
|
||||
answer = model.chat(
|
||||
image=None,
|
||||
msgs=msgs,
|
||||
tokenizer=tokenizer
|
||||
)
|
||||
print(answer)
|
||||
```
|
||||
</details>
|
||||
|
||||
### In-context few-shot learning
|
||||
<details>
|
||||
<summary> 点击查看使用 MiniCPM-V 2.6 进行few-shot推理的Python示例 </summary>
|
||||
|
||||
```python
|
||||
import torch
|
||||
from PIL import Image
|
||||
from transformers import AutoModel, AutoTokenizer
|
||||
|
||||
model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True,
|
||||
attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager
|
||||
model = model.eval().cuda()
|
||||
tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True)
|
||||
|
||||
question = "production date"
|
||||
image1 = Image.open('example1.jpg').convert('RGB')
|
||||
answer1 = "2023.08.04"
|
||||
image2 = Image.open('example2.jpg').convert('RGB')
|
||||
answer2 = "2007.04.24"
|
||||
image_test = Image.open('test.jpg').convert('RGB')
|
||||
|
||||
msgs = [
|
||||
{'role': 'user', 'content': [image1, question]}, {'role': 'assistant', 'content': [answer1]},
|
||||
{'role': 'user', 'content': [image2, question]}, {'role': 'assistant', 'content': [answer2]},
|
||||
{'role': 'user', 'content': [image_test, question]}
|
||||
]
|
||||
|
||||
answer = model.chat(
|
||||
image=None,
|
||||
msgs=msgs,
|
||||
tokenizer=tokenizer
|
||||
)
|
||||
print(answer)
|
||||
```
|
||||
</details>
|
||||
|
||||
### 视频理解
|
||||
<details>
|
||||
<summary> 点击查看使用 MiniCPM-V 2.6 进行视频理解的Python示例 </summary>
|
||||
|
||||
```python
|
||||
import torch
|
||||
from PIL import Image
|
||||
from modelscope import AutoModel, AutoTokenizer
|
||||
from decord import VideoReader, cpu # pip install decord
|
||||
|
||||
params={}
|
||||
|
||||
model = AutoModel.from_pretrained('OpenBMB/MiniCPM-V-2_6', trust_remote_code=True,
|
||||
attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager
|
||||
model = model.eval().cuda()
|
||||
tokenizer = AutoTokenizer.from_pretrained('OpenBMB/MiniCPM-V-2_6', trust_remote_code=True)
|
||||
|
||||
MAX_NUM_FRAMES=64
|
||||
|
||||
def encode_video(video_path):
|
||||
def uniform_sample(l, n):
|
||||
gap = len(l) / n
|
||||
idxs = [int(i * gap + gap / 2) for i in range(n)]
|
||||
return [l[i] for i in idxs]
|
||||
|
||||
vr = VideoReader(video_path, ctx=cpu(0))
|
||||
sample_fps = round(vr.get_avg_fps() / 1) # FPS
|
||||
frame_idx = [i for i in range(0, len(vr), sample_fps)]
|
||||
if len(frame_idx) > MAX_NUM_FRAMES:
|
||||
frame_idx = uniform_sample(frame_idx, MAX_NUM_FRAMES)
|
||||
frames = vr.get_batch(frame_idx).asnumpy()
|
||||
frames = [Image.fromarray(v.astype('uint8')) for v in frames]
|
||||
print('num frames:', len(frames))
|
||||
return frames
|
||||
|
||||
video_path="/mnt/workspace/2.mp4"
|
||||
frames = encode_video(video_path)
|
||||
question = "Describe the video"
|
||||
msgs = [
|
||||
{'role': 'user', 'content': frames + [question]},
|
||||
]
|
||||
|
||||
# Set decode params for video
|
||||
params={}
|
||||
params["use_image_id"] = False
|
||||
params["max_slice_nums"] = 2 # 如果cuda OOM且视频分辨率大于448*448 可设为1
|
||||
|
||||
answer = model.chat(
|
||||
image=None,
|
||||
msgs=msgs,
|
||||
tokenizer=tokenizer,
|
||||
**params
|
||||
)
|
||||
print(answer)
|
||||
```
|
||||
</details>
|
||||
|
||||
更多使用介绍请查看 [GitHub](https://github.com/OpenBMB/MiniCPM-V) 。
|
||||
|
||||
|
||||
## llama.cpp推理 <a id="llamacpp"></a>
|
||||
MiniCPM-V 2.6 支持 llama.cpp 推理. 使用方法请查看我们的fork [llama.cpp](https://github.com/OpenBMB/llama.cpp/tree/minicpm-v2.5/examples/minicpmv).
|
||||
|
||||
|
||||
## Int4 量化版
|
||||
int4 量化版，更低的显存占用(7GB): [MiniCPM-V-2_6-int4](https://modelscope.cn/models/OpenBMB/MiniCPM-V-2_6-int4).
|
||||
|
||||
|
||||
## License
|
||||
#### Model License
|
||||
* The code in this repo is released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License.
|
||||
* The usage of MiniCPM-V series model weights must strictly follow [MiniCPM Model License.md](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md).
|
||||
* The models and weights of MiniCPM are completely free for academic research. after filling out a ["questionnaire"](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g) for registration, are also available for free commercial use.
|
||||
|
||||
|
||||
|
||||
#### Statement
|
||||
* As an LMM, MiniCPM-V 2.6 generates contents by learning a large mount of multimodal corpora, but it cannot comprehend, express personal opinions or make value judgement. Anything generated by MiniCPM-V 2.6 does not represent the views and positions of the model developers
|
||||
* We will not be liable for any problems arising from the use of the MinCPM-V models, including but not limited to data security issues, risk of public opinion, or any risks and problems arising from the misdirection, misuse, dissemination or misuse of the model.
|
||||
|
||||
## Other Multimodal Projects from Our Team
|
||||
|
||||
[VisCPM](https://github.com/OpenBMB/VisCPM/tree/main) | [RLHF-V](https://github.com/RLHF-V/RLHF-V) | [LLaVA-UHD](https://github.com/thunlp/LLaVA-UHD) | [RLAIF-V](https://github.com/RLHF-V/RLAIF-V)
|
||||
|
||||
## Citation
|
||||
If you find our work helpful, please consider citing our papers 📝 and liking this project ❤️！
|
||||
|
||||
```bib
|
||||
@article{yao2024minicpm,
|
||||
title={MiniCPM-V: A GPT-4V Level MLLM on Your Phone},
|
||||
author={Yao, Yuan and Yu, Tianyu and Zhang, Ao and Wang, Chongyi and Cui, Junbo and Zhu, Hongji and Cai, Tianchi and Li, Haoyu and Zhao, Weilin and He, Zhihui and others},
|
||||
journal={arXiv preprint arXiv:2408.01800},
|
||||
year={2024}
|
||||
}
|
||||
```
|
||||
|
|
|
|||
|
|
@ -0,0 +1,25 @@
|
|||
{
|
||||
"</box>": 151651,
|
||||
"</image>": 151647,
|
||||
"</image_id>": 151659,
|
||||
"</point>": 151655,
|
||||
"</quad>": 151653,
|
||||
"</ref>": 151649,
|
||||
"</slice>": 151657,
|
||||
"<box>": 151650,
|
||||
"<image>": 151646,
|
||||
"<image_id>": 151658,
|
||||
"<point>": 151654,
|
||||
"<quad>": 151652,
|
||||
"<ref>": 151648,
|
||||
"<slice>": 151656,
|
||||
"<|endoftext|>": 151643,
|
||||
"<|im_end|>": 151645,
|
||||
"<|im_start|>": 151644,
|
||||
"<|reserved_special_token_0|>": 151660,
|
||||
"<|reserved_special_token_1|>": 151661,
|
||||
"<|reserved_special_token_2|>": 151662,
|
||||
"<|reserved_special_token_3|>": 151663,
|
||||
"<|reserved_special_token_4|>": 151664,
|
||||
"<|reserved_special_token_5|>": 151665
|
||||
}
|
||||
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|
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|
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|
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|
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|
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|
|
@ -0,0 +1,54 @@
|
|||
{
|
||||
"_name_or_path": "openbmb/MiniCPM-V-2_6",
|
||||
"version": 2.6,
|
||||
"architectures": [
|
||||
"MiniCPMV"
|
||||
],
|
||||
"auto_map": {
|
||||
"AutoConfig": "configuration_minicpm.MiniCPMVConfig",
|
||||
"AutoModel": "modeling_minicpmv.MiniCPMV",
|
||||
"AutoModelForCausalLM": "modeling_minicpmv.MiniCPMV"
|
||||
},
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151645,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 3584,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 18944,
|
||||
"max_position_embeddings": 32768,
|
||||
"max_window_layers": 28,
|
||||
"num_attention_heads": 28,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 4,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_theta": 1000000.0,
|
||||
"sliding_window": 131072,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.40.0",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151666,
|
||||
"batch_vision_input": true,
|
||||
"drop_vision_last_layer": false,
|
||||
"image_size": 448,
|
||||
"model_type": "minicpmv",
|
||||
"patch_size": 14,
|
||||
"query_num": 64,
|
||||
"slice_config": {
|
||||
"max_slice_nums": 9,
|
||||
"patch_size": 14,
|
||||
"model_type": "minicpmv"
|
||||
},
|
||||
"slice_mode": true,
|
||||
"vision_config": {
|
||||
"hidden_size": 1152,
|
||||
"image_size": 980,
|
||||
"intermediate_size": 4304,
|
||||
"model_type": "siglip",
|
||||
"num_attention_heads": 16,
|
||||
"num_hidden_layers": 27,
|
||||
"patch_size": 14
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1 @@
|
|||
{"framework":"Pytorch","task":"visual-question-answering"}
|
||||
|
|
@ -0,0 +1,100 @@
|
|||
# coding=utf-8
|
||||
""" MiniCPMV model configuration"""
|
||||
|
||||
import os
|
||||
from typing import Union
|
||||
|
||||
from transformers.utils import logging
|
||||
from transformers import Qwen2Config, PretrainedConfig
|
||||
from .modeling_navit_siglip import SiglipVisionConfig
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class MiniCPMVSliceConfig(PretrainedConfig):
|
||||
model_type = "minicpmv"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
patch_size=14,
|
||||
max_slice_nums=9,
|
||||
scale_resolution=448,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
self.patch_size = patch_size
|
||||
self.max_slice_nums = max_slice_nums
|
||||
self.scale_resolution = scale_resolution
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
|
||||
cls._set_token_in_kwargs(kwargs)
|
||||
|
||||
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
|
||||
|
||||
if config_dict.get("model_type") == "minicpmv":
|
||||
config_dict = config_dict["slice_config"]
|
||||
|
||||
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
|
||||
logger.warning(
|
||||
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
|
||||
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
|
||||
)
|
||||
|
||||
return cls.from_dict(config_dict, **kwargs)
|
||||
|
||||
|
||||
|
||||
class MiniCPMVConfig(Qwen2Config):
|
||||
model_type = "minicpmv"
|
||||
keys_to_ignore_at_inference = ["past_key_values"]
|
||||
|
||||
default_vision_config = {
|
||||
"hidden_size": 1152,
|
||||
"image_size": 980,
|
||||
"intermediate_size": 4304,
|
||||
"model_type": "siglip",
|
||||
"num_attention_heads": 16,
|
||||
"num_hidden_layers": 27,
|
||||
"patch_size": 14,
|
||||
}
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
use_cache=True,
|
||||
query_num=64,
|
||||
image_size=448,
|
||||
drop_vision_last_layer=True,
|
||||
batch_vision_input=True,
|
||||
slice_config=None,
|
||||
vision_config=None,
|
||||
use_image_id=True,
|
||||
vision_batch_size=16,
|
||||
**kwargs,
|
||||
):
|
||||
self.use_cache = use_cache
|
||||
self.query_num = query_num
|
||||
self.image_size = image_size
|
||||
self.drop_vision_last_layer = drop_vision_last_layer
|
||||
self.batch_vision_input = batch_vision_input
|
||||
self.use_image_id = use_image_id
|
||||
self.vision_batch_size = vision_batch_size
|
||||
|
||||
if slice_config is None:
|
||||
self.slice_config = MiniCPMVSliceConfig(max_slice_nums=1)
|
||||
else:
|
||||
self.slice_config = MiniCPMVSliceConfig(**slice_config)
|
||||
self.slice_mode = True
|
||||
|
||||
# same as HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit add tgt_sizes
|
||||
if vision_config is None:
|
||||
self.vision_config = SiglipVisionConfig(**self.default_vision_config)
|
||||
logger.info("vision_config is None, using default vision config")
|
||||
elif isinstance(vision_config, dict):
|
||||
self.vision_config = SiglipVisionConfig(**vision_config)
|
||||
elif isinstance(vision_config, SiglipVisionConfig):
|
||||
self.vision_config = vision_config
|
||||
|
||||
self.patch_size = self.vision_config.patch_size
|
||||
|
||||
super().__init__(**kwargs)
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151645,
|
||||
"transformers_version": "4.40.0"
|
||||
}
|
||||
|
|
@ -0,0 +1,418 @@
|
|||
from typing import Optional, Union, Dict, Any, List
|
||||
|
||||
import torch
|
||||
import math
|
||||
import PIL.Image
|
||||
import PIL.ImageSequence
|
||||
import numpy as np
|
||||
import PIL
|
||||
from PIL import Image
|
||||
|
||||
from transformers.utils import TensorType, requires_backends, is_torch_dtype, is_torch_device
|
||||
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
|
||||
from transformers import AutoImageProcessor
|
||||
from transformers.image_transforms import to_channel_dimension_format
|
||||
from transformers.image_utils import (
|
||||
ImageInput,
|
||||
make_list_of_images,
|
||||
valid_images,
|
||||
is_torch_tensor,
|
||||
is_batched,
|
||||
to_numpy_array,
|
||||
infer_channel_dimension_format,
|
||||
ChannelDimension
|
||||
)
|
||||
|
||||
|
||||
def recursive_converter(converter, value):
|
||||
if isinstance(value, list):
|
||||
new_value = []
|
||||
for v in value:
|
||||
new_value += [recursive_converter(converter, v)]
|
||||
return new_value
|
||||
else:
|
||||
return converter(value)
|
||||
|
||||
|
||||
class MiniCPMVBatchFeature(BatchFeature):
|
||||
r"""
|
||||
Extend from BatchFeature for supporting various image size
|
||||
"""
|
||||
def __init__(self, data: Optional[Dict[str, Any]] = None, tensor_type: Union[None, str, TensorType] = None):
|
||||
super().__init__(data)
|
||||
self.convert_to_tensors(tensor_type=tensor_type)
|
||||
|
||||
def convert_to_tensors(self, tensor_type: Optional[Union[str, TensorType]] = None):
|
||||
if tensor_type is None:
|
||||
return self
|
||||
|
||||
is_tensor, as_tensor = self._get_is_as_tensor_fns(tensor_type)
|
||||
|
||||
def converter(value):
|
||||
try:
|
||||
if not is_tensor(value):
|
||||
tensor = as_tensor(value)
|
||||
return tensor
|
||||
except: # noqa E722
|
||||
if key == "overflowing_values":
|
||||
raise ValueError("Unable to create tensor returning overflowing values of different lengths. ")
|
||||
raise ValueError(
|
||||
"Unable to create tensor, you should probably activate padding "
|
||||
"with 'padding=True' to have batched tensors with the same length."
|
||||
)
|
||||
|
||||
|
||||
for key, value in self.items():
|
||||
self[key] = recursive_converter(converter, value)
|
||||
return self
|
||||
|
||||
def to(self, *args, **kwargs) -> "MiniCPMVBatchFeature":
|
||||
requires_backends(self, ["torch"])
|
||||
import torch
|
||||
|
||||
def cast_tensor(v):
|
||||
# check if v is a floating point
|
||||
if torch.is_floating_point(v):
|
||||
# cast and send to device
|
||||
return v.to(*args, **kwargs)
|
||||
elif device is not None:
|
||||
return v.to(device=device)
|
||||
else:
|
||||
return v
|
||||
|
||||
new_data = {}
|
||||
device = kwargs.get("device")
|
||||
# Check if the args are a device or a dtype
|
||||
if device is None and len(args) > 0:
|
||||
# device should be always the first argument
|
||||
arg = args[0]
|
||||
if is_torch_dtype(arg):
|
||||
# The first argument is a dtype
|
||||
pass
|
||||
elif isinstance(arg, str) or is_torch_device(arg) or isinstance(arg, int):
|
||||
device = arg
|
||||
else:
|
||||
# it's something else
|
||||
raise ValueError(f"Attempting to cast a BatchFeature to type {str(arg)}. This is not supported.")
|
||||
# We cast only floating point tensors to avoid issues with tokenizers casting `LongTensor` to `FloatTensor`
|
||||
for k, v in self.items():
|
||||
new_data[k] = recursive_converter(cast_tensor, v)
|
||||
self.data = new_data
|
||||
return self
|
||||
|
||||
|
||||
class MiniCPMVImageProcessor(BaseImageProcessor):
|
||||
model_input_names = ["pixel_values"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
max_slice_nums=9,
|
||||
scale_resolution=448,
|
||||
patch_size=14,
|
||||
**kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self.max_slice_nums = max_slice_nums
|
||||
self.scale_resolution = scale_resolution
|
||||
self.patch_size = patch_size
|
||||
self.use_image_id = kwargs.pop("use_image_id", False)
|
||||
self.image_feature_size = kwargs.pop("image_feature_size", 64)
|
||||
self.im_start_token = kwargs.pop("im_start", "<image>")
|
||||
self.im_end_token = kwargs.pop("im_end", "</image>")
|
||||
self.slice_start_token = kwargs.pop("slice_start", "<slice>")
|
||||
self.slice_end_token = kwargs.pop("slice_end", "</slice>")
|
||||
self.unk_token = kwargs.pop("unk", "<unk>")
|
||||
self.im_id_start = kwargs.pop("im_id_start", "<image_id>")
|
||||
self.im_id_end = kwargs.pop("im_id_end", "</image_id>")
|
||||
self.slice_mode = kwargs.pop("slice_mode", True)
|
||||
self.mean = np.array(kwargs.pop("norm_mean", [0.5, 0.5, 0.5]))
|
||||
self.std = np.array(kwargs.pop("norm_std", [0.5, 0.5, 0.5]))
|
||||
self.version = kwargs.pop("version", 2.0)
|
||||
|
||||
def ensure_divide(self, length, patch_size):
|
||||
return max(round(length / patch_size) * patch_size, patch_size)
|
||||
|
||||
def find_best_resize(self,
|
||||
original_size,
|
||||
scale_resolution,
|
||||
patch_size,
|
||||
allow_upscale=False):
|
||||
width, height = original_size
|
||||
if (width * height >
|
||||
scale_resolution * scale_resolution) or allow_upscale:
|
||||
r = width / height
|
||||
height = int(scale_resolution / math.sqrt(r))
|
||||
width = int(height * r)
|
||||
best_width = self.ensure_divide(width, patch_size)
|
||||
best_height = self.ensure_divide(height, patch_size)
|
||||
return (best_width, best_height)
|
||||
|
||||
def get_refine_size(self,
|
||||
original_size,
|
||||
grid,
|
||||
scale_resolution,
|
||||
patch_size,
|
||||
allow_upscale=False):
|
||||
width, height = original_size
|
||||
grid_x, grid_y = grid
|
||||
|
||||
refine_width = self.ensure_divide(width, grid_x)
|
||||
refine_height = self.ensure_divide(height, grid_y)
|
||||
|
||||
grid_width = refine_width / grid_x
|
||||
grid_height = refine_height / grid_y
|
||||
|
||||
best_grid_size = self.find_best_resize((grid_width, grid_height),
|
||||
scale_resolution,
|
||||
patch_size,
|
||||
allow_upscale=allow_upscale)
|
||||
refine_size = (best_grid_size[0] * grid_x, best_grid_size[1] * grid_y)
|
||||
return refine_size
|
||||
|
||||
def split_to_patches(self, image, grid):
|
||||
patches = []
|
||||
width, height = image.size
|
||||
grid_x = int(width / grid[0])
|
||||
grid_y = int(height / grid[1])
|
||||
for i in range(0, height, grid_y):
|
||||
images = []
|
||||
for j in range(0, width, grid_x):
|
||||
box = (j, i, j + grid_x, i + grid_y)
|
||||
patch = image.crop(box)
|
||||
images.append(patch)
|
||||
patches.append(images)
|
||||
return patches
|
||||
|
||||
def slice_image(
|
||||
self, image, max_slice_nums=9, scale_resolution=448, patch_size=14, never_split=False
|
||||
):
|
||||
original_size = image.size
|
||||
source_image = None
|
||||
best_grid = self.get_sliced_grid(original_size, max_slice_nums, never_split)
|
||||
patches = []
|
||||
|
||||
if best_grid is None:
|
||||
# dont need to slice, upsample
|
||||
best_size = self.find_best_resize(
|
||||
original_size, scale_resolution, patch_size, allow_upscale=True
|
||||
)
|
||||
source_image = image.resize(best_size, resample=Image.Resampling.BICUBIC)
|
||||
else:
|
||||
# source image, down-sampling and ensure divided by patch_size
|
||||
best_resize = self.find_best_resize(original_size, scale_resolution, patch_size)
|
||||
source_image = image.copy().resize(best_resize, resample=Image.Resampling.BICUBIC)
|
||||
refine_size = self.get_refine_size(
|
||||
original_size, best_grid, scale_resolution, patch_size, allow_upscale=True
|
||||
)
|
||||
refine_image = image.resize(refine_size, resample=Image.Resampling.BICUBIC)
|
||||
patches = self.split_to_patches(refine_image, best_grid)
|
||||
|
||||
return source_image, patches, best_grid
|
||||
|
||||
def get_grid_placeholder(self, grid):
|
||||
if grid is None:
|
||||
return ""
|
||||
slice_image_placeholder = (
|
||||
self.slice_start_token
|
||||
+ self.unk_token * self.image_feature_size
|
||||
+ self.slice_end_token
|
||||
)
|
||||
|
||||
cols = grid[0]
|
||||
rows = grid[1]
|
||||
slices = []
|
||||
for i in range(rows):
|
||||
lines = []
|
||||
for j in range(cols):
|
||||
lines.append(slice_image_placeholder)
|
||||
slices.append("".join(lines))
|
||||
|
||||
slice_placeholder = "\n".join(slices)
|
||||
return slice_placeholder
|
||||
|
||||
def get_image_id_placeholder(self, idx=0):
|
||||
return f"{self.im_id_start}{idx}{self.im_id_end}"
|
||||
|
||||
def get_sliced_images(self, image, max_slice_nums=None):
|
||||
slice_images = []
|
||||
|
||||
if not self.slice_mode:
|
||||
return [image]
|
||||
|
||||
max_slice_nums = self.max_slice_nums if max_slice_nums is None else int(max_slice_nums)
|
||||
assert max_slice_nums > 0
|
||||
source_image, patches, sliced_grid = self.slice_image(
|
||||
image,
|
||||
max_slice_nums, # default: 9
|
||||
self.scale_resolution, # default: 448
|
||||
self.patch_size # default: 14
|
||||
)
|
||||
|
||||
slice_images.append(source_image)
|
||||
if len(patches) > 0:
|
||||
for i in range(len(patches)):
|
||||
for j in range(len(patches[0])):
|
||||
slice_images.append(patches[i][j])
|
||||
return slice_images
|
||||
|
||||
def get_sliced_grid(self, image_size, max_slice_nums, nerver_split=False):
|
||||
original_width, original_height = image_size
|
||||
log_ratio = math.log(original_width / original_height)
|
||||
ratio = original_width * original_height / (self.scale_resolution * self.scale_resolution)
|
||||
multiple = min(math.ceil(ratio), max_slice_nums)
|
||||
if multiple <= 1 or nerver_split:
|
||||
return None
|
||||
candidate_split_grids_nums = []
|
||||
for i in [multiple - 1, multiple, multiple + 1]:
|
||||
if i == 1 or i > max_slice_nums:
|
||||
continue
|
||||
candidate_split_grids_nums.append(i)
|
||||
|
||||
candidate_grids = []
|
||||
for split_grids_nums in candidate_split_grids_nums:
|
||||
m = 1
|
||||
while m <= split_grids_nums:
|
||||
if split_grids_nums % m == 0:
|
||||
candidate_grids.append([m, split_grids_nums // m])
|
||||
m += 1
|
||||
|
||||
best_grid = [1, 1]
|
||||
min_error = float("inf")
|
||||
for grid in candidate_grids:
|
||||
error = abs(log_ratio - math.log(grid[0] / grid[1]))
|
||||
if error < min_error:
|
||||
best_grid = grid
|
||||
min_error = error
|
||||
|
||||
return best_grid
|
||||
|
||||
def get_slice_image_placeholder(self, image_size, image_idx=0, max_slice_nums=None, use_image_id=None):
|
||||
max_slice_nums = self.max_slice_nums if max_slice_nums is None else int(max_slice_nums)
|
||||
assert max_slice_nums > 0
|
||||
grid = self.get_sliced_grid(image_size=image_size, max_slice_nums=max_slice_nums)
|
||||
|
||||
image_placeholder = (
|
||||
self.im_start_token
|
||||
+ self.unk_token * self.image_feature_size
|
||||
+ self.im_end_token
|
||||
)
|
||||
use_image_id = self.use_image_id if use_image_id is None else bool(use_image_id)
|
||||
if use_image_id:
|
||||
final_placeholder = self.get_image_id_placeholder(image_idx) + image_placeholder
|
||||
else:
|
||||
final_placeholder = image_placeholder
|
||||
|
||||
if self.slice_mode:
|
||||
final_placeholder = final_placeholder + self.get_grid_placeholder(grid=grid)
|
||||
return final_placeholder
|
||||
|
||||
def to_pil_image(self, image, rescale=None) -> PIL.Image.Image:
|
||||
"""
|
||||
Converts `image` to a PIL Image. Optionally rescales it and puts the channel dimension back as the last axis if
|
||||
needed.
|
||||
|
||||
Args:
|
||||
image (`PIL.Image.Image` or `numpy.ndarray` or `torch.Tensor`):
|
||||
The image to convert to the PIL Image format.
|
||||
rescale (`bool`, *optional*):
|
||||
Whether or not to apply the scaling factor (to make pixel values integers between 0 and 255). Will
|
||||
default to `True` if the image type is a floating type, `False` otherwise.
|
||||
"""
|
||||
if isinstance(image, PIL.Image.Image):
|
||||
return image
|
||||
if is_torch_tensor(image):
|
||||
image = image.numpy()
|
||||
|
||||
if isinstance(image, np.ndarray):
|
||||
if rescale is None:
|
||||
# rescale default to the array being of floating type.
|
||||
rescale = isinstance(image.flat[0], np.floating)
|
||||
# If the channel as been moved to first dim, we put it back at the end.
|
||||
if image.ndim == 3 and image.shape[0] in [1, 3]:
|
||||
image = image.transpose(1, 2, 0)
|
||||
if rescale:
|
||||
image = image * 255
|
||||
image = image.astype(np.uint8)
|
||||
return PIL.Image.fromarray(image)
|
||||
return image
|
||||
|
||||
def reshape_by_patch(self, image):
|
||||
"""
|
||||
:param image: shape [3, H, W]
|
||||
:param patch_size:
|
||||
:return: [3, patch_size, HW/patch_size]
|
||||
"""
|
||||
image = torch.from_numpy(image)
|
||||
patch_size = self.patch_size
|
||||
patches = torch.nn.functional.unfold(
|
||||
image,
|
||||
(patch_size, patch_size),
|
||||
stride=(patch_size, patch_size)
|
||||
)
|
||||
|
||||
patches = patches.reshape(image.size(0), patch_size, patch_size, -1)
|
||||
patches = patches.permute(0, 1, 3, 2).reshape(image.size(0), patch_size, -1)
|
||||
return patches.numpy()
|
||||
|
||||
def preprocess(
|
||||
self,
|
||||
images: Union[Image.Image, List[Image.Image], List[List[Image.Image]]],
|
||||
do_pad: Optional[bool] = True, # TODO: add pad for MiniCPM-Llama3-V-2_5
|
||||
max_slice_nums: int = None,
|
||||
return_tensors: Optional[Union[str, TensorType]] = None,
|
||||
**kwargs
|
||||
) -> MiniCPMVBatchFeature:
|
||||
if isinstance(images, Image.Image):
|
||||
images_list = [[images]]
|
||||
elif isinstance(images[0], Image.Image):
|
||||
images_list = [images]
|
||||
else:
|
||||
images_list = images
|
||||
|
||||
new_images_list = []
|
||||
image_sizes_list = []
|
||||
tgt_sizes_list = []
|
||||
|
||||
for _images in images_list:
|
||||
if _images is None or len(_images) == 0:
|
||||
new_images_list.append([])
|
||||
image_sizes_list.append([])
|
||||
tgt_sizes_list.append([])
|
||||
continue
|
||||
if not valid_images(_images):
|
||||
raise ValueError(
|
||||
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
|
||||
"torch.Tensor, tf.Tensor or jax.ndarray."
|
||||
)
|
||||
|
||||
_images = [self.to_pil_image(image).convert("RGB") for image in _images]
|
||||
input_data_format = infer_channel_dimension_format(np.array(_images[0]))
|
||||
|
||||
new_images = []
|
||||
image_sizes = [image.size for image in _images]
|
||||
tgt_sizes = []
|
||||
for image in _images:
|
||||
image_patches = self.get_sliced_images(image, max_slice_nums)
|
||||
image_patches = [to_numpy_array(image).astype(np.float32) / 255 for image in image_patches]
|
||||
image_patches = [
|
||||
self.normalize(image=image, mean=self.mean, std=self.std, input_data_format=input_data_format)
|
||||
for image in image_patches
|
||||
]
|
||||
image_patches = [
|
||||
to_channel_dimension_format(image, ChannelDimension.FIRST, input_channel_dim=input_data_format)
|
||||
for image in image_patches
|
||||
]
|
||||
for slice_image in image_patches:
|
||||
new_images.append(self.reshape_by_patch(slice_image))
|
||||
tgt_sizes.append(np.array((slice_image.shape[1] // self.patch_size, slice_image.shape[2] // self.patch_size)))
|
||||
|
||||
if tgt_sizes:
|
||||
tgt_sizes = np.vstack(tgt_sizes)
|
||||
|
||||
new_images_list.append(new_images)
|
||||
image_sizes_list.append(image_sizes)
|
||||
tgt_sizes_list.append(tgt_sizes)
|
||||
return MiniCPMVBatchFeature(
|
||||
data={"pixel_values": new_images_list, "image_sizes": image_sizes_list, "tgt_sizes": tgt_sizes_list}, tensor_type=return_tensors
|
||||
)
|
||||
|
||||
AutoImageProcessor.register("MiniCPMVImageProcessor", MiniCPMVImageProcessor)
|
||||
File diff suppressed because it is too large
Load Diff
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
|
|
@ -0,0 +1,796 @@
|
|||
{
|
||||
"metadata": {
|
||||
"total_size": 16198350304
|
||||
},
|
||||
"weight_map": {
|
||||
"llm.lm_head.weight": "model-00004-of-00004.safetensors",
|
||||
"llm.model.embed_tokens.weight": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.0.input_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.0.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.0.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.0.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.0.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.0.self_attn.k_proj.bias": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.0.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.0.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.0.self_attn.q_proj.bias": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.0.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.0.self_attn.v_proj.bias": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.0.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.1.input_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.1.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.1.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.1.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.1.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.1.self_attn.k_proj.bias": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.1.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.1.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.1.self_attn.q_proj.bias": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.1.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.1.self_attn.v_proj.bias": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.1.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"llm.model.layers.10.input_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"llm.model.layers.10.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"llm.model.layers.10.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"llm.model.layers.10.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"llm.model.layers.10.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"llm.model.layers.10.self_attn.k_proj.bias": "model-00002-of-00004.safetensors",
|
||||
"llm.model.layers.10.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"llm.model.layers.10.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"llm.model.layers.10.self_attn.q_proj.bias": "model-00002-of-00004.safetensors",
|
||||
"llm.model.layers.10.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"llm.model.layers.10.self_attn.v_proj.bias": "model-00002-of-00004.safetensors",
|
||||
"llm.model.layers.10.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
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"vpm.encoder.layers.9.layer_norm2.weight": "model-00004-of-00004.safetensors",
|
||||
"vpm.encoder.layers.9.mlp.fc1.bias": "model-00004-of-00004.safetensors",
|
||||
"vpm.encoder.layers.9.mlp.fc1.weight": "model-00004-of-00004.safetensors",
|
||||
"vpm.encoder.layers.9.mlp.fc2.bias": "model-00004-of-00004.safetensors",
|
||||
"vpm.encoder.layers.9.mlp.fc2.weight": "model-00004-of-00004.safetensors",
|
||||
"vpm.encoder.layers.9.self_attn.k_proj.bias": "model-00004-of-00004.safetensors",
|
||||
"vpm.encoder.layers.9.self_attn.k_proj.weight": "model-00004-of-00004.safetensors",
|
||||
"vpm.encoder.layers.9.self_attn.out_proj.bias": "model-00004-of-00004.safetensors",
|
||||
"vpm.encoder.layers.9.self_attn.out_proj.weight": "model-00004-of-00004.safetensors",
|
||||
"vpm.encoder.layers.9.self_attn.q_proj.bias": "model-00004-of-00004.safetensors",
|
||||
"vpm.encoder.layers.9.self_attn.q_proj.weight": "model-00004-of-00004.safetensors",
|
||||
"vpm.encoder.layers.9.self_attn.v_proj.bias": "model-00004-of-00004.safetensors",
|
||||
"vpm.encoder.layers.9.self_attn.v_proj.weight": "model-00004-of-00004.safetensors",
|
||||
"vpm.post_layernorm.bias": "model-00004-of-00004.safetensors",
|
||||
"vpm.post_layernorm.weight": "model-00004-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,403 @@
|
|||
import math
|
||||
from typing import List, Optional
|
||||
import json
|
||||
import torch
|
||||
import torchvision
|
||||
|
||||
from threading import Thread
|
||||
from copy import deepcopy
|
||||
from PIL import Image
|
||||
from transformers import AutoProcessor, Qwen2PreTrainedModel, Qwen2ForCausalLM, TextIteratorStreamer
|
||||
|
||||
from .configuration_minicpm import MiniCPMVConfig
|
||||
from .modeling_navit_siglip import SiglipVisionTransformer
|
||||
from .resampler import Resampler
|
||||
|
||||
|
||||
|
||||
class MiniCPMVPreTrainedModel(Qwen2PreTrainedModel):
|
||||
config_class = MiniCPMVConfig
|
||||
|
||||
|
||||
class MiniCPMV(MiniCPMVPreTrainedModel):
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
self.llm = Qwen2ForCausalLM(config)
|
||||
self.vpm = self.init_vision_module()
|
||||
self.vision_dim = self.vpm.embed_dim
|
||||
self.embed_dim = self.llm.config.hidden_size
|
||||
self.resampler = self.init_resampler(self.embed_dim, self.vision_dim)
|
||||
self.processor = None
|
||||
|
||||
self.terminators = ['<|im_end|>', '<|endoftext|>']
|
||||
|
||||
def init_vision_module(self):
|
||||
# same as HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit add tgt_sizes
|
||||
if self.config._attn_implementation == 'flash_attention_2':
|
||||
self.config.vision_config._attn_implementation = 'flash_attention_2'
|
||||
else:
|
||||
# not suport sdpa
|
||||
self.config.vision_config._attn_implementation = 'eager'
|
||||
model = SiglipVisionTransformer(self.config.vision_config)
|
||||
if self.config.drop_vision_last_layer:
|
||||
model.encoder.layers = model.encoder.layers[:-1]
|
||||
|
||||
setattr(model, 'embed_dim', model.embeddings.embed_dim)
|
||||
setattr(model, 'patch_size', model.embeddings.patch_size)
|
||||
|
||||
return model
|
||||
|
||||
def init_resampler(self, embed_dim, vision_dim):
|
||||
return Resampler(
|
||||
num_queries=self.config.query_num,
|
||||
embed_dim=embed_dim,
|
||||
num_heads=embed_dim // 128,
|
||||
kv_dim=vision_dim,
|
||||
adaptive=True
|
||||
)
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.llm.get_input_embeddings()
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.llm.embed_tokens = value
|
||||
|
||||
def get_output_embeddings(self):
|
||||
return self.llm.lm_head
|
||||
|
||||
def set_output_embeddings(self, new_embeddings):
|
||||
self.llm.lm_head = new_embeddings
|
||||
|
||||
def set_decoder(self, decoder):
|
||||
self.llm = decoder
|
||||
|
||||
def get_decoder(self):
|
||||
return self.llm
|
||||
|
||||
def get_vllm_embedding(self, data):
|
||||
if 'vision_hidden_states' not in data:
|
||||
dtype = self.llm.model.embed_tokens.weight.dtype
|
||||
device = self.llm.model.embed_tokens.weight.device
|
||||
tgt_sizes = data['tgt_sizes']
|
||||
pixel_values_list = data['pixel_values']
|
||||
vision_hidden_states = []
|
||||
all_pixel_values = []
|
||||
img_cnt = []
|
||||
for pixel_values in pixel_values_list:
|
||||
img_cnt.append(len(pixel_values))
|
||||
all_pixel_values.extend([i.flatten(end_dim=1).permute(1, 0) for i in pixel_values])
|
||||
|
||||
# exist image
|
||||
if all_pixel_values:
|
||||
tgt_sizes = [tgt_size for tgt_size in tgt_sizes if isinstance(tgt_size, torch.Tensor)]
|
||||
tgt_sizes = torch.vstack(tgt_sizes).type(torch.int32)
|
||||
|
||||
max_patches = torch.max(tgt_sizes[:, 0] * tgt_sizes[:, 1])
|
||||
|
||||
all_pixel_values = torch.nn.utils.rnn.pad_sequence(all_pixel_values, batch_first=True,
|
||||
padding_value=0.0)
|
||||
B, L, _ = all_pixel_values.shape
|
||||
all_pixel_values = all_pixel_values.permute(0, 2, 1).reshape(B, 3, -1, L)
|
||||
|
||||
patch_attn_mask = torch.zeros((B, 1, max_patches), dtype=torch.bool, device=device)
|
||||
for i in range(B):
|
||||
patch_attn_mask[i, 0, :tgt_sizes[i][0] * tgt_sizes[i][1]] = True
|
||||
|
||||
vision_batch_size = self.config.vision_batch_size
|
||||
all_pixel_values = all_pixel_values.type(dtype)
|
||||
if B > vision_batch_size:
|
||||
hs = []
|
||||
for i in range(0, B, vision_batch_size):
|
||||
start_idx = i
|
||||
end_idx = i + vision_batch_size
|
||||
tmp_hs = self.vpm(all_pixel_values[start_idx:end_idx], patch_attention_mask=patch_attn_mask[start_idx:end_idx], tgt_sizes=tgt_sizes[start_idx:end_idx]).last_hidden_state
|
||||
hs.append(tmp_hs)
|
||||
vision_embedding = torch.cat(hs, dim=0)
|
||||
else:
|
||||
vision_embedding = self.vpm(all_pixel_values, patch_attention_mask=patch_attn_mask, tgt_sizes=tgt_sizes).last_hidden_state
|
||||
vision_embedding = self.resampler(vision_embedding, tgt_sizes)
|
||||
|
||||
start = 0
|
||||
for pixel_values in pixel_values_list:
|
||||
img_cnt = len(pixel_values)
|
||||
if img_cnt > 0:
|
||||
vision_hidden_states.append(vision_embedding[start: start + img_cnt])
|
||||
start += img_cnt
|
||||
else:
|
||||
vision_hidden_states.append([])
|
||||
else: # no image
|
||||
if self.training:
|
||||
dummy_image = torch.zeros(
|
||||
(1, 3, 224, 224),
|
||||
device=device, dtype=dtype
|
||||
)
|
||||
tgt_sizes = torch.Tensor([[(224 // self.config.patch_size), math.ceil(224 / self.config.patch_size)]]).type(torch.int32)
|
||||
dummy_feature = self.resampler(self.vpm(dummy_image).last_hidden_state, tgt_sizes)
|
||||
else:
|
||||
dummy_feature = []
|
||||
for _ in range(len(pixel_values_list)):
|
||||
vision_hidden_states.append(dummy_feature)
|
||||
|
||||
else:
|
||||
vision_hidden_states = data['vision_hidden_states']
|
||||
|
||||
if hasattr(self.llm.config, 'scale_emb'):
|
||||
vllm_embedding = self.llm.model.embed_tokens(data['input_ids']) * self.llm.config.scale_emb
|
||||
else:
|
||||
vllm_embedding = self.llm.model.embed_tokens(data['input_ids'])
|
||||
|
||||
vision_hidden_states = [i.type(vllm_embedding.dtype) if isinstance(
|
||||
i, torch.Tensor) else i for i in vision_hidden_states]
|
||||
|
||||
bs = len(data['input_ids'])
|
||||
for i in range(bs):
|
||||
cur_vs_hs = vision_hidden_states[i]
|
||||
if len(cur_vs_hs) > 0:
|
||||
cur_vllm_emb = vllm_embedding[i]
|
||||
cur_image_bound = data['image_bound'][i]
|
||||
if len(cur_image_bound) > 0:
|
||||
image_indices = torch.stack(
|
||||
[torch.arange(r[0], r[1], dtype=torch.long) for r in cur_image_bound]
|
||||
).to(vllm_embedding.device)
|
||||
|
||||
cur_vllm_emb.scatter_(0, image_indices.view(-1, 1).repeat(1, cur_vllm_emb.shape[-1]),
|
||||
cur_vs_hs.view(-1, cur_vs_hs.shape[-1]))
|
||||
elif self.training:
|
||||
cur_vllm_emb += cur_vs_hs[0].mean() * 0
|
||||
|
||||
return vllm_embedding, vision_hidden_states
|
||||
|
||||
def forward(self, data, **kwargs):
|
||||
vllm_embedding, vision_hidden_states = self.get_vllm_embedding(data)
|
||||
position_ids = data["position_ids"]
|
||||
if position_ids.dtype != torch.int64:
|
||||
position_ids = position_ids.long()
|
||||
|
||||
return self.llm(
|
||||
input_ids=None,
|
||||
position_ids=position_ids,
|
||||
inputs_embeds=vllm_embedding,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
def _decode(self, inputs_embeds, tokenizer, attention_mask, decode_text=False, **kwargs):
|
||||
terminators = [tokenizer.convert_tokens_to_ids(i) for i in self.terminators]
|
||||
output = self.llm.generate(
|
||||
inputs_embeds=inputs_embeds,
|
||||
pad_token_id=0,
|
||||
eos_token_id=terminators,
|
||||
attention_mask=attention_mask,
|
||||
**kwargs
|
||||
)
|
||||
if decode_text:
|
||||
return self._decode_text(output, tokenizer)
|
||||
return output
|
||||
|
||||
def _decode_stream(self, inputs_embeds, tokenizer, **kwargs):
|
||||
terminators = [tokenizer.convert_tokens_to_ids(i) for i in self.terminators]
|
||||
streamer = TextIteratorStreamer(tokenizer=tokenizer)
|
||||
generation_kwargs = {
|
||||
'inputs_embeds': inputs_embeds,
|
||||
'pad_token_id': 0,
|
||||
'eos_token_id': terminators,
|
||||
'streamer': streamer
|
||||
}
|
||||
generation_kwargs.update(kwargs)
|
||||
|
||||
thread = Thread(target=self.llm.generate, kwargs=generation_kwargs)
|
||||
thread.start()
|
||||
|
||||
return streamer
|
||||
|
||||
def _decode_text(self, result_ids, tokenizer):
|
||||
terminators = [tokenizer.convert_tokens_to_ids(i) for i in self.terminators]
|
||||
result_text = []
|
||||
for result in result_ids:
|
||||
result = result[result != 0]
|
||||
if result[0] == tokenizer.bos_id:
|
||||
result = result[1:]
|
||||
if result[-1] in terminators:
|
||||
result = result[:-1]
|
||||
result_text.append(tokenizer.decode(result).strip())
|
||||
return result_text
|
||||
|
||||
def generate(
|
||||
self,
|
||||
input_ids=None,
|
||||
pixel_values=None,
|
||||
tgt_sizes=None,
|
||||
image_bound=None,
|
||||
attention_mask=None,
|
||||
tokenizer=None,
|
||||
vision_hidden_states=None,
|
||||
return_vision_hidden_states=False,
|
||||
stream=False,
|
||||
decode_text=False,
|
||||
**kwargs
|
||||
):
|
||||
assert input_ids is not None
|
||||
assert len(input_ids) == len(pixel_values)
|
||||
|
||||
model_inputs = {
|
||||
"input_ids": input_ids,
|
||||
"image_bound": image_bound,
|
||||
}
|
||||
|
||||
if vision_hidden_states is None:
|
||||
model_inputs["pixel_values"] = pixel_values
|
||||
model_inputs['tgt_sizes'] = tgt_sizes
|
||||
else:
|
||||
model_inputs["vision_hidden_states"] = vision_hidden_states
|
||||
|
||||
with torch.inference_mode():
|
||||
(
|
||||
model_inputs["inputs_embeds"],
|
||||
vision_hidden_states,
|
||||
) = self.get_vllm_embedding(model_inputs)
|
||||
|
||||
if stream:
|
||||
result = self._decode_stream(model_inputs["inputs_embeds"], tokenizer, **kwargs)
|
||||
else:
|
||||
result = self._decode(model_inputs["inputs_embeds"], tokenizer, attention_mask, decode_text=decode_text, **kwargs)
|
||||
|
||||
if return_vision_hidden_states:
|
||||
return result, vision_hidden_states
|
||||
|
||||
return result
|
||||
|
||||
def chat(
|
||||
self,
|
||||
image,
|
||||
msgs,
|
||||
tokenizer,
|
||||
processor=None,
|
||||
vision_hidden_states=None,
|
||||
max_new_tokens=2048,
|
||||
min_new_tokens=0,
|
||||
sampling=True,
|
||||
max_inp_length=8192,
|
||||
system_prompt='',
|
||||
stream=False,
|
||||
max_slice_nums=None,
|
||||
use_image_id=None,
|
||||
**kwargs
|
||||
):
|
||||
if isinstance(msgs[0], list):
|
||||
batched = True
|
||||
else:
|
||||
batched = False
|
||||
msgs_list = msgs
|
||||
images_list = image
|
||||
|
||||
if batched is False:
|
||||
images_list, msgs_list = [images_list], [msgs_list]
|
||||
else:
|
||||
assert images_list is None, "Please integrate image to msgs when using batch inference."
|
||||
images_list = [None] * len(msgs_list)
|
||||
assert len(images_list) == len(msgs_list), "The batch dim of images_list and msgs_list should be the same."
|
||||
|
||||
if processor is None:
|
||||
if self.processor is None:
|
||||
self.processor = AutoProcessor.from_pretrained(self.config._name_or_path, trust_remote_code=True)
|
||||
processor = self.processor
|
||||
|
||||
assert self.config.query_num == processor.image_processor.image_feature_size, "These two values should be the same. Check `config.json` and `preprocessor_config.json`."
|
||||
assert self.config.patch_size == processor.image_processor.patch_size, "These two values should be the same. Check `config.json` and `preprocessor_config.json`."
|
||||
assert self.config.use_image_id == processor.image_processor.use_image_id, "These two values should be the same. Check `config.json` and `preprocessor_config.json`."
|
||||
assert self.config.slice_config.max_slice_nums == processor.image_processor.max_slice_nums, "These two values should be the same. Check `config.json` and `preprocessor_config.json`."
|
||||
assert self.config.slice_mode == processor.image_processor.slice_mode, "These two values should be the same. Check `config.json` and `preprocessor_config.json`."
|
||||
|
||||
prompts_lists = []
|
||||
input_images_lists = []
|
||||
for image, msgs in zip(images_list, msgs_list):
|
||||
if isinstance(msgs, str):
|
||||
msgs = json.loads(msgs)
|
||||
copy_msgs = deepcopy(msgs)
|
||||
|
||||
assert len(msgs) > 0, "msgs is empty"
|
||||
assert sampling or not stream, "if use stream mode, make sure sampling=True"
|
||||
|
||||
if image is not None and isinstance(copy_msgs[0]["content"], str):
|
||||
copy_msgs[0]["content"] = [image, copy_msgs[0]["content"]]
|
||||
|
||||
images = []
|
||||
for i, msg in enumerate(copy_msgs):
|
||||
role = msg["role"]
|
||||
content = msg["content"]
|
||||
assert role in ["user", "assistant"]
|
||||
if i == 0:
|
||||
assert role == "user", "The role of first msg should be user"
|
||||
if isinstance(content, str):
|
||||
content = [content]
|
||||
cur_msgs = []
|
||||
for c in content:
|
||||
if isinstance(c, Image.Image):
|
||||
images.append(c)
|
||||
cur_msgs.append("(<image>./</image>)")
|
||||
elif isinstance(c, str):
|
||||
cur_msgs.append(c)
|
||||
msg["content"] = "\n".join(cur_msgs)
|
||||
|
||||
if system_prompt:
|
||||
sys_msg = {'role': 'system', 'content': system_prompt}
|
||||
copy_msgs = [sys_msg] + copy_msgs
|
||||
|
||||
prompts_lists.append(processor.tokenizer.apply_chat_template(copy_msgs, tokenize=False, add_generation_prompt=True))
|
||||
input_images_lists.append(images)
|
||||
|
||||
inputs = processor(
|
||||
prompts_lists,
|
||||
input_images_lists,
|
||||
max_slice_nums=max_slice_nums,
|
||||
use_image_id=use_image_id,
|
||||
return_tensors="pt",
|
||||
max_length=max_inp_length
|
||||
).to(self.device)
|
||||
|
||||
if sampling:
|
||||
generation_config = {
|
||||
"top_p": 0.8,
|
||||
"top_k": 100,
|
||||
"temperature": 0.7,
|
||||
"do_sample": True,
|
||||
"repetition_penalty": 1.05
|
||||
}
|
||||
else:
|
||||
generation_config = {
|
||||
"num_beams": 3,
|
||||
"repetition_penalty": 1.2,
|
||||
}
|
||||
|
||||
if min_new_tokens > 0:
|
||||
generation_config['min_new_tokens'] = min_new_tokens
|
||||
|
||||
generation_config.update(
|
||||
(k, kwargs[k]) for k in generation_config.keys() & kwargs.keys()
|
||||
)
|
||||
|
||||
inputs.pop("image_sizes")
|
||||
with torch.inference_mode():
|
||||
res = self.generate(
|
||||
**inputs,
|
||||
tokenizer=tokenizer,
|
||||
max_new_tokens=max_new_tokens,
|
||||
vision_hidden_states=vision_hidden_states,
|
||||
stream=stream,
|
||||
decode_text=True,
|
||||
**generation_config
|
||||
)
|
||||
|
||||
if stream:
|
||||
def stream_gen():
|
||||
for text in res:
|
||||
for term in self.terminators:
|
||||
text = text.replace(term, '')
|
||||
yield text
|
||||
return stream_gen()
|
||||
|
||||
else:
|
||||
if batched:
|
||||
answer = res
|
||||
else:
|
||||
answer = res[0]
|
||||
return answer
|
||||
|
|
@ -0,0 +1,937 @@
|
|||
# coding=utf-8
|
||||
# Copyright 2024 Google AI and The HuggingFace Team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
""" PyTorch Siglip model. """
|
||||
# Copied from HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit and add tgt_sizes
|
||||
|
||||
|
||||
import os
|
||||
import math
|
||||
import warnings
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Optional, Tuple, Union
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import torch.utils.checkpoint
|
||||
from torch import nn
|
||||
from torch.nn.init import _calculate_fan_in_and_fan_out
|
||||
|
||||
from transformers.activations import ACT2FN
|
||||
from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
|
||||
from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
|
||||
from transformers.modeling_utils import PreTrainedModel
|
||||
from transformers.configuration_utils import PretrainedConfig
|
||||
from transformers.utils import (
|
||||
ModelOutput,
|
||||
add_start_docstrings,
|
||||
add_start_docstrings_to_model_forward,
|
||||
is_flash_attn_2_available,
|
||||
logging,
|
||||
replace_return_docstrings,
|
||||
)
|
||||
from transformers.utils import logging
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
class SiglipVisionConfig(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of a [`SiglipVisionModel`]. It is used to instantiate a
|
||||
Siglip vision encoder according to the specified arguments, defining the model architecture. Instantiating a
|
||||
configuration with the defaults will yield a similar configuration to that of the vision encoder of the Siglip
|
||||
[google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) architecture.
|
||||
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||
documentation from [`PretrainedConfig`] for more information.
|
||||
Args:
|
||||
hidden_size (`int`, *optional*, defaults to 768):
|
||||
Dimensionality of the encoder layers and the pooler layer.
|
||||
intermediate_size (`int`, *optional*, defaults to 3072):
|
||||
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
|
||||
num_hidden_layers (`int`, *optional*, defaults to 12):
|
||||
Number of hidden layers in the Transformer encoder.
|
||||
num_attention_heads (`int`, *optional*, defaults to 12):
|
||||
Number of attention heads for each attention layer in the Transformer encoder.
|
||||
num_channels (`int`, *optional*, defaults to 3):
|
||||
Number of channels in the input images.
|
||||
image_size (`int`, *optional*, defaults to 224):
|
||||
The size (resolution) of each image.
|
||||
patch_size (`int`, *optional*, defaults to 16):
|
||||
The size (resolution) of each patch.
|
||||
hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
|
||||
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
|
||||
`"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
|
||||
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
|
||||
The epsilon used by the layer normalization layers.
|
||||
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||||
The dropout ratio for the attention probabilities.
|
||||
Example:
|
||||
```python
|
||||
>>> from transformers import SiglipVisionConfig, SiglipVisionModel
|
||||
>>> # Initializing a SiglipVisionConfig with google/siglip-base-patch16-224 style configuration
|
||||
>>> configuration = SiglipVisionConfig()
|
||||
>>> # Initializing a SiglipVisionModel (with random weights) from the google/siglip-base-patch16-224 style configuration
|
||||
>>> model = SiglipVisionModel(configuration)
|
||||
>>> # Accessing the model configuration
|
||||
>>> configuration = model.config
|
||||
```"""
|
||||
|
||||
model_type = "siglip_vision_model"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
hidden_size=768,
|
||||
intermediate_size=3072,
|
||||
num_hidden_layers=12,
|
||||
num_attention_heads=12,
|
||||
num_channels=3,
|
||||
image_size=224,
|
||||
patch_size=16,
|
||||
hidden_act="gelu_pytorch_tanh",
|
||||
layer_norm_eps=1e-6,
|
||||
attention_dropout=0.0,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.hidden_size = hidden_size
|
||||
self.intermediate_size = intermediate_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.num_channels = num_channels
|
||||
self.patch_size = patch_size
|
||||
self.image_size = image_size
|
||||
self.attention_dropout = attention_dropout
|
||||
self.layer_norm_eps = layer_norm_eps
|
||||
self.hidden_act = hidden_act
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
|
||||
cls._set_token_in_kwargs(kwargs)
|
||||
|
||||
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
|
||||
|
||||
# get the vision config dict if we are loading from SiglipConfig
|
||||
if config_dict.get("model_type") == "siglip":
|
||||
config_dict = config_dict["vision_config"]
|
||||
|
||||
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
|
||||
logger.warning(
|
||||
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
|
||||
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
|
||||
)
|
||||
|
||||
return cls.from_dict(config_dict, **kwargs)
|
||||
|
||||
|
||||
_CHECKPOINT_FOR_DOC = "google/siglip-base-patch16-224"
|
||||
|
||||
SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [
|
||||
"google/siglip-base-patch16-224",
|
||||
# See all SigLIP models at https://huggingface.co/models?filter=siglip
|
||||
]
|
||||
|
||||
if is_flash_attn_2_available():
|
||||
from flash_attn import flash_attn_func, flash_attn_varlen_func
|
||||
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
|
||||
|
||||
|
||||
# Copied from transformers.models.llama.modeling_llama._get_unpad_data
|
||||
def _get_unpad_data(attention_mask):
|
||||
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
|
||||
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
|
||||
max_seqlen_in_batch = seqlens_in_batch.max().item()
|
||||
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
|
||||
return (
|
||||
indices,
|
||||
cu_seqlens,
|
||||
max_seqlen_in_batch,
|
||||
)
|
||||
|
||||
|
||||
def _trunc_normal_(tensor, mean, std, a, b):
|
||||
# Cut & paste from PyTorch official master until it's in a few official releases - RW
|
||||
# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
|
||||
def norm_cdf(x):
|
||||
# Computes standard normal cumulative distribution function
|
||||
return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
|
||||
|
||||
if (mean < a - 2 * std) or (mean > b + 2 * std):
|
||||
warnings.warn(
|
||||
"mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
|
||||
"The distribution of values may be incorrect.",
|
||||
stacklevel=2,
|
||||
)
|
||||
|
||||
# Values are generated by using a truncated uniform distribution and
|
||||
# then using the inverse CDF for the normal distribution.
|
||||
# Get upper and lower cdf values
|
||||
l = norm_cdf((a - mean) / std)
|
||||
u = norm_cdf((b - mean) / std)
|
||||
|
||||
# Uniformly fill tensor with values from [l, u], then translate to
|
||||
# [2l-1, 2u-1].
|
||||
tensor.uniform_(2 * l - 1, 2 * u - 1)
|
||||
|
||||
# Use inverse cdf transform for normal distribution to get truncated
|
||||
# standard normal
|
||||
if tensor.dtype in [torch.float16, torch.bfloat16]:
|
||||
# The `erfinv_` op is not (yet?) defined in float16+cpu, bfloat16+gpu
|
||||
og_dtype = tensor.dtype
|
||||
tensor = tensor.to(torch.float32)
|
||||
tensor.erfinv_()
|
||||
tensor = tensor.to(og_dtype)
|
||||
else:
|
||||
tensor.erfinv_()
|
||||
|
||||
# Transform to proper mean, std
|
||||
tensor.mul_(std * math.sqrt(2.0))
|
||||
tensor.add_(mean)
|
||||
|
||||
# Clamp to ensure it's in the proper range
|
||||
if tensor.dtype == torch.float16:
|
||||
# The `clamp_` op is not (yet?) defined in float16+cpu
|
||||
tensor = tensor.to(torch.float32)
|
||||
tensor.clamp_(min=a, max=b)
|
||||
tensor = tensor.to(torch.float16)
|
||||
else:
|
||||
tensor.clamp_(min=a, max=b)
|
||||
|
||||
|
||||
def trunc_normal_tf_(
|
||||
tensor: torch.Tensor, mean: float = 0.0, std: float = 1.0, a: float = -2.0, b: float = 2.0
|
||||
) -> torch.Tensor:
|
||||
"""Fills the input Tensor with values drawn from a truncated
|
||||
normal distribution. The values are effectively drawn from the
|
||||
normal distribution :math:`\\mathcal{N}(\text{mean}, \text{std}^2)`
|
||||
with values outside :math:`[a, b]` redrawn until they are within
|
||||
the bounds. The method used for generating the random values works
|
||||
best when :math:`a \\leq \text{mean} \\leq b`.
|
||||
NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the
|
||||
bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0
|
||||
and the result is subsquently scaled and shifted by the mean and std args.
|
||||
Args:
|
||||
tensor: an n-dimensional `torch.Tensor`
|
||||
mean: the mean of the normal distribution
|
||||
std: the standard deviation of the normal distribution
|
||||
a: the minimum cutoff value
|
||||
b: the maximum cutoff value
|
||||
"""
|
||||
with torch.no_grad():
|
||||
_trunc_normal_(tensor, 0, 1.0, a, b)
|
||||
tensor.mul_(std).add_(mean)
|
||||
|
||||
|
||||
def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"):
|
||||
fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
|
||||
if mode == "fan_in":
|
||||
denom = fan_in
|
||||
elif mode == "fan_out":
|
||||
denom = fan_out
|
||||
elif mode == "fan_avg":
|
||||
denom = (fan_in + fan_out) / 2
|
||||
|
||||
variance = scale / denom
|
||||
|
||||
if distribution == "truncated_normal":
|
||||
# constant is stddev of standard normal truncated to (-2, 2)
|
||||
trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978)
|
||||
elif distribution == "normal":
|
||||
with torch.no_grad():
|
||||
tensor.normal_(std=math.sqrt(variance))
|
||||
elif distribution == "uniform":
|
||||
bound = math.sqrt(3 * variance)
|
||||
with torch.no_grad():
|
||||
tensor.uniform_(-bound, bound)
|
||||
else:
|
||||
raise ValueError(f"invalid distribution {distribution}")
|
||||
|
||||
|
||||
def lecun_normal_(tensor):
|
||||
variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal")
|
||||
|
||||
|
||||
def default_flax_embed_init(tensor):
|
||||
variance_scaling_(tensor, mode="fan_in", distribution="normal")
|
||||
|
||||
|
||||
@dataclass
|
||||
# Copied from transformers.models.clip.modeling_clip.CLIPVisionModelOutput with CLIP->Siglip
|
||||
class SiglipVisionModelOutput(ModelOutput):
|
||||
"""
|
||||
Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
|
||||
Args:
|
||||
image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
|
||||
The image embeddings obtained by applying the projection layer to the pooler_output.
|
||||
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
|
||||
Sequence of hidden-states at the output of the last layer of the model.
|
||||
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
||||
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
|
||||
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
|
||||
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
|
||||
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||||
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
|
||||
sequence_length)`.
|
||||
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
|
||||
heads.
|
||||
"""
|
||||
|
||||
image_embeds: Optional[torch.FloatTensor] = None
|
||||
last_hidden_state: torch.FloatTensor = None
|
||||
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
|
||||
attentions: Optional[Tuple[torch.FloatTensor]] = None
|
||||
|
||||
|
||||
class SiglipVisionEmbeddings(nn.Module):
|
||||
def __init__(self, config: SiglipVisionConfig):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.embed_dim = config.hidden_size
|
||||
self.image_size = config.image_size
|
||||
self.patch_size = config.patch_size
|
||||
|
||||
self.patch_embedding = nn.Conv2d(
|
||||
in_channels=config.num_channels,
|
||||
out_channels=self.embed_dim,
|
||||
kernel_size=self.patch_size,
|
||||
stride=self.patch_size,
|
||||
padding="valid",
|
||||
)
|
||||
|
||||
self.num_patches_per_side = self.image_size // self.patch_size
|
||||
self.num_patches = self.num_patches_per_side**2
|
||||
self.num_positions = self.num_patches
|
||||
self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
|
||||
|
||||
def forward(self, pixel_values: torch.FloatTensor, patch_attention_mask: torch.BoolTensor, tgt_sizes: Optional[torch.IntTensor]=None) -> torch.Tensor:
|
||||
batch_size = pixel_values.size(0)
|
||||
|
||||
patch_embeds = self.patch_embedding(pixel_values)
|
||||
embeddings = patch_embeds.flatten(2).transpose(1, 2)
|
||||
|
||||
max_im_h, max_im_w = pixel_values.size(2), pixel_values.size(3)
|
||||
max_nb_patches_h, max_nb_patches_w = max_im_h // self.patch_size, max_im_w // self.patch_size
|
||||
boundaries = torch.arange(1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side)
|
||||
position_ids = torch.full(
|
||||
size=(
|
||||
batch_size,
|
||||
max_nb_patches_h * max_nb_patches_w,
|
||||
),
|
||||
fill_value=0,
|
||||
)
|
||||
|
||||
for batch_idx, p_attn_mask in enumerate(patch_attention_mask):
|
||||
if tgt_sizes is not None:
|
||||
nb_patches_h = tgt_sizes[batch_idx][0]
|
||||
nb_patches_w = tgt_sizes[batch_idx][1]
|
||||
else:
|
||||
nb_patches_h = p_attn_mask[:, 0].sum()
|
||||
nb_patches_w = p_attn_mask[0].sum()
|
||||
|
||||
fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / nb_patches_h)
|
||||
fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / nb_patches_w)
|
||||
|
||||
bucket_coords_h = torch.bucketize(fractional_coords_h, boundaries, right=True)
|
||||
bucket_coords_w = torch.bucketize(fractional_coords_w, boundaries, right=True)
|
||||
|
||||
pos_ids = (bucket_coords_h[:, None] * self.num_patches_per_side + bucket_coords_w).flatten()
|
||||
position_ids[batch_idx][p_attn_mask.view(-1).cpu()] = pos_ids
|
||||
|
||||
position_ids = position_ids.to(self.position_embedding.weight.device)
|
||||
|
||||
embeddings = embeddings + self.position_embedding(position_ids)
|
||||
return embeddings
|
||||
|
||||
|
||||
class SiglipAttention(nn.Module):
|
||||
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
||||
|
||||
# Copied from transformers.models.clip.modeling_clip.CLIPAttention.__init__
|
||||
def __init__(self, config):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.embed_dim = config.hidden_size
|
||||
self.num_heads = config.num_attention_heads
|
||||
self.head_dim = self.embed_dim // self.num_heads
|
||||
if self.head_dim * self.num_heads != self.embed_dim:
|
||||
raise ValueError(
|
||||
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
|
||||
f" {self.num_heads})."
|
||||
)
|
||||
self.scale = self.head_dim**-0.5
|
||||
self.dropout = config.attention_dropout
|
||||
|
||||
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
|
||||
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
|
||||
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
|
||||
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
output_attentions: Optional[bool] = False,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
"""Input shape: Batch x Time x Channel"""
|
||||
|
||||
batch_size, q_len, _ = hidden_states.size()
|
||||
|
||||
query_states = self.q_proj(hidden_states)
|
||||
key_states = self.k_proj(hidden_states)
|
||||
value_states = self.v_proj(hidden_states)
|
||||
|
||||
query_states = query_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
key_states = key_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
value_states = value_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
|
||||
k_v_seq_len = key_states.shape[-2]
|
||||
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scale
|
||||
|
||||
if attn_weights.size() != (batch_size, self.num_heads, q_len, k_v_seq_len):
|
||||
raise ValueError(
|
||||
f"Attention weights should be of size {(batch_size, self.num_heads, q_len, k_v_seq_len)}, but is"
|
||||
f" {attn_weights.size()}"
|
||||
)
|
||||
|
||||
if attention_mask is not None:
|
||||
if attention_mask.size() != (batch_size, 1, q_len, k_v_seq_len):
|
||||
raise ValueError(
|
||||
f"Attention mask should be of size {(batch_size, 1, q_len, k_v_seq_len)}, but is {attention_mask.size()}"
|
||||
)
|
||||
attn_weights = attn_weights + attention_mask
|
||||
|
||||
# upcast attention to fp32
|
||||
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
||||
attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
|
||||
attn_output = torch.matmul(attn_weights, value_states)
|
||||
|
||||
if attn_output.size() != (batch_size, self.num_heads, q_len, self.head_dim):
|
||||
raise ValueError(
|
||||
f"`attn_output` should be of size {(batch_size, self.num_heads, q_len, self.head_dim)}, but is"
|
||||
f" {attn_output.size()}"
|
||||
)
|
||||
|
||||
attn_output = attn_output.transpose(1, 2).contiguous()
|
||||
attn_output = attn_output.reshape(batch_size, q_len, self.embed_dim)
|
||||
|
||||
attn_output = self.out_proj(attn_output)
|
||||
|
||||
return attn_output, attn_weights
|
||||
|
||||
|
||||
class SiglipFlashAttention2(SiglipAttention):
|
||||
"""
|
||||
Llama flash attention module. This module inherits from `LlamaAttention` as the weights of the module stays
|
||||
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
|
||||
flash attention and deal with padding tokens in case the input contains any of them.
|
||||
"""
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.is_causal = False # Hack to make sure we don't use a causal mask
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.LongTensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
||||
output_attentions: bool = False,
|
||||
use_cache: bool = False,
|
||||
**kwargs,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
output_attentions = False
|
||||
|
||||
bsz, q_len, _ = hidden_states.size()
|
||||
|
||||
query_states = self.q_proj(hidden_states)
|
||||
key_states = self.k_proj(hidden_states)
|
||||
value_states = self.v_proj(hidden_states)
|
||||
|
||||
# Flash attention requires the input to have the shape
|
||||
# batch_size x seq_length x head_dim x hidden_dim
|
||||
# therefore we just need to keep the original shape
|
||||
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
key_states = key_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
value_states = value_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
|
||||
kv_seq_len = key_states.shape[-2]
|
||||
if past_key_value is not None:
|
||||
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
||||
# cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
||||
# query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
||||
|
||||
# if past_key_value is not None:
|
||||
# cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
|
||||
# key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
||||
|
||||
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
|
||||
# to be able to avoid many of these transpose/reshape/view.
|
||||
query_states = query_states.transpose(1, 2)
|
||||
key_states = key_states.transpose(1, 2)
|
||||
value_states = value_states.transpose(1, 2)
|
||||
|
||||
dropout_rate = self.dropout if self.training else 0.0
|
||||
|
||||
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
||||
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
||||
# cast them back in the correct dtype just to be sure everything works as expected.
|
||||
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
|
||||
# in fp32. (LlamaRMSNorm handles it correctly)
|
||||
|
||||
input_dtype = query_states.dtype
|
||||
if input_dtype == torch.float32:
|
||||
if torch.is_autocast_enabled():
|
||||
target_dtype = torch.get_autocast_gpu_dtype()
|
||||
# Handle the case where the model is quantized
|
||||
elif hasattr(self.config, "_pre_quantization_dtype"):
|
||||
target_dtype = self.config._pre_quantization_dtype
|
||||
else:
|
||||
target_dtype = self.q_proj.weight.dtype
|
||||
|
||||
logger.warning_once(
|
||||
"The input hidden states seems to be silently casted in float32, this might be related to the fact"
|
||||
" you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
||||
f" {target_dtype}."
|
||||
)
|
||||
|
||||
query_states = query_states.to(target_dtype)
|
||||
key_states = key_states.to(target_dtype)
|
||||
value_states = value_states.to(target_dtype)
|
||||
|
||||
attn_output = self._flash_attention_forward(
|
||||
query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
|
||||
)
|
||||
|
||||
attn_output = attn_output.reshape(bsz, q_len, self.embed_dim).contiguous()
|
||||
attn_output = self.out_proj(attn_output)
|
||||
|
||||
if not output_attentions:
|
||||
attn_weights = None
|
||||
|
||||
return attn_output, attn_weights
|
||||
|
||||
def _flash_attention_forward(
|
||||
self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
|
||||
):
|
||||
"""
|
||||
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
|
||||
first unpad the input, then computes the attention scores and pad the final attention scores.
|
||||
Args:
|
||||
query_states (`torch.Tensor`):
|
||||
Input query states to be passed to Flash Attention API
|
||||
key_states (`torch.Tensor`):
|
||||
Input key states to be passed to Flash Attention API
|
||||
value_states (`torch.Tensor`):
|
||||
Input value states to be passed to Flash Attention API
|
||||
attention_mask (`torch.Tensor`):
|
||||
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
|
||||
position of padding tokens and 1 for the position of non-padding tokens.
|
||||
dropout (`int`, *optional*):
|
||||
Attention dropout
|
||||
softmax_scale (`float`, *optional*):
|
||||
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
|
||||
"""
|
||||
|
||||
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
|
||||
causal = self.is_causal and query_length != 1
|
||||
|
||||
# Contains at least one padding token in the sequence
|
||||
if attention_mask is not None:
|
||||
batch_size = query_states.shape[0]
|
||||
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
|
||||
query_states, key_states, value_states, attention_mask, query_length
|
||||
)
|
||||
|
||||
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
||||
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
||||
|
||||
attn_output_unpad = flash_attn_varlen_func(
|
||||
query_states,
|
||||
key_states,
|
||||
value_states,
|
||||
cu_seqlens_q=cu_seqlens_q,
|
||||
cu_seqlens_k=cu_seqlens_k,
|
||||
max_seqlen_q=max_seqlen_in_batch_q,
|
||||
max_seqlen_k=max_seqlen_in_batch_k,
|
||||
dropout_p=dropout,
|
||||
softmax_scale=softmax_scale,
|
||||
causal=causal,
|
||||
)
|
||||
|
||||
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
|
||||
else:
|
||||
attn_output = flash_attn_func(
|
||||
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
|
||||
)
|
||||
|
||||
return attn_output
|
||||
|
||||
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
|
||||
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
||||
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
|
||||
|
||||
key_layer = index_first_axis(
|
||||
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
||||
)
|
||||
value_layer = index_first_axis(
|
||||
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
||||
)
|
||||
if query_length == kv_seq_len:
|
||||
query_layer = index_first_axis(
|
||||
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
|
||||
)
|
||||
cu_seqlens_q = cu_seqlens_k
|
||||
max_seqlen_in_batch_q = max_seqlen_in_batch_k
|
||||
indices_q = indices_k
|
||||
elif query_length == 1:
|
||||
max_seqlen_in_batch_q = 1
|
||||
cu_seqlens_q = torch.arange(
|
||||
batch_size + 1, dtype=torch.int32, device=query_layer.device
|
||||
) # There is a memcpy here, that is very bad.
|
||||
indices_q = cu_seqlens_q[:-1]
|
||||
query_layer = query_layer.squeeze(1)
|
||||
else:
|
||||
# The -q_len: slice assumes left padding.
|
||||
attention_mask = attention_mask[:, -query_length:]
|
||||
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
|
||||
|
||||
return (
|
||||
query_layer,
|
||||
key_layer,
|
||||
value_layer,
|
||||
indices_q,
|
||||
(cu_seqlens_q, cu_seqlens_k),
|
||||
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
||||
)
|
||||
|
||||
|
||||
# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Siglip
|
||||
class SiglipMLP(nn.Module):
|
||||
def __init__(self, config):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.activation_fn = ACT2FN[config.hidden_act]
|
||||
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
|
||||
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
|
||||
|
||||
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||
hidden_states = self.fc1(hidden_states)
|
||||
hidden_states = self.activation_fn(hidden_states)
|
||||
hidden_states = self.fc2(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->Siglip
|
||||
class SiglipEncoderLayer(nn.Module):
|
||||
def __init__(self, config: SiglipVisionConfig):
|
||||
super().__init__()
|
||||
self.embed_dim = config.hidden_size
|
||||
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
|
||||
self.self_attn = (
|
||||
SiglipAttention(config)
|
||||
if not self._use_flash_attention_2
|
||||
else SiglipFlashAttention2(config)
|
||||
)
|
||||
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
|
||||
self.mlp = SiglipMLP(config)
|
||||
self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: torch.Tensor,
|
||||
output_attentions: Optional[bool] = False,
|
||||
) -> Tuple[torch.FloatTensor]:
|
||||
"""
|
||||
Args:
|
||||
hidden_states (`torch.FloatTensor`):
|
||||
Input to the layer of shape `(batch, seq_len, embed_dim)`.
|
||||
attention_mask (`torch.FloatTensor`):
|
||||
Attention mask of shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values.
|
||||
output_attentions (`bool`, *optional*, defaults to `False`):
|
||||
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
||||
returned tensors for more detail.
|
||||
"""
|
||||
residual = hidden_states
|
||||
|
||||
hidden_states = self.layer_norm1(hidden_states)
|
||||
hidden_states, attn_weights = self.self_attn(
|
||||
hidden_states=hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
output_attentions=output_attentions,
|
||||
)
|
||||
hidden_states = residual + hidden_states
|
||||
|
||||
residual = hidden_states
|
||||
hidden_states = self.layer_norm2(hidden_states)
|
||||
hidden_states = self.mlp(hidden_states)
|
||||
hidden_states = residual + hidden_states
|
||||
|
||||
outputs = (hidden_states,)
|
||||
|
||||
if output_attentions:
|
||||
outputs += (attn_weights,)
|
||||
|
||||
return outputs
|
||||
|
||||
|
||||
class SiglipPreTrainedModel(PreTrainedModel):
|
||||
"""
|
||||
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
||||
models.
|
||||
"""
|
||||
|
||||
config_class = SiglipVisionConfig
|
||||
base_model_prefix = "siglip"
|
||||
supports_gradient_checkpointing = True
|
||||
|
||||
def _init_weights(self, module):
|
||||
"""Initialize the weights"""
|
||||
|
||||
if isinstance(module, SiglipVisionEmbeddings):
|
||||
width = self.config.hidden_size
|
||||
nn.init.normal_(module.position_embedding.weight, std=1 / np.sqrt(width))
|
||||
elif isinstance(module, nn.Embedding):
|
||||
default_flax_embed_init(module.weight)
|
||||
elif isinstance(module, SiglipAttention):
|
||||
nn.init.normal_(module.q_proj.weight)
|
||||
nn.init.normal_(module.k_proj.weight)
|
||||
nn.init.normal_(module.v_proj.weight)
|
||||
nn.init.normal_(module.out_proj.weight)
|
||||
nn.init.zeros_(module.q_proj.bias)
|
||||
nn.init.zeros_(module.k_proj.bias)
|
||||
nn.init.zeros_(module.v_proj.bias)
|
||||
nn.init.zeros_(module.out_proj.bias)
|
||||
elif isinstance(module, SiglipMLP):
|
||||
nn.init.normal_(module.fc1.weight)
|
||||
nn.init.normal_(module.fc2.weight)
|
||||
nn.init.normal_(module.fc1.bias, std=1e-6)
|
||||
nn.init.normal_(module.fc2.bias, std=1e-6)
|
||||
elif isinstance(module, (nn.Linear, nn.Conv2d)):
|
||||
lecun_normal_(module.weight)
|
||||
if module.bias is not None:
|
||||
nn.init.zeros_(module.bias)
|
||||
elif isinstance(module, nn.LayerNorm):
|
||||
module.bias.data.zero_()
|
||||
module.weight.data.fill_(1.0)
|
||||
|
||||
|
||||
SIGLIP_START_DOCSTRING = r"""
|
||||
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
||||
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
||||
etc.)
|
||||
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
||||
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
||||
and behavior.
|
||||
Parameters:
|
||||
config ([`SiglipVisionConfig`]): Model configuration class with all the parameters of the model.
|
||||
Initializing with a config file does not load the weights associated with the model, only the
|
||||
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
||||
"""
|
||||
|
||||
|
||||
SIGLIP_VISION_INPUTS_DOCSTRING = r"""
|
||||
Args:
|
||||
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
|
||||
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
|
||||
[`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
|
||||
output_attentions (`bool`, *optional*):
|
||||
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
||||
tensors for more detail.
|
||||
output_hidden_states (`bool`, *optional*):
|
||||
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
||||
more detail.
|
||||
return_dict (`bool`, *optional*):
|
||||
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
||||
"""
|
||||
|
||||
|
||||
# Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->Siglip
|
||||
class SiglipEncoder(nn.Module):
|
||||
"""
|
||||
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
|
||||
[`SiglipEncoderLayer`].
|
||||
Args:
|
||||
config: SiglipConfig
|
||||
"""
|
||||
|
||||
def __init__(self, config: SiglipVisionConfig):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.layers = nn.ModuleList([SiglipEncoderLayer(config) for _ in range(config.num_hidden_layers)])
|
||||
self.gradient_checkpointing = False
|
||||
|
||||
# Ignore copy
|
||||
def forward(
|
||||
self,
|
||||
inputs_embeds,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
) -> Union[Tuple, BaseModelOutput]:
|
||||
r"""
|
||||
Args:
|
||||
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
|
||||
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
|
||||
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
|
||||
than the model's internal embedding lookup matrix.
|
||||
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||||
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
||||
- 1 for tokens that are **not masked**,
|
||||
- 0 for tokens that are **masked**.
|
||||
[What are attention masks?](../glossary#attention-mask)
|
||||
output_attentions (`bool`, *optional*):
|
||||
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
||||
returned tensors for more detail.
|
||||
output_hidden_states (`bool`, *optional*):
|
||||
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
|
||||
for more detail.
|
||||
return_dict (`bool`, *optional*):
|
||||
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
||||
"""
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||||
output_hidden_states = (
|
||||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||||
)
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
encoder_states = () if output_hidden_states else None
|
||||
all_attentions = () if output_attentions else None
|
||||
|
||||
hidden_states = inputs_embeds
|
||||
for encoder_layer in self.layers:
|
||||
if output_hidden_states:
|
||||
encoder_states = encoder_states + (hidden_states,)
|
||||
if self.gradient_checkpointing and self.training:
|
||||
layer_outputs = self._gradient_checkpointing_func(
|
||||
encoder_layer.__call__,
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
output_attentions,
|
||||
)
|
||||
else:
|
||||
layer_outputs = encoder_layer(
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
output_attentions=output_attentions,
|
||||
)
|
||||
|
||||
hidden_states = layer_outputs[0]
|
||||
|
||||
if output_attentions:
|
||||
all_attentions = all_attentions + (layer_outputs[1],)
|
||||
|
||||
if output_hidden_states:
|
||||
encoder_states = encoder_states + (hidden_states,)
|
||||
|
||||
if not return_dict:
|
||||
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
|
||||
return BaseModelOutput(
|
||||
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
|
||||
)
|
||||
|
||||
@add_start_docstrings(
|
||||
"""The vision model from SigLIP without any head or projection on top.""",
|
||||
SIGLIP_START_DOCSTRING
|
||||
)
|
||||
class SiglipVisionTransformer(SiglipPreTrainedModel):
|
||||
config_class = SiglipVisionConfig
|
||||
main_input_name = "pixel_values"
|
||||
_supports_flash_attn_2 = True
|
||||
|
||||
def __init__(self, config: SiglipVisionConfig):
|
||||
super().__init__(config)
|
||||
self.config = config
|
||||
embed_dim = config.hidden_size
|
||||
|
||||
self.embeddings = SiglipVisionEmbeddings(config)
|
||||
self.encoder = SiglipEncoder(config)
|
||||
self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
|
||||
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
|
||||
|
||||
# Initialize weights and apply final processing
|
||||
self.post_init()
|
||||
|
||||
def get_input_embeddings(self) -> nn.Module:
|
||||
return self.embeddings.patch_embedding
|
||||
|
||||
@add_start_docstrings_to_model_forward(SIGLIP_VISION_INPUTS_DOCSTRING)
|
||||
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipVisionConfig)
|
||||
def forward(
|
||||
self,
|
||||
pixel_values,
|
||||
patch_attention_mask: Optional[torch.BoolTensor] = None,
|
||||
tgt_sizes: Optional[torch.IntTensor] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
) -> Union[Tuple, BaseModelOutputWithPooling]:
|
||||
r"""
|
||||
Returns:
|
||||
"""
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||||
output_hidden_states = (
|
||||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||||
)
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
batch_size = pixel_values.size(0)
|
||||
if patch_attention_mask is None:
|
||||
patch_attention_mask = torch.ones(
|
||||
size=(
|
||||
batch_size,
|
||||
pixel_values.size(2) // self.config.patch_size,
|
||||
pixel_values.size(3) // self.config.patch_size,
|
||||
),
|
||||
dtype=torch.bool,
|
||||
device=pixel_values.device,
|
||||
)
|
||||
|
||||
hidden_states = self.embeddings(pixel_values=pixel_values, patch_attention_mask=patch_attention_mask, tgt_sizes=tgt_sizes)
|
||||
|
||||
patch_attention_mask = patch_attention_mask.view(batch_size, -1)
|
||||
# The call to `_upad_input` in `_flash_attention_forward` is expensive
|
||||
# So when the `patch_attention_mask` is full of 1s (i.e. attending to the whole sequence),
|
||||
# avoiding passing the attention_mask, which is equivalent to attending to the full sequence
|
||||
if not torch.any(~patch_attention_mask):
|
||||
attention_mask=None
|
||||
else:
|
||||
attention_mask = (
|
||||
_prepare_4d_attention_mask(patch_attention_mask, hidden_states.dtype)
|
||||
if not self._use_flash_attention_2
|
||||
else patch_attention_mask
|
||||
)
|
||||
|
||||
encoder_outputs = self.encoder(
|
||||
inputs_embeds=hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
)
|
||||
|
||||
last_hidden_state = encoder_outputs[0]
|
||||
last_hidden_state = self.post_layernorm(last_hidden_state)
|
||||
|
||||
if not return_dict:
|
||||
return (last_hidden_state, None) + encoder_outputs[1:]
|
||||
|
||||
return BaseModelOutputWithPooling(
|
||||
last_hidden_state=last_hidden_state,
|
||||
pooler_output=None,
|
||||
hidden_states=encoder_outputs.hidden_states,
|
||||
attentions=encoder_outputs.attentions,
|
||||
)
|
||||
|
|
@ -0,0 +1,24 @@
|
|||
{
|
||||
"image_processor_type": "MiniCPMVImageProcessor",
|
||||
"auto_map": {
|
||||
"AutoProcessor": "processing_minicpmv.MiniCPMVProcessor",
|
||||
"AutoImageProcessor": "image_processing_minicpmv.MiniCPMVImageProcessor"
|
||||
},
|
||||
"processor_class": "MiniCPMVProcessor",
|
||||
"max_slice_nums": 9,
|
||||
"scale_resolution": 448,
|
||||
"patch_size": 14,
|
||||
"use_image_id": true,
|
||||
"image_feature_size": 64,
|
||||
"im_start": "<image>",
|
||||
"im_end": "</image>",
|
||||
"slice_start": "<slice>",
|
||||
"slice_end": "</slice>",
|
||||
"unk": "<unk>",
|
||||
"im_id_start": "<image_id>",
|
||||
"im_id_end": "</image_id>",
|
||||
"slice_mode": true,
|
||||
"norm_mean": [0.5, 0.5, 0.5],
|
||||
"norm_std": [0.5, 0.5, 0.5],
|
||||
"version": 2.6
|
||||
}
|
||||
|
|
@ -0,0 +1,240 @@
|
|||
# coding=utf-8
|
||||
# Copyright 2024 The HuggingFace Inc. team.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""
|
||||
Processor class for MiniCPMV.
|
||||
"""
|
||||
|
||||
from typing import List, Optional, Union, Dict, Any
|
||||
import torch
|
||||
import re
|
||||
|
||||
from transformers.image_processing_utils import BatchFeature
|
||||
from transformers.image_utils import ImageInput
|
||||
from transformers.processing_utils import ProcessorMixin
|
||||
from transformers.tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
|
||||
from transformers.utils import TensorType, requires_backends, is_torch_dtype, is_torch_device
|
||||
|
||||
from .image_processing_minicpmv import MiniCPMVBatchFeature
|
||||
|
||||
|
||||
class MiniCPMVProcessor(ProcessorMixin):
|
||||
r"""
|
||||
Constructs a MiniCPMV processor which wraps a MiniCPMV image processor and a MiniCPMV tokenizer into a single processor.
|
||||
|
||||
[`MiniCPMVProcessor`] offers all the functionalities of [`MiniCPMVImageProcessor`] and [`LlamaTokenizerWrapper`]. See the
|
||||
[`~MiniCPMVProcessor.__call__`] and [`~MiniCPMVProcessor.decode`] for more information.
|
||||
|
||||
Args:
|
||||
image_processor ([`MiniCPMVImageProcessor`], *optional*):
|
||||
The image processor is a required input.
|
||||
tokenizer ([`LlamaTokenizerWrapper`], *optional*):
|
||||
The tokenizer is a required input.
|
||||
"""
|
||||
attributes = ["image_processor", "tokenizer"]
|
||||
image_processor_class = "AutoImageProcessor"
|
||||
tokenizer_class = "AutoTokenizer"
|
||||
|
||||
def __init__(self, image_processor=None, tokenizer=None):
|
||||
super().__init__(image_processor, tokenizer)
|
||||
self.version = image_processor.version
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
|
||||
images: ImageInput = None,
|
||||
max_length: Optional[int] = None,
|
||||
do_pad: Optional[bool] = True,
|
||||
max_slice_nums: int = None,
|
||||
use_image_id: bool = None,
|
||||
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
|
||||
**kwargs
|
||||
) -> MiniCPMVBatchFeature:
|
||||
|
||||
if images is not None:
|
||||
image_inputs = self.image_processor(images, do_pad=do_pad, max_slice_nums=max_slice_nums, return_tensors=return_tensors)
|
||||
return self._convert_images_texts_to_inputs(image_inputs, text, max_slice_nums=max_slice_nums, use_image_id=use_image_id, max_length=max_length, **kwargs)
|
||||
|
||||
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
|
||||
def batch_decode(self, *args, **kwargs):
|
||||
"""
|
||||
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
|
||||
refer to the docstring of this method for more information.
|
||||
"""
|
||||
output_ids = args[0]
|
||||
result_text = []
|
||||
for result in output_ids:
|
||||
result = result[result != 0]
|
||||
if result[0] == self.tokenizer.bos_id:
|
||||
result = result[1:]
|
||||
if result[-1] == self.tokenizer.eos_id:
|
||||
result = result[:-1]
|
||||
result_text.append(self.tokenizer.decode(result, *args[1:], **kwargs).strip())
|
||||
return result_text
|
||||
# return self.tokenizer.batch_decode(*args, **kwargs)
|
||||
|
||||
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
|
||||
def decode(self, *args, **kwargs):
|
||||
"""
|
||||
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
|
||||
the docstring of this method for more information.
|
||||
"""
|
||||
result = args[0]
|
||||
result = result[result != 0]
|
||||
if result[0] == self.tokenizer.bos_id:
|
||||
result = result[1:]
|
||||
if result[-1] == self.tokenizer.eos_id or (hasattr(self.tokenizer, "eot_id") and result[-1] == self.tokenizer.eot_id):
|
||||
result = result[:-1]
|
||||
return self.tokenizer.decode(result, *args[1:], **kwargs).strip()
|
||||
|
||||
def _convert(
|
||||
self, input_str, max_inp_length: Optional[int] = None
|
||||
):
|
||||
if self.version > 2.5 or not getattr(self.tokenizer, "add_bos_token", False):
|
||||
input_ids = self.tokenizer.encode(input_str)
|
||||
else:
|
||||
input_ids = [self.tokenizer.bos_id] + self.tokenizer.encode(input_str)
|
||||
if max_inp_length is not None:
|
||||
input_ids = input_ids[:max_inp_length]
|
||||
input_ids = torch.tensor(input_ids, dtype=torch.int32)
|
||||
|
||||
start_cond = (input_ids == self.tokenizer.im_start_id) | (input_ids == self.tokenizer.slice_start_id)
|
||||
end_cond = (input_ids == self.tokenizer.im_end_id) | (input_ids == self.tokenizer.slice_end_id)
|
||||
|
||||
image_start_tokens = torch.where(start_cond)[0]
|
||||
image_start_tokens += 1
|
||||
image_end_tokens = torch.where(end_cond)[0]
|
||||
|
||||
valid_image_nums = max(len(image_start_tokens), len(image_end_tokens))
|
||||
|
||||
image_bounds = torch.hstack(
|
||||
[
|
||||
image_start_tokens[:valid_image_nums].unsqueeze(-1),
|
||||
image_end_tokens[:valid_image_nums].unsqueeze(-1),
|
||||
]
|
||||
)
|
||||
return input_ids, image_bounds
|
||||
|
||||
def _convert_images_texts_to_inputs(
|
||||
self,
|
||||
images,
|
||||
texts: Union[str, List[str]],
|
||||
truncation=None,
|
||||
max_length=None,
|
||||
max_slice_nums=None,
|
||||
use_image_id=None,
|
||||
return_tensors=None,
|
||||
**kwargs
|
||||
):
|
||||
if images is None or not len(images):
|
||||
model_inputs = self.tokenizer(texts, return_tensors=return_tensors, truncation=truncation, max_length=max_length, **kwargs)
|
||||
return MiniCPMVBatchFeature(data={**model_inputs})
|
||||
|
||||
pattern = "(<image>./</image>)"
|
||||
images, image_sizes, tgt_sizes = images["pixel_values"], images["image_sizes"], images["tgt_sizes"]
|
||||
|
||||
if isinstance(texts, str):
|
||||
texts = [texts]
|
||||
input_ids_list = []
|
||||
image_bounds_list = []
|
||||
for index, text in enumerate(texts):
|
||||
image_tags = re.findall(pattern, text)
|
||||
assert len(image_tags) == len(image_sizes[index])
|
||||
text_chunks = text.split(pattern)
|
||||
final_text = ""
|
||||
for i in range(len(image_tags)):
|
||||
final_text = final_text + text_chunks[i] + \
|
||||
self.image_processor.get_slice_image_placeholder(
|
||||
image_sizes[index][i],
|
||||
i,
|
||||
max_slice_nums,
|
||||
use_image_id
|
||||
)
|
||||
final_text += text_chunks[-1]
|
||||
input_ids, image_bounds = self._convert(final_text, max_length)
|
||||
input_ids_list.append(input_ids)
|
||||
image_bounds_list.append(image_bounds)
|
||||
padded_input_ids, padding_lengths = self.pad(
|
||||
input_ids_list,
|
||||
padding_side="left"
|
||||
)
|
||||
for i, length in enumerate(padding_lengths):
|
||||
image_bounds_list[i] = image_bounds_list[i] + length
|
||||
attention_mask = padded_input_ids.ne(0)
|
||||
|
||||
return MiniCPMVBatchFeature(data={
|
||||
"input_ids": padded_input_ids,
|
||||
"attention_mask": attention_mask,
|
||||
"pixel_values": images,
|
||||
"image_sizes": image_sizes,
|
||||
"image_bound": image_bounds_list,
|
||||
"tgt_sizes": tgt_sizes
|
||||
})
|
||||
|
||||
@property
|
||||
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
|
||||
def model_input_names(self):
|
||||
tokenizer_input_names = self.tokenizer.model_input_names
|
||||
image_processor_input_names = self.image_processor.model_input_names
|
||||
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
|
||||
|
||||
|
||||
def pad(self, inputs, max_length=None, padding_value=0, padding_side="left"):
|
||||
items = []
|
||||
if isinstance(inputs[0], list):
|
||||
assert isinstance(inputs[0][0], torch.Tensor)
|
||||
for it in inputs:
|
||||
for tr in it:
|
||||
items.append(tr)
|
||||
else:
|
||||
assert isinstance(inputs[0], torch.Tensor)
|
||||
items = inputs
|
||||
|
||||
batch_size = len(items)
|
||||
shape = items[0].shape
|
||||
dim = len(shape)
|
||||
assert dim <= 2
|
||||
if max_length is None:
|
||||
max_length = 0
|
||||
max_length = max(max_length, max(item.shape[-1] for item in items))
|
||||
min_length = min(item.shape[-1] for item in items)
|
||||
dtype = items[0].dtype
|
||||
|
||||
if dim == 0:
|
||||
return torch.stack([item for item in items], dim=0), [0]
|
||||
elif dim == 1:
|
||||
if max_length == min_length:
|
||||
return torch.stack([item for item in items], dim=0), [0] * batch_size
|
||||
tensor = torch.zeros((batch_size, max_length), dtype=dtype) + padding_value
|
||||
else:
|
||||
tensor = (
|
||||
torch.zeros((batch_size, max_length, shape[-1]), dtype=dtype)
|
||||
+ padding_value
|
||||
)
|
||||
|
||||
padding_length = []
|
||||
for i, item in enumerate(items):
|
||||
if dim == 1:
|
||||
if padding_side == "left":
|
||||
tensor[i, -len(item) :] = item.clone()
|
||||
else:
|
||||
tensor[i, : len(item)] = item.clone()
|
||||
elif dim == 2:
|
||||
if padding_side == "left":
|
||||
tensor[i, -len(item) :, :] = item.clone()
|
||||
else:
|
||||
tensor[i, : len(item), :] = item.clone()
|
||||
padding_length.append(tensor.shape[-1] - len(item))
|
||||
|
||||
return tensor, padding_length
|
||||
|
|
@ -0,0 +1,782 @@
|
|||
from functools import partial
|
||||
from typing import Optional, Tuple
|
||||
import numpy as np
|
||||
import warnings
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch import Tensor
|
||||
import torch.nn.functional as F
|
||||
from torch.nn.functional import *
|
||||
from torch.nn.modules.activation import *
|
||||
from torch.nn.init import trunc_normal_, constant_, xavier_normal_, xavier_uniform_
|
||||
|
||||
from transformers.integrations import is_deepspeed_zero3_enabled
|
||||
|
||||
def get_2d_sincos_pos_embed(embed_dim, image_size):
|
||||
"""
|
||||
image_size: image_size or (image_height, image_width)
|
||||
return:
|
||||
pos_embed: [image_height, image_width, embed_dim]
|
||||
"""
|
||||
if isinstance(image_size, int):
|
||||
grid_h_size, grid_w_size = image_size, image_size
|
||||
else:
|
||||
grid_h_size, grid_w_size = image_size[0], image_size[1]
|
||||
|
||||
grid_h = np.arange(grid_h_size, dtype=np.float32)
|
||||
grid_w = np.arange(grid_w_size, dtype=np.float32)
|
||||
grid = np.meshgrid(grid_w, grid_h) # here w goes first
|
||||
grid = np.stack(grid, axis=0)
|
||||
|
||||
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
|
||||
return pos_embed
|
||||
|
||||
|
||||
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
|
||||
assert embed_dim % 2 == 0
|
||||
|
||||
# use half of dimensions to encode grid_h
|
||||
emb_h = get_1d_sincos_pos_embed_from_grid_new(embed_dim // 2, grid[0]) # (H, W, D/2)
|
||||
emb_w = get_1d_sincos_pos_embed_from_grid_new(embed_dim // 2, grid[1]) # (H, W, D/2)
|
||||
|
||||
emb = np.concatenate([emb_h, emb_w], axis=-1) # (H, W, D)
|
||||
return emb
|
||||
|
||||
|
||||
def get_1d_sincos_pos_embed_from_grid_new(embed_dim, pos):
|
||||
"""
|
||||
embed_dim: output dimension for each position
|
||||
pos: a list of positions to be encoded: size (H, W)
|
||||
out: (H, W, D)
|
||||
"""
|
||||
assert embed_dim % 2 == 0
|
||||
omega = np.arange(embed_dim // 2, dtype=np.float32)
|
||||
omega /= embed_dim / 2.
|
||||
omega = 1. / 10000 ** omega # (D/2,)
|
||||
|
||||
out = np.einsum('hw,d->hwd', pos, omega) # (H, W, D/2), outer product
|
||||
|
||||
emb_sin = np.sin(out) # (H, W, D/2)
|
||||
emb_cos = np.cos(out) # (H, W, D/2)
|
||||
|
||||
emb = np.concatenate([emb_sin, emb_cos], axis=-1) # (H, W, D)
|
||||
return emb
|
||||
|
||||
|
||||
class Resampler(nn.Module):
|
||||
"""
|
||||
A 2D perceiver-resampler network with one cross attention layers by
|
||||
given learnable queries and 2d sincos pos_emb
|
||||
Outputs:
|
||||
A tensor with the shape of (batch_size, num_queries, embed_dim)
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
num_queries,
|
||||
embed_dim,
|
||||
num_heads,
|
||||
kv_dim=None,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
||||
adaptive=False,
|
||||
max_size=(70, 70),
|
||||
):
|
||||
super().__init__()
|
||||
self.num_queries = num_queries
|
||||
self.embed_dim = embed_dim
|
||||
self.num_heads = num_heads
|
||||
self.adaptive = adaptive
|
||||
self.max_size = max_size
|
||||
|
||||
self.query = nn.Parameter(torch.zeros(self.num_queries, embed_dim))
|
||||
|
||||
if kv_dim is not None and kv_dim != embed_dim:
|
||||
self.kv_proj = nn.Linear(kv_dim, embed_dim, bias=False)
|
||||
else:
|
||||
self.kv_proj = nn.Identity()
|
||||
|
||||
self.attn = MultiheadAttention(embed_dim, num_heads)
|
||||
self.ln_q = norm_layer(embed_dim)
|
||||
self.ln_kv = norm_layer(embed_dim)
|
||||
|
||||
self.ln_post = norm_layer(embed_dim)
|
||||
self.proj = nn.Parameter((embed_dim ** -0.5) * torch.randn(embed_dim, embed_dim))
|
||||
|
||||
self._set_2d_pos_cache(self.max_size)
|
||||
|
||||
def _set_2d_pos_cache(self, max_size, device='cpu'):
|
||||
if is_deepspeed_zero3_enabled():
|
||||
device='cuda'
|
||||
pos_embed = torch.from_numpy(get_2d_sincos_pos_embed(self.embed_dim, max_size)).float().to(device)
|
||||
self.register_buffer("pos_embed", pos_embed, persistent=False)
|
||||
|
||||
def _adjust_pos_cache(self, tgt_sizes, device):
|
||||
max_h = torch.max(tgt_sizes[:, 0])
|
||||
max_w = torch.max(tgt_sizes[:, 1])
|
||||
if max_h > self.max_size[0] or max_w > self.max_size[1]:
|
||||
self.max_size = [max(max_h, self.max_size[0]), max(max_w, self.max_size[1])]
|
||||
self._set_2d_pos_cache(self.max_size, device)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
|
||||
def forward(self, x, tgt_sizes=None):
|
||||
assert x.shape[0] == tgt_sizes.shape[0]
|
||||
bs = x.shape[0]
|
||||
|
||||
device = x.device
|
||||
dtype = x.dtype
|
||||
|
||||
patch_len = tgt_sizes[:, 0] * tgt_sizes[:, 1]
|
||||
|
||||
self._adjust_pos_cache(tgt_sizes, device=device)
|
||||
|
||||
max_patch_len = torch.max(patch_len)
|
||||
key_padding_mask = torch.zeros((bs, max_patch_len), dtype=torch.bool, device=device)
|
||||
|
||||
pos_embed = []
|
||||
for i in range(bs):
|
||||
tgt_h, tgt_w = tgt_sizes[i]
|
||||
pos_embed.append(self.pos_embed[:tgt_h, :tgt_w, :].reshape((tgt_h * tgt_w, -1)).to(dtype)) # patches * D
|
||||
key_padding_mask[i, patch_len[i]:] = True
|
||||
|
||||
pos_embed = torch.nn.utils.rnn.pad_sequence(
|
||||
pos_embed, batch_first=True, padding_value=0.0).permute(1, 0, 2) # BLD => L * B * D
|
||||
|
||||
x = self.kv_proj(x) # B * L * D
|
||||
x = self.ln_kv(x).permute(1, 0, 2) # L * B * D
|
||||
|
||||
q = self.ln_q(self.query) # Q * D
|
||||
|
||||
out = self.attn(
|
||||
self._repeat(q, bs), # Q * B * D
|
||||
x + pos_embed, # L * B * D + L * B * D
|
||||
x,
|
||||
key_padding_mask=key_padding_mask)[0]
|
||||
# out: Q * B * D
|
||||
x = out.permute(1, 0, 2) # B * Q * D
|
||||
|
||||
x = self.ln_post(x)
|
||||
x = x @ self.proj
|
||||
return x
|
||||
|
||||
def _repeat(self, query, N: int):
|
||||
return query.unsqueeze(1).repeat(1, N, 1)
|
||||
|
||||
|
||||
class MultiheadAttention(nn.MultiheadAttention):
|
||||
def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bias_kv=False,
|
||||
add_zero_attn=False, kdim=None, vdim=None, batch_first=False, device=None, dtype=None):
|
||||
super().__init__(embed_dim, num_heads, dropout, bias, add_bias_kv, add_zero_attn, kdim, vdim, batch_first, device, dtype)
|
||||
|
||||
# rewrite out_proj layer，with nn.Linear
|
||||
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
query: Tensor,
|
||||
key: Tensor,
|
||||
value: Tensor,
|
||||
key_padding_mask: Optional[Tensor] = None,
|
||||
need_weights: bool = True,
|
||||
attn_mask: Optional[Tensor] = None,
|
||||
average_attn_weights: bool = True,
|
||||
is_causal : bool = False) -> Tuple[Tensor, Optional[Tensor]]:
|
||||
why_not_fast_path = ''
|
||||
if ((attn_mask is not None and torch.is_floating_point(attn_mask))
|
||||
or (key_padding_mask is not None) and torch.is_floating_point(key_padding_mask)):
|
||||
why_not_fast_path = "floating-point masks are not supported for fast path."
|
||||
|
||||
is_batched = query.dim() == 3
|
||||
|
||||
key_padding_mask = _canonical_mask(
|
||||
mask=key_padding_mask,
|
||||
mask_name="key_padding_mask",
|
||||
other_type=F._none_or_dtype(attn_mask),
|
||||
other_name="attn_mask",
|
||||
target_type=query.dtype
|
||||
)
|
||||
|
||||
attn_mask = _canonical_mask(
|
||||
mask=attn_mask,
|
||||
mask_name="attn_mask",
|
||||
other_type=None,
|
||||
other_name="",
|
||||
target_type=query.dtype,
|
||||
check_other=False,
|
||||
)
|
||||
|
||||
|
||||
if not is_batched:
|
||||
why_not_fast_path = f"input not batched; expected query.dim() of 3 but got {query.dim()}"
|
||||
elif query is not key or key is not value:
|
||||
# When lifting this restriction, don't forget to either
|
||||
# enforce that the dtypes all match or test cases where
|
||||
# they don't!
|
||||
why_not_fast_path = "non-self attention was used (query, key, and value are not the same Tensor)"
|
||||
elif self.in_proj_bias is not None and query.dtype != self.in_proj_bias.dtype:
|
||||
why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_bias ({self.in_proj_bias.dtype}) don't match"
|
||||
elif self.in_proj_weight is None:
|
||||
why_not_fast_path = "in_proj_weight was None"
|
||||
elif query.dtype != self.in_proj_weight.dtype:
|
||||
# this case will fail anyway, but at least they'll get a useful error message.
|
||||
why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_weight ({self.in_proj_weight.dtype}) don't match"
|
||||
elif self.training:
|
||||
why_not_fast_path = "training is enabled"
|
||||
elif (self.num_heads % 2) != 0:
|
||||
why_not_fast_path = "self.num_heads is not even"
|
||||
elif not self.batch_first:
|
||||
why_not_fast_path = "batch_first was not True"
|
||||
elif self.bias_k is not None:
|
||||
why_not_fast_path = "self.bias_k was not None"
|
||||
elif self.bias_v is not None:
|
||||
why_not_fast_path = "self.bias_v was not None"
|
||||
elif self.add_zero_attn:
|
||||
why_not_fast_path = "add_zero_attn was enabled"
|
||||
elif not self._qkv_same_embed_dim:
|
||||
why_not_fast_path = "_qkv_same_embed_dim was not True"
|
||||
elif query.is_nested and (key_padding_mask is not None or attn_mask is not None):
|
||||
why_not_fast_path = "supplying both src_key_padding_mask and src_mask at the same time \
|
||||
is not supported with NestedTensor input"
|
||||
elif torch.is_autocast_enabled():
|
||||
why_not_fast_path = "autocast is enabled"
|
||||
|
||||
if not why_not_fast_path:
|
||||
tensor_args = (
|
||||
query,
|
||||
key,
|
||||
value,
|
||||
self.in_proj_weight,
|
||||
self.in_proj_bias,
|
||||
self.out_proj.weight,
|
||||
self.out_proj.bias,
|
||||
)
|
||||
# We have to use list comprehensions below because TorchScript does not support
|
||||
# generator expressions.
|
||||
if torch.overrides.has_torch_function(tensor_args):
|
||||
why_not_fast_path = "some Tensor argument has_torch_function"
|
||||
elif _is_make_fx_tracing():
|
||||
why_not_fast_path = "we are running make_fx tracing"
|
||||
elif not all(_check_arg_device(x) for x in tensor_args):
|
||||
why_not_fast_path = ("some Tensor argument's device is neither one of "
|
||||
f"cpu, cuda or {torch.utils.backend_registration._privateuse1_backend_name}")
|
||||
elif torch.is_grad_enabled() and any(_arg_requires_grad(x) for x in tensor_args):
|
||||
why_not_fast_path = ("grad is enabled and at least one of query or the "
|
||||
"input/output projection weights or biases requires_grad")
|
||||
if not why_not_fast_path:
|
||||
merged_mask, mask_type = self.merge_masks(attn_mask, key_padding_mask, query)
|
||||
|
||||
if self.in_proj_bias is not None and self.in_proj_weight is not None:
|
||||
return torch._native_multi_head_attention(
|
||||
query,
|
||||
key,
|
||||
value,
|
||||
self.embed_dim,
|
||||
self.num_heads,
|
||||
self.in_proj_weight,
|
||||
self.in_proj_bias,
|
||||
self.out_proj.weight,
|
||||
self.out_proj.bias,
|
||||
merged_mask,
|
||||
need_weights,
|
||||
average_attn_weights,
|
||||
mask_type)
|
||||
|
||||
any_nested = query.is_nested or key.is_nested or value.is_nested
|
||||
assert not any_nested, ("MultiheadAttention does not support NestedTensor outside of its fast path. " +
|
||||
f"The fast path was not hit because {why_not_fast_path}")
|
||||
|
||||
if self.batch_first and is_batched:
|
||||
# make sure that the transpose op does not affect the "is" property
|
||||
if key is value:
|
||||
if query is key:
|
||||
query = key = value = query.transpose(1, 0)
|
||||
else:
|
||||
query, key = (x.transpose(1, 0) for x in (query, key))
|
||||
value = key
|
||||
else:
|
||||
query, key, value = (x.transpose(1, 0) for x in (query, key, value))
|
||||
|
||||
if not self._qkv_same_embed_dim:
|
||||
attn_output, attn_output_weights = self.multi_head_attention_forward(
|
||||
query, key, value, self.embed_dim, self.num_heads,
|
||||
self.in_proj_weight, self.in_proj_bias,
|
||||
self.bias_k, self.bias_v, self.add_zero_attn,
|
||||
self.dropout, self.out_proj.weight, self.out_proj.bias,
|
||||
training=self.training,
|
||||
key_padding_mask=key_padding_mask, need_weights=need_weights,
|
||||
attn_mask=attn_mask,
|
||||
use_separate_proj_weight=True,
|
||||
q_proj_weight=self.q_proj_weight, k_proj_weight=self.k_proj_weight,
|
||||
v_proj_weight=self.v_proj_weight,
|
||||
average_attn_weights=average_attn_weights,
|
||||
is_causal=is_causal)
|
||||
else:
|
||||
attn_output, attn_output_weights = self.multi_head_attention_forward(
|
||||
query, key, value, self.embed_dim, self.num_heads,
|
||||
self.in_proj_weight, self.in_proj_bias,
|
||||
self.bias_k, self.bias_v, self.add_zero_attn,
|
||||
self.dropout, self.out_proj.weight, self.out_proj.bias,
|
||||
training=self.training,
|
||||
key_padding_mask=key_padding_mask,
|
||||
need_weights=need_weights,
|
||||
attn_mask=attn_mask,
|
||||
average_attn_weights=average_attn_weights,
|
||||
is_causal=is_causal)
|
||||
if self.batch_first and is_batched:
|
||||
return attn_output.transpose(1, 0), attn_output_weights
|
||||
else:
|
||||
return attn_output, attn_output_weights
|
||||
|
||||
def multi_head_attention_forward(
|
||||
self,
|
||||
query: Tensor,
|
||||
key: Tensor,
|
||||
value: Tensor,
|
||||
embed_dim_to_check: int,
|
||||
num_heads: int,
|
||||
in_proj_weight: Optional[Tensor],
|
||||
in_proj_bias: Optional[Tensor],
|
||||
bias_k: Optional[Tensor],
|
||||
bias_v: Optional[Tensor],
|
||||
add_zero_attn: bool,
|
||||
dropout_p: float,
|
||||
out_proj_weight: Tensor,
|
||||
out_proj_bias: Optional[Tensor],
|
||||
training: bool = True,
|
||||
key_padding_mask: Optional[Tensor] = None,
|
||||
need_weights: bool = True,
|
||||
attn_mask: Optional[Tensor] = None,
|
||||
use_separate_proj_weight: bool = False,
|
||||
q_proj_weight: Optional[Tensor] = None,
|
||||
k_proj_weight: Optional[Tensor] = None,
|
||||
v_proj_weight: Optional[Tensor] = None,
|
||||
static_k: Optional[Tensor] = None,
|
||||
static_v: Optional[Tensor] = None,
|
||||
average_attn_weights: bool = True,
|
||||
is_causal: bool = False,
|
||||
) -> Tuple[Tensor, Optional[Tensor]]:
|
||||
tens_ops = (query, key, value, in_proj_weight, in_proj_bias, bias_k, bias_v, out_proj_weight, out_proj_bias)
|
||||
|
||||
is_batched = _mha_shape_check(query, key, value, key_padding_mask, attn_mask, num_heads)
|
||||
|
||||
# For unbatched input, we unsqueeze at the expected batch-dim to pretend that the input
|
||||
# is batched, run the computation and before returning squeeze the
|
||||
# batch dimension so that the output doesn't carry this temporary batch dimension.
|
||||
if not is_batched:
|
||||
# unsqueeze if the input is unbatched
|
||||
query = query.unsqueeze(1)
|
||||
key = key.unsqueeze(1)
|
||||
value = value.unsqueeze(1)
|
||||
if key_padding_mask is not None:
|
||||
key_padding_mask = key_padding_mask.unsqueeze(0)
|
||||
|
||||
# set up shape vars
|
||||
tgt_len, bsz, embed_dim = query.shape
|
||||
src_len, _, _ = key.shape
|
||||
|
||||
key_padding_mask = _canonical_mask(
|
||||
mask=key_padding_mask,
|
||||
mask_name="key_padding_mask",
|
||||
other_type=_none_or_dtype(attn_mask),
|
||||
other_name="attn_mask",
|
||||
target_type=query.dtype
|
||||
)
|
||||
|
||||
if is_causal and attn_mask is None:
|
||||
raise RuntimeError(
|
||||
"Need attn_mask if specifying the is_causal hint. "
|
||||
"You may use the Transformer module method "
|
||||
"`generate_square_subsequent_mask` to create this mask."
|
||||
)
|
||||
|
||||
if is_causal and key_padding_mask is None and not need_weights:
|
||||
# when we have a kpm or need weights, we need attn_mask
|
||||
# Otherwise, we use the is_causal hint go as is_causal
|
||||
# indicator to SDPA.
|
||||
attn_mask = None
|
||||
else:
|
||||
attn_mask = _canonical_mask(
|
||||
mask=attn_mask,
|
||||
mask_name="attn_mask",
|
||||
other_type=None,
|
||||
other_name="",
|
||||
target_type=query.dtype,
|
||||
check_other=False,
|
||||
)
|
||||
|
||||
if key_padding_mask is not None:
|
||||
# We have the attn_mask, and use that to merge kpm into it.
|
||||
# Turn off use of is_causal hint, as the merged mask is no
|
||||
# longer causal.
|
||||
is_causal = False
|
||||
|
||||
assert embed_dim == embed_dim_to_check, \
|
||||
f"was expecting embedding dimension of {embed_dim_to_check}, but got {embed_dim}"
|
||||
if isinstance(embed_dim, torch.Tensor):
|
||||
# embed_dim can be a tensor when JIT tracing
|
||||
head_dim = embed_dim.div(num_heads, rounding_mode='trunc')
|
||||
else:
|
||||
head_dim = embed_dim // num_heads
|
||||
assert head_dim * num_heads == embed_dim, f"embed_dim {embed_dim} not divisible by num_heads {num_heads}"
|
||||
if use_separate_proj_weight:
|
||||
# allow MHA to have different embedding dimensions when separate projection weights are used
|
||||
assert key.shape[:2] == value.shape[:2], \
|
||||
f"key's sequence and batch dims {key.shape[:2]} do not match value's {value.shape[:2]}"
|
||||
else:
|
||||
assert key.shape == value.shape, f"key shape {key.shape} does not match value shape {value.shape}"
|
||||
|
||||
#
|
||||
# compute in-projection
|
||||
#
|
||||
if not use_separate_proj_weight:
|
||||
assert in_proj_weight is not None, "use_separate_proj_weight is False but in_proj_weight is None"
|
||||
q, k, v = _in_projection_packed(query, key, value, in_proj_weight, in_proj_bias)
|
||||
else:
|
||||
assert q_proj_weight is not None, "use_separate_proj_weight is True but q_proj_weight is None"
|
||||
assert k_proj_weight is not None, "use_separate_proj_weight is True but k_proj_weight is None"
|
||||
assert v_proj_weight is not None, "use_separate_proj_weight is True but v_proj_weight is None"
|
||||
if in_proj_bias is None:
|
||||
b_q = b_k = b_v = None
|
||||
else:
|
||||
b_q, b_k, b_v = in_proj_bias.chunk(3)
|
||||
q, k, v = _in_projection(query, key, value, q_proj_weight, k_proj_weight, v_proj_weight, b_q, b_k, b_v)
|
||||
|
||||
# prep attention mask
|
||||
|
||||
if attn_mask is not None:
|
||||
# ensure attn_mask's dim is 3
|
||||
if attn_mask.dim() == 2:
|
||||
correct_2d_size = (tgt_len, src_len)
|
||||
if attn_mask.shape != correct_2d_size:
|
||||
raise RuntimeError(f"The shape of the 2D attn_mask is {attn_mask.shape}, but should be {correct_2d_size}.")
|
||||
attn_mask = attn_mask.unsqueeze(0)
|
||||
elif attn_mask.dim() == 3:
|
||||
correct_3d_size = (bsz * num_heads, tgt_len, src_len)
|
||||
if attn_mask.shape != correct_3d_size:
|
||||
raise RuntimeError(f"The shape of the 3D attn_mask is {attn_mask.shape}, but should be {correct_3d_size}.")
|
||||
else:
|
||||
raise RuntimeError(f"attn_mask's dimension {attn_mask.dim()} is not supported")
|
||||
|
||||
# add bias along batch dimension (currently second)
|
||||
if bias_k is not None and bias_v is not None:
|
||||
assert static_k is None, "bias cannot be added to static key."
|
||||
assert static_v is None, "bias cannot be added to static value."
|
||||
k = torch.cat([k, bias_k.repeat(1, bsz, 1)])
|
||||
v = torch.cat([v, bias_v.repeat(1, bsz, 1)])
|
||||
if attn_mask is not None:
|
||||
attn_mask = pad(attn_mask, (0, 1))
|
||||
if key_padding_mask is not None:
|
||||
key_padding_mask = pad(key_padding_mask, (0, 1))
|
||||
else:
|
||||
assert bias_k is None
|
||||
assert bias_v is None
|
||||
|
||||
#
|
||||
# reshape q, k, v for multihead attention and make em batch first
|
||||
#
|
||||
q = q.view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1)
|
||||
if static_k is None:
|
||||
k = k.view(k.shape[0], bsz * num_heads, head_dim).transpose(0, 1)
|
||||
else:
|
||||
# TODO finish disentangling control flow so we don't do in-projections when statics are passed
|
||||
assert static_k.size(0) == bsz * num_heads, \
|
||||
f"expecting static_k.size(0) of {bsz * num_heads}, but got {static_k.size(0)}"
|
||||
assert static_k.size(2) == head_dim, \
|
||||
f"expecting static_k.size(2) of {head_dim}, but got {static_k.size(2)}"
|
||||
k = static_k
|
||||
if static_v is None:
|
||||
v = v.view(v.shape[0], bsz * num_heads, head_dim).transpose(0, 1)
|
||||
else:
|
||||
# TODO finish disentangling control flow so we don't do in-projections when statics are passed
|
||||
assert static_v.size(0) == bsz * num_heads, \
|
||||
f"expecting static_v.size(0) of {bsz * num_heads}, but got {static_v.size(0)}"
|
||||
assert static_v.size(2) == head_dim, \
|
||||
f"expecting static_v.size(2) of {head_dim}, but got {static_v.size(2)}"
|
||||
v = static_v
|
||||
|
||||
# add zero attention along batch dimension (now first)
|
||||
if add_zero_attn:
|
||||
zero_attn_shape = (bsz * num_heads, 1, head_dim)
|
||||
k = torch.cat([k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)], dim=1)
|
||||
v = torch.cat([v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)], dim=1)
|
||||
if attn_mask is not None:
|
||||
attn_mask = pad(attn_mask, (0, 1))
|
||||
if key_padding_mask is not None:
|
||||
key_padding_mask = pad(key_padding_mask, (0, 1))
|
||||
|
||||
# update source sequence length after adjustments
|
||||
src_len = k.size(1)
|
||||
|
||||
# merge key padding and attention masks
|
||||
if key_padding_mask is not None:
|
||||
assert key_padding_mask.shape == (bsz, src_len), \
|
||||
f"expecting key_padding_mask shape of {(bsz, src_len)}, but got {key_padding_mask.shape}"
|
||||
key_padding_mask = key_padding_mask.view(bsz, 1, 1, src_len). \
|
||||
expand(-1, num_heads, -1, -1).reshape(bsz * num_heads, 1, src_len)
|
||||
if attn_mask is None:
|
||||
attn_mask = key_padding_mask
|
||||
else:
|
||||
attn_mask = attn_mask + key_padding_mask
|
||||
|
||||
# adjust dropout probability
|
||||
if not training:
|
||||
dropout_p = 0.0
|
||||
|
||||
#
|
||||
# (deep breath) calculate attention and out projection
|
||||
#
|
||||
|
||||
if need_weights:
|
||||
B, Nt, E = q.shape
|
||||
q_scaled = q / math.sqrt(E)
|
||||
|
||||
assert not (is_causal and attn_mask is None), "FIXME: is_causal not implemented for need_weights"
|
||||
|
||||
if attn_mask is not None:
|
||||
attn_output_weights = torch.baddbmm(attn_mask, q_scaled, k.transpose(-2, -1))
|
||||
else:
|
||||
attn_output_weights = torch.bmm(q_scaled, k.transpose(-2, -1))
|
||||
attn_output_weights = softmax(attn_output_weights, dim=-1)
|
||||
if dropout_p > 0.0:
|
||||
attn_output_weights = dropout(attn_output_weights, p=dropout_p)
|
||||
|
||||
attn_output = torch.bmm(attn_output_weights, v)
|
||||
|
||||
attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len * bsz, embed_dim)
|
||||
attn_output = self.out_proj(attn_output)
|
||||
attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1))
|
||||
|
||||
# optionally average attention weights over heads
|
||||
attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len)
|
||||
if average_attn_weights:
|
||||
attn_output_weights = attn_output_weights.mean(dim=1)
|
||||
|
||||
if not is_batched:
|
||||
# squeeze the output if input was unbatched
|
||||
attn_output = attn_output.squeeze(1)
|
||||
attn_output_weights = attn_output_weights.squeeze(0)
|
||||
return attn_output, attn_output_weights
|
||||
else:
|
||||
# attn_mask can be either (L,S) or (N*num_heads, L, S)
|
||||
# if attn_mask's shape is (1, L, S) we need to unsqueeze to (1, 1, L, S)
|
||||
# in order to match the input for SDPA of (N, num_heads, L, S)
|
||||
if attn_mask is not None:
|
||||
if attn_mask.size(0) == 1 and attn_mask.dim() == 3:
|
||||
attn_mask = attn_mask.unsqueeze(0)
|
||||
else:
|
||||
attn_mask = attn_mask.view(bsz, num_heads, -1, src_len)
|
||||
|
||||
q = q.view(bsz, num_heads, tgt_len, head_dim)
|
||||
k = k.view(bsz, num_heads, src_len, head_dim)
|
||||
v = v.view(bsz, num_heads, src_len, head_dim)
|
||||
|
||||
attn_output = F.scaled_dot_product_attention(q, k, v, attn_mask, dropout_p, is_causal)
|
||||
attn_output = attn_output.permute(2, 0, 1, 3).contiguous().view(bsz * tgt_len, embed_dim)
|
||||
|
||||
attn_output = self.out_proj(attn_output)
|
||||
attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1))
|
||||
if not is_batched:
|
||||
# squeeze the output if input was unbatched
|
||||
attn_output = attn_output.squeeze(1)
|
||||
return attn_output, None
|
||||
|
||||
|
||||
def _mha_shape_check(query: Tensor, key: Tensor, value: Tensor,
|
||||
key_padding_mask: Optional[Tensor], attn_mask: Optional[Tensor], num_heads: int):
|
||||
# Verifies the expected shape for `query, `key`, `value`, `key_padding_mask` and `attn_mask`
|
||||
# and returns if the input is batched or not.
|
||||
# Raises an error if `query` is not 2-D (unbatched) or 3-D (batched) tensor.
|
||||
|
||||
# Shape check.
|
||||
if query.dim() == 3:
|
||||
# Batched Inputs
|
||||
is_batched = True
|
||||
assert key.dim() == 3 and value.dim() == 3, \
|
||||
("For batched (3-D) `query`, expected `key` and `value` to be 3-D"
|
||||
f" but found {key.dim()}-D and {value.dim()}-D tensors respectively")
|
||||
if key_padding_mask is not None:
|
||||
assert key_padding_mask.dim() == 2, \
|
||||
("For batched (3-D) `query`, expected `key_padding_mask` to be `None` or 2-D"
|
||||
f" but found {key_padding_mask.dim()}-D tensor instead")
|
||||
if attn_mask is not None:
|
||||
assert attn_mask.dim() in (2, 3), \
|
||||
("For batched (3-D) `query`, expected `attn_mask` to be `None`, 2-D or 3-D"
|
||||
f" but found {attn_mask.dim()}-D tensor instead")
|
||||
elif query.dim() == 2:
|
||||
# Unbatched Inputs
|
||||
is_batched = False
|
||||
assert key.dim() == 2 and value.dim() == 2, \
|
||||
("For unbatched (2-D) `query`, expected `key` and `value` to be 2-D"
|
||||
f" but found {key.dim()}-D and {value.dim()}-D tensors respectively")
|
||||
|
||||
if key_padding_mask is not None:
|
||||
assert key_padding_mask.dim() == 1, \
|
||||
("For unbatched (2-D) `query`, expected `key_padding_mask` to be `None` or 1-D"
|
||||
f" but found {key_padding_mask.dim()}-D tensor instead")
|
||||
|
||||
if attn_mask is not None:
|
||||
assert attn_mask.dim() in (2, 3), \
|
||||
("For unbatched (2-D) `query`, expected `attn_mask` to be `None`, 2-D or 3-D"
|
||||
f" but found {attn_mask.dim()}-D tensor instead")
|
||||
if attn_mask.dim() == 3:
|
||||
expected_shape = (num_heads, query.shape[0], key.shape[0])
|
||||
assert attn_mask.shape == expected_shape, \
|
||||
(f"Expected `attn_mask` shape to be {expected_shape} but got {attn_mask.shape}")
|
||||
else:
|
||||
raise AssertionError(
|
||||
f"query should be unbatched 2D or batched 3D tensor but received {query.dim()}-D query tensor")
|
||||
|
||||
return is_batched
|
||||
|
||||
|
||||
def _canonical_mask(
|
||||
mask: Optional[Tensor],
|
||||
mask_name: str,
|
||||
other_type: Optional[DType],
|
||||
other_name: str,
|
||||
target_type: DType,
|
||||
check_other: bool = True,
|
||||
) -> Optional[Tensor]:
|
||||
|
||||
if mask is not None:
|
||||
_mask_dtype = mask.dtype
|
||||
_mask_is_float = torch.is_floating_point(mask)
|
||||
if _mask_dtype != torch.bool and not _mask_is_float:
|
||||
raise AssertionError(
|
||||
f"only bool and floating types of {mask_name} are supported")
|
||||
if check_other and other_type is not None:
|
||||
if _mask_dtype != other_type:
|
||||
warnings.warn(
|
||||
f"Support for mismatched {mask_name} and {other_name} "
|
||||
"is deprecated. Use same type for both instead."
|
||||
)
|
||||
if not _mask_is_float:
|
||||
mask = (
|
||||
torch.zeros_like(mask, dtype=target_type)
|
||||
.masked_fill_(mask, float("-inf"))
|
||||
)
|
||||
return mask
|
||||
|
||||
|
||||
def _none_or_dtype(input: Optional[Tensor]) -> Optional[DType]:
|
||||
if input is None:
|
||||
return None
|
||||
elif isinstance(input, torch.Tensor):
|
||||
return input.dtype
|
||||
raise RuntimeError("input to _none_or_dtype() must be None or torch.Tensor")
|
||||
|
||||
def _in_projection_packed(
|
||||
q: Tensor,
|
||||
k: Tensor,
|
||||
v: Tensor,
|
||||
w: Tensor,
|
||||
b: Optional[Tensor] = None,
|
||||
) -> List[Tensor]:
|
||||
r"""
|
||||
Performs the in-projection step of the attention operation, using packed weights.
|
||||
Output is a triple containing projection tensors for query, key and value.
|
||||
Args:
|
||||
q, k, v: query, key and value tensors to be projected. For self-attention,
|
||||
these are typically the same tensor; for encoder-decoder attention,
|
||||
k and v are typically the same tensor. (We take advantage of these
|
||||
identities for performance if they are present.) Regardless, q, k and v
|
||||
must share a common embedding dimension; otherwise their shapes may vary.
|
||||
w: projection weights for q, k and v, packed into a single tensor. Weights
|
||||
are packed along dimension 0, in q, k, v order.
|
||||
b: optional projection biases for q, k and v, packed into a single tensor
|
||||
in q, k, v order.
|
||||
Shape:
|
||||
Inputs:
|
||||
- q: :math:`(..., E)` where E is the embedding dimension
|
||||
- k: :math:`(..., E)` where E is the embedding dimension
|
||||
- v: :math:`(..., E)` where E is the embedding dimension
|
||||
- w: :math:`(E * 3, E)` where E is the embedding dimension
|
||||
- b: :math:`E * 3` where E is the embedding dimension
|
||||
Output:
|
||||
- in output list :math:`[q', k', v']`, each output tensor will have the
|
||||
same shape as the corresponding input tensor.
|
||||
"""
|
||||
E = q.size(-1)
|
||||
if k is v:
|
||||
if q is k:
|
||||
# self-attention
|
||||
proj = linear(q, w, b)
|
||||
# reshape to 3, E and not E, 3 is deliberate for better memory coalescing and keeping same order as chunk()
|
||||
proj = proj.unflatten(-1, (3, E)).unsqueeze(0).transpose(0, -2).squeeze(-2).contiguous()
|
||||
return proj[0], proj[1], proj[2]
|
||||
else:
|
||||
# encoder-decoder attention
|
||||
w_q, w_kv = w.split([E, E * 2])
|
||||
if b is None:
|
||||
b_q = b_kv = None
|
||||
else:
|
||||
b_q, b_kv = b.split([E, E * 2])
|
||||
q_proj = linear(q, w_q, b_q)
|
||||
kv_proj = linear(k, w_kv, b_kv)
|
||||
# reshape to 2, E and not E, 2 is deliberate for better memory coalescing and keeping same order as chunk()
|
||||
kv_proj = kv_proj.unflatten(-1, (2, E)).unsqueeze(0).transpose(0, -2).squeeze(-2).contiguous()
|
||||
return (q_proj, kv_proj[0], kv_proj[1])
|
||||
else:
|
||||
w_q, w_k, w_v = w.chunk(3)
|
||||
if b is None:
|
||||
b_q = b_k = b_v = None
|
||||
else:
|
||||
b_q, b_k, b_v = b.chunk(3)
|
||||
return linear(q, w_q, b_q), linear(k, w_k, b_k), linear(v, w_v, b_v)
|
||||
|
||||
|
||||
def _in_projection(
|
||||
q: Tensor,
|
||||
k: Tensor,
|
||||
v: Tensor,
|
||||
w_q: Tensor,
|
||||
w_k: Tensor,
|
||||
w_v: Tensor,
|
||||
b_q: Optional[Tensor] = None,
|
||||
b_k: Optional[Tensor] = None,
|
||||
b_v: Optional[Tensor] = None,
|
||||
) -> Tuple[Tensor, Tensor, Tensor]:
|
||||
r"""
|
||||
Performs the in-projection step of the attention operation. This is simply
|
||||
a triple of linear projections, with shape constraints on the weights which
|
||||
ensure embedding dimension uniformity in the projected outputs.
|
||||
Output is a triple containing projection tensors for query, key and value.
|
||||
Args:
|
||||
q, k, v: query, key and value tensors to be projected.
|
||||
w_q, w_k, w_v: weights for q, k and v, respectively.
|
||||
b_q, b_k, b_v: optional biases for q, k and v, respectively.
|
||||
Shape:
|
||||
Inputs:
|
||||
- q: :math:`(Qdims..., Eq)` where Eq is the query embedding dimension and Qdims are any
|
||||
number of leading dimensions.
|
||||
- k: :math:`(Kdims..., Ek)` where Ek is the key embedding dimension and Kdims are any
|
||||
number of leading dimensions.
|
||||
- v: :math:`(Vdims..., Ev)` where Ev is the value embedding dimension and Vdims are any
|
||||
number of leading dimensions.
|
||||
- w_q: :math:`(Eq, Eq)`
|
||||
- w_k: :math:`(Eq, Ek)`
|
||||
- w_v: :math:`(Eq, Ev)`
|
||||
- b_q: :math:`(Eq)`
|
||||
- b_k: :math:`(Eq)`
|
||||
- b_v: :math:`(Eq)`
|
||||
Output: in output triple :math:`(q', k', v')`,
|
||||
- q': :math:`[Qdims..., Eq]`
|
||||
- k': :math:`[Kdims..., Eq]`
|
||||
- v': :math:`[Vdims..., Eq]`
|
||||
"""
|
||||
Eq, Ek, Ev = q.size(-1), k.size(-1), v.size(-1)
|
||||
assert w_q.shape == (Eq, Eq), f"expecting query weights shape of {(Eq, Eq)}, but got {w_q.shape}"
|
||||
assert w_k.shape == (Eq, Ek), f"expecting key weights shape of {(Eq, Ek)}, but got {w_k.shape}"
|
||||
assert w_v.shape == (Eq, Ev), f"expecting value weights shape of {(Eq, Ev)}, but got {w_v.shape}"
|
||||
assert b_q is None or b_q.shape == (Eq,), f"expecting query bias shape of {(Eq,)}, but got {b_q.shape}"
|
||||
assert b_k is None or b_k.shape == (Eq,), f"expecting key bias shape of {(Eq,)}, but got {b_k.shape}"
|
||||
assert b_v is None or b_v.shape == (Eq,), f"expecting value bias shape of {(Eq,)}, but got {b_v.shape}"
|
||||
return linear(q, w_q, b_q), linear(k, w_k, b_k), linear(v, w_v, b_v)
|
||||
|
|
@ -0,0 +1,172 @@
|
|||
{
|
||||
"additional_special_tokens": [
|
||||
{
|
||||
"content": "<image>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
{
|
||||
"content": "</image>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
{
|
||||
"content": "<ref>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
{
|
||||
"content": "</ref>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
{
|
||||
"content": "<box>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
{
|
||||
"content": "</box>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
{
|
||||
"content": "<quad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
{
|
||||
"content": "</quad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
{
|
||||
"content": "<point>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
{
|
||||
"content": "</point>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
{
|
||||
"content": "<slice>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
{
|
||||
"content": "</slice>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
{
|
||||
"content": "<image_id>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
{
|
||||
"content": "</image_id>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
{
|
||||
"content": "<|reserved_special_token_0|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
{
|
||||
"content": "<|reserved_special_token_1|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
{
|
||||
"content": "<|reserved_special_token_2|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
{
|
||||
"content": "<|reserved_special_token_3|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
{
|
||||
"content": "<|reserved_special_token_4|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
{
|
||||
"content": "<|reserved_special_token_5|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
],
|
||||
"bos_token": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,66 @@
|
|||
from transformers.models.qwen2 import Qwen2TokenizerFast
|
||||
|
||||
|
||||
class MiniCPMVTokenizerFast(Qwen2TokenizerFast):
|
||||
def __init__(self, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self.im_start = "<image>"
|
||||
self.im_end = "</image>"
|
||||
self.ref_start = "<ref>"
|
||||
self.ref_end = "</ref>"
|
||||
self.box_start = "<box>"
|
||||
self.box_end = "</box>"
|
||||
self.quad_start = "<quad>"
|
||||
self.quad_end = "</quad>"
|
||||
self.slice_start = "<slice>"
|
||||
self.slice_end = "</slice>"
|
||||
self.im_id_start = "<image_id>"
|
||||
self.im_id_end = "</image_id>"
|
||||
|
||||
@property
|
||||
def eos_id(self):
|
||||
return self.eos_token_id
|
||||
|
||||
@property
|
||||
def bos_id(self):
|
||||
return self.bos_token_id
|
||||
|
||||
@property
|
||||
def unk_id(self):
|
||||
return self.unk_token_id
|
||||
|
||||
@property
|
||||
def im_start_id(self):
|
||||
return self.convert_tokens_to_ids(self.im_start)
|
||||
|
||||
@property
|
||||
def im_end_id(self):
|
||||
return self.convert_tokens_to_ids(self.im_end)
|
||||
|
||||
@property
|
||||
def slice_start_id(self):
|
||||
return self.convert_tokens_to_ids(self.slice_start)
|
||||
|
||||
@property
|
||||
def slice_end_id(self):
|
||||
return self.convert_tokens_to_ids(self.slice_end)
|
||||
|
||||
@property
|
||||
def im_id_start_id(self):
|
||||
return self.convert_tokens_to_ids(self.im_id_start)
|
||||
|
||||
@property
|
||||
def im_id_end_id(self):
|
||||
return self.convert_tokens_to_ids(self.im_id_end)
|
||||
|
||||
@property
|
||||
def newline_id(self):
|
||||
return self.convert_tokens_to_ids('\n')
|
||||
|
||||
@staticmethod
|
||||
def escape(text: str) -> str:
|
||||
return text
|
||||
|
||||
@staticmethod
|
||||
def unescape(text: str) -> str:
|
||||
return text
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,235 @@
|
|||
{
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"128244": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<image>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "</image>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<ref>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "</ref>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<box>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "</box>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<quad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "</quad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<point>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "</point>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<slice>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "</slice>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151658": {
|
||||
"content": "<image_id>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151659": {
|
||||
"content": "</image_id>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|reserved_special_token_0|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|reserved_special_token_1|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|reserved_special_token_2|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|reserved_special_token_3|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|reserved_special_token_4|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151665": {
|
||||
"content": "<|reserved_special_token_5|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<image>",
|
||||
"</image>",
|
||||
"<ref>",
|
||||
"</ref>",
|
||||
"<box>",
|
||||
"</box>",
|
||||
"<quad>",
|
||||
"</quad>",
|
||||
"<point>",
|
||||
"</point>",
|
||||
"<slice>",
|
||||
"</slice>",
|
||||
"<image_id>",
|
||||
"</image_id>",
|
||||
"<|reserved_special_token_0|>",
|
||||
"<|reserved_special_token_1|>",
|
||||
"<|reserved_special_token_2|>",
|
||||
"<|reserved_special_token_3|>",
|
||||
"<|reserved_special_token_4|>",
|
||||
"<|reserved_special_token_5|>"
|
||||
],
|
||||
"bos_token": "<|im_start|>",
|
||||
"chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"auto_map": {
|
||||
"AutoTokenizer": [
|
||||
"tokenization_minicpmv_fast.MiniCPMVTokenizerFast",
|
||||
null
|
||||
]
|
||||
},
|
||||
"tokenizer_class": "MiniCPMVTokenizerFast",
|
||||
"unk_token": "<unk>"
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
Loading…
Reference in New Issue