update checkerpoints

README.md

@@ -1,3 +1,222 @@
----
-license: apache-2.0
----
+# UnrealEngineGPT: 虚幻引擎对话式大语言模型
+
+<div align="left">
+
+![GitHub Repo stars](https://img.shields.io/github/stars/jiayaozhang/UnrealEngineGPT?style=social)
+[![Generic badge](https://img.shields.io/badge/🤗-Huggingface%20Repo-green.svg)](https://huggingface.co/Jiayao)
+[![Generic badge](https://img.shields.io/badge/🔥-Modelscope-white)](https://www.modelscope.cn/profile/Jannie)
+
+</div>
+
+
+欢迎加入虚幻引擎大模型的学习🤗。
+
+
+## News: 😎 开源模型
+
+**在1/2*V100上进行训练**，经过中文词表扩充、增量预训练、多轮指令微调。该项目也支持对Baichuan、Qwen、InternLM、LLaMA、Falcon等模型进行高效增量预训练。
+🔔 使用本项目的训练代码，以及上述训练数据，我们训练并开源了以下模型。
+
+中文模型：
+
+| 模型                                                                             | Huggingface                                | Modelscope|
+|--------------------------------------------------------------------------------|-------------------------------------|------------|
+| InternLM-UEGPT-7b    | [Huggingface-internLM2-7b](https://huggingface.co/Jiayao/UEGPT-internlm-7b-qlora-sft-merge/tree/main)  | [UEGPT-internLM2-7b](https://www.modelscope.cn/models/Jannie/UEGPT-internlm-7b-qlora-sft-merge/files) |  
+| InternLM-UEGPT-20b   | [Huggingface-internLM2-20b](https://huggingface.co/Jiayao/UnrealGPT-internlm-20b-qlora-sft-merge) | [Modelscope-internLM2-20b](https://www.modelscope.cn/models/Jannie/UEGPT-internlm-20b-qlora-sft-merge/files)|  
+| UEGPT-Datasets       | [Huggingface-UEGPT-Datasets](https://huggingface.co/datasets/Jiayao/UEGPT-Datasets)     |[Modelscope-UEGPT-Datasets](https://www.modelscope.cn/datasets/Jannie/UEGPT-Datasets/files)     |  
+
+
+## 相关项目
+
+| Project                                                                                        | Description                                                                                    |
+|------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------|
+| [InternLM Notes](https://github.com/jiayaozhang/InternLM_Notes)  | RAG检索增强生成，支持使用QLoRA增量预训练大模型推理优化、大模型参数高效微调（PEFT）、大模型上下文扩展|
+| [LongQLoRA](https://github.com/yangjianxin1/LongQLoRA) | 大模型长度扩展项目，可在单卡V100上将LLaMA-13B的长度扩展至8192，且性能逼近MPT-8K                                            |
+
+## 项目简介
+
+
+**虚幻引擎对话式大语言模型** 是一个开源的虚幻引擎大语言模型项目，支持QLoRA和全量参数微调InternLM等开源模型。
+
+正如项目名称一样，希望本项目能够为为虚幻引擎社区尽绵薄之力，促进游戏以及游戏引擎的发展。
+如果你的训练资源有限，我们极力推荐使用QLoRA的指令微调方式，因为我们在Open LLM Leaderboard上验证了该方法的有效性。
+
+
+🔔 本项目主要内容如下：
+- 📗 支持全量参数指令微调、QLoRA低成本高效指令微调、其中QLoRA是我们主推的一种高效的训练方式。
+- 📗 支持绝大部分主流的开源大模型，如Baichuan2、CodeLLaMA、LLaMA2、LLaMA、Qwen、Baichuan、ChatGLM2、InternLM、Ziya、Bloom、XVERSE等。
+- 📗 支持lora与base model进行权重合并，推理更便捷。
+- 📗️ 模型裁剪：通过[LLMPruner：大语言模型裁剪工具](https://github.com/yangjianxin1/LLMPruner) ，开源[裁剪后的Bloom模型权重](https://huggingface.co/Jiayao) 。在保留预训练中文知识的前提下，有效减少模型参数量，降低训练成本，提高训练效率。
+- 📗 开源[UEGPT系列指令微调模型权重](https://huggingface.co/Jiayao) 。
+- 📗 在Open LLM Leaderboard上验证了QLoRA训练流程的有效性。
+
+
+## 安装环境
+在requirements.txt下固定了几个主要的python包的版本，执行如下脚本即可。
+
+**注意：Baichuan2需要安装pytorch 2.0。除Baichuan2以外，其他模型的训练，我们均在torch==1.13上进行训练。**
+```bash
+pip install requirements.txt
+```
+
+<!-- ## 模型列表
+
+🔔 使用本项目的训练代码，以及上述训练数据，我们训练并开源了以下模型。
+
+中文模型：
+
+| 模型                                                                             | 基座模型                                | Max Length |
+|--------------------------------------------------------------------------------|-------------------------------------|------------|
+| [InternLM-UEGPT-7b](https://huggingface.co/YeungNLP/firefly-baichuan2-13b) | internlm/internlm-7b     |1024     |  
+| [InternLM-UEGPT-20b](https://huggingface.co/YeungNLP/firefly-baichuan-13b)   | internlm/internlm-20b      |1024     |  
+ -->
+
+## 训练数据
+
+| [moss-003-sft-data](https://huggingface.co/datasets/YeungNLP/moss-003-sft-data)                               | 由复旦大学MOSS团队开源的中英文多轮对话数据，包含100万+数据                                                                       
+
+🔔 目前本项目参考复旦大学MOSS团队开源的中英文多轮对话数据统一的数据格式,整理了上千条虚幻引擎问答多轮数据对话
+
+训练数据为jsonl格式，每一行的数据格式如下，其中conversation字段是必需的，可以根据实际需求添加或删除其他字段。
+可以参考项目中的data/dummy_data.jsonl文件。
+```json
+  {
+    "conversation_id": 390,
+    "category": "Brainstorming",
+    "conversation": [
+      {
+        "human": "如何在虚幻引擎中实现水面波纹效果？",
+        "assistant": "实现水面波纹效果在虚幻引擎中通常涉及材质编辑器的使用，以及可能应用一些动态模拟技术。以下是一个基础的步骤指南，介绍如何在虚幻引擎中创建水面波纹效果：\n\n### 材质创建\n\n1. **创建水面材质**：首先，你需要创建一个新的材质，用来应用到你的水面对象上。\n   \n2. **使用波纹纹理**：找到适合的波纹纹理或者使用程序生成的模式，比如使用Perlin噪声或简单的正弦波函数来模拟波纹。\n\n3. **纹理坐标和变换**：使用材质编辑器中的纹理坐标（Texture Coordinates）节点与时间（Time）节点，结合一些数学运算，来对纹理进行动态变换，产生波动效果。\n\n4. **反射与折射**：利用虚幻的反射和折射功能使水面更逼真。通常，通过设置材质的反射（Specular）和粗糙度（Roughness）参数，以及可能的折射参数来模拟水光效果。\n\n5. **透明与深度效果**：为了使水面更逼真，还需要调整透明度（Opacity）以及深度效果，这可能包括水底颜色渐变、深度模糊等。\n\n### 动态波纹\n\n1. **顶点位移**：如果需要波纹影响水面的形态（3D效果），可以使用顶点位移（Vertex Displacement）技术，根据波纹纹理来上下移动水面的顶点。\n\n2. **材质参数调整**：创建材质实例，允许在游戏运行时动态调整波纹的强度、速度等。\n\n3. **触发波纹**：对于交互性的波纹（例如物体掉入水面），需要编写蓝图或C++代码来在特定位置触发波纹效果，可以通过在材质中设置参数，并用蓝图或代码动态更新它们来实现。\n\n### 更高级的技术\n\n- **物理模拟**：对于更高级的波纹效果，可以实现基于物理的模拟，如采用一个动态模拟系统或使用顶点着色器和计算着色器进行模拟。\n\n- **涟漪扩散**：为了模拟从一个点向外扩散的涟漪效果，可以采用某种扩散算法，并将其表现在水面材质上。\n\n- **多层波纹效果**：通过叠加不同的波纹图层来创建更复杂的波纹效果。\n\n- **动态环境交互**：实现环境要素（如风、雨滴）与水面交互产生的动态波纹效果。\n\n在实现水面波纹效果时，监视性能也很重要，因为这些效果有时会非常消耗GPU资源。务必管理好材质的复杂度，并考虑只在玩家视野范围内使用更高级的效果，而在远处采用更简化的表现。"
+     },
+     {
+        "human": "虚幻引擎中的自适应曝光是如何调整场景曝光的？",
+        "assistant": "对于大量的问题和广泛的主题，回答每个问题都是非常耗时的。这样的问题列表看起来像是为了开发或学习虚幻引擎(Unreal Engine)渲染技术的目的而准备的。为了提供一些有用的信息，我将试着总结出关键的回答，这样可以涵盖大部分问到的主题。\n\n**虚幻引擎渲染管线概述：**\n虚幻引擎的渲染管线负责将3D场景转换为平面图像。主要步骤包括场景几何体的提取、光照和阴影的计算、材质的应用、以及最终的图像后处理。\n\n**创建和编辑材质：**\n材质是定义对象表面外观，如颜色、纹理和光泽等的属性。在虚幻引擎中，你可以使用材质编辑器来创建和编辑材质，它提供了一个可视化的节点网络来调整材质属性。\n\n**Post-Processing效果：**\n后期处理效果涉及到在3D渲染后对图像进行的处理，这包括颜色校正、布鲁光(Bloom)、景深(Depth of Field)、HDR曝光、镜面折射和反射等效果。\n\n**虚幻引擎粒子系统：**\n虚幻引擎提供了强大的Niagara和Cascade粒子系统，用于创建如火、烟、雾、尘埃等效果。这可以通过蓝图或材质与粒子参数交互进行增强。\n\n**光线追踪技术：**\n虚幻引擎支持实时光线追踪，它提供了更逼真的光影效果，像几乎真实的反射、阴影、折射和全局光照(Global Illumination, GI)。\n\n**阴影技术：**\n虚幻引擎支持多种阴影技术，包括静态阴影贴图、动态阴影和软阴影。这些技术考虑了不同光源和物体的特性来生成逼真的阴影。\n\n**全局光照��决方案：**\n全局光照是指光在场景中的多次散射。虚幻引擎提供了静态的光照贴图以及动态GI解决方案，如LPV(Light Propagation Volumes)。\n\n**图像空间光线追踪 (ISRT) 和 GPU粒子系统：**\nISRT允许在图像空间来模拟更复杂的光影效果。GPU粒子系统利用了图形处理器的强大计算能力，可以实现大量粒子实时模拟。\n\n**抗锯齿技术：**\n虚幻引擎支持多种抗锯齿技术，包括传统的MSAA、更现代的技术如FXAA、TXAA和MLAA等。\n\n**渲染优化技术：**\n为了提高性能，虚幻引擎实现了多种优化技术，例如LOD系统（Level of Detail，细节层次）、剔除（Culling，移除视野外的物体）、纹理压缩和流式加载等。\n\n**动态天气系统和环境效果：**\n动态天气系统可以通过改变光照、粒子系统（如下雨或下雪），以及天空盒的动态变化来实现。环境效果则可以通过材料更改（如湿地效果）和PBR材料的逼真性来提升。"
+     },
+     {
+        "human": "如何在虚幻引擎中创建动态的火焰和爆炸效果？",
+        "assistant": "创建和编辑材质的过程依赖于虚幻引擎的材质编辑器。这是一个非常直观的界面，它允许用户通过材质图（Material Graph）来创建和编辑材质。在这个图中，材质的各个部分是通过节点表示的。每个节点代表了一种材质属性，比如颜色、纹理、透明度、高光等，这些节点可以通过线缆相连，以定义材质的最终外观。\n\n以下是创建和编辑材质的基本步骤：\n\n1. 创建新材质：\n   - 在内容浏览器中右键单击，选择“材质”选项。\n   - 这将创建一个新的材质资产，并在材质编辑器中打开它。\n\n2. 编辑材质属性：\n   - 使用材质编辑器左侧的节点面板来添加各种节点到材质图中。\n   - 您可以添加纹理采样器节点来使用贴图，常量节点来定义颜色或数值，以及各种材质函数节点来实现更复杂的效果。\n\n3. 连接节点：\n   - 节点之间的连接定义了材质的运算逻辑。拖拽节点之间的针脚来连接它们。\n   - 您需要将结果连接到材质的输出节点，这些输出定义了材质在光照、颜色、金属度、粗糙度等方面的表现。\n\n4. 参数化材质：\n   - 通过添加材质参数，比如“参数化纹理”、“参数变量”等，您可以创建可以在蓝图中或在材质实例中动态调整的材质。\n   - 参数化让您可以重用同一个材质，通过更改参数来实现不同的外观，这非常有用。\n\n5. 预览和调整：\n   - 您可以实时预览材质在材质编辑器中的球体或立方体预览上的效果。\n   - 根据预览进行实时调整，直到您满意为止。\n\n6. 保存并应用：\n   - 一旦完成材质的编辑，点击“保存”按钮来保存您的工作。\n   - 您可以将此材质拖动到场景中的对象上，或者在静态网格的材质槽中指定它。\n\n请记住，材质制作是一个迭代过程，可能需要多次调整和预览以达到理想的效果。虚幻引擎的材质编辑器支持PBR（基于物理的渲染）工作流，使得创建的材质可以更逼真地模拟真实世界中的物体表面。"
+     },
+     {
+        "human": "虚幻引擎中的LOD系统如何影响渲染性能？",
+        "assistant": "Unreal Engine（虚幻引擎）提供了一系列功能来优化渲染渲染性能，其中LOD系统（Level Of Detail，详细等级）是很重要的一部分。LOD系统是通过为在不同距离上观察的对象提供不同的细节层次，从而优化渲染性能。\n\nLOD 系统如何影响渲染性能：\n\n1. **模型复杂度降低**：当物体距离摄像机较远时，渲染器会选择使用更低细节的模型。这些模型通常拥有较少的顶点和面片，从而减少了GPU的负担。\n\n2. **渲染调用减少**：如果多个远处物体使用相同的LOD层级，可以将它们的绘制调用合并为单次调用，从而降低CPU负担。\n\n3. **纹理贴图和材质复杂度降低**：与模型类似，远处物体也可以使用更低分辨率的纹理和简化的材质，这进一步降低了显存的使用量和纹理采样的性能消耗。\n\n4. **影子及反射代理**：面向远距离物体时，可以采用更低分辨率的影子贴图（Shadow Maps）和反射代理（Reflection Probes），从而达到性能节省的目的。\n\n5. **发卡负载均衡**：通过根据摄像机的视角和距离自动调整模型的细节级别，LOD系统可以在不牺牲太多视觉效果的情况下对发卡负载进行均衡。\n\n6. **流畅的视觉体验**：设置不同LOD阈值可以在性能和视觉质量之间取得平衡。当玩家在游戏中移动时，通过平滑的转换来确保不会有突变的视觉变化。\n\n为了最大化渲染的性能，通常需要对物体进行LOD的设置，确保在视觉效果可接受的前提下最小化性能的消耗。Unreal Engine 提供了自动和��动创建LOD的工具，开发者可以根据项目的需要来设定不同级别的LOD，以获得最佳的性能和视觉效果。"
+     }
+    ]
+  }
+```
+
+
+
+
+## 模型训练
+
+
+
+目前支持全量参数指令微调、QLoRA指令微调。我们将训练中使用的各种组件抽取出来，以便后续的扩展和优化，详见component目录下的实现。训练时的参数配置存储在train_args目录下，方便统一管理和更改。大家可以在train_args目录下查看不同模型的训练配置。
+
+### 数据格式
+训练时，我们将多轮对话拼接成如下格式，然后进行tokenize。其中<s\>表示bos_token，</s\> 表示eos_token。
+```
+<s>input1</s>target1</s>input2</s>target2</s>...
+```
+在计算loss时，我们通过mask的方式，input部分的loss不参与参数更新，只有“target</s>”部分的loss参与参数更新。
+这种方式充分利用了模型并行计算的优势，训练更加高效，且多轮对话中的每个target部分都参与了训练，训练更充分。
+否则，就需要把一个n轮对话，拆分成n条数据，且只计算最后一个target的loss，大大降低了训练效率。
+
+### 全量参数微调
+💻 执行如下命令即可进行全量参数微调：
+```bash
+deepspeed --num_gpus={num_gpus} train.py --train_args_file train_args/sft.json
+```
+
+📝 train_args/sft.json中的主要参数说明如下，以下参数可以根据需求进行修改，其他参数建议不做修改：
+- output_dir：训练输出目录，存储checkpoint、tokenizer、tensorboard等
+- model_name_or_path：预训练模型的本地目录，或者在huggingface上的模型名称。
+- train_file：训练数据集路径。可以使用data/dummy_data.jsonl进行debug。
+- num_train_epochs：训练的轮次。如果数据量足够大，一般建议只训一个epoch。
+- per_device_train_batch_size：每张显卡的batch size。
+- gradient_accumulation_steps：梯度累计步数。global batch=num_gpus * per_device_train_batch_size * gradient_accumulation_steps。
+- gradient_checkpointing：如果显存捉襟见肘，可以开启。以时间换空间，模型不缓存激活状态，会进行两次forward计算，以节省显存。
+- learning_rate：学习率。全量参数微调的时候，建议小一些，1e-5或5e-6。
+- max_seq_length：训练时的最大长度。按照自己的设备进行设置，越长需要占用越多显存。
+- logging_steps：每隔多少步统计一次train loss。
+- save_steps：每隔多少步保存一个模型。
+- save_total_limit：output_dir目录中最多保存多少个checkpoint，超出则会将最旧的删除。
+- lr_scheduler_type：学习率变化策略。
+- warmup_steps：warm up步数。学习率经过多少步，增长到指定的数值。
+- optim：优化器。如果是全量参数微调，建议使用adamw_hf。
+- seed：随机种子，用于复现实验结果。
+- fp16：使用使用fp16混合精度。V100建议开启。
+- bf16：使用使用bf16混合精度。A100建议开启。
+
+
+
+### QLoRA微调
+
+关于QLoRA的详细介绍可参考文章：[【QLoRA实战】使用单卡高效微调bloom-7b1，效果惊艳](https://mp.weixin.qq.com/s/DED7yeiE0DibsVzTmMeDOw)
+
+QLoRA通过4-bit的nf4量化，且加入更多adapter，在大幅减少显存消耗的同时，尽可能逼近全量参数微调的效果。
+QLoRA论文指出，该方法可以在一张V100上对33B的模型进行微调，并且性能逼近全量参数微调。
+
+我们在bloom-7b1上使用qlora，adapter的参数量约1.2亿，超过bert-base模型参数量，可以在V100上使用1024的长度进行训练。
+
+💻 单卡时建议使用python命令启动脚本：
+```bash
+python train_qlora.py --train_args_file train_args/qlora/baichuan-7b-sft-qlora.json
+```
+
+💻 多卡时使用torchrun命令启动脚本：
+```bash
+torchrun --nproc_per_node={num_gpus} train_qlora.py --train_args_file train_args/qlora/baichuan-7b-sft-qlora.json
+```
+
+📝 train_args/sft-qlora.json中的主要参数说明如下，基本与全量微调的参数一致，几个较为特殊：
+- lora_rank：qlora矩阵的秩。一般设置为8、16、32、64等，在qlora论文中作者设为64。越大则参与训练的参数量越大，一般来说效果会更好，但需要更多显存，。
+- lora_alpha: qlora中的缩放参数。一般设为16、32即可。
+- lora_dropout: lora权重的dropout rate。
+- learning_rate：qlora中的学习率设置更大一些，一般为1e-4、2e-4。
+
+
+## 模型使用
+
+### 权重合并
+如果使用LoRA或者QLoRA进行训练，本项目仅保存adapter的权重和配置文件，需要将adapter权重与base model进行合并。脚本见script/merge_lora.py
+
+### 模型推理
+我们提供了单轮对话和多轮对话的脚本，详见script/chat目录，该脚本可同时兼容本项目训练的所有模型进行推理，不适用于非本项目训练的模型。
+```bash
+cd script/chat
+python single_chat.py
+```
+
+生成脚本中的top_p、temperature、repetition_penalty、do_sample等参数对模型的���成效果影响较大，可按照自己的使用场景进行调试修改。
+
+推理脚本中支持使用base model和adapter进行推理，缺点是每次启动脚本都需要合并一次权重，等待时间较久。
+
+支持使用4bit进行推理，显存要求低，效果会略有下降。
+
+### 服务部署
+本项目支持将模型部署成HTTP服务，脚本在script/http下，使用flask进行开发。start_service.py为启动服务，post为发送请求，可按需进行修改。
+
+
+## 生成效果
+
+下面的样例均为UEGPT-InternLM2-20b模型所生成，未经修改，仅供参考。
+
+
+
+
+</details>
+
+
+## InternLM大模型与虚幻引擎在线结合
+
+后期把虚幻引擎和书生大模型联合，可以实时调试，之后会把虚幻的项目工程放出来
+
+😎Stay tune!
+
+license: Apache License 2.0
+
+
+
+
+

config.json

@@ -0,0 +1,36 @@
+{
+  "_name_or_path": "internlm/internlm2-chat-7b",
+  "architectures": [
+    "InternLM2ForCausalLM"
+  ],
+  "attn_implementation": "eager",
+  "auto_map": {
+    "AutoConfig": "configuration_internlm2.InternLM2Config",
+    "AutoModel": "internlm/internlm2-chat-7b--modeling_internlm2.InternLM2ForCausalLM",
+    "AutoModelForCausalLM": "modeling_internlm2.InternLM2ForCausalLM"
+  },
+  "bias": false,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "internlm2",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pad_token_id": 2,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 2.0,
+    "type": "dynamic"
+  },
+  "rope_theta": 1000000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.36.0",
+  "use_cache": true,
+  "vocab_size": 92544
+}

configuration.json

@@ -0,0 +1 @@
+{"framework":"Pytorch","task":"multimodal-dialogue"}

configuration_internlm2.py

@@ -0,0 +1,151 @@
+# coding=utf-8
+# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on transformers/src/transformers/models/llama/configuration_llama.py
+#
+# 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.
+""" InternLM2 model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+INTERNLM2_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+# Modified from transformers.model.llama.configuration_llama.LlamaConfig
+class InternLM2Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`InternLM2Model`]. It is used to instantiate
+    an InternLM2 model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the InternLM2-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the InternLM2 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`InternLM2Model`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        Example:
+
+    """
+    model_type = "internlm2"
+    _auto_class = "AutoConfig"
+
+    def __init__(  # pylint: disable=W0102
+        self,
+        vocab_size=103168,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        bias=True,
+        rope_theta=10000,
+        rope_scaling=None,
+        attn_implementation="eager",
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.bias = bias
+
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+
+        self.attn_implementation = attn_implementation
+        if self.attn_implementation is None:
+            self.attn_implementation = "eager"
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor < 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {rope_scaling_factor}")

generation_config.json

@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 2,
+  "transformers_version": "4.36.0"
+}

model-00001-of-00004.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c287bf4150010a440fd99c9fbcd1aea5c226e0e0ab5abc917b870bea3f2df4ad
+size 4885470560

model-00002-of-00004.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e9db8f5b2ffd9e06b80367386619ebfd01e3bc85503ca949c32f8dbb11c43c1
+size 4915913344

model-00003-of-00004.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:61fbfa00297b4383f5b512c604c9c9bc9eacd1fe67c0c6e4b47f6f22fda12bb3
+size 4915938224

model-00004-of-00004.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:31cc277fe6a5796985ec4c125b4577514f9fe7548b3b5a311e055b974bcd5d5e
+size 758120576

model.safetensors.index.json

@@ -0,0 +1,234 @@
+{
+  "metadata": {
+    "total_size": 15475417088
+  },
+  "weight_map": {
+    "model.layers.0.attention.wo.weight": "model-00001-of-00004.safetensors",
+    "model.layers.0.attention.wqkv.weight": "model-00001-of-00004.safetensors",
+    "model.layers.0.attention_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.0.feed_forward.w1.weight": "model-00001-of-00004.safetensors",
+    "model.layers.0.feed_forward.w2.weight": "model-00001-of-00004.safetensors",
+    "model.layers.0.feed_forward.w3.weight": "model-00001-of-00004.safetensors",
+    "model.layers.0.ffn_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.1.attention.wo.weight": "model-00001-of-00004.safetensors",
+    "model.layers.1.attention.wqkv.weight": "model-00001-of-00004.safetensors",
+    "model.layers.1.attention_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.1.feed_forward.w1.weight": "model-00001-of-00004.safetensors",
+    "model.layers.1.feed_forward.w2.weight": "model-00001-of-00004.safetensors",
+    "model.layers.1.feed_forward.w3.weight": "model-00001-of-00004.safetensors",
+    "model.layers.1.ffn_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.10.attention.wo.weight": "model-00002-of-00004.safetensors",
+    "model.layers.10.attention.wqkv.weight": "model-00002-of-00004.safetensors",
+    "model.layers.10.attention_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.10.feed_forward.w1.weight": "model-00002-of-00004.safetensors",
+    "model.layers.10.feed_forward.w2.weight": "model-00002-of-00004.safetensors",
+    "model.layers.10.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.10.ffn_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.11.attention.wo.weight": "model-00002-of-00004.safetensors",
+    "model.layers.11.attention.wqkv.weight": "model-00002-of-00004.safetensors",
+    "model.layers.11.attention_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.11.feed_forward.w1.weight": "model-00002-of-00004.safetensors",
+    "model.layers.11.feed_forward.w2.weight": "model-00002-of-00004.safetensors",
+    "model.layers.11.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.11.ffn_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.12.attention.wo.weight": "model-00002-of-00004.safetensors",
+    "model.layers.12.attention.wqkv.weight": "model-00002-of-00004.safetensors",
+    "model.layers.12.attention_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.12.feed_forward.w1.weight": "model-00002-of-00004.safetensors",
+    "model.layers.12.feed_forward.w2.weight": "model-00002-of-00004.safetensors",
+    "model.layers.12.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.12.ffn_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.13.attention.wo.weight": "model-00002-of-00004.safetensors",
+    "model.layers.13.attention.wqkv.weight": "model-00002-of-00004.safetensors",
+    "model.layers.13.attention_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.13.feed_forward.w1.weight": "model-00002-of-00004.safetensors",
+    "model.layers.13.feed_forward.w2.weight": "model-00002-of-00004.safetensors",
+    "model.layers.13.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.13.ffn_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.14.attention.wo.weight": "model-00002-of-00004.safetensors",
+    "model.layers.14.attention.wqkv.weight": "model-00002-of-00004.safetensors",
+    "model.layers.14.attention_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.14.feed_forward.w1.weight": "model-00002-of-00004.safetensors",
+    "model.layers.14.feed_forward.w2.weight": "model-00002-of-00004.safetensors",
+    "model.layers.14.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.14.ffn_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.15.attention.wo.weight": "model-00002-of-00004.safetensors",
+    "model.layers.15.attention.wqkv.weight": "model-00002-of-00004.safetensors",
+    "model.layers.15.attention_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.15.feed_forward.w1.weight": "model-00002-of-00004.safetensors",
+    "model.layers.15.feed_forward.w2.weight": "model-00002-of-00004.safetensors",
+    "model.layers.15.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.15.ffn_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.16.attention.wo.weight": "model-00002-of-00004.safetensors",
+    "model.layers.16.attention.wqkv.weight": "model-00002-of-00004.safetensors",
+    "model.layers.16.attention_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.16.feed_forward.w1.weight": "model-00002-of-00004.safetensors",
+    "model.layers.16.feed_forward.w2.weight": "model-00002-of-00004.safetensors",
+    "model.layers.16.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.16.ffn_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.17.attention.wo.weight": "model-00002-of-00004.safetensors",
+    "model.layers.17.attention.wqkv.weight": "model-00002-of-00004.safetensors",
+    "model.layers.17.attention_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.17.feed_forward.w1.weight": "model-00002-of-00004.safetensors",
+    "model.layers.17.feed_forward.w2.weight": "model-00002-of-00004.safetensors",
+    "model.layers.17.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.17.ffn_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.18.attention.wo.weight": "model-00002-of-00004.safetensors",
+    "model.layers.18.attention.wqkv.weight": "model-00002-of-00004.safetensors",
+    "model.layers.18.attention_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.18.feed_forward.w1.weight": "model-00002-of-00004.safetensors",
+    "model.layers.18.feed_forward.w2.weight": "model-00002-of-00004.safetensors",
+    "model.layers.18.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.18.ffn_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.19.attention.wo.weight": "model-00002-of-00004.safetensors",
+    "model.layers.19.attention.wqkv.weight": "model-00002-of-00004.safetensors",
+    "model.layers.19.attention_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.19.feed_forward.w1.weight": "model-00002-of-00004.safetensors",
+    "model.layers.19.feed_forward.w2.weight": "model-00002-of-00004.safetensors",
+    "model.layers.19.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.19.ffn_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.2.attention.wo.weight": "model-00001-of-00004.safetensors",
+    "model.layers.2.attention.wqkv.weight": "model-00001-of-00004.safetensors",
+    "model.layers.2.attention_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.2.feed_forward.w1.weight": "model-00001-of-00004.safetensors",
+    "model.layers.2.feed_forward.w2.weight": "model-00001-of-00004.safetensors",
+    "model.layers.2.feed_forward.w3.weight": "model-00001-of-00004.safetensors",
+    "model.layers.2.ffn_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.20.attention.wo.weight": "model-00002-of-00004.safetensors",
+    "model.layers.20.attention.wqkv.weight": "model-00002-of-00004.safetensors",
+    "model.layers.20.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.20.feed_forward.w1.weight": "model-00002-of-00004.safetensors",
+    "model.layers.20.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.20.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.20.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.21.attention.wo.weight": "model-00003-of-00004.safetensors",
+    "model.layers.21.attention.wqkv.weight": "model-00003-of-00004.safetensors",
+    "model.layers.21.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.21.feed_forward.w1.weight": "model-00003-of-00004.safetensors",
+    "model.layers.21.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.21.feed_forward.w3.weight": "model-00003-of-00004.safetensors",
+    "model.layers.21.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.22.attention.wo.weight": "model-00003-of-00004.safetensors",
+    "model.layers.22.attention.wqkv.weight": "model-00003-of-00004.safetensors",
+    "model.layers.22.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.22.feed_forward.w1.weight": "model-00003-of-00004.safetensors",
+    "model.layers.22.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.22.feed_forward.w3.weight": "model-00003-of-00004.safetensors",
+    "model.layers.22.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.23.attention.wo.weight": "model-00003-of-00004.safetensors",
+    "model.layers.23.attention.wqkv.weight": "model-00003-of-00004.safetensors",
+    "model.layers.23.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.23.feed_forward.w1.weight": "model-00003-of-00004.safetensors",
+    "model.layers.23.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.23.feed_forward.w3.weight": "model-00003-of-00004.safetensors",
+    "model.layers.23.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.24.attention.wo.weight": "model-00003-of-00004.safetensors",
+    "model.layers.24.attention.wqkv.weight": "model-00003-of-00004.safetensors",
+    "model.layers.24.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.24.feed_forward.w1.weight": "model-00003-of-00004.safetensors",
+    "model.layers.24.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.24.feed_forward.w3.weight": "model-00003-of-00004.safetensors",
+    "model.layers.24.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.25.attention.wo.weight": "model-00003-of-00004.safetensors",
+    "model.layers.25.attention.wqkv.weight": "model-00003-of-00004.safetensors",
+    "model.layers.25.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.25.feed_forward.w1.weight": "model-00003-of-00004.safetensors",
+    "model.layers.25.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.25.feed_forward.w3.weight": "model-00003-of-00004.safetensors",
+    "model.layers.25.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.26.attention.wo.weight": "model-00003-of-00004.safetensors",
+    "model.layers.26.attention.wqkv.weight": "model-00003-of-00004.safetensors",
+    "model.layers.26.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.26.feed_forward.w1.weight": "model-00003-of-00004.safetensors",
+    "model.layers.26.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.26.feed_forward.w3.weight": "model-00003-of-00004.safetensors",
+    "model.layers.26.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.27.attention.wo.weight": "model-00003-of-00004.safetensors",
+    "model.layers.27.attention.wqkv.weight": "model-00003-of-00004.safetensors",
+    "model.layers.27.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.27.feed_forward.w1.weight": "model-00003-of-00004.safetensors",
+    "model.layers.27.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.27.feed_forward.w3.weight": "model-00003-of-00004.safetensors",
+    "model.layers.27.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.28.attention.wo.weight": "model-00003-of-00004.safetensors",
+    "model.layers.28.attention.wqkv.weight": "model-00003-of-00004.safetensors",
+    "model.layers.28.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.28.feed_forward.w1.weight": "model-00003-of-00004.safetensors",
+    "model.layers.28.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.28.feed_forward.w3.weight": "model-00003-of-00004.safetensors",
+    "model.layers.28.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.29.attention.wo.weight": "model-00003-of-00004.safetensors",
+    "model.layers.29.attention.wqkv.weight": "model-00003-of-00004.safetensors",
+    "model.layers.29.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.29.feed_forward.w1.weight": "model-00003-of-00004.safetensors",
+    "model.layers.29.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.29.feed_forward.w3.weight": "model-00003-of-00004.safetensors",
+    "model.layers.29.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.3.attention.wo.weight": "model-00001-of-00004.safetensors",
+    "model.layers.3.attention.wqkv.weight": "model-00001-of-00004.safetensors",
+    "model.layers.3.attention_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.3.feed_forward.w1.weight": "model-00001-of-00004.safetensors",
+    "model.layers.3.feed_forward.w2.weight": "model-00001-of-00004.safetensors",
+    "model.layers.3.feed_forward.w3.weight": "model-00001-of-00004.safetensors",
+    "model.layers.3.ffn_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.30.attention.wo.weight": "model-00003-of-00004.safetensors",
+    "model.layers.30.attention.wqkv.weight": "model-00003-of-00004.safetensors",
+    "model.layers.30.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.30.feed_forward.w1.weight": "model-00003-of-00004.safetensors",
+    "model.layers.30.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.30.feed_forward.w3.weight": "model-00003-of-00004.safetensors",
+    "model.layers.30.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.31.attention.wo.weight": "model-00003-of-00004.safetensors",
+    "model.layers.31.attention.wqkv.weight": "model-00003-of-00004.safetensors",
+    "model.layers.31.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.31.feed_forward.w1.weight": "model-00003-of-00004.safetensors",
+    "model.layers.31.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.31.feed_forward.w3.weight": "model-00003-of-00004.safetensors",
+    "model.layers.31.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.4.attention.wo.weight": "model-00001-of-00004.safetensors",
+    "model.layers.4.attention.wqkv.weight": "model-00001-of-00004.safetensors",
+    "model.layers.4.attention_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.4.feed_forward.w1.weight": "model-00001-of-00004.safetensors",
+    "model.layers.4.feed_forward.w2.weight": "model-00001-of-00004.safetensors",
+    "model.layers.4.feed_forward.w3.weight": "model-00001-of-00004.safetensors",
+    "model.layers.4.ffn_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.5.attention.wo.weight": "model-00001-of-00004.safetensors",
+    "model.layers.5.attention.wqkv.weight": "model-00001-of-00004.safetensors",
+    "model.layers.5.attention_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.5.feed_forward.w1.weight": "model-00001-of-00004.safetensors",
+    "model.layers.5.feed_forward.w2.weight": "model-00001-of-00004.safetensors",
+    "model.layers.5.feed_forward.w3.weight": "model-00001-of-00004.safetensors",
+    "model.layers.5.ffn_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.6.attention.wo.weight": "model-00001-of-00004.safetensors",
+    "model.layers.6.attention.wqkv.weight": "model-00001-of-00004.safetensors",
+    "model.layers.6.attention_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.6.feed_forward.w1.weight": "model-00001-of-00004.safetensors",
+    "model.layers.6.feed_forward.w2.weight": "model-00001-of-00004.safetensors",
+    "model.layers.6.feed_forward.w3.weight": "model-00001-of-00004.safetensors",
+    "model.layers.6.ffn_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.7.attention.wo.weight": "model-00001-of-00004.safetensors",
+    "model.layers.7.attention.wqkv.weight": "model-00001-of-00004.safetensors",
+    "model.layers.7.attention_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.7.feed_forward.w1.weight": "model-00001-of-00004.safetensors",
+    "model.layers.7.feed_forward.w2.weight": "model-00001-of-00004.safetensors",
+    "model.layers.7.feed_forward.w3.weight": "model-00001-of-00004.safetensors",
+    "model.layers.7.ffn_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.8.attention.wo.weight": "model-00001-of-00004.safetensors",
+    "model.layers.8.attention.wqkv.weight": "model-00001-of-00004.safetensors",
+    "model.layers.8.attention_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.8.feed_forward.w1.weight": "model-00001-of-00004.safetensors",
+    "model.layers.8.feed_forward.w2.weight": "model-00001-of-00004.safetensors",
+    "model.layers.8.feed_forward.w3.weight": "model-00001-of-00004.safetensors",
+    "model.layers.8.ffn_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.9.attention.wo.weight": "model-00001-of-00004.safetensors",
+    "model.layers.9.attention.wqkv.weight": "model-00001-of-00004.safetensors",
+    "model.layers.9.attention_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.9.feed_forward.w1.weight": "model-00001-of-00004.safetensors",
+    "model.layers.9.feed_forward.w2.weight": "model-00002-of-00004.safetensors",
+    "model.layers.9.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.9.ffn_norm.weight": "model-00002-of-00004.safetensors",
+    "model.norm.weight": "model-00003-of-00004.safetensors",
+    "model.tok_embeddings.weight": "model-00001-of-00004.safetensors",
+    "output.weight": "model-00004-of-00004.safetensors"
+  }
+}

modeling_internlm2.py

@@ -0,0 +1,1391 @@
+# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on transformers/src/transformers/models/llama/modeling_llama.py
+#
+# 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 InternLM2 model."""
+import math
+import queue
+import threading
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from einops import rearrange
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    SequenceClassifierOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
+
+try:
+    from transformers.generation.streamers import BaseStreamer
+except:  # noqa # pylint: disable=bare-except
+    BaseStreamer = None
+
+from .configuration_internlm2 import InternLM2Config
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "InternLM2Config"
+
+flash_attn_func, flash_attn_varlen_func = None, None
+pad_input, index_first_axis, unpad_input = None, None, None
+def _import_flash_attn():
+    global flash_attn_func, flash_attn_varlen_func
+    global pad_input, index_first_axis, unpad_input
+    try:
+        from flash_attn import flash_attn_func as _flash_attn_func, flash_attn_varlen_func as _flash_attn_varlen_func
+        from flash_attn.bert_padding import pad_input as _pad_input, index_first_axis as _index_first_axis, unpad_input as _unpad_input
+        flash_attn_func, flash_attn_varlen_func = _flash_attn_func, _flash_attn_varlen_func
+        pad_input, index_first_axis, unpad_input = _pad_input, _index_first_axis, _unpad_input
+    except ImportError:
+        raise ImportError("flash_attn is not installed.")
+
+# 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,
+    )
+
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+    """
+    Make causal mask used for bi-directional self-attention.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->InternLM2
+class InternLM2RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        InternLM2RMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+# Copied from transformers.model.llama.modeling_llama.LlamaRotaryEmbedding with Llama->InternLM2
+class InternLM2RotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.float32)
+
+        return (
+            self.cos_cached[:seq_len].to(dtype=x.dtype),
+            self.sin_cached[:seq_len].to(dtype=x.dtype),
+        )
+
+
+# Copied from transformers.model.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->InternLM2
+class InternLM2LinearScalingRotaryEmbedding(InternLM2RotaryEmbedding):
+    """InternLM2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+        t = t / self.scaling_factor
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+# Copied from transformers.model.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->InternLM2
+class InternLM2DynamicNTKScalingRotaryEmbedding(InternLM2RotaryEmbedding):
+    """InternLM2RotaryEmbedding extended with Dynamic NTK scaling.
+    Credits to the Reddit users /u/bloc97 and /u/emozilla.
+    """
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+            self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+# Copied from transformers.model.llama.modeling_llama.rotate_half
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.model.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors."""
+    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class InternLM2MLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.w1 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.w3 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.w2 = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        down_proj = self.w2(self.act_fn(self.w1(x)) * self.w3(x))
+
+        return down_proj
+
+
+# Copied from transformers.model.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+# Modified from transformers.model.llama.modeling_llama.LlamaAttention
+class InternLM2Attention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: InternLM2Config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.is_causal = True
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+
+        self.wqkv = nn.Linear(
+            self.hidden_size,
+            (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
+            bias=config.bias,
+        )
+
+        self.wo = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias)
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = InternLM2RotaryEmbedding(
+                self.head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.config.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "dynamic":
+                self.rotary_emb = InternLM2DynamicNTKScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    base=self.config.rope_theta,
+                    scaling_factor=scaling_factor,
+                )
+            elif scaling_type == "linear":
+                self.rotary_emb = InternLM2LinearScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    base=self.config.rope_theta,
+                    scaling_factor=scaling_factor,
+                )
+            else:
+                raise ValueError("Currently we only support rotary embedding's type being 'dynamic' or 'linear'.")
+        return self.rotary_emb
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = 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]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. "
+                "Please make sure use `attention_mask` instead.`"
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv_states = self.wqkv(hidden_states)
+
+        qkv_states = rearrange(
+            qkv_states,
+            "b q (h gs d) -> b q h gs d",
+            gs=2 + self.num_key_value_groups,
+            d=self.head_dim,
+        )
+
+        query_states = qkv_states[..., : self.num_key_value_groups, :]
+        query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
+        key_states = qkv_states[..., -2, :]
+        value_states = qkv_states[..., -1, :]
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+        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:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_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_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, 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(bsz, q_len, self.hidden_size)
+
+        attn_output = self.wo(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+# Modified from transformers.model.llama.modeling_llama.InternLM2FlashAttention2
+class InternLM2FlashAttention2(InternLM2Attention):
+    """
+    InternLM2 flash attention module. This module inherits from `InternLM2Attention` 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 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]]]:
+        # InternLM2FlashAttention2 attention does not support output_attentions
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. "
+                "Please make sure use `attention_mask` instead.`"
+            )
+
+            # overwrite attention_mask with padding_mask
+            attention_mask = kwargs.pop("padding_mask")
+
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv_states = self.wqkv(hidden_states)
+
+        qkv_states = rearrange(
+            qkv_states,
+            "b q (h gs d) -> b q h gs d",
+            gs=2 + self.num_key_value_groups,
+            d=self.head_dim,
+        )
+
+        query_states = qkv_states[..., : self.num_key_value_groups, :]
+        query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
+        key_states = qkv_states[..., -2, :]
+        value_states = qkv_states[..., -1, :]
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+
+        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:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        attn_output = self._flash_attention_forward(
+            query_states, key_states, value_states, attention_mask, q_len
+        )
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.wo(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+    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)
+        """
+        # Contains at least one padding token in the sequence
+        causal = self.is_causal and query_length != 1
+        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._unpad_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 _unpad_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.to(torch.int64),
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+INTERNLM2_ATTENTION_CLASSES = {
+    "eager": InternLM2Attention,
+    "flash_attention_2": InternLM2FlashAttention2,
+}
+
+# Modified from transformers.model.llama.modeling_llama.LlamaDecoderLayer
+class InternLM2DecoderLayer(nn.Module):
+    def __init__(self, config: InternLM2Config):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.attention = INTERNLM2_ATTENTION_CLASSES[config.attn_implementation](config=config)
+
+        self.feed_forward = InternLM2MLP(config)
+        self.attention_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.ffn_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. "
+                "Please make sure use `attention_mask` instead.`"
+            )
+
+        residual = hidden_states
+
+        hidden_states = self.attention_norm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.attention(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.ffn_norm(hidden_states)
+        hidden_states = self.feed_forward(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+InternLM2_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 ([`InternLM2Config`]):
+            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.
+"""
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->InternLM2
+@add_start_docstrings(
+    "The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2PreTrainedModel(PreTrainedModel):
+    config_class = InternLM2Config
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["InternLM2DecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+InternLM2_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        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)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or
+            when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
+            `(batch_size, num_heads, decoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            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.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        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.
+"""
+
+
+# Modified from transformers.model.llama.modeling_llama.LlamaModel
+@add_start_docstrings(
+    "The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2Model(InternLM2PreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InternLM2DecoderLayer`]
+
+    Args:
+        config: InternLM2Config
+    """
+
+    _auto_class = "AutoModel"
+
+    def __init__(self, config: InternLM2Config):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.config = config
+
+        self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+
+        self.layers = nn.ModuleList([InternLM2DecoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.tok_embeddings = value
+
+    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                inputs_embeds.dtype,
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
+                inputs_embeds.device
+            )
+            combined_attention_mask = (
+                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+            )
+
+        return combined_attention_mask
+
+    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if self.config.attn_implementation == "flash_attention_2":
+            _import_flash_attn()
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape[:2]
+        elif inputs_embeds is not None:
+            batch_size, seq_length = inputs_embeds.shape[:2]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.tok_embeddings(input_ids)
+
+        if self.config.attn_implementation == "flash_attention_2":
+            # 2d mask is passed through the layers
+            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+        else:
+            if attention_mask is None:
+                attention_mask = torch.ones(
+                    (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
+                )
+            attention_mask = self._prepare_decoder_attention_mask(
+                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+            )
+
+        # embed positions
+        hidden_states = inputs_embeds
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = () if use_cache else None
+
+        for idx, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, output_attentions, None)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    None,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+# Modified from transformers.model.llama.modeling_llama.LlamaForCausalLM
+class InternLM2ForCausalLM(InternLM2PreTrainedModel):
+    _auto_class = "AutoModelForCausalLM"
+
+    _tied_weights_keys = ["output.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = InternLM2Model(config)
+        self.vocab_size = config.vocab_size
+        self.output = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.tok_embeddings = value
+
+    def get_output_embeddings(self):
+        return self.output
+
+    def set_output_embeddings(self, new_embeddings):
+        self.output = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, InternLM2ForCausalLM
+
+        >>> model = InternLM2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+
+        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
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.output(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        if past_key_values is not None:
+            past_length = past_key_values[0][0].shape[2]
+
+            # Some generation methods already pass only the last input ID
+            if input_ids.shape[1] > past_length:
+                remove_prefix_length = past_length
+            else:
+                # Default to old behavior: keep only final ID
+                remove_prefix_length = input_ids.shape[1] - 1
+
+            input_ids = input_ids[:, remove_prefix_length:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+
+    def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = [], meta_instruction=""):
+        if tokenizer.add_bos_token:
+            prompt = ""
+        else:
+            prompt = tokenizer.bos_token
+        if meta_instruction:
+            prompt += f"""<|im_start|>system\n{meta_instruction}<|im_end|>\n"""
+        for record in history:
+            prompt += f"""<|im_start|>user\n{record[0]}<|im_end|>\n<|im_start|>assistant\n{record[1]}<|im_end|>\n"""
+        prompt += f"""<|im_start|>user\n{query}<|im_end|>\n<|im_start|>assistant\n"""
+        return tokenizer([prompt], return_tensors="pt")
+
+    @torch.no_grad()
+    def chat(
+        self,
+        tokenizer,
+        query: str,
+        history: List[Tuple[str, str]] = [],
+        streamer: Optional[BaseStreamer] = None,
+        max_new_tokens: int = 1024,
+        do_sample: bool = True,
+        temperature: float = 0.8,
+        top_p: float = 0.8,
+        meta_instruction: str = "You are an AI assistant whose name is InternLM (书生·浦语).\n"
+        "- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n"
+        "- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.",
+        **kwargs,
+    ):
+        inputs = self.build_inputs(tokenizer, query, history, meta_instruction)
+        inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)}
+        # also add end-of-assistant token in eos token id to avoid unnecessary generation
+        eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids(["<|im_end|>"])[0]]
+        outputs = self.generate(
+            **inputs,
+            streamer=streamer,
+            max_new_tokens=max_new_tokens,
+            do_sample=do_sample,
+            temperature=temperature,
+            top_p=top_p,
+            eos_token_id=eos_token_id,
+            **kwargs,
+        )
+        outputs = outputs[0].cpu().tolist()[len(inputs["input_ids"][0]) :]
+        response = tokenizer.decode(outputs, skip_special_tokens=True)
+        response = response.split("<|im_end|>")[0]
+        history = history + [(query, response)]
+        return response, history
+
+    @torch.no_grad()
+    def stream_chat(
+        self,
+        tokenizer,
+        query: str,
+        history: List[Tuple[str, str]] = [],
+        max_new_tokens: int = 1024,
+        do_sample: bool = True,
+        temperature: float = 0.8,
+        top_p: float = 0.8,
+        **kwargs,
+    ):
+        """
+        Return a generator in format: (response, history)
+        Eg.
+        ('你好，有什么可以帮助您的吗', [('你好', '你好，有什么可以帮助您的吗')])
+        ('你好，有什么可以帮助您的吗？', [('你好', '你好，有什么可以帮助您的吗？')])
+        """
+        if BaseStreamer is None:
+            raise ModuleNotFoundError(
+                "The version of `transformers` is too low. Please make sure "
+                "that you have installed `transformers>=4.28.0`."
+            )
+
+        response_queue = queue.Queue(maxsize=20)
+
+        class ChatStreamer(BaseStreamer):
+            def __init__(self, tokenizer) -> None:
+                super().__init__()
+                self.tokenizer = tokenizer
+                self.queue = response_queue
+                self.query = query
+                self.history = history
+                self.response = ""
+                self.cache = []
+                self.received_inputs = False
+                self.queue.put((self.response, history + [(self.query, self.response)]))
+
+            def put(self, value):
+                if len(value.shape) > 1 and value.shape[0] > 1:
+                    raise ValueError("ChatStreamer only supports batch size 1")
+                elif len(value.shape) > 1:
+                    value = value[0]
+
+                if not self.received_inputs:
+                    # The first received value is input_ids, ignore here
+                    self.received_inputs = True
+                    return
+
+                self.cache.extend(value.tolist())
+                token = self.tokenizer.decode(self.cache, skip_special_tokens=True)
+                if token.strip() != "<|im_end|>":
+                    self.response = self.response + token
+                    history = self.history + [(self.query, self.response)]
+                    self.queue.put((self.response, history))
+                    self.cache = []
+                else:
+                    self.end()
+
+            def end(self):
+                self.queue.put(None)
+
+        def stream_producer():
+            return self.chat(
+                tokenizer=tokenizer,
+                query=query,
+                streamer=ChatStreamer(tokenizer=tokenizer),
+                history=history,
+                max_new_tokens=max_new_tokens,
+                do_sample=do_sample,
+                temperature=temperature,
+                top_p=top_p,
+                **kwargs,
+            )
+
+        def consumer():
+            producer = threading.Thread(target=stream_producer)
+            producer.start()
+            while True:
+                res = response_queue.get()
+                if res is None:
+                    return
+                yield res
+
+        return consumer()
+
+
+# Copied from transformers.model.llama.modeling_llama.LlamaForSequenceClassification with Llama->InternLM2
+@add_start_docstrings(
+    """
+    The InternLM2 Model transformer with a sequence classification head on top (linear layer).
+
+    [`InternLM2ForSequenceClassification`] uses the last token in order to do the classification,
+    as other causal models (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = InternLM2Model(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.tok_embeddings = value
+
+    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
+                    logits.device
+                )
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )

special_tokens_map.json

@@ -0,0 +1,34 @@
+{
+  "additional_special_tokens": [
+    "<|im_end|>",
+    "<|im_start|>"
+  ],
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenization_internlm2.py

@@ -0,0 +1,236 @@
+# coding=utf-8
+# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on transformers/src/transformers/models/llama/tokenization_llama.py
+#
+# 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.
+
+"""Tokenization classes for InternLM."""
+import os
+from shutil import copyfile
+from typing import Any, Dict, List, Optional, Tuple
+
+import sentencepiece as spm
+from transformers.tokenization_utils import PreTrainedTokenizer
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
+
+PRETRAINED_VOCAB_FILES_MAP = {}
+
+
+# Modified from transformers.model.llama.tokenization_llama.LlamaTokenizer
+class InternLM2Tokenizer(PreTrainedTokenizer):
+    """
+    Construct a InternLM2 tokenizer. Based on byte-level Byte-Pair-Encoding.
+
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    model_input_names = ["input_ids", "attention_mask"]
+    _auto_class = "AutoTokenizer"
+
+    def __init__(
+        self,
+        vocab_file,
+        unk_token="<unk>",
+        bos_token="<s>",
+        eos_token="</s>",
+        pad_token="</s>",
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        add_bos_token=True,
+        add_eos_token=False,
+        decode_with_prefix_space=False,
+        clean_up_tokenization_spaces=False,
+        **kwargs,
+    ):
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+        self.vocab_file = vocab_file
+        self.add_bos_token = add_bos_token
+        self.add_eos_token = add_eos_token
+        self.decode_with_prefix_space = decode_with_prefix_space
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(vocab_file)
+        self._no_prefix_space_tokens = None
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+    @property
+    def no_prefix_space_tokens(self):
+        if self._no_prefix_space_tokens is None:
+            vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
+            self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")}
+        return self._no_prefix_space_tokens
+
+    @property
+    def vocab_size(self):
+        """Returns vocab size"""
+        return self.sp_model.get_piece_size()
+
+    @property
+    def bos_token_id(self) -> Optional[int]:
+        return self.sp_model.bos_id()
+
+    @property
+    def eos_token_id(self) -> Optional[int]:
+        return self.sp_model.eos_id()
+
+    def get_vocab(self):
+        """Returns vocab as a dict"""
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def _tokenize(self, text):
+        """Returns a tokenized string."""
+        return self.sp_model.encode(text, out_type=str)
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.sp_model.piece_to_id(token)
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        token = self.sp_model.IdToPiece(index)
+        return token
+
+    def _maybe_add_prefix_space(self, tokens, decoded):
+        if tokens and tokens[0] not in self.no_prefix_space_tokens:
+            return " " + decoded
+        else:
+            return decoded
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        current_sub_tokens = []
+        out_string = ""
+        prev_is_special = False
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                if not prev_is_special:
+                    out_string += " "
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                prev_is_special = True
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+                prev_is_special = False
+        out_string += self.sp_model.decode(current_sub_tokens)
+        out_string = self.clean_up_tokenization(out_string)
+        out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
+        return out_string[1:]
+
+    def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        """
+        Save the vocabulary and special tokens file to a directory.
+
+        Args:
+            save_directory (`str`):
+                The directory in which to save the vocabulary.
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        out_vocab_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+        elif not os.path.isfile(self.vocab_file):
+            with open(out_vocab_file, "wb") as fi:
+                content_spiece_model = self.sp_model.serialized_model_proto()
+                fi.write(content_spiece_model)
+
+        return (out_vocab_file,)
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        if self.add_bos_token:
+            bos_token_ids = [self.bos_token_id]
+        else:
+            bos_token_ids = []
+
+        output = bos_token_ids + token_ids_0
+
+        if token_ids_1 is not None:
+            output = output + token_ids_1
+
+        if self.add_eos_token:
+            output = output + [self.eos_token_id]
+
+        return output
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        if token_ids_1 is None:
+            return [1] + ([0] * len(token_ids_0)) + [1]
+        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
+        use of token type ids, therefore a list of zeros is returned.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of zeros.
+        """
+        eos = [self.eos_token_id]
+
+        if token_ids_1 is None:
+            return len(token_ids_0 + eos) * [0]
+        return len(token_ids_0 + eos + token_ids_1 + eos) * [0]

tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f868398fc4e05ee1e8aeba95ddf18ddcc45b8bce55d5093bead5bbf80429b48b
+size 1477754

tokenizer_config.json

@@ -0,0 +1,94 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92538": {
+      "content": "<|plugin|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92539": {
+      "content": "<|interpreter|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92540": {
+      "content": "<|action_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92541": {
+      "content": "<|action_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92542": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92543": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_end|>",
+    "<|im_start|>"
+  ],
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_internlm2.InternLM2Tokenizer",
+      null
+    ]
+  },
+  "bos_token": "<s>",
+  "chat_template": "{{ bos_token }}{% for message in messages %}{{'<|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": "</s>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "</s>",
+  "tokenizer_class": "InternLM2Tokenizer",
+  "unk_token": "<unk>"
+}