add checkpoints

GPT_SoVITS/pretrained_models/.gitignore

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Qwen/Qwen-1_8B-Chat/LICENSE

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Qwen/Qwen-1_8B-Chat/NOTICE

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Qwen/Qwen-1_8B-Chat/README.md

@@ -0,0 +1,418 @@
+---
+language:
+- zh
+- en
+tags:
+- qwen
+pipeline_tag: text-generation
+inference: false
+---
+
+# Qwen-1.8B-Chat
+
+<p align="center">
+    <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/logo_qwen.jpg" width="400"/>
+<p>
+<br>
+
+<p align="center">
+        🤗 <a href="https://huggingface.co/Qwen">Hugging Face</a>&nbsp&nbsp | &nbsp&nbsp🤖 <a href="https://modelscope.cn/organization/qwen">ModelScope</a>&nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://arxiv.org/abs/2309.16609">Paper</a> &nbsp&nbsp ｜ &nbsp&nbsp🖥️ <a href="https://www.modelscope.cn/studios/qwen/Qwen-1_8B-Chat-Demo/summary">Demo</a>
+<br>
+<a href="https://github.com/QwenLM/Qwen/blob/main/assets/wechat.png">WeChat (微信)</a>&nbsp&nbsp | &nbsp&nbsp<a href="https://discord.gg/z3GAxXZ9Ce">Discord</a>&nbsp&nbsp ｜  &nbsp&nbsp<a href="https://dashscope.aliyun.com">API</a> 
+</p>
+<br>
+
+## 介绍（Introduction）
+**通义千问-1.8B（Qwen-1.8B）**是阿里云研发的通义千问大模型系列的18亿参数规模的模型。Qwen-1.8B是基于Transformer的大语言模型, 在超大规模的预训练数据上进行训练得到。预训练数据类型多样，覆盖广泛，包括大量网络文本、专业书籍、代码等。同时，在Qwen-1.8B的基础上，我们使用对齐机制打造了基于大语言模型的AI助手Qwen-1.8B-Chat。本仓库为Qwen-1.8B-Chat的仓库。
+
+通义千问-1.8B（Qwen-1.8B）主要有以下特点：
+1. **低成本部署**：提供int8和int4量化版本，推理最低仅需不到2GB显存，生成2048 tokens仅需3GB显存占用。微调最低仅需6GB。
+2. **大规模高质量训练语料**：使用超过2.2万亿tokens的数据进行预训练，包含高质量中、英、多语言、代码、数学等数据，涵盖通用及专业领域的训练语料。通过大量对比实验对预训练语料分布进行了优化。
+3. **优秀的性能**：Qwen-1.8B支持8192上下文长度，在多个中英文下游评测任务上（涵盖常识推理、代码、数学、翻译等），效果显著超越现有的相近规模开源模型，具体评测结果请详见下文。
+4. **覆盖更全面的词表**：相比目前以中英词表为主的开源模型，Qwen-1.8B使用了约15万大小的词表。该词表对多语言更加友好，方便用户在不扩展词表的情况下对部分语种进行能力增强和扩展。
+5. **系统指令跟随**：Qwen-1.8B-Chat可以通过调整系统指令，实现**角色扮演**，**语言风格迁移**，**任务设定**，和**行为设定**等能力。
+
+
+如果您想了解更多关于通义千问1.8B开源模型的细节，我们建议您参阅[GitHub代码库](https://github.com/QwenLM/Qwen)。
+
+**Qwen-1.8B** is the 1.8B-parameter version of the large language model series, Qwen (abbr. Tongyi Qianwen), proposed by Aibaba Cloud. Qwen-1.8B is a Transformer-based large language model, which is pretrained on a large volume of data, including web texts, books, codes, etc. Additionally, based on the pretrained Qwen-1.8B, we release Qwen-1.8B-Chat, a large-model-based AI assistant, which is trained with alignment techniques. This repository is the one for Qwen-1.8B-Chat.
+
+The features of Qwen-1.8B include:
+1. **Low-cost deployment**: We provide int4 and int8 quantized versions, the minimum memory requirment for inference is less than 2GB, generating 2048 tokens only 3GB of memory usage. The minimum memory requirment of finetuning is only 6GB.
+
+2. **Large-scale high-quality training corpora**: It is pretrained on over 2.2 trillion tokens, including Chinese, English, multilingual texts, code, and mathematics, covering general and professional fields. The distribution of the pre-training corpus has been optimized through a large number of ablation experiments.
+3. **Good performance**: It supports 8192 context length and significantly surpasses existing open-source models of similar scale on multiple Chinese and English downstream evaluation tasks (including commonsense, reasoning, code, mathematics, etc.), and even surpasses some larger-scale models in several benchmarks. See below for specific evaluation results.
+4. **More comprehensive vocabulary coverage**: Compared with other open-source models based on Chinese and English vocabularies, Qwen-1.8B uses a vocabulary of over 150K tokens. This vocabulary is more friendly to multiple languages, enabling users to directly further enhance the capability for certain languages without expanding the vocabulary.
+5. **System prompt**: Qwen-1.8B-Chat can realize roly playing, language style transfer, task setting, and behavior setting by using system prompt.
+
+For more details about the open-source model of Qwen-1.8B-Chat, please refer to the [GitHub](https://github.com/QwenLM/Qwen) code repository.
+                                                                                        
+
+<br>
+
+## 要求（Requirements）
+
+* python 3.8及以上版本
+* pytorch 1.12及以上版本，推荐2.0及以上版本
+* 建议使用CUDA 11.4及以上（GPU用户、flash-attention用户等需考虑��选项）
+* python 3.8 and above
+* pytorch 1.12 and above, 2.0 and above are recommended
+* CUDA 11.4 and above are recommended (this is for GPU users, flash-attention users, etc.)
+
+## 依赖项（Dependency）
+
+运行Qwen-1.8B-Chat，请确保满足上述要求，再执行以下pip命令安装依赖库
+
+To run Qwen-1.8B-Chat, please make sure you meet the above requirements, and then execute the following pip commands to install the dependent libraries.
+
+```bash
+pip install transformers==4.32.0 accelerate tiktoken einops scipy transformers_stream_generator==0.0.4 peft deepspeed
+```
+
+另外，推荐安装`flash-attention`库（**当前已支持flash attention 2**），以实现更高的效率和更低的显存占用。
+
+In addition, it is recommended to install the `flash-attention` library (**we support flash attention 2 now.**) for higher efficiency and lower memory usage.
+
+```bash
+git clone https://github.com/Dao-AILab/flash-attention
+cd flash-attention && pip install .
+# 下方安装可选，安装可能比较缓慢。
+# pip install csrc/layer_norm
+# pip install csrc/rotary
+```
+<br>
+
+## 快速使用（Quickstart）
+
+下面我们展示了一个使用Qwen-1.8B-Chat模型，进行多轮对话交互的样例：
+
+We show an example of multi-turn interaction with Qwen-1.8B-Chat in the following code:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.generation import GenerationConfig
+
+# Note: The default behavior now has injection attack prevention off.
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-1_8B-Chat", trust_remote_code=True)
+
+# use bf16
+# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-1_8B-Chat", device_map="auto", trust_remote_code=True, bf16=True).eval()
+# use fp16
+# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-1_8B-Chat", device_map="auto", trust_remote_code=True, fp16=True).eval()
+# use cpu only
+# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-1_8B-Chat", device_map="cpu", trust_remote_code=True).eval()
+# use auto mode, automatically select precision based on the device.
+model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-1_8B-Chat", device_map="auto", trust_remote_code=True).eval()
+
+# Specify hyperparameters for generation. But if you use transformers>=4.32.0, there is no need to do this.
+# model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-1_8B-Chat", trust_remote_code=True) # 可指定不同的生成长度、top_p等相关超参
+
+# 第一轮对话 1st dialogue turn
+response, history = model.chat(tokenizer, "你好", history=None)
+print(response)
+# 你好！很高兴为你提供帮助。
+
+# 第二轮对话 2nd dialogue turn
+response, history = model.chat(tokenizer, "给我讲一个年轻人奋斗创业最终取得成功的故事。", history=history)
+print(response)
+# 这是一个关于一个年轻人奋斗创业最终取得成功的故事。
+# 故事的主人公叫李明，他来自一个普通的家庭，父母都是普通的工人。从小，李明就立下了一个目标：要成为一名成功的企业家。
+# 为了实现这个目标，李明勤奋学习，考上了大学。在大学期间，他积极参加各种创业比赛，获得了不少奖项。他还利用课余时间去实习，积累了宝贵的经验。
+# 毕业后，李明决定开始自己的创业之路。他开始寻找投资机会，但多次都被拒绝了。然而，他并没有放弃。他继续努力，不断改进自己的创业计划，并寻找新的投资机会。
+# 最终，李明成功地获得了一笔投资，开始了自己的创业之路。他成立了一家科技公司，专注于开发新型软件。在他的领导下，公司迅速发展起来，成为了一家成功的科技企业。
+# 李明的成功并不是偶然的。他勤奋、坚韧、勇于冒险，不断学习和改进自己。他的成功也证明了，只要努力奋斗，任何人都有可能取得成功。
+
+# 第三轮对话 3rd dialogue turn
+response, history = model.chat(tokenizer, "给这个故事起一个标题", history=history)
+print(response)
+# 《奋斗创业：一个年轻人的成功之路》
+
+# Qwen-1.8B-Chat现在可以通过调整系统指令（System Prompt），实现角色扮演，语言风格迁移，任务设定，行为设定等能力。
+# Qwen-1.8B-Chat can realize roly playing, language style transfer, task setting, and behavior setting by system prompt.
+response, _ = model.chat(tokenizer, "你好呀", history=None, system="请用二次元可爱语气和我说话")
+print(response)
+# 你好啊！我是一只可爱的二次元猫咪哦，不知道你有什么问题需要我帮忙解答吗？
+
+response, _ = model.chat(tokenizer, "My colleague works diligently", history=None, system="You will write beautiful compliments according to needs")
+print(response)
+# Your colleague is an outstanding worker! Their dedication and hard work are truly inspiring. They always go above and beyond to ensure that 
+# their tasks are completed on time and to the highest standard. I am lucky to have them as a colleague, and I know I can count on them to handle any challenge that comes their way.
+```
+
+关于更多的使用说明，请参考我们的[GitHub repo](https://github.com/QwenLM/Qwen)获取更多信息。
+
+For more information, please refer to our [GitHub repo](https://github.com/QwenLM/Qwen) for more information.
+
+## Tokenizer
+
+> 注：作为术语的“tokenization”在中文中尚无共识的概念对应，本文档采用英文表达以利说明。
+
+基于tiktoken的分词器有别于其他分词器，比如sentencepiece分词器。尤其在微调阶段，需要特别注意特殊token的使用。关于tokenizer的更多信息，以及微调时涉及的相关使用，请参阅[文档](https://github.com/QwenLM/Qwen/blob/main/tokenization_note_zh.md)。
+
+Our tokenizer based on tiktoken is different from other tokenizers, e.g., sentencepiece tokenizer. You need to pay attention to special tokens, especially in finetuning. For more detailed information on the tokenizer and related use in fine-tuning, please refer to the [documentation](https://github.com/QwenLM/Qwen/blob/main/tokenization_note.md).
+
+## 量化 (Quantization)
+
+### 用法 (Usage)
+
+**请注意：我们更新量化方案为基于[AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ)的量化，提供Qwen-1.8B-Chat的Int4量化模型[点击这里](https://huggingface.co/Qwen/Qwen-1_8B-Chat-Int4)。相比此前方案，该方案在模型评测效果几乎无损，且存储需求更低，推理速度更优。**
+
+**Note: we provide a new solution based on [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), and release an Int4 quantized model for Qwen-1.8B-Chat [Click here](https://huggingface.co/Qwen/Qwen-1_8B-Chat-Int4), which achieves nearly lossless model effects but improved performance on both memory costs and inference speed, in comparison with the previous solution.**
+
+以下我们提供示例说明如何使用Int4量化模型。在开始使用前，请先保证满足要求（如torch 2.0及以上，transformers版本为4.32.0及以上，等等），并安装所需安装包：
+
+Here we demonstrate how to use our provided quantized models for inference. Before you start, make sure you meet the requirements of auto-gptq (e.g., torch 2.0 and above, transformers 4.32.0 and above, etc.) and install the required packages:
+
+```bash
+pip install auto-gptq optimum
+```
+
+如安装`auto-gptq`遇到问题，我们建议您到官方[repo](https://github.com/PanQiWei/AutoGPTQ)搜索合适的预编译wheel。
+
+随后即可使用和上述一致的用法调用量化模型：
+
+If you meet problems installing `auto-gptq`, we advise you to check out the official [repo](https://github.com/PanQiWei/AutoGPTQ) to find a pre-build wheel.
+
+Then you can load the quantized model easily and run inference as same as usual:
+
+```python
+model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen-1_8B-Chat-Int4",
+    device_map="auto",
+    trust_remote_code=True
+).eval()
+response, history = model.chat(tokenizer, "你好", history=None)
+```
+
+### 效果评测
+
+我们使用原始模型的FP32和BF16精度，以及量化过的Int8和Int4模型在基准评测上做了测试，结果如下所示：
+
+We illustrate the model performance of both FP32, BF16, Int8 and Int4 models on the benchmark. Results are shown below:
+
+| Quantization | MMLU | CEval (val) | GSM8K | Humaneval |
+|--------------|:----:|:-----------:|:-----:|:---------:|
+| FP32         | 43.4 |    57.0     | 33.0  |   26.8    |
+| BF16         | 43.3 |    55.6     | 33.7  |   26.2    |
+| Int8         | 43.1 |    55.8     | 33.0  |   27.4    |
+| Int4         | 42.9 |    52.8     | 31.2  |   25.0    |
+
+### 推理速度 (Inference Speed)
+
+我们测算了FP32、BF16精度和Int8、Int4量化模型生成2048和8192个token的平均推理速度。如图所示：
+
+We measured the average inference speed of generating 2048 and 8192 tokens under FP32, BF16 precision and Int8, Int4 quantization level, respectively.
+
+| Quantization | FlashAttn | Speed (2048 tokens) | Speed (8192 tokens) |
+|--------------| :-------: |:-------------------:|:-------------------:|
+| FP32         |   v2      |        52.96        |        47.35        |
+| BF16         |   v2      |        54.09        |        54.04        |
+| Int8         |   v2      |        55.56        |        55.62        |
+| Int4         |   v2      |        71.07        |        76.45        |
+| FP32         |   v1      |        52.00        |        45.80        |
+| BF16         |   v1      |        51.70        |        55.04        |
+| Int8         |   v1      |        53.16        |        53.33        |
+| Int4         |   v1      |        69.82        |        67.44        |
+| FP32         |  Disabled |        52.28        |        44.95        |
+| BF16         |  Disabled |        48.17        |        45.01        |
+| Int8         |  Disabled |        52.16        |        52.99        |
+| Int4         |  Disabled |        68.37        |        65.94        |
+
+具体而言，我们记录在长度为1的上下文的条件下生成8192个token的性能。评测运行于单张A100-SXM4-80G GPU，使用PyTorch 2.0.1和CUDA 11.4。推理速度是生成8192个token的速度均值。
+
+In detail, the setting of profiling is generating 8192 new tokens with 1 context token. The profiling runs on a single A100-SXM4-80G GPU with PyTorch 2.0.1 and CUDA 11.4. The inference speed is averaged over the generated 8192 tokens.
+
+### 显存使用 (GPU Memory Usage)
+
+我们测算了FP32、BF16精度和Int8、Int4量化模型生成2048个及8192个token（单个token作为输入）的峰值显存占用情况。结果如下所示：
+
+We also profile the peak GPU memory usage for generating 2048 tokens and 8192 tokens (with single token as context) under FP32, BF16 or Int8, Int4 quantization level, respectively. The results are shown below.
+
+| Quantization Level | Peak Usage for Encoding 2048 Tokens | Peak Usage for Generating 8192 Tokens |
+|--------------------|:-----------------------------------:|:-------------------------------------:|
+| FP32               |               8.45GB                |                13.06GB                |
+| BF16               |               4.23GB                |                6.48GB                 |
+| Int8               |               3.48GB                |                5.34GB                 |
+| Int4               |               2.91GB                |                4.80GB                 |
+
+上述性能测算使用[此脚本](https://qianwen-res.oss-cn-beijing.aliyuncs.com/profile.py)完成。
+
+The above speed and memory profiling are conducted using [this script](https://qianwen-res.oss-cn-beijing.aliyuncs.com/profile.py).
+<br>
+
+## 模型细节（Model）
+
+与Qwen-1.8B预训练模型相同，Qwen-1.8B-Chat模型规模基本情况如下所示
+
+The details of the model architecture of Qwen-1.8B-Chat are listed as follows
+
+| Hyperparameter  | Value  |
+|:----------------|:------:|
+| n_layers        |   24   |
+| n_heads         |   16   |
+| d_model         |  2048  |
+| vocab size      | 151851 |
+| sequence length |  8192  |
+
+在位置编码、FFN激活函数和normalization的实现方式上，我们也采用了目前最流行的做法，
+即RoPE相对位置编码、SwiGLU激活函数、RMSNorm（可选安装flash-attention加速）。
+
+在分词器方面，相比目前主流开源模型以中英词表为主，Qwen-1.8B-Chat使用了约15万token大小的词表。
+该词表在GPT-4使用的BPE词表`cl100k_base`基础上，对中文、多语言进行了优化，在对中、英、代码数据的高效编解码的基础上，对部分多语言更加友好，方便用户在不扩展词表的情况下对部分语种进行能力增强。
+词表对数字按单个数字位切分。调用较为高效的[tiktoken分词库](https://github.com/openai/tiktoken)进行分词。
+
+For position encoding, FFN activation function, and normalization calculation methods, we adopt the prevalent practices, i.e., RoPE relative position encoding, SwiGLU for activation function, and RMSNorm for normalization (optional installation of flash-attention for acceleration).
+
+For tokenization, compared to the current mainstream open-source models based on Chinese and English vocabularies, Qwen-1.8B-Chat uses a vocabulary of over 150K tokens.
+It first considers efficient encoding of Chinese, English, and code data, and is also more friendly to multilingual languages, enabling users to directly enhance the capability of some languages without expanding the vocabulary.
+It segments numbers by single digit, and calls the [tiktoken](https://github.com/openai/tiktoken) tokenizer library for efficient tokenization.
+
+## 评测效果（Evaluation）
+
+对于Qwen-1.8B-Chat模型，我们同样评测了常规的中文理解（C-Eval）、英文理解（MMLU）、代码（HumanEval）和数学（GSM8K）等权威任务，同时包含了长序列任务的评测结果。由于Qwen-1.8B-Chat模型经过对齐后，激发了较强的外部系统调用能力，我们还进行了工具使用能力方面的评测。
+
+提示：由于硬件和框架造成的舍入误差，复现结果如有波动属于正常现象。
+
+For Qwen-1.8B-Chat, we also evaluate the model on C-Eval, MMLU, HumanEval, GSM8K, etc., as well as the benchmark evaluation for long-context understanding, and tool usage.
+
+Note: Due to rounding errors caused by hardware and framework, differences in reproduced results are possible.
+
+### 中文评测（Chinese Evaluation）
+
+#### C-Eval
+
+在[C-Eval](https://arxiv.org/abs/2305.08322)验证集上，我们评价了Qwen-1.8B-Chat模型的准确率
+
+We demonstrate the accuracy of Qwen-1.8B-Chat on C-Eval validation set
+
+|          Model                   |    Acc.   |
+|:--------------------------------:|:---------:|
+| RedPajama-INCITE-Chat-3B         |   18.3    |
+|       OpenBuddy-3B               |   23.5    |
+|    Firefly-Bloom-1B4             |   23.6    |
+|   OpenLLaMA-Chinese-3B           |   24.4    |
+|          LLaMA2-7B-Chat          |   31.9    |
+|         ChatGLM2-6B-Chat         |   52.6    |
+|         InternLM-7B-Chat         |   53.6    |
+|    **Qwen-1.8B-Chat (0-shot)**   |   55.6    |
+|    **Qwen-7B-Chat (0-shot)**     |   59.7    |
+|    **Qwen-7B-Chat (5-shot)**     |   59.3    |
+
+C-Eval测试集上，Qwen-1.8B-Chat模型的zero-shot准确率结果如下：
+
+The zero-shot accuracy of Qwen-1.8B-Chat on C-Eval testing set is provided below:
+
+| Model                   |   Avg.   | STEM | Social Sciences | Humanities | Others |
+| :---------------------: | :------: | :--: | :-------------: | :--------: | :----: |
+| Chinese-Alpaca-Plus-13B |   41.5   | 36.6 |      49.7       |    43.1    |  41.2  |
+| Chinese-Alpaca-2-7B     |   40.3   |  -   |        -        |     -      |   -    |
+| ChatGLM2-6B-Chat        |   50.1   | 46.4 |      60.4       |    50.6    |  46.9  |
+| Baichuan-13B-Chat       |   51.5   | 43.7 |      64.6       |    56.2    |  49.2  |
+| **Qwen-1.8B-Chat**      |   53.8   | 48.4 |      68.0       |    56.5    |  48.3  |
+| **Qwen-7B-Chat**        |   58.6   | 53.3 |      72.1       |    62.8    |  52.0  |
+
+### 英文评测（English Evaluation）
+
+#### MMLU
+
+[MMLU](https://arxiv.org/abs/2009.03300)评测集上，Qwen-1.8B-Chat模型的准确率如下，效果同样在同类对齐模型中同样表现较优。
+
+The accuracy of Qwen-1.8B-Chat on MMLU is provided below.
+The performance of Qwen-1.8B-Chat still on the top between other human-aligned models with comparable size.
+
+|          Model                   |   Acc.    |
+|:--------------------------------:|:---------:|
+|    Firefly-Bloom-1B4             |   23.8    |
+|       OpenBuddy-3B               |   25.5    |
+| RedPajama-INCITE-Chat-3B         |   25.5    |
+|   OpenLLaMA-Chinese-3B           |   25.7    |
+|         ChatGLM2-6B-Chat         |   46.0    |
+|          LLaMA2-7B-Chat          |   46.2    |
+|         InternLM-7B-Chat         |   51.1    |
+|        Baichuan2-7B-Chat         |   52.9    |
+|    **Qwen-1.8B-Chat (0-shot)**   |   43.3    |
+|    **Qwen-7B-Chat (0-shot)**     |   55.8    |
+|    **Qwen-7B-Chat (5-shot)**     |   57.0    |
+
+### 代码评测（Coding Evaluation）
+
+Qwen-1.8B-Chat在[HumanEval](https://github.com/openai/human-eval)的zero-shot Pass@1效果如下
+
+The zero-shot Pass@1 of Qwen-1.8B-Chat on [HumanEval](https://github.com/openai/human-eval) is demonstrated below
+
+|          Model           | Pass@1 |
+|:------------------------:|:------:|
+|    Firefly-Bloom-1B4     |  0.6   |
+|   OpenLLaMA-Chinese-3B   |  4.9   |
+| RedPajama-INCITE-Chat-3B |  6.1   |
+|       OpenBuddy-3B       |  10.4  |
+|    ChatGLM2-6B-Chat      |  11.0  |
+|     LLaMA2-7B-Chat       |  12.2  |
+|    Baichuan2-7B-Chat     |  13.4  |
+|    InternLM-7B-Chat      |  14.6  |
+|    **Qwen-1.8B-Chat**    |  26.2  |
+|    **Qwen-7B-Chat**      |  37.2  |
+
+### 数学评测（Mathematics Evaluation）
+
+在评测数学能力的[GSM8K](https://github.com/openai/grade-school-math)上，Qwen-1.8B-Chat的准确率结果如下
+
+The accuracy of Qwen-1.8B-Chat on GSM8K is shown below
+
+|                 Model                |    Acc.  |
+|:------------------------------------:|:--------:|
+|         Firefly-Bloom-1B4            |   2.4    |
+|      RedPajama-INCITE-Chat-3B        |   2.5    |
+|         OpenLLaMA-Chinese-3B         |   3.0    |
+|            OpenBuddy-3B              |   12.6   |
+|            LLaMA2-7B-Chat            |   26.3   |
+|           ChatGLM2-6B-Chat           |   28.8   |
+|          Baichuan2-7B-Chat           |   32.8   |
+|           InternLM-7B-Chat           |   33.0   |
+|    **Qwen-1.8B-Chat (0-shot)**       |   33.7   |
+|      **Qwen-7B-Chat (0-shot)**       |   50.3   |
+|      **Qwen-7B-Chat (8-shot)**       |   54.1   |
+
+## 评测复现（Reproduction）
+
+我们提供了评测脚本，方便大家复现模型效果，详见[链接](https://github.com/QwenLM/Qwen/tree/main/eval)。提示：由于硬件和框架造成的舍入误差，复现结果如有小幅波动属于正常现象。
+
+We have provided evaluation scripts to reproduce the performance of our model, details as [link](https://github.com/QwenLM/Qwen/tree/main/eval).
+<br>
+
+## FAQ
+
+如遇到问题，敬请查阅[FAQ](https://github.com/QwenLM/Qwen/blob/main/FAQ_zh.md)以及issue区，如仍无法解决再提交issue。
+
+If you meet problems, please refer to [FAQ](https://github.com/QwenLM/Qwen/blob/main/FAQ.md) and the issues first to search a solution before you launch a new issue.
+<br>
+
+## 引用 (Citation)
+
+如果你觉得我们的工作对你有帮助，欢迎引用！
+
+If you find our work helpful, feel free to give us a cite.
+
+```
+@article{qwen,
+  title={Qwen Technical Report},
+  author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu},
+  journal={arXiv preprint arXiv:2309.16609},
+  year={2023}
+}
+```
+<br>
+
+## 使用协议（License Agreement）
+
+我们的代码和模型权重对学术研究完全开放。请查看[LICENSE](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20RESEARCH%20LICENSE%20AGREEMENT)文件了解具体的开源协议细节。如需商用，请联系我们。
+
+Our code and checkpoints are open to research purpose. Check the [LICENSE](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20RESEARCH%20LICENSE%20AGREEMENT) for more details about the license. For commercial use, please contact us.
+<br>
+
+## 联系我们（Contact Us）
+
+如果你想给我们的研发团队和产品团队留言，欢迎加入我们的微信群、钉钉群以及Discord！同时，也欢迎通过邮件（qianwen_opensource@alibabacloud.com）联系我们。
+
+If you are interested to leave a message to either our research team or product team, join our Discord or WeChat groups! Also, feel free to send an email to qianwen_opensource@alibabacloud.com.
+

Qwen/Qwen-1_8B-Chat/cache_autogptq_cuda_256.cpp

@@ -0,0 +1,198 @@
+#include <torch/all.h>
+#include <torch/python.h>
+#include <c10/cuda/CUDAGuard.h>
+
+// adapted from https://github.com/PanQiWei/AutoGPTQ/blob/main/autogptq_extension/cuda_256/autogptq_cuda_256.cpp
+void vecquant8matmul_cuda(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros,
+  torch::Tensor g_idx
+);
+
+void vecquant8matmul(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros,
+  torch::Tensor g_idx
+) {
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
+  vecquant8matmul_cuda(vec, mat, mul, scales, zeros, g_idx);
+}
+
+void vecquant8matmul_batched_cuda(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+);
+
+void vecquant8matmul_batched(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+) {
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
+  vecquant8matmul_batched_cuda(vec, mat, mul, scales, zeros);
+}
+
+void vecquant8matmul_batched_column_compression_cuda(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+);
+
+void vecquant8matmul_batched_column_compression(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+) {
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
+  vecquant8matmul_batched_column_compression_cuda(vec, mat, mul, scales, zeros);
+}
+
+void vecquant4matmul_batched_cuda(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+);
+
+void vecquant4matmul_batched(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+) {
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
+  vecquant4matmul_batched_cuda(vec, mat, mul, scales, zeros);
+}
+
+void vecquant4matmul_batched_column_compression_cuda(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+);
+
+void vecquant4matmul_batched_column_compression(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+) {
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
+  vecquant4matmul_batched_column_compression_cuda(vec, mat, mul, scales, zeros);
+}
+
+void vecquant8matmul_batched_old_cuda(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+);
+
+void vecquant8matmul_batched_old(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+) {
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
+  vecquant8matmul_batched_old_cuda(vec, mat, mul, scales, zeros);
+}
+
+
+void vecquant4matmul_batched_old_cuda(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+);
+
+void vecquant4matmul_batched_old(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+) {
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
+  vecquant4matmul_batched_old_cuda(vec, mat, mul, scales, zeros);
+}
+
+void vecquant8matmul_batched_column_compression_old_cuda(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+);
+
+void vecquant8matmul_batched_column_compression_old(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+) {
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
+  vecquant8matmul_batched_column_compression_old_cuda(vec, mat, mul, scales, zeros);
+}
+
+void vecquant4matmul_batched_column_compression_old_cuda(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+);
+
+void vecquant4matmul_batched_column_compression_old(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+) {
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
+  vecquant4matmul_batched_column_compression_old_cuda(vec, mat, mul, scales, zeros);
+}
+
+
+
+void vecquant8matmul_batched_faster_cuda(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+);
+
+void vecquant8matmul_batched_faster(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+) {
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
+  vecquant8matmul_batched_faster_cuda(vec, mat, mul, scales, zeros);
+}
+
+
+void vecquant8matmul_batched_faster_old_cuda(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+);
+
+void vecquant8matmul_batched_faster_old(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+) {
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
+  vecquant8matmul_batched_faster_old_cuda(vec, mat, mul, scales, zeros);
+}
+
+void vecquant8matmul_batched_column_compression_faster_cuda(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+);
+
+void vecquant8matmul_batched_column_compression_faster(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+) {
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
+  vecquant8matmul_batched_column_compression_faster_cuda(vec, mat, mul, scales, zeros);
+}
+
+
+void vecquant8matmul_batched_column_compression_faster_old_cuda(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+);
+
+void vecquant8matmul_batched_column_compression_faster_old(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros
+) {
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
+  vecquant8matmul_batched_column_compression_faster_old_cuda(vec, mat, mul, scales, zeros);
+}
+
+
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("vecquant8matmul", &vecquant8matmul, "Vector 8-bit Quantized Matrix Multiplication (CUDA) (desc_act)");
+  m.def("vecquant8matmul_batched", &vecquant8matmul_batched, "Vector 8-bit Batched Quantized Matrix Multiplication (CUDA) (desc_act)");
+  m.def("vecquant8matmul_batched_old", &vecquant8matmul_batched_old, "Vector 8-bit old Batched Quantized Matrix Multiplication (CUDA) (desc_act)");
+  m.def("vecquant8matmul_batched_faster", &vecquant8matmul_batched_faster, "Vector 8-bit old Batched Quantized Matrix Multiplication (CUDA) (desc_act)");
+  m.def("vecquant8matmul_batched_faster_old", &vecquant8matmul_batched_faster_old, "Vector 8-bit old Batched Quantized Matrix Multiplication (CUDA) (desc_act)");
+  m.def("vecquant4matmul_batched_old", &vecquant4matmul_batched_old, "Vector 4-bit old Batched Quantized Matrix Multiplication (CUDA) (desc_act)");
+  m.def("vecquant8matmul_batched_column_compression", &vecquant8matmul_batched_column_compression, "Vector 8-bit Batched Quantized Matrix Multiplication (CUDA) with weight's column compressed (desc_act)");
+  m.def("vecquant8matmul_batched_column_compression_old", &vecquant8matmul_batched_column_compression_old, "Vector old 8-bit Batched Quantized Matrix Multiplication (CUDA) with weight's column compressed (desc_act)");
+  m.def("vecquant8matmul_batched_column_compression_faster", &vecquant8matmul_batched_column_compression_faster, "Vector old 8-bit Batched Quantized Matrix Multiplication (CUDA) with weight's column compressed (desc_act)");
+  m.def("vecquant8matmul_batched_column_compression_faster_old", &vecquant8matmul_batched_column_compression_faster_old, "Vector old 8-bit Batched Quantized Matrix Multiplication (CUDA) with weight's column compressed (desc_act)");
+  m.def("vecquant4matmul_batched_column_compression_old", &vecquant4matmul_batched_column_compression_old, "Vector old 4-bit Batched Quantized Matrix Multiplication (CUDA) with weight's column compressed (desc_act)");
+  m.def("vecquant4matmul_batched", &vecquant4matmul_batched, "Vector 4-bit Batched Quantized Matrix Multiplication (CUDA) (desc_act)");
+  m.def("vecquant4matmul_batched_column_compression", &vecquant4matmul_batched_column_compression, "Vector 4-bit Batched Quantized Matrix Multiplication (CUDA) with weight's column compressed (desc_act)");
+}

Qwen/Qwen-1_8B-Chat/cache_autogptq_cuda_kernel_256.cu

@@ -0,0 +1,1708 @@
+#define _CRT_SECURE_NO_WARNINGS
+#include <torch/all.h>
+#include <torch/python.h>
+#include <cuda.h>
+#include <cuda_runtime.h>
+#include <cuda_fp16.h>
+#include <stdint.h>
+
+#if (defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 700) || defined(USE_ROCM)
+// adapted from https://github.com/PanQiWei/AutoGPTQ/blob/main/autogptq_extension/cuda_256/autogptq_cuda_kernel_256.cu
+__device__ __forceinline__ void atomicAdd(c10::Half* address, c10::Half val) {
+    unsigned int *address_as_ui = reinterpret_cast<unsigned int *>(reinterpret_cast<char *>(address) - (reinterpret_cast<size_t>(address) & 2));
+    unsigned int old = *address_as_ui;
+    unsigned int assumed;
+
+    do {
+        assumed = old;
+        unsigned short hsum = reinterpret_cast<size_t>(address) & 2 ? (old >> 16) : (old & 0xffff);
+        hsum += val;
+        old = reinterpret_cast<size_t>(address) & 2
+                 ? (old & 0xffff) | (hsum << 16)
+                 : (old & 0xffff0000) | hsum;
+        old = atomicCAS(address_as_ui, assumed, old);
+
+    // Note: uses integer comparison to avoid hang in case of NaN (since NaN != NaN)
+    } while (assumed != old);
+}
+__device__ __forceinline__ void atomicAdd(__half* address, c10::Half val) {
+    unsigned int * address_as_ui = (unsigned int *) ((char *)address - ((size_t)address & 2));
+    unsigned int old = *address_as_ui;
+    unsigned int assumed;
+
+    do {
+        assumed = old;
+        __half_raw hsum;
+        hsum.x = (size_t)address & 2 ? (old >> 16) : (old & 0xffff);
+        half tmpres = __hadd(hsum, val);
+        hsum = __half_raw(tmpres);
+        old = (size_t)address & 2 ? (old & 0xffff) | (hsum.x << 16) : (old & 0xffff0000) | hsum.x;
+        old = atomicCAS(address_as_ui, assumed, old);
+    } while (assumed != old);
+}
+#endif
+
+template <typename scalar_t>
+__global__ void VecQuant8MatMulKernel(
+    const  scalar_t* __restrict__ vec,
+    const       int* __restrict__ mat,
+           scalar_t* __restrict__ mul,
+    const  scalar_t* __restrict__ scales,
+    const       int* __restrict__ zeros,
+    const       int* __restrict__ g_idx,
+    int batch,
+    int vec_height,
+    int height,
+    int width,
+    int zero_width
+);
+
+template <typename scalar_t>
+__global__ void VecQuant8BatchMatMulColumnCompressionKernel(
+    const  scalar_t* __restrict__ vec,
+    const       int* __restrict__ mat,
+           scalar_t* __restrict__ mul,
+    const  scalar_t* __restrict__ scales,
+    const       int* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int height,
+    int width
+);
+
+template <typename scalar_t>
+__global__ void VecQuant4BatchMatMulColumnCompressionKernel(
+    const  scalar_t* __restrict__ vec,
+    const       int* __restrict__ mat,
+           scalar_t* __restrict__ mul,
+    const  scalar_t* __restrict__ scales,
+    const       int* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int height,
+    int width
+);
+
+template <typename scalar_t>
+__global__ void VecQuant8BatchMatMulKernel(
+    const  scalar_t* __restrict__ vec,
+    const       int* __restrict__ mat,
+           scalar_t* __restrict__ mul,
+    const  scalar_t* __restrict__ scales,
+    const       int* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int vec_height,
+    int height,
+    int width,
+    int zero_width
+);
+
+template <typename scalar_t>
+__global__ void VecQuant4BatchMatMulKernel(
+    const  scalar_t* __restrict__ vec,
+    const       int* __restrict__ mat,
+           scalar_t* __restrict__ mul,
+    const  scalar_t* __restrict__ scales,
+    const       int* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int vec_height,
+    int height,
+    int width,
+    int zero_width
+);
+
+
+
+template <typename scalar_t>
+__global__ void VecQuant8BatchMatMulKernel_old(
+    const  scalar_t* __restrict__ vec,
+    const  uint8_t* __restrict__ mat,
+           scalar_t* __restrict__ mul,
+    const  scalar_t* __restrict__ scales,
+    const  scalar_t* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int vec_height,
+    int height,
+    int width,
+    int zero_width
+);
+
+__global__ void VecQuant8BatchMatMulKernel_faster(
+    const  half* __restrict__ vec,
+    const  uint8_t* __restrict__ mat,
+           half* __restrict__ mul,
+    const  half* __restrict__ scales,
+    const  half* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int vec_height,
+    int height,
+    int width,
+    int zero_width
+);
+
+
+
+__global__ void VecQuant8BatchMatMulKernel_faster_old(
+    const  half* __restrict__ vec,
+    const  uint8_t* __restrict__ mat,
+           half* __restrict__ mul,
+    const  half* __restrict__ scales,
+    const  half* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int vec_height,
+    int height,
+    int width
+);
+
+
+template <typename scalar_t>
+__global__ void VecQuant4BatchMatMulKernel_old(
+    const  scalar_t* __restrict__ vec,
+    const  uint8_t* __restrict__ mat,
+           scalar_t* __restrict__ mul,
+    const  scalar_t* __restrict__ scales,
+    const  scalar_t* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int vec_height,
+    int height,
+    int width,
+    int zero_width
+);
+
+
+template <typename scalar_t>
+__global__ void VecQuant8BatchMatMulColumnCompressionKernel_old(
+    const  scalar_t* __restrict__ vec,
+    const  uint8_t* __restrict__ mat,
+           scalar_t* __restrict__ mul,
+    const  scalar_t* __restrict__ scales,
+    const  scalar_t* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int height,
+    int width
+);
+
+__global__ void VecQuant8BatchMatMulColumnCompressionKernel_faster(
+    const  half* __restrict__ vec,
+    const  uint8_t* __restrict__ mat,
+           half* __restrict__ mul,
+    const  half* __restrict__ scales,
+    const  half* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int height,
+    int width
+);
+
+__global__ void VecQuant8BatchMatMulColumnCompressionKernel_faster_old(
+    const  half* __restrict__ vec,
+    const  uint8_t* __restrict__ mat,
+           half* __restrict__ mul,
+    const  half* __restrict__ scales,
+    const  half* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int height,
+    int width
+);
+
+
+template <typename scalar_t>
+__global__ void VecQuant4BatchMatMulColumnCompressionKernel_old(
+    const  scalar_t* __restrict__ vec,
+    const  uint8_t* __restrict__ mat,
+           scalar_t* __restrict__ mul,
+    const  scalar_t* __restrict__ scales,
+    const  scalar_t* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int height,
+    int width
+);
+
+
+__global__ void VecQuant8BatchMatMulKernel_faster(
+    const  half* __restrict__ vec,
+    const  uint8_t* __restrict__ mat,
+           half* __restrict__ mul,
+    const  half* __restrict__ scales,
+    const  half* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int vec_height,
+    int height,
+    int width
+);
+
+
+__global__ void VecQuant8BatchMatMulColumnCompressionKernel_faster(
+    const  half* __restrict__ vec,
+    const  uint8_t* __restrict__ mat,
+           half* __restrict__ mul,
+    const  half* __restrict__ scales,
+    const  half* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int height,
+    int width
+);
+
+const int BLOCKWIDTH  = 128;
+const int BLOCKHEIGHT8 =  32;
+const int BLOCKHEIGHT4 =  16;
+const int BLOCKHEIGHT_OLD4 =  128;
+//const int BLOCKHEIGHT_OLD8 =  128;
+
+__device__ inline unsigned int as_unsigned(int i) {
+  return *reinterpret_cast<unsigned int*>(&i);
+}
+
+__device__ inline int as_int(int i) {
+  return *reinterpret_cast<int*>(&i);
+}
+
+void vecquant8matmul_batched_column_compression_cuda(
+  torch::Tensor vec,
+  torch::Tensor mat,
+  torch::Tensor mul,
+  torch::Tensor scales,
+  torch::Tensor zeros
+) {
+  int batch = vec.size(0);
+  int heads = vec.size(1);
+  int vec_row = vec.size(2);
+  int height = vec.size(3);
+  int width = mat.size(3) * 4;
+
+  dim3 blocks(
+    (height + BLOCKWIDTH - 1) / BLOCKWIDTH,
+    (width + BLOCKWIDTH - 1) / BLOCKWIDTH
+  );
+  dim3 threads(BLOCKWIDTH);
+
+  AT_DISPATCH_FLOATING_TYPES(
+    vec.type(), "vecquant8matmul_batched_cuda", ([&] {
+      VecQuant8BatchMatMulColumnCompressionKernel<<<blocks, threads>>>(
+        vec.data<scalar_t>(), mat.data<int>(), mul.data<scalar_t>(),
+        scales.data<scalar_t>(), zeros.data<int>(),
+        batch, heads, vec_row, height, width
+      );
+    })
+  );
+
+}
+
+template <typename scalar_t>
+__global__ void VecQuant8BatchMatMulColumnCompressionKernel(
+    const  scalar_t* __restrict__ vec,
+    const       int* __restrict__ mat,
+           scalar_t* __restrict__ mul,
+    const  scalar_t* __restrict__ scales,
+    const       int* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int height,
+    int width
+) {
+  int weight_total = batch * heads * height * width / 4;
+  int input_total = batch * heads * vec_row * height;
+  int out_total = batch * heads * vec_row * width;
+  int tid = threadIdx.x;
+  // h is index of height with step being BLOCKWIDTH
+  int h = BLOCKWIDTH * blockIdx.x;
+  // w is index of width with step being 1
+  int w = BLOCKWIDTH * blockIdx.y + tid;
+  if (w >= width && tid >= height) {
+    return;
+  }
+
+  __shared__ scalar_t blockvec[BLOCKWIDTH];
+  int k;
+  scalar_t w_tmp;
+
+  float weight[BLOCKWIDTH];
+
+  for (int b = 0; b < batch; ++b){
+    for (int head = 0; head < heads; ++head){
+      int batch_shift = b * heads + head;
+      for (k = 0; k <  BLOCKWIDTH && h + k < height; ++k){
+        int i_w = (w / 4);
+        int w_bit = (w % 4) * 8;
+
+        int w_index = (batch_shift * height + h + k) * width / 4 + i_w;
+        if (w_index >= weight_total || w >= width) {
+          weight[k] = 0;
+        } else {
+          scalar_t scale = scales[batch_shift * height + h + k];
+          scalar_t zero = zeros[batch_shift * height + h + k];
+          w_tmp = ((as_unsigned(mat[w_index]) >> w_bit) & 0xFF);
+          weight[k] = scale * (w_tmp - zero);
+        }
+      }
+
+      scalar_t res;
+      for (int vr = 0; vr < vec_row; ++vr){
+          res = 0;
+        int vec_index = (batch_shift * vec_row + vr) * height + blockIdx.x * BLOCKWIDTH + tid;
+        if (vec_index < input_total) {
+            blockvec[tid] = vec[vec_index];
+        } else {
+            blockvec[tid] = 0;
+        }
+
+        __syncthreads();
+          for (k = 0; k <  BLOCKWIDTH && h + k < height; ++k){
+          // res is the dot product of BLOCKWIDTH elements (part of width)
+            res += weight[k] * blockvec[k];
+        }
+        // add res to the final result, final matrix shape: (batch, vec_row, width)
+        int out_index = (batch_shift * vec_row + vr) * width + w;
+        if (out_index < out_total) {
+            atomicAdd(&mul[out_index], res);
+        }
+        __syncthreads();
+      }
+    }
+  }
+}
+
+void vecquant8matmul_batched_cuda(
+  torch::Tensor vec,
+  torch::Tensor mat,
+  torch::Tensor mul,
+  torch::Tensor scales,
+  torch::Tensor zeros
+) {
+  int batch = vec.size(0);
+  int heads = vec.size(1);
+  int vec_row = vec.size(2);
+  int vec_height = vec.size(3);
+  int height = mat.size(2);
+  int width = mat.size(3);
+  int zero_width = zeros.size(2);
+
+  dim3 blocks(
+    (height + BLOCKHEIGHT8 - 1) / BLOCKHEIGHT8,
+    (width + BLOCKWIDTH - 1) / BLOCKWIDTH
+  );
+  dim3 threads(BLOCKWIDTH);
+
+  AT_DISPATCH_FLOATING_TYPES(
+    vec.type(), "vecquant8matmul_batched_cuda", ([&] {
+      VecQuant8BatchMatMulKernel<<<blocks, threads>>>(
+        vec.data<scalar_t>(), mat.data<int>(), mul.data<scalar_t>(),
+        scales.data<scalar_t>(), zeros.data<int>(),
+        batch, heads, vec_row, vec_height, height, width, zero_width
+      );
+    })
+  );
+
+}
+
+template <typename scalar_t>
+__global__ void VecQuant8BatchMatMulKernel(
+    const  scalar_t* __restrict__ vec,
+    const       int* __restrict__ mat,
+           scalar_t* __restrict__ mul,
+    const  scalar_t* __restrict__ scales,
+    const       int* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int vec_height,
+    int height,
+    int width,
+    int zero_width
+) {
+  int weight_total = batch * heads * height * width;
+  int input_total = batch * heads * vec_row * vec_height;
+  int out_total = batch * heads * vec_row * width;
+  int tid = threadIdx.x;
+  // h is index of height with step being BLOCKHEIGHT8
+  int h = BLOCKHEIGHT8 * blockIdx.x;
+  // w is index of width with step being 1
+  int w = BLOCKWIDTH * blockIdx.y + tid;
+  if (w >= width && tid >= vec_height) {
+    return;
+  }
+
+  __shared__ scalar_t blockvec[BLOCKWIDTH];
+  // i is index of mat of block first row
+  int i = width * h + w;
+  // if (i >= width * height) {
+  //   return;
+  // }
+  int k;
+  scalar_t w_tmp;
+
+  int z_w = w / 4;
+  int z_mod = (w % 4) * 8;
+
+  float weight[BLOCKWIDTH];
+
+  for (int b = 0; b < batch; ++b){
+    for (int head = 0; head < heads; ++head){
+      int batch_shift = b * heads + head;
+      for (k = 0; k <  BLOCKWIDTH && h * 4 + k < vec_height; ++k){
+        int k_w = (k / 4);
+        int k_bit = (k % 4) * 8;
+
+        int w_index = batch_shift * height * width + i + (k_w * width);
+        if (w_index >= weight_total || w >= width) {
+          weight[k] = 0;
+        } else {
+          scalar_t scale = scales[batch_shift * width + w];
+          scalar_t zero;
+          if (zero_width == width) {
+            zero = zeros[batch_shift * width + w];
+          } else {
+            zero = scalar_t(((as_unsigned(zeros[batch_shift * zero_width + z_w]) >> z_mod) & 0xFF) + 1);
+          }
+          w_tmp = ((as_unsigned(mat[w_index]) >> k_bit) & 0xFF);
+          weight[k] = scale * (w_tmp - zero);
+        }
+      }
+
+      scalar_t res;
+      for (int vr = 0; vr < vec_row; ++vr){
+          res = 0;
+        int vec_index = (batch_shift * vec_row + vr) * vec_height + blockIdx.x * BLOCKWIDTH + tid;
+        if (vec_index < input_total) {
+            blockvec[tid] = vec[vec_index];
+        } else {
+            blockvec[tid] = 0;
+        }
+
+        __syncthreads();
+          for (k = 0; k <  BLOCKWIDTH && h * 4 + k < vec_height; ++k){
+          // res is the dot product of BLOCKWIDTH elements (part of width)
+            res += weight[k] * blockvec[k];
+        }
+        // add res to the final result, final matrix shape: (batch, vec_row, width)
+        int out_index = (batch_shift * vec_row + vr) * width + w;
+        if (out_index < out_total) {
+            atomicAdd(&mul[out_index], res);
+        }
+        __syncthreads();
+      }
+    }
+  }
+}
+
+
+void vecquant8matmul_cuda(
+  torch::Tensor vec,
+  torch::Tensor mat,
+  torch::Tensor mul,
+  torch::Tensor scales,
+  torch::Tensor zeros,
+  torch::Tensor g_idx
+) {
+  int batch = vec.size(0);
+  int vec_height = vec.size(1);
+  int height = mat.size(0);
+  int width = mat.size(1);
+  int zero_width = zeros.size(1);
+
+  dim3 blocks(
+    (height + BLOCKHEIGHT8 - 1) / BLOCKHEIGHT8,
+    (width + BLOCKWIDTH - 1) / BLOCKWIDTH
+  );
+  dim3 threads(BLOCKWIDTH);
+
+  AT_DISPATCH_FLOATING_TYPES(
+    vec.type(), "vecquant8matmul_cuda", ([&] {
+      VecQuant8MatMulKernel<<<blocks, threads>>>(
+        vec.data<scalar_t>(), mat.data<int>(), mul.data<scalar_t>(),
+        scales.data<scalar_t>(), zeros.data<int>(), g_idx.data<int>(),
+        batch, vec_height, height, width, zero_width
+      );
+    })
+  );
+}
+
+template <typename scalar_t>
+__global__ void VecQuant8MatMulKernel(
+    const  scalar_t* __restrict__ vec,
+    const       int* __restrict__ mat,
+           scalar_t* __restrict__ mul,
+    const  scalar_t* __restrict__ scales,
+    const       int* __restrict__ zeros,
+    const       int* __restrict__ g_idx,
+    int batch,
+    int vec_height,
+    int height,
+    int width,
+    int zero_width
+) {
+  int h = BLOCKHEIGHT8 * blockIdx.x;
+  int w = BLOCKWIDTH * blockIdx.y + threadIdx.x;
+
+  __shared__ scalar_t blockvec[BLOCKWIDTH];
+  int i = width * h + w;
+  int g_h = h * 4;
+  int k;
+  unsigned int g;
+  scalar_t w_tmp;
+
+  int z_w = w / 4;
+  int z_mod = (w % 4) * 8;
+
+  float weight[BLOCKWIDTH];
+
+  for (k = 0; k <  BLOCKWIDTH; ++k){
+    int k_w = (k / 4);
+    int k_bit = (k % 4) * 8;
+
+      g = as_int(g_idx[g_h + k]);
+      scalar_t scale = scales[g * width + w];
+      scalar_t zero = scalar_t(((as_unsigned(zeros[g * zero_width + z_w]) >> z_mod) & 0xFF) + 1);
+
+      w_tmp = ((as_unsigned(mat[i + (k_w * width)]) >> k_bit) & 0xFF);
+
+    weight[k] = scale * (w_tmp - zero);
+  }
+
+
+  scalar_t res;
+  for (int b = 0; b < batch; ++b){
+      res = 0;
+    blockvec[threadIdx.x] = vec[b * vec_height + blockIdx.x * BLOCKWIDTH + threadIdx.x];
+    __syncthreads();
+    for (k = 0; k <  BLOCKWIDTH; ++k){
+      res += weight[k] * blockvec[k];
+    }
+    atomicAdd(&mul[b * width + w], res);
+    __syncthreads();
+  }
+}
+
+
+
+void vecquant4matmul_batched_cuda(
+  torch::Tensor vec,
+  torch::Tensor mat,
+  torch::Tensor mul,
+  torch::Tensor scales,
+  torch::Tensor zeros
+) {
+  int batch = vec.size(0);
+  int heads = vec.size(1);
+  int vec_row = vec.size(2);
+  int vec_height = vec.size(3);
+  int height = mat.size(2);
+  int width = mat.size(3);
+  int zero_width = zeros.size(2);
+
+  dim3 blocks(
+    (height + BLOCKHEIGHT4 - 1) / BLOCKHEIGHT4,
+    (width + BLOCKWIDTH - 1) / BLOCKWIDTH
+  );
+  dim3 threads(BLOCKWIDTH);
+
+  AT_DISPATCH_FLOATING_TYPES(
+    vec.type(), "vecquant4matmul_batched_cuda", ([&] {
+      VecQuant4BatchMatMulKernel<<<blocks, threads>>>(
+        vec.data<scalar_t>(), mat.data<int>(), mul.data<scalar_t>(),
+        scales.data<scalar_t>(), zeros.data<int>(),
+        batch, heads, vec_row, vec_height, height, width, zero_width
+      );
+    })
+  );
+
+}
+
+template <typename scalar_t>
+__global__ void VecQuant4BatchMatMulKernel(
+    const  scalar_t* __restrict__ vec,
+    const       int* __restrict__ mat,
+           scalar_t* __restrict__ mul,
+    const  scalar_t* __restrict__ scales,
+    const       int* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int vec_height,
+    int height,
+    int width,
+    int zero_width
+) {
+  int weight_total = batch * heads * height * width;
+  int input_total = batch * heads * vec_row * vec_height;
+  int out_total = batch * heads * vec_row * width;
+  int tid = threadIdx.x;
+  // h is index of height with step being BLOCKHEIGHT4
+  int h = BLOCKHEIGHT4 * blockIdx.x;
+  // w is index of width with step being 1
+  int w = BLOCKWIDTH * blockIdx.y + tid;
+  if (w >= width && tid >= vec_height) {
+    return;
+  }
+
+  __shared__ scalar_t blockvec[BLOCKWIDTH];
+  // i is index of mat of block first row
+  int i = width * h + w;
+  int k;
+  scalar_t w_tmp;
+
+  int z_w = w / 8;
+  int z_mod = (w % 8) * 4;
+
+  float weight[BLOCKWIDTH];
+
+  for (int b = 0; b < batch; ++b){
+    for (int head = 0; head < heads; ++head){
+      int batch_shift = b * heads + head;
+      for (k = 0; k <  BLOCKWIDTH && h * 8 + k < vec_height; ++k){
+        int k_w = (k / 8);
+        int k_bit = (k % 8) * 4;
+
+        int w_index = batch_shift * height * width + i + (k_w * width);
+        if (w_index >= weight_total || w >= width) {
+          weight[k] = 0;
+        } else {
+          scalar_t scale = scales[batch_shift * width + w];
+          scalar_t zero;
+          if (zero_width == width) {
+            zero = zeros[batch_shift * width + w];
+          } else {
+            zero = scalar_t(((as_unsigned(zeros[batch_shift * zero_width + z_w]) >> z_mod) & 0xF));
+          }
+          w_tmp = ((as_unsigned(mat[w_index]) >> k_bit) & 0xF);
+          weight[k] = scale * (w_tmp - zero);
+        }
+      }
+
+      scalar_t res;
+      for (int vr = 0; vr < vec_row; ++vr){
+          res = 0;
+        int vec_index = (batch_shift * vec_row + vr) * vec_height + blockIdx.x * BLOCKWIDTH + tid;
+        if (vec_index < input_total) {
+            blockvec[tid] = vec[vec_index];
+        } else {
+            blockvec[tid] = 0;
+        }
+
+        __syncthreads();
+          for (k = 0; k <  BLOCKWIDTH && h * 8 + k < vec_height; ++k){
+          // res is the dot product of BLOCKWIDTH elements (part of width)
+            res += weight[k] * blockvec[k];
+        }
+        // add res to the final result, final matrix shape: (batch, vec_row, width)
+        int out_index = (batch_shift * vec_row + vr) * width + w;
+        if (out_index < out_total) {
+            atomicAdd(&mul[out_index], res);
+        }
+        __syncthreads();
+      }
+    }
+  }
+}
+
+
+
+void vecquant4matmul_batched_column_compression_cuda(
+  torch::Tensor vec,
+  torch::Tensor mat,
+  torch::Tensor mul,
+  torch::Tensor scales,
+  torch::Tensor zeros
+) {
+  int batch = vec.size(0);
+  int heads = vec.size(1);
+  int vec_row = vec.size(2);
+  int height = vec.size(3);
+  int width = mat.size(3) * 8;
+
+  dim3 blocks(
+    (height + BLOCKWIDTH - 1) / BLOCKWIDTH,
+    (width + BLOCKWIDTH - 1) / BLOCKWIDTH
+  );
+  dim3 threads(BLOCKWIDTH);
+
+  AT_DISPATCH_FLOATING_TYPES(
+    vec.type(), "vecquant4matmul_batched_cuda", ([&] {
+      VecQuant4BatchMatMulColumnCompressionKernel<<<blocks, threads>>>(
+        vec.data<scalar_t>(), mat.data<int>(), mul.data<scalar_t>(),
+        scales.data<scalar_t>(), zeros.data<int>(),
+        batch, heads, vec_row, height, width
+      );
+    })
+  );
+
+}
+
+template <typename scalar_t>
+__global__ void VecQuant4BatchMatMulColumnCompressionKernel(
+    const  scalar_t* __restrict__ vec,
+    const       int* __restrict__ mat,
+           scalar_t* __restrict__ mul,
+    const  scalar_t* __restrict__ scales,
+    const       int* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int height,
+    int width
+) {
+  int weight_total = batch * heads * height * width / 8;
+  int input_total = batch * heads * vec_row * height;
+  int out_total = batch * heads * vec_row * width;
+  int tid = threadIdx.x;
+  // h is index of height with step being BLOCKWIDTH
+  int h = BLOCKWIDTH * blockIdx.x;
+  // w is index of width with step being 1
+  int w = BLOCKWIDTH * blockIdx.y + tid;
+  if (w >= width && tid >= height) {
+    return;
+  }
+
+  __shared__ scalar_t blockvec[BLOCKWIDTH];
+  int k;
+  scalar_t w_tmp;
+
+  float weight[BLOCKWIDTH];
+
+  for (int b = 0; b < batch; ++b){
+    for (int head = 0; head < heads; ++head){
+      int batch_shift = b * heads + head;
+      for (k = 0; k <  BLOCKWIDTH && h + k < height; ++k){
+        int i_w = (w / 8);
+        int w_bit = (w % 8) * 4;
+
+        int w_index = (batch_shift * height + h + k) * width / 8 + i_w;
+        if (w_index >= weight_total || w >= width) {
+          weight[k] = 0;
+        } else {
+          scalar_t scale = scales[batch_shift * height + h + k];
+          scalar_t zero = zeros[batch_shift * height + h + k];
+          w_tmp = ((as_unsigned(mat[w_index]) >> w_bit) & 0xF);
+          weight[k] = scale * (w_tmp - zero);
+        }
+      }
+
+      scalar_t res;
+      for (int vr = 0; vr < vec_row; ++vr){
+          res = 0;
+        int vec_index = (batch_shift * vec_row + vr) * height + blockIdx.x * BLOCKWIDTH + tid;
+        if (vec_index < input_total) {
+            blockvec[tid] = vec[vec_index];
+        } else {
+            blockvec[tid] = 0;
+        }
+
+        __syncthreads();
+          for (k = 0; k <  BLOCKWIDTH && h + k < height; ++k){
+          // res is the dot product of BLOCKWIDTH elements (part of width)
+            res += weight[k] * blockvec[k];
+        }
+        // add res to the final result, final matrix shape: (batch, vec_row, width)
+        int out_index = (batch_shift * vec_row + vr) * width + w;
+        if (out_index < out_total) {
+            atomicAdd(&mul[out_index], res);
+        }
+        __syncthreads();
+      }
+    }
+  }
+}
+
+
+void vecquant8matmul_batched_old_cuda(
+  torch::Tensor vec,
+  torch::Tensor mat,
+  torch::Tensor mul,
+  torch::Tensor scales,
+  torch::Tensor zeros
+) {
+  int batch = vec.size(0);
+  int heads = vec.size(1);
+  int vec_row = vec.size(2);
+  int vec_height = vec.size(3);
+  int height = mat.size(2);
+  int width = mat.size(3);
+  int zero_width = zeros.size(2);
+
+  dim3 blocks(
+    (height + BLOCKWIDTH - 1) / BLOCKWIDTH,
+    (width + BLOCKWIDTH - 1) / BLOCKWIDTH
+  );
+  dim3 threads(BLOCKWIDTH);
+
+  AT_DISPATCH_FLOATING_TYPES(
+    vec.type(), "vecquant8matmul_batched_old_cuda", ([&] {
+      VecQuant8BatchMatMulKernel_old<<<blocks, threads>>>(
+        vec.data<scalar_t>(), mat.data<uint8_t>(), mul.data<scalar_t>(),
+        scales.data<scalar_t>(), zeros.data<scalar_t>(),
+        batch, heads, vec_row, vec_height, height, width, zero_width
+      );
+    })
+  );
+}
+
+
+template <typename scalar_t>
+__global__ void VecQuant8BatchMatMulKernel_old(
+    const  scalar_t* __restrict__ vec,
+    const  uint8_t* __restrict__ mat,
+           scalar_t* __restrict__ mul,
+    const  scalar_t* __restrict__ scales,
+    const  scalar_t* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int vec_height,
+    int height,
+    int width,
+    int zero_width
+) {
+  int weight_total = batch * heads * height * width;
+  int input_total = batch * heads * vec_row * vec_height;
+  int out_total = batch * heads * vec_row * width;
+  int tid = threadIdx.x;
+  // h is index of height with step being BLOCKHEIGHT8
+  int h = BLOCKWIDTH * blockIdx.x;
+  // w is index of width with step being 1
+  int w = BLOCKWIDTH * blockIdx.y + tid;
+  if (w >= width && tid >= vec_height) {
+    return;
+  }
+
+  __shared__ scalar_t blockvec[BLOCKWIDTH];
+  // i is index of mat of block first row
+  int i = width * h + w;
+  int k;
+  scalar_t w_tmp;
+
+  float weight[BLOCKWIDTH];
+  for (int b = 0; b < batch; ++b){
+    for (int head = 0; head < heads; ++head){
+      int batch_shift = b * heads + head;
+      for (k = 0; k <  BLOCKWIDTH && h + k < vec_height; ++k){
+        int k_w = k;
+        int w_index = batch_shift * height * width + i + (k_w * width);
+        if (w_index >= weight_total || w >= width) {
+          weight[k] = 0;
+        } else {
+          scalar_t scale = scales[batch_shift * width + w];
+          scalar_t zero = zeros[batch_shift * width + w];
+          w_tmp = as_unsigned(mat[w_index]);
+          weight[k] = scale * (w_tmp - zero);
+        }
+      }
+
+      scalar_t res;
+      for (int vr = 0; vr < vec_row; ++vr){
+          res = 0;
+        int vec_index = (batch_shift * vec_row + vr) * vec_height + blockIdx.x * BLOCKWIDTH + tid;
+        if (vec_index < input_total) {
+            blockvec[tid] = vec[vec_index];
+        } else {
+            blockvec[tid] = 0;
+        }
+
+        __syncthreads();
+          for (k = 0; k <  BLOCKWIDTH && h + k < vec_height; ++k){
+          // res is the dot product of BLOCKWIDTH elements (part of width)
+            res += weight[k] * blockvec[k];
+        }
+        // add res to the final result, final matrix shape: (batch, vec_row, width)
+        int out_index = (batch_shift * vec_row + vr) * width + w;
+        if (out_index < out_total) {
+            atomicAdd(&mul[out_index], res);
+        }
+        __syncthreads();
+      }
+    }
+  }
+}
+
+
+
+void vecquant8matmul_batched_faster_cuda(
+  torch::Tensor vec,
+  torch::Tensor mat,
+  torch::Tensor mul,
+  torch::Tensor scales,
+  torch::Tensor zeros
+) {
+  int batch = vec.size(0);
+  int heads = vec.size(1);
+  int vec_row = vec.size(2);
+  int vec_height = vec.size(3);
+  int height = mat.size(2);
+  int width = mat.size(3);
+  int zero_width = zeros.size(2);
+
+  dim3 blocks(
+    (height + BLOCKWIDTH - 1) / BLOCKWIDTH,
+    (width + BLOCKWIDTH - 1) / BLOCKWIDTH
+  );
+  dim3 threads(BLOCKWIDTH);
+
+  VecQuant8BatchMatMulKernel_faster<<<blocks, threads>>>(
+    (half*) vec.data_ptr(),
+    (uint8_t*) mat.data_ptr(),
+    (half*) mul.data_ptr(),
+    (half*) scales.data_ptr(),
+    (half*) zeros.data_ptr(),
+    batch, heads, vec_row, vec_height, height, width, zero_width
+  );
+}
+
+
+
+__global__ void VecQuant8BatchMatMulKernel_faster(
+    const  half* __restrict__ vec,
+    const  uint8_t* __restrict__ mat,
+           half* __restrict__ mul,
+    const  half* __restrict__ scales,
+    const  half* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int vec_height,
+    int height,
+    int width,
+    int zero_width
+) {
+  //int weight_total = batch * heads * height * width;
+  int input_total = batch * heads * vec_row * vec_height;
+  int out_total = batch * heads * vec_row * width;
+  int tid = threadIdx.x;
+  int h = BLOCKWIDTH * blockIdx.x;
+  int w = BLOCKWIDTH * blockIdx.y + tid;
+  if (w >= width && tid >= height) {
+    return;
+  }
+
+  __shared__ float blockvec[BLOCKWIDTH];
+  int i = width * h + w;
+  int k;
+  float w_tmp;
+
+  float weight[BLOCKWIDTH];
+  for (int b = 0; b < batch; ++b){
+    for (int head = 0; head < heads; ++head){
+      int batch_shift = b * heads + head;
+      for (k = 0; k <  BLOCKWIDTH && h + k < vec_height; ++k){
+        int k_w = k;
+        int w_index = batch_shift * height * width + i + (k_w * width);
+        float scale = __half2float(scales[batch_shift * width + w]);
+        float zero = __half2float(zeros[batch_shift * width + w]);
+        w_tmp = as_unsigned(mat[w_index]);
+        weight[k] = scale *(w_tmp-zero);
+      }
+
+      float res;
+      for (int vr = 0; vr < vec_row; ++vr){
+        res = 0;
+        int vec_index = (batch_shift * vec_row + vr) * vec_height + blockIdx.x * BLOCKWIDTH + tid;
+        if (vec_index < input_total) {
+            blockvec[tid] = __half2float(vec[vec_index]);
+        } else {
+            blockvec[tid] = 0;
+        }
+        __syncthreads();
+          for (k = 0; k <  BLOCKWIDTH && h + k < vec_height; ++k){
+            float temp_res = weight[k]*blockvec[k];
+            res += temp_res;
+        }
+        int out_index = (batch_shift * vec_row + vr) * width + w;
+        if (out_index < out_total) {
+            atomicAdd(&mul[out_index], __float2half(res));
+        }
+        __syncthreads();
+      }
+    }
+  }
+}
+
+
+
+
+void vecquant8matmul_batched_column_compression_faster_cuda(
+  torch::Tensor vec,
+  torch::Tensor mat,
+  torch::Tensor mul,
+  torch::Tensor scales,
+  torch::Tensor zeros
+) {
+  int batch = vec.size(0);
+  int heads = vec.size(1);
+  int vec_row = vec.size(2);
+  int height = vec.size(3);
+  int width = mat.size(3);
+
+  dim3 blocks(
+    (height + BLOCKWIDTH - 1) / BLOCKWIDTH,
+    (width + BLOCKWIDTH - 1) / BLOCKWIDTH
+  );
+  dim3 threads(BLOCKWIDTH);
+
+  VecQuant8BatchMatMulColumnCompressionKernel_faster<<<blocks, threads>>>(
+    (half*) vec.data_ptr(),
+    (uint8_t*) mat.data_ptr(),
+    (half*) mul.data_ptr(),
+    (half*) scales.data_ptr(),
+    (half*) zeros.data_ptr(),
+    batch, heads, vec_row, height, width
+  );
+
+}
+
+__global__ void VecQuant8BatchMatMulColumnCompressionKernel_faster(
+    const  half* __restrict__ vec,
+    const  uint8_t* __restrict__ mat,
+           half* __restrict__ mul,
+    const  half* __restrict__ scales,
+    const  half* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int height,
+    int width
+) {
+  //int weight_total = batch * heads * height * width;
+  int input_total = batch * heads * vec_row * height;
+  int out_total = batch * heads * vec_row * width;
+  int tid = threadIdx.x;
+  int h = BLOCKWIDTH * blockIdx.x;
+  int w = BLOCKWIDTH * blockIdx.y + tid;
+  if (w >= width && tid >= height) {
+    return;
+  }
+
+  __shared__ float blockvec[BLOCKWIDTH];
+  int k;
+  float w_tmp;
+  float weight[BLOCKWIDTH];
+
+  for (int b = 0; b < batch; ++b){
+    for (int head = 0; head < heads; ++head){
+      int batch_shift = b * heads + head;
+      for (k = 0; k <  BLOCKWIDTH; ++k){
+        int w_index = (batch_shift * height + h + k) * width  + w;
+        float scale = __half2float(scales[batch_shift * height + h + k]);
+        float zero = __half2float(zeros[batch_shift * height + h + k]);
+        w_tmp = mat[w_index];
+        weight[k] = scale * (w_tmp-zero);
+      }
+
+      float res;
+      for (int vr = 0; vr < vec_row; ++vr){
+        res = 0;
+        int vec_index = (batch_shift * vec_row + vr) * height + blockIdx.x * BLOCKWIDTH + tid;
+        if (vec_index < input_total) {
+            blockvec[tid] = __half2float(vec[vec_index]);
+        } else {
+            blockvec[tid] = 0;
+        }
+        __syncthreads();
+          for (k = 0; k <  BLOCKWIDTH; ++k){
+            res += weight[k]*blockvec[k];
+        }
+        int out_index = (batch_shift * vec_row + vr) * width + w;
+        if (out_index < out_total) {
+            atomicAdd(&mul[out_index], __float2half(res));
+        }
+        __syncthreads();
+      }
+    }
+  }
+}
+
+
+
+void vecquant8matmul_batched_column_compression_old_cuda(
+  torch::Tensor vec,
+  torch::Tensor mat,
+  torch::Tensor mul,
+  torch::Tensor scales,
+  torch::Tensor zeros
+) {
+  int batch = vec.size(0);
+  int heads = vec.size(1);
+  int vec_row = vec.size(2);
+  int height = vec.size(3);
+  int width = mat.size(3);
+
+  dim3 blocks(
+    (height + BLOCKWIDTH - 1) / BLOCKWIDTH,
+    (width + BLOCKWIDTH - 1) / BLOCKWIDTH
+  );
+  dim3 threads(BLOCKWIDTH);
+
+  AT_DISPATCH_FLOATING_TYPES(
+    vec.type(), "vecquant8matmul_batched_column_compression_old_cuda", ([&] {
+      VecQuant8BatchMatMulColumnCompressionKernel_old<<<blocks, threads>>>(
+        vec.data<scalar_t>(), mat.data<uint8_t>(), mul.data<scalar_t>(),
+        scales.data<scalar_t>(), zeros.data<scalar_t>(),
+        batch, heads, vec_row, height, width
+      );
+    })
+  );
+
+}
+
+template <typename scalar_t>
+__global__ void VecQuant8BatchMatMulColumnCompressionKernel_old(
+    const  scalar_t* __restrict__ vec,
+    const  uint8_t* __restrict__ mat,
+           scalar_t* __restrict__ mul,
+    const  scalar_t* __restrict__ scales,
+    const  scalar_t* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int height,
+    int width
+) {
+  int weight_total = batch * heads * height * width;
+  int input_total = batch * heads * vec_row * height;
+  int out_total = batch * heads * vec_row * width;
+  int tid = threadIdx.x;
+  // h is index of height with step being BLOCKWIDTH
+  int h = BLOCKWIDTH * blockIdx.x;
+  // w is index of width with step being 1
+  int w = BLOCKWIDTH * blockIdx.y + tid;
+  if (w >= width && tid >= height) {
+    return;
+  }
+
+  __shared__ scalar_t blockvec[BLOCKWIDTH];
+  int k;
+  scalar_t w_tmp;
+
+  float weight[BLOCKWIDTH];
+
+  for (int b = 0; b < batch; ++b){
+    for (int head = 0; head < heads; ++head){
+      int batch_shift = b * heads + head;
+      for (k = 0; k <  BLOCKWIDTH && h + k < height; ++k){
+        int w_index = (batch_shift * height + h + k) * width  + w;
+        if (w_index >= weight_total || w >= width) {
+          weight[k] = 0;
+        } else {
+          scalar_t scale = scales[batch_shift * height + h + k];
+          scalar_t zero = zeros[batch_shift * height + h + k];
+          w_tmp = mat[w_index];
+          weight[k] = scale * (w_tmp - zero);
+        }
+      }
+
+      scalar_t res;
+      for (int vr = 0; vr < vec_row; ++vr){
+          res = 0;
+        int vec_index = (batch_shift * vec_row + vr) * height + blockIdx.x * BLOCKWIDTH + tid;
+        if (vec_index < input_total) {
+            blockvec[tid] = vec[vec_index];
+        } else {
+            blockvec[tid] = 0;
+        }
+
+        __syncthreads();
+          for (k = 0; k <  BLOCKWIDTH && h + k < height; ++k){
+          // res is the dot product of BLOCKWIDTH elements (part of width)
+            res += weight[k] * blockvec[k];
+        }
+        // add res to the final result, final matrix shape: (batch, vec_row, width)
+        int out_index = (batch_shift * vec_row + vr) * width + w;
+        if (out_index < out_total) {
+            atomicAdd(&mul[out_index], res);
+        }
+        __syncthreads();
+      }
+    }
+  }
+}
+
+
+void vecquant4matmul_batched_old_cuda(
+  torch::Tensor vec,
+  torch::Tensor mat,
+  torch::Tensor mul,
+  torch::Tensor scales,
+  torch::Tensor zeros
+) {
+  int batch = vec.size(0);
+  int heads = vec.size(1);
+  int vec_row = vec.size(2);
+  int vec_height = vec.size(3);
+  int height = mat.size(2);
+  int width = mat.size(3);
+  int zero_width = zeros.size(2);
+
+  dim3 blocks(
+    (height + BLOCKHEIGHT_OLD4 - 1) / BLOCKHEIGHT_OLD4,
+    (width + BLOCKWIDTH - 1) / BLOCKWIDTH
+  );
+  dim3 threads(BLOCKWIDTH);
+
+  AT_DISPATCH_FLOATING_TYPES(
+    vec.type(), "vecquant4matmul_batched_old_cuda", ([&] {
+      VecQuant4BatchMatMulKernel_old<<<blocks, threads>>>(
+        vec.data<scalar_t>(), mat.data<uint8_t>(), mul.data<scalar_t>(),
+        scales.data<scalar_t>(), zeros.data<scalar_t>(),
+        batch, heads, vec_row, vec_height, height, width, zero_width
+      );
+    })
+  );
+
+}
+
+template <typename scalar_t>
+__global__ void VecQuant4BatchMatMulKernel_old(
+    const  scalar_t* __restrict__ vec,
+    const  uint8_t* __restrict__ mat,
+           scalar_t* __restrict__ mul,
+    const  scalar_t* __restrict__ scales,
+    const  scalar_t* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int vec_height,
+    int height,
+    int width,
+    int zero_width
+) {
+  int weight_total = batch * heads * height * width;
+  int input_total = batch * heads * vec_row * vec_height;
+  int out_total = batch * heads * vec_row * width;
+  int tid = threadIdx.x;
+  // h is index of height with step being BLOCKHEIGHT_OLD4
+  int h = BLOCKHEIGHT_OLD4 * blockIdx.x;
+  // w is index of width with step being 1
+  int w = BLOCKWIDTH * blockIdx.y + tid;
+  if (w >= width && tid >= vec_height) {
+    return;
+  }
+
+  __shared__ scalar_t blockvec[BLOCKWIDTH];
+  // i is index of mat of block first row
+  int i = width * h + w;
+  int k;
+  scalar_t w_tmp;
+
+  float weight[BLOCKWIDTH];
+  for (int b = 0; b < batch; ++b){
+    for (int head = 0; head < heads; ++head){
+      int batch_shift = b * heads + head;
+      for (k = 0; k <  BLOCKWIDTH && h*2 + k < vec_height; ++k){
+        int k_w = (k / 2);
+        int k_bit = (k % 2) * 4;
+        int w_index = batch_shift * height * width + i + (k_w * width);
+        if (w_index >= weight_total || w >= width) {
+          weight[k] = 0;
+        } else {
+          scalar_t scale = scales[batch_shift * width + w];
+          scalar_t zero = zeros[batch_shift * width + w];
+          w_tmp = ((as_unsigned(mat[w_index]) >> k_bit) & 0xF);
+          weight[k] = scale * (w_tmp - zero);
+        }
+      }
+
+      scalar_t res;
+      for (int vr = 0; vr < vec_row; ++vr){
+          res = 0;
+        int vec_index = (batch_shift * vec_row + vr) * vec_height + blockIdx.x * BLOCKWIDTH + tid;
+        if (vec_index < input_total) {
+            blockvec[tid] = vec[vec_index];
+        } else {
+            blockvec[tid] = 0;
+        }
+
+        __syncthreads();
+          for (k = 0; k <  BLOCKWIDTH && h*2 + k < vec_height; ++k){
+          // res is the dot product of BLOCKWIDTH elements (part of width)
+            res += weight[k] * blockvec[k];
+        }
+        // add res to the final result, final matrix shape: (batch, vec_row, width)
+        int out_index = (batch_shift * vec_row + vr) * width + w;
+        if (out_index < out_total) {
+            atomicAdd(&mul[out_index], res);
+        }
+        __syncthreads();
+      }
+    }
+  }
+}
+
+
+
+
+
+void vecquant4matmul_batched_column_compression_old_cuda(
+  torch::Tensor vec,
+  torch::Tensor mat,
+  torch::Tensor mul,
+  torch::Tensor scales,
+  torch::Tensor zeros
+) {
+  int batch = vec.size(0);
+  int heads = vec.size(1);
+  int vec_row = vec.size(2);
+  int height = vec.size(3);
+  int width = mat.size(3);
+
+  dim3 blocks(
+    (height + BLOCKHEIGHT_OLD4 - 1) / BLOCKHEIGHT_OLD4,
+    (width + BLOCKWIDTH - 1) / BLOCKWIDTH
+  );
+  dim3 threads(BLOCKWIDTH);
+
+  AT_DISPATCH_FLOATING_TYPES(
+    vec.type(), "vecquant4matmul_batched_column_compression_old_cuda", ([&] {
+      VecQuant4BatchMatMulColumnCompressionKernel_old<<<blocks, threads>>>(
+        vec.data<scalar_t>(), mat.data<uint8_t>(), mul.data<scalar_t>(),
+        scales.data<scalar_t>(), zeros.data<scalar_t>(),
+        batch, heads, vec_row, height, width
+      );
+    })
+  );
+
+}
+
+template <typename scalar_t>
+__global__ void VecQuant4BatchMatMulColumnCompressionKernel_old(
+    const  scalar_t* __restrict__ vec,
+    const  uint8_t* __restrict__ mat,
+           scalar_t* __restrict__ mul,
+    const  scalar_t* __restrict__ scales,
+    const  scalar_t* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int height,
+    int width
+) {
+  int weight_total = batch * heads * height * width;
+  int input_total = batch * heads * vec_row * height;
+  int out_total = batch * heads * vec_row * width;
+  int tid = threadIdx.x;
+  // h is index of height with step being BLOCKWIDTH
+  int h = BLOCKHEIGHT_OLD4 * blockIdx.x;
+  // w is index of width with step being 1
+  int w = BLOCKWIDTH * blockIdx.y + tid;
+  if (w >= width && tid >= height) {
+    return;
+  }
+
+  __shared__ scalar_t blockvec[BLOCKWIDTH];
+  int k;
+  scalar_t w_tmp;
+
+  float weight[BLOCKWIDTH];
+
+  for (int b = 0; b < batch; ++b){
+    for (int head = 0; head < heads; ++head){
+      int batch_shift = b * heads + head;
+      for (k = 0; k <  BLOCKWIDTH && h*2 + k < height; ++k){
+        int k_w = (k / 2);
+        int k_bit = (k % 2) * 4;
+        int w_index = (batch_shift * height + h + k) * width  + k_w;
+        if (w_index >= weight_total || w >= width) {
+          weight[k] = 0;
+        } else {
+          scalar_t scale = scales[batch_shift * height + h + k];
+          scalar_t zero = zeros[batch_shift * height + h + k];
+          w_tmp = ((as_unsigned(mat[w_index]) >> k_bit) & 0xF);
+          weight[k] = scale * (w_tmp - zero);
+        }
+      }
+
+      scalar_t res;
+      for (int vr = 0; vr < vec_row; ++vr){
+          res = 0;
+        int vec_index = (batch_shift * vec_row + vr) * height + blockIdx.x * BLOCKWIDTH + tid;
+        if (vec_index < input_total) {
+            blockvec[tid] = vec[vec_index];
+        } else {
+            blockvec[tid] = 0;
+        }
+
+        __syncthreads();
+          for (k = 0; k <  BLOCKWIDTH && h*2 + k < height; ++k){
+          // res is the dot product of BLOCKWIDTH elements (part of width)
+            res += weight[k] * blockvec[k];
+        }
+        // add res to the final result, final matrix shape: (batch, vec_row, width)
+        int out_index = (batch_shift * vec_row + vr) * width + w;
+        if (out_index < out_total) {
+            atomicAdd(&mul[out_index], res);
+        }
+        __syncthreads();
+      }
+    }
+  }
+}
+
+
+
+
+
+void vecquant8matmul_batched_faster_old_cuda(
+  torch::Tensor vec,
+  torch::Tensor mat,
+  torch::Tensor mul,
+  torch::Tensor scales,
+  torch::Tensor zeros
+) {
+  int batch = vec.size(0);
+  int heads = vec.size(1);
+  int vec_row = vec.size(2);
+  int vec_height = vec.size(3);
+  int height = mat.size(2);
+  int width = mat.size(3);
+
+  dim3 blocks(
+    (height + BLOCKWIDTH - 1) / BLOCKWIDTH,
+    (width + BLOCKWIDTH - 1) / BLOCKWIDTH
+  );
+  dim3 threads(BLOCKWIDTH);
+
+  VecQuant8BatchMatMulKernel_faster_old<<<blocks, threads>>>(
+    (half*) vec.data_ptr(),
+    (uint8_t*) mat.data_ptr(),
+    (half*) mul.data_ptr(),
+    (half*) scales.data_ptr(),
+    (half*) zeros.data_ptr(),
+    batch, heads, vec_row, vec_height, height, width
+  );
+}
+
+
+__global__ void VecQuant8BatchMatMulKernel_faster_old(
+    const  half* __restrict__ vec,
+    const  uint8_t* __restrict__ mat,
+           half* __restrict__ mul,
+    const  half* __restrict__ scales,
+    const  half* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row,
+    int vec_height,
+    int height,
+    int width
+) {
+ int weight_total = batch * heads * height * width;
+  int input_total = batch * heads * vec_row * vec_height;
+  int out_total = batch * heads * vec_row * width;
+  int tid = threadIdx.x;
+  const int BLOCKWIDTH_half = BLOCKWIDTH/2;
+
+  int h = BLOCKWIDTH * blockIdx.x; //head_dim, dim=-1
+  int w = BLOCKWIDTH * blockIdx.y + tid; //seq-len, +0-256 ,dim=-2
+  /*
+  if (w >= width && tid >= vec_height) {
+    return;
+  }
+  */
+  __shared__ half blockvec[BLOCKWIDTH]; //256
+  int i = width * h + w;
+  int k;
+
+  half w_tmp1 = __float2half(0);
+  half w_tmp2 = __float2half(0);
+
+  half2 weight[BLOCKWIDTH_half];
+  for (int b = 0; b < batch; ++b){
+    for (int head = 0; head < heads; ++head){
+      int batch_shift = b * heads + head;
+      //int zero_index = batch_shift;
+      for (k = 0; k <  BLOCKWIDTH_half; ++k){
+        int w_index1 = batch_shift * height * width + i + (2 * k * width); // [batch,head,h+k, w]
+        int w_index2 = batch_shift * height * width + i + ((2 * k + 1) * width);
+        int zero_index = batch_shift * width + w; // [batch,head, w]
+        if (w_index1 >= weight_total || w >= width || (2 * k + h) >= height) {
+          weight[k] = __float2half2_rn(0);
+        } else {
+            float zero_f=__half2float(zeros[zero_index]);
+            float scale_f= __half2float(scales[zero_index]);
+            if (w_index2 >= weight_total){
+              w_tmp1 = __float2half((as_unsigned(mat[w_index1]) -zero_f)*scale_f);
+              w_tmp2 = __float2half(0);
+              weight[k] = __halves2half2(w_tmp1,w_tmp2);
+              //printf("zero_index is %d w is %d height is %d width is %d w_index1 is %d w_tmp1 is %f w_tmp2 is %f zero is %f scale is %f low is %f high is %f \n ",zero_index,w,height, width,w_index1,__half2float(w_tmp1),__half2float(w_tmp2),zero_f,scale_f,__low2float(weight[k]),__high2float(weight[k]));
+            }else{
+              w_tmp1 = __int2half_rn(as_unsigned(mat[w_index1]));
+              w_tmp2 = __int2half_rn(as_unsigned(mat[w_index2]));
+
+              //weight[k] = __hmul2(__hsub2(__halves2half2(w_tmp1,w_tmp2), __halves2half2(zero,zero)),__halves2half2(scale,scale));
+              weight[k] = __hfma2(__halves2half2(w_tmp1,w_tmp2), __float2half2_rn(scale_f), __float2half2_rn(-(scale_f * zero_f)));
+              //printf("zero_index1 is %d zero_index2 is %d k is %d head is %d w is %d h is %d height is %d width is %d w_index1 is %d w_index2 is %d zero is %f scale is %f low is %f high is %f \n ",zero_index1,zero_index2,k,head,w,h,height, width,w_index1,w_index2,__half2float(zero1),__half2float(scale1),__low2float(weight[k]),__high2float(weight[k]));
+            }
+        }
+      }
+
+
+      for (int vr = 0; vr < vec_row; ++vr){
+        float res=0;
+        int vec_index = (batch_shift * vec_row + vr) * height + blockIdx.x * BLOCKWIDTH + tid;
+        int out_index = (batch_shift * vec_row + vr) * width + w;
+        if (vec_index < input_total) {
+            //blockvec[tid] = __half2float(vec[vec_index]);// [batch, head, vr, tid(seq_len dim+)]
+            blockvec[tid] = vec[vec_index];
+            //printf("width is %d height is %d h is %d w is %d vec_index is %d out_index is %d vec_row is %d vec_height is %d,vr is %d tid is %d blockvec is %f\n",width,height, h,w,vec_index,out_index,vec_row,vec_height,vr,tid,blockvec[tid]);
+        } else {
+            blockvec[tid] = __float2half(0);
+        }
+        __syncthreads();
+        if (out_index < out_total) {
+          for (k = 0; k <  BLOCKWIDTH_half; ++k){
+            half2 res2 = __hmul2(weight[k],__halves2half2(blockvec[2*k],blockvec[2*k+1]));
+            res += __low2float(res2) + __high2float(res2);
+          }
+          atomicAdd(&mul[out_index], __float2half(res));
+        }
+        __syncthreads();
+      }
+    }
+  }
+}
+
+
+void vecquant8matmul_batched_column_compression_faster_old_cuda(
+  torch::Tensor vec,  // [batch,heads, seq_q, seq_v]
+  torch::Tensor mat, // [batch,heads, seq_v, head_dim]
+  torch::Tensor mul,  // [batch,heads, seq_q,head_dim]
+  torch::Tensor scales, // [batch,heads, head_dim]
+  torch::Tensor zeros
+) {
+  int batch = vec.size(0);
+  int heads = vec.size(1);
+  int vec_row = vec.size(2); //ql
+  int height = mat.size(2); //vl
+  int width = mat.size(3); //head_dim
+
+  dim3 blocks(
+    (height + BLOCKWIDTH - 1) / BLOCKWIDTH,
+    (width + BLOCKWIDTH - 1) / BLOCKWIDTH
+  );
+  dim3 threads(BLOCKWIDTH);
+
+  VecQuant8BatchMatMulColumnCompressionKernel_faster_old<<<blocks, threads>>>(
+    (half*) vec.data_ptr(),
+    (uint8_t*) mat.data_ptr(),
+    (half*) mul.data_ptr(),
+    (half*) scales.data_ptr(),
+    (half*) zeros.data_ptr(),
+    batch, heads, vec_row, height, width
+  );
+
+}
+
+
+__global__ void VecQuant8BatchMatMulColumnCompressionKernel_faster_old(
+    const  half* __restrict__ vec,  // [batch,heads, seq_q, seq_v]
+    const  uint8_t* __restrict__ mat, // [batch,heads, seq_v, head_dim]
+           half* __restrict__ mul, // [batch,heads, seq_q,head_dim]
+    const  half* __restrict__ scales, // [batch,heads, seq_v]
+    const  half* __restrict__ zeros,
+    int batch,
+    int heads,
+    int vec_row, //seq_q
+    int height, //seq_v
+    int width //head_dim
+) {
+  int weight_total = batch * heads * height * width;
+  int input_total = batch * heads * vec_row * height;
+  int out_total = batch * heads * vec_row * width;
+  int tid = threadIdx.x;
+  int h = BLOCKWIDTH * blockIdx.x; // vl
+  int w = BLOCKWIDTH * blockIdx.y + tid; //head_dim + block
+  if (w >= width && tid >= height) {
+    return;
+  }
+  __shared__ half blockvec[BLOCKWIDTH];
+  int k;
+  half w_tmp1 = __float2half(0);
+  half w_tmp2 = __float2half(0);
+  int i = width * h + w;
+  const int BLOCKWIDTH_half = BLOCKWIDTH/2;
+  half2 weight[BLOCKWIDTH_half];
+
+  for (int b = 0; b < batch; ++b){
+    for (int head = 0; head < heads; ++head){
+      int batch_shift = b * heads + head;
+      //int zero_index = batch_shift;
+      for (k = 0; k <  BLOCKWIDTH_half; ++k){
+        int w_index1 = batch_shift * height * width + i + (2 * k) * width; // [batch,head, h+k, w]
+        int w_index2 = batch_shift * height * width + i + ((2 * k + 1) * width);
+        int zero_index1 = batch_shift * height + h + 2*k; // [batch,head, w]
+        int zero_index2 = batch_shift * height + h + 2*k+1; // [batch,head, w]
+
+        if (w_index1 >= weight_total || (2 * k + h)>=height) {
+          weight[k]=__float2half2_rn(0);
+        } else{
+            //int zero_index = batch_shift + h; // [batch,head, w]
+            //float scale_f1 = __half2float(scales[zero_index1]);
+            //float zero_f1 =  __half2float(zeros[zero_index1]);
+            if (w_index2>=weight_total){
+              w_tmp1 = __float2half((as_unsigned(mat[w_index1]) - __half2float(zeros[zero_index1]))* __half2float(scales[zero_index1]));
+              w_tmp2 = __float2half(0);
+              weight[k] = __halves2half2(w_tmp1,w_tmp2);
+              //printf("zero_index is %d k is %d w is %d head is %d height is %d width is %d w_index1 is %d w_tmp1 is %f w_tmp2 is %f zero is %f scale is %f low is %f high is %f \n ",zero_index,k,w,head,height, width,w_index1,__half2float(w_tmp1),__half2float(w_tmp2),zero_f,scale_f,__low2float(weight[k]),__high2float(weight[k]));
+            }else{
+              w_tmp1 = __int2half_rn(as_unsigned(mat[w_index1]));
+              w_tmp2 = __int2half_rn(as_unsigned(mat[w_index2]));
+              half zero1=zeros[zero_index1];
+              half zero2=zeros[zero_index2];
+              half scale1=scales[zero_index1];
+              half scale2=scales[zero_index2];
+              weight[k] = __hmul2(__hsub2(__halves2half2(w_tmp1,w_tmp2), __halves2half2(zero1,zero2)),__halves2half2(scale1,scale2));
+              //weight[k] = __hfma2(__halves2half2(w_tmp1,w_tmp2), __float2half2_rn(scale_f), __float2half2_rn(-(scale_f * zero_f)));
+              //printf("zero_index1 is %d zero_index2 is %d k is %d head is %d w is %d h is %d height is %d width is %d w_index1 is %d w_index2 is %d zero is %f scale is %f low is %f high is %f \n ",zero_index1,zero_index2,k,head,w,h,height, width,w_index1,w_index2,__half2float(zero1),__half2float(scale1),__low2float(weight[k]),__high2float(weight[k]));
+            }
+          }
+       }
+
+
+      for (int vr = 0; vr < vec_row; ++vr){
+        float res=0;
+        int vec_index = (batch_shift * vec_row + vr) * height + blockIdx.x * BLOCKWIDTH + tid;
+        int out_index = (batch_shift * vec_row + vr) * width + w;
+
+        if (vec_index < input_total) {
+            //blockvec[tid] = __half2float(vec[vec_index]);
+            blockvec[tid] = vec[vec_index];
+            //printf("vec_index is %d out_index is %d vec_row is %d ,vr is %d tid is %d blockvec is %f\n",vec_index,out_index,vec_row,vr,tid,blockvec[tid]);
+        } else {
+            blockvec[tid] = __float2half(0);
+            //blockvec[tid] = 0;
+        }
+        __syncthreads();
+        if (out_index < out_total) {
+            for (k = 0; k <  BLOCKWIDTH_half; ++k){
+                half2 res2 = __hmul2(weight[k],__halves2half2(blockvec[2*k],blockvec[2*k+1]));
+                res += __low2float(res2) + __high2float(res2);
+            }
+            atomicAdd(&mul[out_index], __float2half(res));
+        }
+        __syncthreads();
+      }
+    }
+  }
+}

Qwen/Qwen-1_8B-Chat/config.json

@@ -0,0 +1,37 @@
+{
+  "architectures": [
+    "QWenLMHeadModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_qwen.QWenConfig",
+    "AutoModelForCausalLM": "modeling_qwen.QWenLMHeadModel"
+  },
+  "attn_dropout_prob": 0.0,
+  "bf16": false,
+  "emb_dropout_prob": 0.0,
+  "fp16": false,
+  "fp32": false,
+  "hidden_size": 2048,
+  "intermediate_size": 11008,
+  "initializer_range": 0.02,
+  "kv_channels": 128,
+  "layer_norm_epsilon": 1e-06,
+  "max_position_embeddings": 8192,
+  "model_type": "qwen",
+  "no_bias": true,
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "onnx_safe": null,
+  "rotary_emb_base": 10000,
+  "rotary_pct": 1.0,
+  "scale_attn_weights": true,
+  "seq_length": 8192,
+  "tie_word_embeddings": false,
+  "tokenizer_class": "QWenTokenizer",
+  "transformers_version": "4.32.0",
+  "use_cache": true,
+  "use_dynamic_ntk": true,
+  "use_flash_attn": "auto",
+  "use_logn_attn": true,
+  "vocab_size": 151936
+}

Qwen/Qwen-1_8B-Chat/configuration_qwen.py

@@ -0,0 +1,71 @@
+# Copyright (c) Alibaba Cloud.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from transformers import PretrainedConfig
+
+
+class QWenConfig(PretrainedConfig):
+    model_type = "qwen"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=151936,
+        hidden_size=4096,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        emb_dropout_prob=0.0,
+        attn_dropout_prob=0.0,
+        layer_norm_epsilon=1e-6,
+        initializer_range=0.02,
+        max_position_embeddings=8192,
+        scale_attn_weights=True,
+        use_cache=True,
+        bf16=False,
+        fp16=False,
+        fp32=False,
+        kv_channels=128,
+        rotary_pct=1.0,
+        rotary_emb_base=10000,
+        use_dynamic_ntk=True,
+        use_logn_attn=True,
+        use_flash_attn="auto",
+        intermediate_size=22016,
+        no_bias=True,
+        tie_word_embeddings=False,
+        use_cache_quantization=False,
+        use_cache_kernel=False,
+        softmax_in_fp32=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        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.emb_dropout_prob = emb_dropout_prob
+        self.attn_dropout_prob = attn_dropout_prob
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_range = initializer_range
+        self.scale_attn_weights = scale_attn_weights
+        self.use_cache = use_cache
+        self.max_position_embeddings = max_position_embeddings
+        self.bf16 = bf16
+        self.fp16 = fp16
+        self.fp32 = fp32
+        self.kv_channels = kv_channels
+        self.rotary_pct = rotary_pct
+        self.rotary_emb_base = rotary_emb_base
+        self.use_dynamic_ntk = use_dynamic_ntk
+        self.use_logn_attn = use_logn_attn
+        self.use_flash_attn = use_flash_attn
+        self.no_bias = no_bias
+        self.use_cache_quantization = use_cache_quantization
+        self.use_cache_kernel = use_cache_kernel
+        self.softmax_in_fp32 = softmax_in_fp32
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs
+        )

Qwen/Qwen-1_8B-Chat/cpp_kernels.py

@@ -0,0 +1,55 @@
+from torch.utils import cpp_extension
+import pathlib
+import os
+import subprocess
+
+def _get_cuda_bare_metal_version(cuda_dir):
+    raw_output = subprocess.check_output([cuda_dir + "/bin/nvcc", "-V"],
+                                         universal_newlines=True)
+    output = raw_output.split()
+    release_idx = output.index("release") + 1
+    release = output[release_idx].split(".")
+    bare_metal_major = release[0]
+    bare_metal_minor = release[1][0]
+
+    return raw_output, bare_metal_major, bare_metal_minor
+
+def _create_build_dir(buildpath):
+    try:
+        os.mkdir(buildpath)
+    except OSError:
+        if not os.path.isdir(buildpath):
+            print(f"Creation of the build directory {buildpath} failed")
+
+# Check if cuda 11 is installed for compute capability 8.0
+cc_flag = []
+_, bare_metal_major, bare_metal_minor = _get_cuda_bare_metal_version(cpp_extension.CUDA_HOME)
+if int(bare_metal_major) >= 11:
+    cc_flag.append('-gencode')
+    cc_flag.append('arch=compute_80,code=sm_80')
+    if int(bare_metal_minor) >= 7:
+        cc_flag.append('-gencode')
+        cc_flag.append('arch=compute_90,code=sm_90')
+
+# Build path
+srcpath = pathlib.Path(__file__).parent.absolute()
+buildpath = srcpath / 'build'
+_create_build_dir(buildpath)
+
+def _cpp_extention_load_helper(name, sources, extra_cuda_flags):
+    return cpp_extension.load(
+        name=name,
+        sources=sources,
+        build_directory=buildpath,
+        extra_cflags=['-O3', ],
+        extra_cuda_cflags=['-O3',
+                           '-gencode', 'arch=compute_70,code=sm_70',
+                           '--use_fast_math'] + extra_cuda_flags + cc_flag,
+        verbose=1
+    )
+
+extra_flags = []
+
+cache_autogptq_cuda_256_sources = ["./cache_autogptq_cuda_256.cpp",
+           "./cache_autogptq_cuda_kernel_256.cu"]
+cache_autogptq_cuda_256 = _cpp_extention_load_helper("cache_autogptq_cuda_256", cache_autogptq_cuda_256_sources, extra_flags)

Qwen/Qwen-1_8B-Chat/examples/react_prompt.md

@@ -0,0 +1,249 @@
+# ReAct Prompting 示例
+
+本文档将介绍如何用 ReAct Prompting 技术命令千问使用工具。
+
+本文档主要基本的原理概念介绍，并在文末附上了一些具体实现相关的 FAQ，但不含被调用插件的实际实现。如果您更喜欢一边调试实际可执行的代码、一边理解原理，可以转而阅读整合了 LangChain 常用工具的这个 [ipython notebook](https://github.com/QwenLM/Qwen-7B/blob/main/examples/langchain_tooluse.ipynb)。
+
+此外，本文档和前述的 ipython notebook 都仅介绍单轮对话的实现。如果想了解多轮对话下的实现，可参见 [react_demo.py](https://github.com/QwenLM/Qwen-7B/blob/main/examples/react_demo.py)。
+
+## 准备工作一：样例问题、样例工具
+
+假设我们有如下的一个适合用工具处理的 query，以及有夸克搜索、通义万相文生图这两个工具：
+
+```py
+query = '现在给我画个五彩斑斓的黑。'
+
+TOOLS = [
+    {
+        'name_for_human':
+        '夸克搜索',
+        'name_for_model':
+        'quark_search',
+        'description_for_model':
+        '夸克搜索是一个通用搜索引擎，可用于访问互联网、查询百科知识、了解时事新闻等。',
+        'parameters': [{
+            'name': 'search_query',
+            'description': '搜索关键词或短语',
+            'required': True,
+            'schema': {
+                'type': 'string'
+            },
+        }],
+    },
+    {
+        'name_for_human':
+        '通义万相',
+        'name_for_model':
+        'image_gen',
+        'description_for_model':
+        '通义万相是一个AI绘画（图像生成）服务，输入文本描述，返回根据文本作画得到的图片的URL',
+        'parameters': [{
+            'name': 'query',
+            'description': '中文关键词，描述了希望图像具有什么内容',
+            'required': True,
+            'schema': {
+                'type': 'string'
+            },
+        }],
+    },
+]
+```
+
+## 准备工作二：ReAct 模版
+
+我们将使用如下的 ReAct prompt 模版来激发千问使用工具的能力。
+
+```py
+TOOL_DESC = """{name_for_model}: Call this tool to interact with the {name_for_human} API. What is the {name_for_human} API useful for? {description_for_model} Parameters: {parameters} Format the arguments as a JSON object."""
+
+REACT_PROMPT = """Answer the following questions as best you can. You have access to the following tools:
+
+{tool_descs}
+
+Use the following format:
+
+Question: the input question you must answer
+Thought: you should always think about what to do
+Action: the action to take, should be one of [{tool_names}]
+Action Input: the input to the action
+Observation: the result of the action
+... (this Thought/Action/Action Input/Observation can be repeated zero or more times)
+Thought: I now know the final answer
+Final Answer: the final answer to the original input question
+
+Begin!
+
+Question: {query}"""
+```
+
+## 步骤一：让千问判断要调用什么工具、生成工具入参
+
+首先我们需要根据 ReAct prompt 模版、query、工具的信息构建 prompt：
+
+```py
+tool_descs = []
+tool_names = []
+for info in TOOLS:
+    tool_descs.append(
+        TOOL_DESC.format(
+            name_for_model=info['name_for_model'],
+            name_for_human=info['name_for_human'],
+            description_for_model=info['description_for_model'],
+            parameters=json.dumps(
+                info['parameters'], ensure_ascii=False),
+        )
+    )
+    tool_names.append(info['name_for_model'])
+tool_descs = '\n\n'.join(tool_descs)
+tool_names = ','.join(tool_names)
+
+prompt = REACT_PROMPT.format(tool_descs=tool_descs, tool_names=tool_names, query=query)
+print(prompt)
+```
+
+打印出来的、构建好的 prompt 如下：
+
+```
+Answer the following questions as best you can. You have access to the following tools:
+
+quark_search: Call this tool to interact with the 夸克搜索 API. What is the 夸克搜索 API useful for? 夸克搜索是一个通用搜索引擎，可用于访问互联网、查询百科知识、了解时事新闻等。 Parameters: [{"name": "search_query", "description": "搜索关键词或短语", "required": true, "schema": {"type": "string"}}] Format the arguments as a JSON object.
+
+image_gen: Call this tool to interact with the 通义万相 API. What is the 通义万相 API useful for? 通义万相是一个AI绘画（图像生成）服务，输入文本描述，返回根据文本作画得到的图片的URL Parameters: [{"name": "query", "description": "中文关键词，描述了希望图像具有什么内容", "required": true, "schema": {"type": "string"}}] Format the arguments as a JSON object.
+
+Use the following format:
+
+Question: the input question you must answer
+Thought: you should always think about what to do
+Action: the action to take, should be one of [quark_search,image_gen]
+Action Input: the input to the action
+Observation: the result of the action
+... (this Thought/Action/Action Input/Observation can be repeated zero or more times)
+Thought: I now know the final answer
+Final Answer: the final answer to the original input question
+
+Begin!
+
+Question: 现在给我画个五彩斑斓的黑。
+```
+
+将这个 prompt 送入千问，并记得设置 "Observation" 为 stop word （见本文末尾的 FAQ）—— 即让千问在预测到要生成的下一个词是 "Observation" 时马上停止生成 —— 则千问在得到这个 prompt 后会生成如下的结果：
+
+![](../assets/react_tutorial_001.png)
+
+```
+Thought: 我应该使用通义万相API来生成一张五彩斑斓的黑的图片。
+Action: image_gen
+Action Input: {"query": "五彩斑斓的黑"}
+```
+
+在得到这个结果后，调用千问的开发者可以通过简单的解析提取出 `{"query": "五彩斑斓的黑"}` 并基于这个解析结果调用文生图服务 —— 这部分逻辑需要开发者自行实现，或者也可以使用千问商业版，商业版本将内部集成相关逻辑。
+
+## 步骤二：让千问根据插件返回结果继续作答
+
+让我们假设文生图插件返回了如下结果：
+
+```
+{"status_code": 200, "request_id": "3d894da2-0e26-9b7c-bd90-102e5250ae03", "code": null, "message": "", "output": {"task_id": "2befaa09-a8b3-4740-ada9-4d00c2758b05", "task_status": "SUCCEEDED", "results": [{"url": "https://dashscope-result-sh.oss-cn-shanghai.aliyuncs.com/1e5e2015/20230801/1509/6b26bb83-469e-4c70-bff4-a9edd1e584f3-1.png"}], "task_metrics": {"TOTAL": 1, "SUCCEEDED": 1, "FAILED": 0}}, "usage": {"image_count": 1}}
+```
+
+![](../assets/wanx_colorful_black.png)
+
+接下来，我们可以将之前首次请求千问时用的 prompt 和 调用文生图插件的结果拼接成如下的新 prompt：
+
+```
+Answer the following questions as best you can. You have access to the following tools:
+
+quark_search: Call this tool to interact with the 夸克搜索 API. What is the 夸克搜索 API useful for? 夸克搜索是一个通用搜索引擎，可用于访问互联网、查询百科知识、了解时事新闻等。 Parameters: [{"name": "search_query", "description": "搜索关键词或短语", "required": true, "schema": {"type": "string"}}] Format the arguments as a JSON object.
+
+image_gen: Call this tool to interact with the 通义万相 API. What is the 通义万相 API useful for? 通义万相是一个AI绘画（图像生成）服务，输入文本描述，返回根据文本作画得到的图片的URL Parameters: [{"name": "query", "description": "中文关键词，描述了希望图像具有什么内容", "required": true, "schema": {"type": "string"}}] Format the arguments as a JSON object.
+
+Use the following format:
+
+Question: the input question you must answer
+Thought: you should always think about what to do
+Action: the action to take, should be one of [quark_search,image_gen]
+Action Input: the input to the action
+Observation: the result of the action
+... (this Thought/Action/Action Input/Observation can be repeated zero or more times)
+Thought: I now know the final answer
+Final Answer: the final answer to the original input question
+
+Begin!
+
+Question: 现在给我画个五彩斑斓的黑。
+Thought: 我应该使用通义万相API来生成一张五彩斑斓的黑的图片。
+Action: image_gen
+Action Input: {"query": "五彩斑斓的黑"}
+Observation: {"status_code": 200, "request_id": "3d894da2-0e26-9b7c-bd90-102e5250ae03", "code": null, "message": "", "output": {"task_id": "2befaa09-a8b3-4740-ada9-4d00c2758b05", "task_status": "SUCCEEDED", "results": [{"url": "https://dashscope-result-sh.oss-cn-shanghai.aliyuncs.com/1e5e2015/20230801/1509/6b26bb83-469e-4c70-bff4-a9edd1e584f3-1.png"}], "task_metrics": {"TOTAL": 1, "SUCCEEDED": 1, "FAILED": 0}}, "usage": {"image_count": 1}}
+```
+
+用这个新的拼接了文生图插件结果的新 prompt 去调用千问，将得到如下的最终回复：
+
+![](../assets/react_tutorial_002.png)
+
+```
+Thought: 我已经成功使用通义万相API生成了一张五彩斑斓的黑的图片。
+Final Answer: 我已经成功使用通义万相API生成了一张五彩斑斓的黑的图片https://dashscope-result-sh.oss-cn-shanghai.aliyuncs.com/1e5e2015/20230801/1509/6b26bb83-469e-4c70-bff4-a9edd1e584f3-1.png。
+```
+
+虽然对于文生图来说，这个第二次调用千问的步骤显得多余。但是对于搜索插件、代码执行插件、计算器插件等别的插件来说，这个第二次调用千问的步骤给了千问提炼、总结插件返回结果的机会。
+
+## FAQ
+
+**怎么配置 "Observation" 这个 stop word？**
+
+通过 chat 接口的 stop_words_ids 指定：
+```py
+react_stop_words = [
+    # tokenizer.encode('Observation'),  # [37763, 367]
+    tokenizer.encode('Observation:'),  # [37763, 367, 25]
+    tokenizer.encode('Observation:\n'),  # [37763, 367, 510]
+]
+response, history = model.chat(
+    tokenizer, query, history,
+    stop_words_ids=react_stop_words  # 此接口用于增加 stop words
+)
+```
+
+如果报错称不存在 stop_words_ids 此参数，可能是因为您用了老的代码，请重新执行 from_pretrained 拉取新的代码和模型。
+
+需要注意的是，当前的 tokenizer 对 `\n` 有一系列较复杂的聚合操作。比如例子中的`:\n`这两个字符便被聚合成了一个 token。因此配置 stop words 需要非常细致地预估 tokenizer 的行为。
+
+**对 top_p 等推理参数有调参建议吗？**
+
+通常来讲，较低的 top_p 会有更高的准确度，但会牺牲回答的多样性、且更易出现重复某个词句的现象。
+
+可以按如下方式调整 top_p 为 0.5：
+```py
+model.generation_config.top_p = 0.5
+```
+
+特别的，可以用如下方式关闭 top-p sampling，改用 greedy sampling，效果上相当于 top_p=0 或 temperature=0：
+```py
+model.generation_config.do_sample = False  # greedy decoding
+```
+
+此外，我们在 `model.chat()` 接口也提供了调整 top_p 等参数的接口。
+
+**有解析Action、Action Input的参考代码吗？**
+
+有的，可以参考：
+```py
+def parse_latest_plugin_call(text: str) -> Tuple[str, str]:
+    i = text.rfind('\nAction:')
+    j = text.rfind('\nAction Input:')
+    k = text.rfind('\nObservation:')
+    if 0 <= i < j:  # If the text has `Action` and `Action input`,
+        if k < j:  # but does not contain `Observation`,
+            # then it is likely that `Observation` is ommited by the LLM,
+            # because the output text may have discarded the stop word.
+            text = text.rstrip() + '\nObservation:'  # Add it back.
+            k = text.rfind('\nObservation:')
+    if 0 <= i < j < k:
+        plugin_name = text[i + len('\nAction:'):j].strip()
+        plugin_args = text[j + len('\nAction Input:'):k].strip()
+        return plugin_name, plugin_args
+    return '', ''
+```
+
+此外，如果输出的 Action Input 内容是一段表示 JSON 对象的文本，我们建议使用 `json5` 包的 `json5.loads(...)` 方法加载。

Qwen/Qwen-1_8B-Chat/generation_config.json

@@ -0,0 +1,12 @@
+{
+  "chat_format": "chatml",
+  "eos_token_id": 151643,
+  "pad_token_id": 151643,
+  "max_window_size": 6144,
+  "max_new_tokens": 512,
+  "do_sample": true,
+  "top_k": 0,
+  "top_p": 0.8,
+  "repetition_penalty": 1.1,
+  "transformers_version": "4.31.0"
+}

Qwen/Qwen-1_8B-Chat/model-00001-of-00002.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:afe102dfe02cbf973a5647a4ccef97dad0f088ae12a749e67000f25af3c6c997
+size 2039259008

Qwen/Qwen-1_8B-Chat/model-00002-of-00002.safetensors

@@ -0,0 +1,3 @@
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+oid sha256:f710454106cacb0bee8138866573f1ecd453c3cd55f122b74e70a0e392d21435
+size 1634419264

Qwen/Qwen-1_8B-Chat/model.safetensors.index.json

@@ -0,0 +1,202 @@
+{
+  "metadata": {
+    "total_size": 3673657344
+  },
+  "weight_map": {
+    "lm_head.weight": "model-00002-of-00002.safetensors",
+    "transformer.h.0.attn.c_attn.bias": "model-00001-of-00002.safetensors",
+    "transformer.h.0.attn.c_attn.weight": "model-00001-of-00002.safetensors",
+    "transformer.h.0.attn.c_proj.weight": "model-00001-of-00002.safetensors",
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Qwen/Qwen-1_8B-Chat/modeling_qwen.py

@@ -0,0 +1,1363 @@
+# Copyright (c) Alibaba Cloud.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import copy
+import importlib
+import math
+import pathlib
+from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List, Any, Generator
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+import warnings
+
+from torch.nn import CrossEntropyLoss
+from transformers import PreTrainedTokenizer, GenerationConfig, StoppingCriteriaList
+from transformers.generation.logits_process import LogitsProcessorList
+
+if TYPE_CHECKING:
+    from transformers.generation.streamers import BaseStreamer
+from transformers.generation.utils import GenerateOutput
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+
+try:
+    from einops import rearrange
+except ImportError:
+    rearrange = None
+from torch import nn
+
+SUPPORT_CUDA = torch.cuda.is_available()
+SUPPORT_BF16 = SUPPORT_CUDA and torch.cuda.is_bf16_supported()
+SUPPORT_FP16 = SUPPORT_CUDA and torch.cuda.get_device_capability(0)[0] >= 7
+SUPPORT_TORCH2 = hasattr(torch, '__version__') and int(torch.__version__.split(".")[0]) >= 2
+
+
+from .configuration_qwen import QWenConfig
+from .qwen_generation_utils import (
+    HistoryType,
+    make_context,
+    decode_tokens,
+    get_stop_words_ids,
+    StopWordsLogitsProcessor,
+)
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "qwen"
+_CONFIG_FOR_DOC = "QWenConfig"
+
+QWen_PRETRAINED_MODEL_ARCHIVE_LIST = ["qwen-7b"]
+
+_ERROR_BAD_CHAT_FORMAT = """\
+We detect you are probably using the pretrained model (rather than chat model) for chatting, since the chat_format in generation_config is not "chatml".
+If you are directly using the model downloaded from Huggingface, please make sure you are using our "Qwen/Qwen-7B-Chat" Huggingface model (rather than "Qwen/Qwen-7B") when you call model.chat().
+我们检测到您可能在使用预训练模型（而非chat模型）进行多轮chat，因为您当前在generation_config指定的chat_format，并未设置为我们在对话中所支持的"chatml"格式。
+如果您在直接使用我们从Huggingface提供的模型，请确保您在调用model.chat()时，使用的是"Qwen/Qwen-7B-Chat"模型（而非"Qwen/Qwen-7B"预训练模型）。
+"""
+
+_SENTINEL = object()
+_ERROR_STREAM_IN_CHAT = """\
+Pass argument `stream` to model.chat() is buggy, deprecated, and marked for removal. Please use model.chat_stream(...) instead of model.chat(..., stream=True).
+向model.chat()传入参数stream的用法可能存在Bug，该用法已被废弃，将在未来被移除。请使用model.chat_stream(...)代替model.chat(..., stream=True)。
+"""
+
+_ERROR_INPUT_CPU_QUERY_WITH_FLASH_ATTN_ACTIVATED = """\
+We detect you have activated flash attention support, but running model computation on CPU. Please make sure that your input data has been placed on GPU. If you actually want to run CPU computation, please following the readme and set device_map="cpu" to disable flash attention when loading the model (calling AutoModelForCausalLM.from_pretrained).
+检测到您的模型已激活了flash attention支持，但正在执行CPU运算任务。如使用flash attention，请您确认模型输入已经传到GPU上。如果您确认要执行CPU运算，请您在载入模型（调用AutoModelForCausalLM.from_pretrained）时，按照readme说法，指定device_map="cpu"以禁用flash attention。
+"""
+
+apply_rotary_emb_func = None
+rms_norm = None
+flash_attn_unpadded_func = None
+flash_attn_func = None
+
+def _import_flash_attn():
+    global apply_rotary_emb_func, rms_norm, flash_attn_unpadded_func, flash_attn_func
+    try:
+        from flash_attn.layers.rotary import apply_rotary_emb_func as __apply_rotary_emb_func
+        apply_rotary_emb_func = __apply_rotary_emb_func
+    except ImportError:
+        logger.warn(
+            "Warning: import flash_attn rotary fail, please install FlashAttention rotary to get higher efficiency "
+            "https://github.com/Dao-AILab/flash-attention/tree/main/csrc/rotary"
+        )
+
+    try:
+        from flash_attn.ops.rms_norm import rms_norm as __rms_norm
+        rms_norm = __rms_norm
+    except ImportError:
+        logger.warn(
+            "Warning: import flash_attn rms_norm fail, please install FlashAttention layer_norm to get higher efficiency "
+            "https://github.com/Dao-AILab/flash-attention/tree/main/csrc/layer_norm"
+        )
+
+    try:
+        import flash_attn
+        _flash_attn_func = None
+        if not hasattr(flash_attn, '__version__'):
+            from flash_attn.flash_attn_interface import flash_attn_unpadded_func as __flash_attn_unpadded_func
+        else:
+            if int(flash_attn.__version__.split(".")[0]) >= 2:
+                if int(flash_attn.__version__.split(".")[1]) >= 1:
+                    from flash_attn.flash_attn_interface import flash_attn_func as _flash_attn_func
+                from flash_attn.flash_attn_interface import flash_attn_varlen_func as __flash_attn_unpadded_func
+            else:
+                from flash_attn.flash_attn_interface import flash_attn_unpadded_func as __flash_attn_unpadded_func
+        flash_attn_unpadded_func = __flash_attn_unpadded_func
+        flash_attn_func = _flash_attn_func
+    except ImportError:
+        logger.warn(
+            "Warning: import flash_attn fail, please install FlashAttention to get higher efficiency "
+            "https://github.com/Dao-AILab/flash-attention"
+        )
+
+def quantize_cache_v(fdata, bits, qmax, qmin):
+    # b, s, head, h-dim->b, head, s, h-dim
+    qtype = torch.uint8
+    device = fdata.device
+    shape = fdata.shape
+
+    fdata_cal = torch.flatten(fdata, 2)
+    fmax = torch.amax(fdata_cal, dim=-1, keepdim=True)
+    fmin = torch.amin(fdata_cal, dim=-1, keepdim=True)
+    # Compute params
+    if qmax.device != fmax.device:
+        qmax = qmax.to(device)
+        qmin = qmin.to(device)
+    scale = (fmax - fmin) / (qmax - qmin)
+    zero = qmin - fmin / scale
+    scale = scale.unsqueeze(-1).repeat(1,1,shape[2],1).contiguous()
+    zero = zero.unsqueeze(-1).repeat(1,1,shape[2],1).contiguous()
+    # Quantize
+    res_data = fdata / scale + zero
+    qdata = torch.clamp(res_data, qmin, qmax).to(qtype)
+    return qdata.contiguous(), scale, zero
+
+def dequantize_cache_torch(qdata, scale, zero):
+    data = scale * (qdata - zero)
+    return data
+
+class FlashSelfAttention(torch.nn.Module):
+    def __init__(
+        self,
+        causal=False,
+        softmax_scale=None,
+        attention_dropout=0.0,
+    ):
+        super().__init__()
+        assert flash_attn_unpadded_func is not None, (
+            "Please install FlashAttention first, " "e.g., with pip install flash-attn"
+        )
+        assert (
+            rearrange is not None
+        ), "Please install einops first, e.g., with pip install einops"
+        self.causal = causal
+        self.softmax_scale = softmax_scale
+        self.dropout_p = attention_dropout
+
+    def unpad_input(self, hidden_states, attention_mask):
+        valid_mask = attention_mask.squeeze(1).squeeze(1).eq(0)
+        seqlens_in_batch = valid_mask.sum(dim=-1, dtype=torch.int32)
+        indices = torch.nonzero(valid_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))
+        hidden_states = hidden_states[indices]
+        return hidden_states, indices, cu_seqlens, max_seqlen_in_batch
+
+    def pad_input(self, hidden_states, indices, batch, seqlen):
+        output = torch.zeros(batch * seqlen, *hidden_states.shape[1:], device=hidden_states.device,
+                             dtype=hidden_states.dtype)
+        output[indices] = hidden_states
+        return rearrange(output, '(b s) ... -> b s ...', b=batch)
+
+    def forward(self, q, k, v, attention_mask=None):
+        assert all((i.dtype in [torch.float16, torch.bfloat16] for i in (q, k, v)))
+        assert all((i.is_cuda for i in (q, k, v)))
+        batch_size, seqlen_q = q.shape[0], q.shape[1]
+        seqlen_k = k.shape[1]
+        seqlen_out = seqlen_q
+
+        if flash_attn_func is not None and batch_size == 1:
+            dropout_p = self.dropout_p if self.training else 0
+            output = flash_attn_func(q, k, v, dropout_p, softmax_scale=self.softmax_scale, causal=self.causal)
+            return output
+
+        q, k, v = [rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v]]
+        cu_seqlens_q = torch.arange(
+            0,
+            (batch_size + 1) * seqlen_q,
+            step=seqlen_q,
+            dtype=torch.int32,
+            device=q.device,
+        )
+
+        if batch_size > 1 and attention_mask is not None:
+            k, indices_k, cu_seqlens_k, seqlen_k = self.unpad_input(k, attention_mask)
+            if q.size(0) == v.size(0):
+                q = q[indices_k]
+                cu_seqlens_q = cu_seqlens_k
+                seqlen_q = seqlen_k
+            v = v[indices_k]
+        else:
+            cu_seqlens_k = torch.arange(
+                0,
+                (batch_size + 1) * seqlen_k,
+                step=seqlen_k,
+                dtype=torch.int32,
+                device=q.device,
+            )
+
+        if self.training:
+            assert seqlen_k == seqlen_q
+            is_causal = self.causal
+            dropout_p = self.dropout_p
+        else:
+            is_causal = seqlen_q == seqlen_k
+            dropout_p = 0
+
+        output = flash_attn_unpadded_func(
+            q,
+            k,
+            v,
+            cu_seqlens_q,
+            cu_seqlens_k,
+            seqlen_q,
+            seqlen_k,
+            dropout_p,
+            softmax_scale=self.softmax_scale,
+            causal=is_causal,
+        )
+        if batch_size > 1 and attention_mask is not None and seqlen_q == seqlen_k:
+            output = self.pad_input(output, indices_k, batch_size, seqlen_out)
+        else:
+            new_shape = (batch_size, output.shape[0] // batch_size) + output.shape[1:]
+            output = output.view(new_shape)
+        return output
+
+
+class QWenAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+
+        self.register_buffer("masked_bias", torch.tensor(-1e4), persistent=False)
+        self.seq_length = config.seq_length
+
+        self.hidden_size = config.hidden_size
+        self.split_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+
+        self.use_flash_attn = config.use_flash_attn
+        self.scale_attn_weights = True
+
+        self.projection_size = config.kv_channels * config.num_attention_heads
+
+        assert self.projection_size % config.num_attention_heads == 0
+        self.hidden_size_per_attention_head = (
+            self.projection_size // config.num_attention_heads
+        )
+
+        self.c_attn = nn.Linear(config.hidden_size, 3 * self.projection_size)
+
+        self.c_proj = nn.Linear(
+            config.hidden_size, self.projection_size, bias=not config.no_bias
+        )
+
+        self.is_fp32 = not (config.bf16 or config.fp16)
+        if (
+            self.use_flash_attn
+            and flash_attn_unpadded_func is not None
+            and not self.is_fp32
+        ):
+            self.core_attention_flash = FlashSelfAttention(
+                causal=True, attention_dropout=config.attn_dropout_prob
+            )
+        self.bf16 = config.bf16
+
+        self.use_dynamic_ntk = config.use_dynamic_ntk
+        self.use_logn_attn = config.use_logn_attn
+
+        logn_list = [
+            math.log(i, self.seq_length) if i > self.seq_length else 1
+            for i in range(1, 32768)
+        ]
+        logn_tensor = torch.tensor(logn_list)[None, :, None, None]
+        self.register_buffer("logn_tensor", logn_tensor, persistent=False)
+
+        self.attn_dropout = nn.Dropout(config.attn_dropout_prob)
+        self.softmax_in_fp32 = config.softmax_in_fp32 if hasattr(config, 'softmax_in_fp32') else False
+        self.use_cache_quantization = config.use_cache_quantization if hasattr(config, 'use_cache_quantization') else False
+        self.use_cache_kernel = config.use_cache_kernel if hasattr(config,'use_cache_kernel') else False
+        cache_dtype = torch.float
+        if self.bf16:
+            cache_dtype=torch.bfloat16
+        elif config.fp16:
+            cache_dtype = torch.float16
+        self.cache_qmax = torch.tensor(torch.iinfo(torch.uint8).max, dtype=cache_dtype)
+        self.cache_qmin = torch.tensor(torch.iinfo(torch.uint8).min, dtype=cache_dtype)
+
+        if config.use_cache_quantization and config.use_cache_kernel:
+            # pre check if the support files existing
+            module_root = pathlib.Path(__file__).parent
+            src_files = ("cache_autogptq_cuda_256.cpp", "cache_autogptq_cuda_kernel_256.cu")
+            if any(not (module_root/src).is_file() for src in src_files):
+                warnings.warn("KV cache kernel source files (.cpp and .cu) not found.")
+                self.cache_kernels = None
+            else:
+                try:
+                    from .cpp_kernels import cache_autogptq_cuda_256
+                    self.cache_kernels = cache_autogptq_cuda_256
+                except ImportError:
+                    warnings.warn("Failed to import KV cache kernels.")
+                    self.cache_kernels = None
+
+    def _attn(self, query, key, value, causal_mask=None, attention_mask=None, head_mask=None):
+        device = query.device
+        if self.use_cache_quantization:
+            qk, qk_scale, qk_zero = key
+            if self.use_cache_kernel and self.cache_kernels is not None:
+                shape = query.shape[:-1] + (qk.shape[-2],)
+                attn_weights = torch.zeros(shape, dtype=torch.float16, device=device)
+                self.cache_kernels.vecquant8matmul_batched_faster_old(
+                    query.contiguous() if query.dtype == torch.float16 else query.to(torch.float16).contiguous(),
+                    qk.transpose(-1, -2).contiguous(),
+                    attn_weights,
+                    qk_scale.contiguous() if qk_scale.dtype == torch.float16 else qk_scale.to(torch.float16).contiguous(),
+                    qk_zero.contiguous()if qk_zero.dtype == torch.float16 else qk_zero.to(torch.float16).contiguous())
+                # attn_weights = attn_weights.to(query.dtype).contiguous()
+            else:
+                key = dequantize_cache_torch(qk, qk_scale, qk_zero)
+                attn_weights = torch.matmul(query, key.transpose(-1, -2))
+        else:
+            attn_weights = torch.matmul(query, key.transpose(-1, -2))
+
+        if self.scale_attn_weights:
+            if self.use_cache_quantization:
+                size_temp = value[0].size(-1)
+            else:
+                size_temp = value.size(-1)
+            attn_weights = attn_weights / (size_temp ** 0.5)
+
+        mask_value = torch.finfo(attn_weights.dtype).min
+        if causal_mask is not None:
+            attn_weights = torch.where(
+                causal_mask, attn_weights.to(attn_weights.dtype), mask_value
+            )
+
+        if attention_mask is not None:
+            attn_weights = attn_weights + attention_mask
+
+        if self.softmax_in_fp32:
+            attn_weights = nn.functional.softmax(attn_weights.float(), dim=-1)
+        else:
+            attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+
+        attn_weights = attn_weights.type(query.dtype)
+        attn_weights = self.attn_dropout(attn_weights)
+
+        if head_mask is not None:
+            attn_weights = attn_weights * head_mask
+
+        if self.use_cache_quantization:
+            qv, qv_scale, qv_zero = value
+            if self.use_cache_kernel and self.cache_kernels is not None:
+                shape = attn_weights.shape[:-1] + (query.shape[-1],)
+                attn_output = torch.zeros(shape, dtype=torch.float16, device=device)
+                self.cache_kernels.vecquant8matmul_batched_column_compression_faster_old(
+                    attn_weights.contiguous() if attn_weights.dtype == torch.float16 else attn_weights.to(torch.float16).contiguous(),
+                    qv.contiguous(),  # dtype: int32
+                    attn_output,
+                    qv_scale.contiguous() if qv_scale.dtype == torch.float16 else qv_scale.to(torch.float16).contiguous(),
+                    qv_zero.contiguous() if qv_zero.dtype == torch.float16 else qv_zero.to(torch.float16).contiguous())
+                if attn_output.dtype != query.dtype:
+                    attn_output = attn_output.to(query.dtype)
+                    attn_weights = attn_weights.to(query.dtype)
+            else:
+                value = dequantize_cache_torch(qv, qv_scale, qv_zero)
+                attn_output = torch.matmul(attn_weights, value)
+        else:
+            attn_output = torch.matmul(attn_weights, value)
+
+        attn_output = attn_output.transpose(1, 2)
+
+        return attn_output, attn_weights
+
+    def _split_heads(self, tensor, num_heads, attn_head_size):
+        new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
+        tensor = tensor.view(new_shape)
+        return tensor
+
+    def _merge_heads(self, tensor, num_heads, attn_head_size):
+        tensor = tensor.contiguous()
+        new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
+        return tensor.view(new_shape)
+
+    def forward(
+        self,
+        hidden_states: Optional[Tuple[torch.FloatTensor]],
+        rotary_pos_emb_list: Optional[List[List[torch.Tensor]]] = None,
+        layer_past: Optional[Tuple[torch.Tensor]] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ):
+        mixed_x_layer = self.c_attn(hidden_states)
+
+        query, key, value = mixed_x_layer.split(self.split_size, dim=2)
+
+        query = self._split_heads(query, self.num_heads, self.head_dim)
+        key = self._split_heads(key, self.num_heads, self.head_dim)
+        value = self._split_heads(value, self.num_heads, self.head_dim)
+
+        if rotary_pos_emb_list is not None:
+            cur_len = query.shape[1]
+            if len(rotary_pos_emb_list) == 1:
+                rotary_pos_emb = rotary_pos_emb_list[0]
+                rotary_pos_emb = [i[:, -cur_len:, :, :] for i in rotary_pos_emb]
+                rotary_pos_emb = (rotary_pos_emb,) * 2
+                q_pos_emb, k_pos_emb = rotary_pos_emb
+                # Slice the pos emb for current inference
+                query = apply_rotary_pos_emb(query, q_pos_emb)
+                key = apply_rotary_pos_emb(key, k_pos_emb)
+            else:
+                query_list = []
+                key_list = []
+                for i, rotary_pos_emb in enumerate(rotary_pos_emb_list):
+                    rotary_pos_emb = [i[:, -cur_len:, :, :] for i in rotary_pos_emb]
+                    rotary_pos_emb = (rotary_pos_emb,) * 2
+                    q_pos_emb, k_pos_emb = rotary_pos_emb
+                    # Slice the pos emb for current inference
+                    query_list += [apply_rotary_pos_emb(query[i:i+1, :, :], q_pos_emb)]
+                    key_list += [apply_rotary_pos_emb(key[i:i+1, :, :], k_pos_emb)]
+                query = torch.cat(query_list, dim=0)
+                key = torch.cat(key_list, dim=0)
+
+        if self.use_cache_quantization:
+            key = quantize_cache_v(key.permute(0, 2, 1, 3),
+                                       bits=8,
+                                       qmin=self.cache_qmin,
+                                       qmax=self.cache_qmax)
+            value = quantize_cache_v(value.permute(0, 2, 1, 3),
+                                         bits=8,
+                                         qmin=self.cache_qmin,
+                                         qmax=self.cache_qmax)
+
+
+        if layer_past is not None:
+            past_key, past_value = layer_past[0], layer_past[1]
+            if self.use_cache_quantization:
+                # use_cache_quantization:
+                # present=((q_key,key_scale,key_zero_point),
+                #          (q_value,value_scale,value_zero_point))
+                key = (torch.cat((past_key[0], key[0]), dim=2),
+                       torch.cat((past_key[1], key[1]), dim=2),
+                       torch.cat((past_key[2], key[2]), dim=2))
+                value = (torch.cat((past_value[0], value[0]), dim=2),
+                         torch.cat((past_value[1], value[1]), dim=2),
+                         torch.cat((past_value[2], value[2]), dim=2))
+            else:
+                # not use_cache_quantization:
+                # present=(key,value)
+                key = torch.cat((past_key, key), dim=1)
+                value = torch.cat((past_value, value), dim=1)
+
+        if use_cache:
+            present = (key, value)
+        else:
+            present = None
+
+        key_size = key[0].size(2) if self.use_cache_quantization else key.size(1)
+        if key_size > self.seq_length and self.use_logn_attn and not self.training:
+            if self.use_cache_quantization:
+                seq_start = key[0].size(2) - query.size(1)
+                seq_end = key[0].size(2)
+            else:
+                seq_start = key.size(1) - query.size(1)
+                seq_end = key.size(1)
+            logn_tensor = self.logn_tensor[:, seq_start:seq_end, :, :].type_as(query)
+            query = query * logn_tensor.expand_as(query)
+
+        if (
+            self.use_flash_attn
+            and flash_attn_unpadded_func is not None
+            and not self.is_fp32
+            and query.is_cuda
+        ):
+            q, k, v = query, key, value
+            attn_output = self.core_attention_flash(q, k, v, attention_mask=attention_mask)
+        else:
+            key_size = key[0].size(2) if self.use_cache_quantization else key.size(1)
+            if query.size(1) == key_size:
+                causal_mask = torch.tril(
+                    torch.ones((key_size, key_size), dtype=torch.bool, device=query.device)
+                ).view(1, 1, key_size, key_size)
+            else:
+                causal_mask = None
+            query = query.permute(0, 2, 1, 3)
+            if not self.use_cache_quantization:
+                key = key.permute(0, 2, 1, 3)
+                value = value.permute(0, 2, 1, 3)
+            if (
+                causal_mask is None
+                and self.use_flash_attn
+                and flash_attn_unpadded_func is not None
+                and not self.is_fp32
+                and not query.is_cuda
+            ):
+                raise Exception(_ERROR_INPUT_CPU_QUERY_WITH_FLASH_ATTN_ACTIVATED)
+
+            if not self.use_cache_quantization and SUPPORT_TORCH2:
+                if attention_mask is not None:
+                    attention_mask = attention_mask.expand(-1, -1, query.size(2), -1)
+                    if causal_mask is not None:
+                        attention_mask = attention_mask.masked_fill(~causal_mask, torch.finfo(query.dtype).min)
+                else:
+                    attention_mask = causal_mask
+                attn_output = F.scaled_dot_product_attention(
+                    query, key, value, attn_mask=attention_mask
+                ).transpose(1, 2)
+                attn_weight = None
+            else:
+                attn_output, attn_weight = self._attn(
+                    query, key, value, causal_mask, attention_mask, head_mask
+                )
+        context_layer = self._merge_heads(
+            attn_output, self.num_heads, self.head_dim
+        )
+
+        attn_output = self.c_proj(context_layer)
+
+        outputs = (attn_output, present)
+        if output_attentions:
+            if (
+                self.use_flash_attn
+                and flash_attn_unpadded_func is not None
+                and not self.is_fp32
+            ):
+                raise ValueError("Cannot output attentions while using flash-attn")
+            elif not self.use_cache_quantization and SUPPORT_TORCH2:
+                raise ValueError("Cannot output attentions while using scaled_dot_product_attention")
+            else:
+                outputs += (attn_weight,)
+
+        return outputs
+
+
+class QWenMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.w1 = nn.Linear(
+            config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias
+        )
+        self.w2 = nn.Linear(
+            config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias
+        )
+        ff_dim_in = config.intermediate_size // 2
+        self.c_proj = nn.Linear(ff_dim_in, config.hidden_size, bias=not config.no_bias)
+
+    def forward(self, hidden_states):
+        a1 = self.w1(hidden_states)
+        a2 = self.w2(hidden_states)
+        intermediate_parallel = a1 * F.silu(a2)
+        output = self.c_proj(intermediate_parallel)
+        return output
+
+
+class QWenBlock(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        hidden_size = config.hidden_size
+        self.bf16 = config.bf16
+
+        self.ln_1 = RMSNorm(
+            hidden_size,
+            eps=config.layer_norm_epsilon,
+        )
+        self.attn = QWenAttention(config)
+        self.ln_2 = RMSNorm(
+            hidden_size,
+            eps=config.layer_norm_epsilon,
+        )
+
+        self.mlp = QWenMLP(config)
+
+    def forward(
+        self,
+        hidden_states: Optional[Tuple[torch.FloatTensor]],
+        rotary_pos_emb_list: Optional[List[List[torch.Tensor]]] = None,
+        layer_past: Optional[Tuple[torch.Tensor]] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = False,
+        output_attentions: Optional[bool] = False,
+    ):
+        layernorm_output = self.ln_1(hidden_states)
+
+        attn_outputs = self.attn(
+            layernorm_output,
+            rotary_pos_emb_list,
+            layer_past=layer_past,
+            attention_mask=attention_mask,
+            head_mask=head_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+        )
+        attn_output = attn_outputs[0]
+
+        outputs = attn_outputs[1:]
+
+        residual = hidden_states
+        layernorm_input = attn_output + residual
+
+        layernorm_output = self.ln_2(layernorm_input)
+
+        residual = layernorm_input
+        mlp_output = self.mlp(layernorm_output)
+        hidden_states = residual + mlp_output
+
+        if use_cache:
+            outputs = (hidden_states,) + outputs
+        else:
+            outputs = (hidden_states,) + outputs[1:]
+
+        return outputs
+
+
+class QWenPreTrainedModel(PreTrainedModel):
+    config_class = QWenConfig
+    base_model_prefix = "transformer"
+    is_parallelizable = False
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["QWenBlock"]
+    _skip_keys_device_placement = "past_key_values"
+
+    def __init__(self, *inputs, **kwargs):
+        super().__init__(*inputs, **kwargs)
+
+    def _init_weights(self, module):
+        """Initialize the weights."""
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, RMSNorm):
+            module.weight.data.fill_(1.0)
+
+        for name, p in module.named_parameters():
+            if name == "c_proj.weight":
+                p.data.normal_(
+                    mean=0.0,
+                    std=(
+                        self.config.initializer_range
+                        / math.sqrt(2 * self.config.num_hidden_layers)
+                    ),
+                )
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, QWenModel):
+            module.gradient_checkpointing = value
+
+
+class QWenModel(QWenPreTrainedModel):
+    _keys_to_ignore_on_load_missing = ["attn.masked_bias"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.vocab_size = config.vocab_size
+        self.num_hidden_layers = config.num_hidden_layers
+        self.embed_dim = config.hidden_size
+        self.use_cache_quantization = self.config.use_cache_quantization if hasattr(self.config, 'use_cache_quantization') else False
+
+        self.gradient_checkpointing = False
+        self.use_dynamic_ntk = config.use_dynamic_ntk
+        self.seq_length = config.seq_length
+
+        self.wte = nn.Embedding(self.vocab_size, self.embed_dim)
+
+        self.drop = nn.Dropout(config.emb_dropout_prob)
+
+        if config.rotary_pct == 1.0:
+            self.rotary_ndims = None
+        else:
+            assert config.rotary_pct < 1
+            self.rotary_ndims = int(
+                config.kv_channels * config.rotary_pct
+            )
+        dim = (
+            self.rotary_ndims
+            if self.rotary_ndims is not None
+            else config.kv_channels
+        )
+        self.rotary_emb = RotaryEmbedding(dim, base=config.rotary_emb_base)
+
+        self.use_flash_attn = config.use_flash_attn
+        self.is_fp32 = not (config.bf16 or config.fp16)
+
+        self.h = nn.ModuleList(
+            [
+                QWenBlock(
+                    config
+                )
+                for i in range(config.num_hidden_layers)
+            ]
+        )
+        self.ln_f = RMSNorm(
+            self.embed_dim,
+            eps=config.layer_norm_epsilon,
+        )
+
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.wte
+
+    def set_input_embeddings(self, new_embeddings):
+        self.wte = new_embeddings
+
+    def get_ntk_alpha(self, true_seq_len):
+        context_value = math.log(true_seq_len / self.seq_length, 2) + 1
+        ntk_alpha = 2 ** math.ceil(context_value) - 1
+        ntk_alpha = max(ntk_alpha, 1)
+        return ntk_alpha
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: 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,
+    ):
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        if input_ids is 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:
+            input_shape = input_ids.size()
+            input_ids = input_ids.view(-1, input_shape[-1])
+            batch_size = input_ids.shape[0]
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+            batch_size = inputs_embeds.shape[0]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+        if token_type_ids is not None:
+            token_type_ids = token_type_ids.view(-1, input_shape[-1])
+        if position_ids is not None:
+            position_ids = position_ids.view(-1, input_shape[-1])
+
+        if past_key_values is None:
+            past_length = 0
+            past_key_values = tuple([None] * len(self.h))
+        else:
+            if self.use_cache_quantization:
+                past_length = past_key_values[0][0][0].size(2)
+            else:
+                past_length = past_key_values[0][0].size(-2)
+        if position_ids is None:
+            position_ids = torch.arange(
+                past_length,
+                input_shape[-1] + past_length,
+                dtype=torch.long,
+                device=device,
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
+
+        if attention_mask is not None:
+            if batch_size <= 0:
+                raise ValueError("batch_size has to be defined and > 0")
+            attention_mask = attention_mask.view(batch_size, -1)
+            attention_mask = attention_mask[:, None, None, :]
+            attention_mask = attention_mask.to(dtype=self.dtype)
+            attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
+
+        encoder_attention_mask = None
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.wte(input_ids)
+        hidden_states = inputs_embeds
+
+        kv_seq_len = hidden_states.size()[1]
+        if past_key_values[0] is not None:
+            # past key values[0][0] shape: bs * seq_len * head_num * dim
+            if self.use_cache_quantization:
+                kv_seq_len += past_key_values[0][0][0].shape[2]
+            else:
+                kv_seq_len += past_key_values[0][0].shape[1]
+
+        if self.training or not self.use_dynamic_ntk:
+            ntk_alpha_list = [1.0]
+        elif kv_seq_len != hidden_states.size()[1]:
+            ntk_alpha_list = self.rotary_emb._ntk_alpha_cached_list
+        else:
+            ntk_alpha_list = []
+            if attention_mask is not None and kv_seq_len > self.seq_length:
+                true_seq_lens = attention_mask.squeeze(1).squeeze(1).eq(0).sum(dim=-1, dtype=torch.int32)
+                for i in range(hidden_states.size()[0]):
+                    true_seq_len = true_seq_lens[i].item()
+                    ntk_alpha = self.get_ntk_alpha(true_seq_len)
+                    ntk_alpha_list.append(ntk_alpha)
+            else:
+                ntk_alpha = self.get_ntk_alpha(kv_seq_len)
+                ntk_alpha_list.append(ntk_alpha)
+        self.rotary_emb._ntk_alpha_cached_list = ntk_alpha_list
+        rotary_pos_emb_list = [
+            self.rotary_emb(kv_seq_len, ntk_alpha=ntk_alpha) for ntk_alpha in ntk_alpha_list
+        ]
+
+        hidden_states = self.drop(hidden_states)
+        output_shape = input_shape + (hidden_states.size(-1),)
+
+        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
+
+        presents = () if use_cache else None
+        all_self_attentions = () if output_attentions else None
+        all_hidden_states = () if output_hidden_states else None
+        for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
+
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, use_cache, output_attentions)
+
+                    return custom_forward
+
+                outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    rotary_pos_emb_list,
+                    None,
+                    attention_mask,
+                    head_mask[i],
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                )
+            else:
+                outputs = block(
+                    hidden_states,
+                    layer_past=layer_past,
+                    rotary_pos_emb_list=rotary_pos_emb_list,
+                    attention_mask=attention_mask,
+                    head_mask=head_mask[i],
+                    encoder_hidden_states=encoder_hidden_states,
+                    encoder_attention_mask=encoder_attention_mask,
+                    use_cache=use_cache,
+                    output_attentions=output_attentions,
+                )
+
+            hidden_states = outputs[0]
+            if use_cache is True:
+                presents = presents + (outputs[1],)
+
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
+
+        hidden_states = self.ln_f(hidden_states)
+        hidden_states = hidden_states.view(output_shape)
+        # Add last hidden state
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(
+                v for v in [hidden_states, presents, all_hidden_states] if v is not None
+            )
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=presents,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+        )
+
+
+class QWenLMHeadModel(QWenPreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.rotary_emb\.inv_freq"]
+    _keys_to_ignore_on_load_unexpected = [r"h\.\d+\.attn\.masked_bias"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        assert (
+            config.bf16 + config.fp16 + config.fp32 <= 1
+        ), "Only one of \"bf16\", \"fp16\", \"fp32\" can be true"
+
+        autoset_precision = config.bf16 + config.fp16 + config.fp32 == 0
+
+        if autoset_precision:
+            if SUPPORT_BF16:
+                logger.warn(
+                    "The model is automatically converting to bf16 for faster inference. "
+                    "If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"."
+                )
+                config.bf16 = True
+            elif SUPPORT_FP16:
+                logger.warn(
+                    "The model is automatically converting to fp16 for faster inference. "
+                    "If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"."
+                )
+                config.fp16 = True
+            else:
+                config.fp32 = True
+
+        if config.bf16 and SUPPORT_CUDA and not SUPPORT_BF16:
+            logger.warn("Your device does NOT seem to support bf16, you can switch to fp16 or fp32 by by passing fp16/fp32=True in \"AutoModelForCausalLM.from_pretrained\".")
+        if config.fp16 and SUPPORT_CUDA and not SUPPORT_FP16:
+            logger.warn("Your device does NOT support faster inference with fp16, please switch to fp32 which is likely to be faster")
+        if config.fp32:
+            if SUPPORT_BF16:
+                logger.warn("Your device support faster inference by passing bf16=True in \"AutoModelForCausalLM.from_pretrained\".")
+            elif SUPPORT_FP16:
+                logger.warn("Your device support faster inference by passing fp16=True in \"AutoModelForCausalLM.from_pretrained\".")
+
+        if config.use_flash_attn == "auto":
+            if config.bf16 or config.fp16:
+                logger.warn("Try importing flash-attention for faster inference...")
+                config.use_flash_attn = True
+            else:
+                config.use_flash_attn = False
+        if config.use_flash_attn and config.fp32:
+            logger.warn("Flash attention will be disabled because it does NOT support fp32.")
+
+        if config.use_flash_attn:
+            _import_flash_attn()
+
+        self.transformer = QWenModel(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        if config.bf16:
+            self.transformer.bfloat16()
+            self.lm_head.bfloat16()
+        if config.fp16:
+            self.transformer.half()
+            self.lm_head.half()
+        self.post_init()
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs
+    ):
+        if past_key_values:
+            input_ids = input_ids[:, -1].unsqueeze(-1)
+
+        if input_ids.size(0) == 1:
+            attention_mask = None
+        else:
+            attention_mask = kwargs.get("attention_mask", None)
+
+        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(
+            {
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: 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]:
+
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        transformer_outputs = self.transformer(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+
+        lm_logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(lm_logits.device)
+            shift_logits = lm_logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(
+                shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
+            )
+
+        if not return_dict:
+            output = (lm_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=lm_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+    @staticmethod
+    def _reorder_cache(
+        past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
+    ) -> Tuple[Tuple[torch.Tensor]]:
+
+        return tuple(
+            tuple(
+                past_state.index_select(0, beam_idx.to(past_state.device))
+                for past_state in layer_past
+            )
+            for layer_past in past_key_values
+        )
+
+    def chat(
+        self,
+        tokenizer: PreTrainedTokenizer,
+        query: str,
+        history: Optional[HistoryType],
+        system: str = "You are a helpful assistant.",
+        stream: Optional[bool] = _SENTINEL,
+        stop_words_ids: Optional[List[List[int]]] = None,
+        generation_config: Optional[GenerationConfig] = None,
+        **kwargs,
+    ) -> Tuple[str, HistoryType]:
+        generation_config = generation_config if generation_config is not None else self.generation_config
+
+        assert stream is _SENTINEL, _ERROR_STREAM_IN_CHAT
+        assert generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT
+        if history is None:
+            history = []
+        else:
+            # make a copy of the user's input such that is is left untouched
+            history = copy.deepcopy(history)
+
+        if stop_words_ids is None:
+            stop_words_ids = []
+
+        max_window_size = kwargs.get('max_window_size', None)
+        if max_window_size is None:
+            max_window_size = generation_config.max_window_size
+        raw_text, context_tokens = make_context(
+            tokenizer,
+            query,
+            history=history,
+            system=system,
+            max_window_size=max_window_size,
+            chat_format=generation_config.chat_format,
+        )
+
+        stop_words_ids.extend(get_stop_words_ids(
+            generation_config.chat_format, tokenizer
+        ))
+        input_ids = torch.tensor([context_tokens]).to(self.device)
+        outputs = self.generate(
+                    input_ids,
+                    stop_words_ids=stop_words_ids,
+                    return_dict_in_generate=False,
+                    generation_config=generation_config,
+                    **kwargs,
+                )
+
+        response = decode_tokens(
+            outputs[0],
+            tokenizer,
+            raw_text_len=len(raw_text),
+            context_length=len(context_tokens),
+            chat_format=generation_config.chat_format,
+            verbose=False,
+            errors='replace'
+        )
+
+        # as history is a copy of the user inputs,
+        # we can always return the new turn to the user.
+        # separating input history and output history also enables the user
+        # to implement more complex history management
+        history.append((query, response))
+
+        return response, history
+
+    def chat_stream(
+            self,
+            tokenizer: PreTrainedTokenizer,
+            query: str,
+            history: Optional[HistoryType],
+            system: str = "You are a helpful assistant.",
+            stop_words_ids: Optional[List[List[int]]] = None,
+            logits_processor: Optional[LogitsProcessorList] = None,
+            generation_config: Optional[GenerationConfig] = None,
+            **kwargs,
+    ) -> Generator[str, Any, None]:
+        generation_config = generation_config if generation_config is not None else self.generation_config
+        assert generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT
+        if history is None:
+            history = []
+        if stop_words_ids is None:
+            stop_words_ids = []
+
+        max_window_size = kwargs.get('max_window_size', None)
+        if max_window_size is None:
+            max_window_size = generation_config.max_window_size
+        raw_text, context_tokens = make_context(
+            tokenizer,
+            query,
+            history=history,
+            system=system,
+            max_window_size=max_window_size,
+            chat_format=generation_config.chat_format,
+        )
+
+        stop_words_ids.extend(get_stop_words_ids(
+            generation_config.chat_format, tokenizer
+        ))
+        if stop_words_ids is not None:
+            stop_words_logits_processor = StopWordsLogitsProcessor(
+                stop_words_ids=stop_words_ids,
+                eos_token_id=generation_config.eos_token_id,
+            )
+            if logits_processor is None:
+                logits_processor = LogitsProcessorList([stop_words_logits_processor])
+            else:
+                logits_processor.append(stop_words_logits_processor)
+        input_ids = torch.tensor([context_tokens]).to(self.device)
+
+        from transformers_stream_generator.main import NewGenerationMixin, StreamGenerationConfig
+        self.__class__.generate_stream = NewGenerationMixin.generate
+        self.__class__.sample_stream = NewGenerationMixin.sample_stream
+        stream_config = StreamGenerationConfig(**generation_config.to_dict(), do_stream=True)
+
+        def stream_generator():
+            outputs = []
+            for token in self.generate_stream(
+                    input_ids,
+                    return_dict_in_generate=False,
+                    generation_config=stream_config,
+                    logits_processor=logits_processor,
+                    seed=-1,
+                    **kwargs):
+                outputs.append(token.item())
+                yield tokenizer.decode(outputs, skip_special_tokens=True, errors='ignore')
+
+        return stream_generator()
+
+    def generate(
+        self,
+        inputs: Optional[torch.Tensor] = None,
+        generation_config: Optional[GenerationConfig] = None,
+        logits_processor: Optional[LogitsProcessorList] = None,
+        stopping_criteria: Optional[StoppingCriteriaList] = None,
+        prefix_allowed_tokens_fn: Optional[
+            Callable[[int, torch.Tensor], List[int]]
+        ] = None,
+        synced_gpus: Optional[bool] = None,
+        assistant_model: Optional["PreTrainedModel"] = None,
+        streamer: Optional["BaseStreamer"] = None,
+        **kwargs,
+    ) -> Union[GenerateOutput, torch.LongTensor]:
+        generation_config = generation_config if generation_config is not None else self.generation_config
+
+        # Process stop_words_ids.
+        stop_words_ids = kwargs.pop("stop_words_ids", None)
+        if stop_words_ids is None and generation_config is not None:
+            stop_words_ids = getattr(generation_config, "stop_words_ids", None)
+        if stop_words_ids is None:
+            stop_words_ids = getattr(generation_config, "stop_words_ids", None)
+
+        if stop_words_ids is not None:
+            stop_words_logits_processor = StopWordsLogitsProcessor(
+                stop_words_ids=stop_words_ids,
+                eos_token_id=generation_config.eos_token_id,
+            )
+            if logits_processor is None:
+                logits_processor = LogitsProcessorList([stop_words_logits_processor])
+            else:
+                logits_processor.append(stop_words_logits_processor)
+
+        return super().generate(
+            inputs,
+            generation_config=generation_config,
+            logits_processor=logits_processor,
+            stopping_criteria=stopping_criteria,
+            prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
+            synced_gpus=synced_gpus,
+            assistant_model=assistant_model,
+            streamer=streamer,
+            **kwargs,
+        )
+
+
+class RotaryEmbedding(torch.nn.Module):
+    def __init__(self, dim, base=10000):
+        super().__init__()
+        self.dim = dim
+        self.base = base
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        if importlib.util.find_spec("einops") is None:
+            raise RuntimeError("einops is required for Rotary Embedding")
+
+        self._rotary_pos_emb_cache = None
+        self._seq_len_cached = 0
+        self._ntk_alpha_cached = 1.0
+        self._ntk_alpha_cached_list = [1.0]
+
+    def update_rotary_pos_emb_cache(self, seqlen, ntk_alpha=1.0):
+        if seqlen > self._seq_len_cached or ntk_alpha != self._ntk_alpha_cached:
+            base = self.base * ntk_alpha ** (self.dim / (self.dim - 2))
+            self.inv_freq = 1.0 / (
+                base
+                ** (
+                    torch.arange(0, self.dim, 2, device=self.inv_freq.device).float()
+                    / self.dim
+                )
+            )
+            self._seq_len_cached = max(2 * seqlen, 16)
+            self._ntk_alpha_cached = ntk_alpha
+            seq = torch.arange(self._seq_len_cached, device=self.inv_freq.device)
+            freqs = torch.outer(seq.type_as(self.inv_freq), self.inv_freq)
+
+            emb = torch.cat((freqs, freqs), dim=-1)
+            from einops import rearrange
+
+            emb = rearrange(emb, "n d -> 1 n 1 d")
+
+            cos, sin = emb.cos(), emb.sin()
+            self._rotary_pos_emb_cache = [cos, sin]
+
+    def forward(self, max_seq_len, ntk_alpha=1.0):
+        self.update_rotary_pos_emb_cache(max_seq_len, ntk_alpha)
+        cos, sin = self._rotary_pos_emb_cache
+        return [cos[:, :max_seq_len], sin[:, :max_seq_len]]
+
+
+def _rotate_half(x):
+    from einops import rearrange
+
+    x = rearrange(x, "... (j d) -> ... j d", j=2)
+    x1, x2 = x.unbind(dim=-2)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(t, freqs):
+    """ Apply rotary embedding to the first rotary_dim of the iput
+
+    Arguments:
+      t (tensor(batch_size, seq_len, n_head, head_dim)):
+        the input embedding/hidden states
+      freqs (list[tensor(1, seq_len, 1, rotary_dim), tensor(1, seq_len, 1, rotary_dim)]):
+        the cached cos/sin position embeddings
+    """
+    rot_dim = freqs[0].shape[-1]
+    cos, sin = freqs
+    t_float = t.float()
+    if apply_rotary_emb_func is not None and t.is_cuda:
+        # apply_rotary_emb in flash_attn requires cos/sin to be of
+        # shape (seqlen, rotary_dim / 2) and apply rotary embedding
+        # to the first rotary_dim of the input
+        cos = cos.squeeze(0).squeeze(1)[:, : rot_dim // 2]
+        sin = sin.squeeze(0).squeeze(1)[:, : rot_dim // 2]
+        return apply_rotary_emb_func(t_float, cos, sin).type_as(t)
+    else:
+        t_rot, t_pass = t_float[..., :rot_dim], t_float[..., rot_dim:]
+        t_rot = (t_rot * cos) + (_rotate_half(t_rot) * sin)
+        return torch.cat((t_rot, t_pass), dim=-1).type_as(t)
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        if rms_norm is not None and x.is_cuda:
+            return rms_norm(x, self.weight, self.eps)
+        else:
+            output = self._norm(x.float()).type_as(x)
+            return output * self.weight

Qwen/Qwen-1_8B-Chat/qwen_generation_utils.py

@@ -0,0 +1,416 @@
+# Copyright (c) Alibaba Cloud.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Generation support."""
+
+from typing import Tuple, List, Union, Iterable
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from transformers import PreTrainedTokenizer
+from transformers import logging
+from transformers.generation import LogitsProcessor
+
+logger = logging.get_logger(__name__)
+
+# Types.
+HistoryType = List[Tuple[str, str]]
+TokensType = List[int]
+BatchTokensType = List[List[int]]
+
+
+def pad_batch(batch: BatchTokensType, pad_id: int, seq_length: int) -> BatchTokensType:
+    for tokens in batch:
+        context_length = len(tokens)
+        if context_length < seq_length:
+            tokens.extend([pad_id] * (seq_length - context_length))
+    return batch
+
+
+def get_ltor_masks_and_position_ids(
+    data,
+    eod_token,
+    reset_position_ids,
+    reset_attention_mask,
+    eod_mask_loss,
+):
+    """Build masks and position id for left to right model."""
+
+    # Extract batch size and sequence length.
+    micro_batch_size, seq_length = data.size()
+
+    # Attention mask (lower triangular).
+    if reset_attention_mask:
+        att_mask_batch = micro_batch_size
+    else:
+        att_mask_batch = 1
+    attention_mask = torch.tril(
+        torch.ones((att_mask_batch, seq_length, seq_length), device=data.device)
+    ).view(att_mask_batch, 1, seq_length, seq_length)
+
+    # Loss mask.
+    loss_mask = torch.ones(data.size(), dtype=torch.float, device=data.device)
+    if eod_mask_loss:
+        loss_mask[data == eod_token] = 0.0
+
+    # Position ids.
+    position_ids = torch.arange(seq_length, dtype=torch.long, device=data.device)
+    position_ids = position_ids.unsqueeze(0).expand_as(data)
+    # We need to clone as the ids will be modifed based on batch index.
+    if reset_position_ids:
+        position_ids = position_ids.clone()
+
+    if reset_position_ids or reset_attention_mask:
+        # Loop through the batches:
+        for b in range(micro_batch_size):
+
+            # Find indecies where EOD token is.
+            eod_index = position_ids[b, data[b] == eod_token]
+            # Detach indecies from positions if going to modify positions.
+            if reset_position_ids:
+                eod_index = eod_index.clone()
+
+            # Loop through EOD indecies:
+            prev_index = 0
+            for j in range(eod_index.size()[0]):
+                i = eod_index[j]
+                # Mask attention loss.
+                if reset_attention_mask:
+                    attention_mask[b, 0, (i + 1) :, : (i + 1)] = 0
+                # Reset positions.
+                if reset_position_ids:
+                    position_ids[b, (i + 1) :] -= i + 1 - prev_index
+                    prev_index = i + 1
+
+    # Convert attention mask to binary:
+    attention_mask = attention_mask < 0.5
+
+    return attention_mask, loss_mask, position_ids
+
+
+def get_batch(context_tokens: torch.LongTensor, eod_id: int):
+    """Generate batch from context tokens."""
+    # Move to GPU.
+    tokens = context_tokens.contiguous().to(context_tokens.device)
+    # Get the attention mask and postition ids.
+    attention_mask, _, position_ids = get_ltor_masks_and_position_ids(
+        tokens,
+        eod_id,
+        reset_position_ids=False,
+        reset_attention_mask=False,
+        eod_mask_loss=False,
+    )
+    return tokens, attention_mask, position_ids
+
+
+def get_stop_words_ids(chat_format, tokenizer):
+    if chat_format == "raw":
+        stop_words_ids = [tokenizer.encode("Human:"), [tokenizer.eod_id]]
+    elif chat_format == "chatml":
+        stop_words_ids = [[tokenizer.im_end_id], [tokenizer.im_start_id]]
+    else:
+        raise NotImplementedError(f"Unknown chat format {chat_format!r}")
+    return stop_words_ids
+
+
+def make_context(
+    tokenizer: PreTrainedTokenizer,
+    query: str,
+    history: List[Tuple[str, str]] = None,
+    system: str = "",
+    max_window_size: int = 6144,
+    chat_format: str = "chatml",
+):
+    if history is None:
+        history = []
+
+    if chat_format == "chatml":
+        im_start, im_end = "<|im_start|>", "<|im_end|>"
+        im_start_tokens = [tokenizer.im_start_id]
+        im_end_tokens = [tokenizer.im_end_id]
+        nl_tokens = tokenizer.encode("\n")
+
+        def _tokenize_str(role, content):
+            return f"{role}\n{content}", tokenizer.encode(
+                role, allowed_special=set()
+            ) + nl_tokens + tokenizer.encode(content, allowed_special=set())
+
+        system_text, system_tokens_part = _tokenize_str("system", system)
+        system_tokens = im_start_tokens + system_tokens_part + im_end_tokens
+
+        raw_text = ""
+        context_tokens = []
+
+        for turn_query, turn_response in reversed(history):
+            query_text, query_tokens_part = _tokenize_str("user", turn_query)
+            query_tokens = im_start_tokens + query_tokens_part + im_end_tokens
+            response_text, response_tokens_part = _tokenize_str(
+                "assistant", turn_response
+            )
+            response_tokens = im_start_tokens + response_tokens_part + im_end_tokens
+
+            next_context_tokens = nl_tokens + query_tokens + nl_tokens + response_tokens
+            prev_chat = (
+                f"\n{im_start}{query_text}{im_end}\n{im_start}{response_text}{im_end}"
+            )
+
+            current_context_size = (
+                len(system_tokens) + len(next_context_tokens) + len(context_tokens)
+            )
+            if current_context_size < max_window_size:
+                context_tokens = next_context_tokens + context_tokens
+                raw_text = prev_chat + raw_text
+            else:
+                break
+
+        context_tokens = system_tokens + context_tokens
+        raw_text = f"{im_start}{system_text}{im_end}" + raw_text
+        context_tokens += (
+            nl_tokens
+            + im_start_tokens
+            + _tokenize_str("user", query)[1]
+            + im_end_tokens
+            + nl_tokens
+            + im_start_tokens
+            + tokenizer.encode("assistant")
+            + nl_tokens
+        )
+        raw_text += f"\n{im_start}user\n{query}{im_end}\n{im_start}assistant\n"
+
+    elif chat_format == "raw":
+        raw_text = query
+        context_tokens = tokenizer.encode(raw_text)
+    else:
+        raise NotImplementedError(f"Unknown chat format {chat_format!r}")
+
+    return raw_text, context_tokens
+
+
+def _decode_default(
+    tokens: List[int],
+    *,
+    stop_words: List[str],
+    eod_words: List[str],
+    tokenizer: PreTrainedTokenizer,
+    raw_text_len: int,
+    verbose: bool = False,
+    return_end_reason: bool = False,
+    errors: str='replace',
+):
+    trim_decode_tokens = tokenizer.decode(tokens, errors=errors)[raw_text_len:]
+    if verbose:
+        print("\nRaw Generate: ", trim_decode_tokens)
+
+    end_reason = f"Gen length {len(tokens)}"
+    for stop_word in stop_words:
+        trim_decode_tokens = trim_decode_tokens.replace(stop_word, "").strip()
+    for eod_word in eod_words:
+        if eod_word in trim_decode_tokens:
+            end_reason = f"Gen {eod_word!r}"
+        trim_decode_tokens = trim_decode_tokens.split(eod_word)[0]
+    trim_decode_tokens = trim_decode_tokens.strip()
+    if verbose:
+        print("\nEnd Reason:", end_reason)
+        print("\nGenerate: ", trim_decode_tokens)
+
+    if return_end_reason:
+        return trim_decode_tokens, end_reason
+    else:
+        return trim_decode_tokens
+
+
+def _decode_chatml(
+    tokens: List[int],
+    *,
+    stop_words: List[str],
+    eod_token_ids: List[int],
+    tokenizer: PreTrainedTokenizer,
+    raw_text_len: int,
+    context_length: int,
+    verbose: bool = False,
+    return_end_reason: bool = False,
+    errors: str='replace'
+):
+    end_reason = f"Gen length {len(tokens)}"
+    eod_token_idx = context_length
+    for eod_token_idx in range(context_length, len(tokens)):
+        if tokens[eod_token_idx] in eod_token_ids:
+            end_reason = f"Gen {tokenizer.decode([tokens[eod_token_idx]])!r}"
+            break
+
+    trim_decode_tokens = tokenizer.decode(tokens[:eod_token_idx], errors=errors)[raw_text_len:]
+    if verbose:
+        print("\nRaw Generate w/o EOD:", tokenizer.decode(tokens, errors=errors)[raw_text_len:])
+        print("\nRaw Generate:", trim_decode_tokens)
+        print("\nEnd Reason:", end_reason)
+    for stop_word in stop_words:
+        trim_decode_tokens = trim_decode_tokens.replace(stop_word, "").strip()
+    trim_decode_tokens = trim_decode_tokens.strip()
+    if verbose:
+        print("\nGenerate:", trim_decode_tokens)
+
+    if return_end_reason:
+        return trim_decode_tokens, end_reason
+    else:
+        return trim_decode_tokens
+
+
+def decode_tokens(
+    tokens: Union[torch.LongTensor, TokensType],
+    tokenizer: PreTrainedTokenizer,
+    raw_text_len: int,
+    context_length: int,
+    chat_format: str,
+    verbose: bool = False,
+    return_end_reason: bool = False,
+    errors: str="replace",
+) -> str:
+    if torch.is_tensor(tokens):
+        tokens = tokens.cpu().numpy().tolist()
+
+    if chat_format == "chatml":
+        return _decode_chatml(
+            tokens,
+            stop_words=[],
+            eod_token_ids=[tokenizer.im_start_id, tokenizer.im_end_id],
+            tokenizer=tokenizer,
+            raw_text_len=raw_text_len,
+            context_length=context_length,
+            verbose=verbose,
+            return_end_reason=return_end_reason,
+            errors=errors,
+        )
+    elif chat_format == "raw":
+        return _decode_default(
+            tokens,
+            stop_words=["<|endoftext|>"],
+            eod_words=["<|endoftext|>"],
+            tokenizer=tokenizer,
+            raw_text_len=raw_text_len,
+            verbose=verbose,
+            return_end_reason=return_end_reason,
+            errors=errors,
+        )
+    else:
+        raise NotImplementedError(f"Unknown chat format {chat_format!r}")
+
+
+class StopWordsLogitsProcessor(LogitsProcessor):
+    """
+    :class:`transformers.LogitsProcessor` that enforces that when specified sequences appear, stop geration.
+
+    Args:
+        stop_words_ids (:obj:`List[List[int]]`):
+            List of list of token ids of stop ids. In order to get the tokens of the words
+            that should not appear in the generated text, use :obj:`tokenizer(bad_word,
+            add_prefix_space=True).input_ids`.
+        eos_token_id (:obj:`int`):
+            The id of the `end-of-sequence` token.
+    """
+
+    def __init__(self, stop_words_ids: Iterable[Iterable[int]], eos_token_id: int):
+
+        if not isinstance(stop_words_ids, List) or len(stop_words_ids) == 0:
+            raise ValueError(
+                f"`stop_words_ids` has to be a non-emtpy list, but is {stop_words_ids}."
+            )
+        if any(not isinstance(bad_word_ids, list) for bad_word_ids in stop_words_ids):
+            raise ValueError(
+                f"`stop_words_ids` has to be a list of lists, but is {stop_words_ids}."
+            )
+        if any(
+            any(
+                (not isinstance(token_id, (int, np.integer)) or token_id < 0)
+                for token_id in stop_word_ids
+            )
+            for stop_word_ids in stop_words_ids
+        ):
+            raise ValueError(
+                f"Each list in `stop_words_ids` has to be a list of positive integers, but is {stop_words_ids}."
+            )
+
+        self.stop_words_ids = list(
+            filter(
+                lambda bad_token_seq: bad_token_seq != [eos_token_id], stop_words_ids
+            )
+        )
+        self.eos_token_id = eos_token_id
+        for stop_token_seq in self.stop_words_ids:
+            assert (
+                len(stop_token_seq) > 0
+            ), "Stop words token sequences {} cannot have an empty list".format(
+                stop_words_ids
+            )
+
+    def __call__(
+        self, input_ids: torch.LongTensor, scores: torch.FloatTensor
+    ) -> torch.FloatTensor:
+        stopped_samples = self._calc_stopped_samples(input_ids)
+        for i, should_stop in enumerate(stopped_samples):
+            if should_stop:
+                scores[i, self.eos_token_id] = float(2**15)
+        return scores
+
+    def _tokens_match(self, prev_tokens: torch.LongTensor, tokens: List[int]) -> bool:
+        if len(tokens) == 0:
+            # if bad word tokens is just one token always ban it
+            return True
+        elif len(tokens) > len(prev_tokens):
+            # if bad word tokens are longer then prev input_ids they can't be equal
+            return False
+        elif prev_tokens[-len(tokens) :].tolist() == tokens:
+            # if tokens match
+            return True
+        else:
+            return False
+
+    def _calc_stopped_samples(self, prev_input_ids: Iterable[int]) -> Iterable[int]:
+        stopped_samples = []
+        for prev_input_ids_slice in prev_input_ids:
+            match = False
+            for stop_token_seq in self.stop_words_ids:
+                if self._tokens_match(prev_input_ids_slice, stop_token_seq):
+                    # if tokens do not match continue
+                    match = True
+                    break
+            stopped_samples.append(match)
+
+        return stopped_samples
+
+
+def top_k_logits(logits, top_k=0, top_p=0.0, filter_value=-float("Inf")):
+    """This function has been mostly taken from huggingface conversational
+    ai code at
+        https://medium.com/huggingface/how-to-build-a-state-of-the-art-
+             conversational-ai-with-transfer-learning-2d818ac26313"""
+
+    if top_k > 0:
+        # Remove all tokens with a probability less than the
+        # last token of the top-k
+        indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
+        logits[indices_to_remove] = filter_value
+
+    if top_p > 0.0:
+        # Cconvert to 1D
+        sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1)
+        cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+
+        # Remove tokens with cumulative probability above the threshold
+        sorted_indices_to_remove = cumulative_probs > top_p
+        # Shift the indices to the right to keep also the first token
+        # above the threshold
+        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+        sorted_indices_to_remove[..., 0] = 0
+        for i in range(sorted_indices.size(0)):
+            indices_to_remove = sorted_indices[i][sorted_indices_to_remove[i]]
+            logits[i][indices_to_remove] = filter_value
+
+    return logits
+
+
+def switch(val1, val2, boolean):
+    boolean = boolean.type_as(val1)
+    return (1 - boolean) * val1 + boolean * val2

Qwen/Qwen-1_8B-Chat/tokenization_qwen.py

@@ -0,0 +1,276 @@
+# Copyright (c) Alibaba Cloud.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Tokenization classes for QWen."""
+
+import base64
+import logging
+import os
+import unicodedata
+from typing import Collection, Dict, List, Set, Tuple, Union
+
+import tiktoken
+from transformers import PreTrainedTokenizer, AddedToken
+
+logger = logging.getLogger(__name__)
+
+
+VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken"}
+
+PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
+ENDOFTEXT = "<|endoftext|>"
+IMSTART = "<|im_start|>"
+IMEND = "<|im_end|>"
+# as the default behavior is changed to allow special tokens in
+# regular texts, the surface forms of special tokens need to be
+# as different as possible to minimize the impact
+EXTRAS = tuple((f"<|extra_{i}|>" for i in range(205)))
+# changed to use actual index to avoid misconfiguration with vocabulary expansion
+SPECIAL_START_ID = 151643
+SPECIAL_TOKENS = tuple(
+    enumerate(
+        (
+            (
+                ENDOFTEXT,
+                IMSTART,
+                IMEND,
+            )
+            + EXTRAS
+        ),
+        start=SPECIAL_START_ID,
+    )
+)
+SPECIAL_TOKENS_SET = set(t for i, t in SPECIAL_TOKENS)
+
+
+def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
+    with open(tiktoken_bpe_file, "rb") as f:
+        contents = f.read()
+    return {
+        base64.b64decode(token): int(rank)
+        for token, rank in (line.split() for line in contents.splitlines() if line)
+    }
+
+
+class QWenTokenizer(PreTrainedTokenizer):
+    """QWen tokenizer."""
+
+    vocab_files_names = VOCAB_FILES_NAMES
+
+    def __init__(
+        self,
+        vocab_file,
+        errors="replace",
+        extra_vocab_file=None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        # how to handle errors in decoding UTF-8 byte sequences
+        # use ignore if you are in streaming inference
+        self.errors = errors  
+
+        self.mergeable_ranks = _load_tiktoken_bpe(vocab_file)  # type: Dict[bytes, int]
+        self.special_tokens = {
+            token: index
+            for index, token in SPECIAL_TOKENS
+        }
+
+        # try load extra vocab from file
+        if extra_vocab_file is not None:
+            used_ids = set(self.mergeable_ranks.values()) | set(self.special_tokens.values())
+            extra_mergeable_ranks = _load_tiktoken_bpe(extra_vocab_file)
+            for token, index in extra_mergeable_ranks.items():
+                if token in self.mergeable_ranks:
+                    logger.info(f"extra token {token} exists, skipping")
+                    continue
+                if index in used_ids:
+                    logger.info(f'the index {index} for extra token {token} exists, skipping')
+                    continue
+                self.mergeable_ranks[token] = index
+            # the index may be sparse after this, but don't worry tiktoken.Encoding will handle this
+
+        enc = tiktoken.Encoding(
+            "Qwen",
+            pat_str=PAT_STR,
+            mergeable_ranks=self.mergeable_ranks,
+            special_tokens=self.special_tokens,
+        )
+        assert (
+            len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
+        ), f"{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding"
+
+        self.decoder = {
+            v: k for k, v in self.mergeable_ranks.items()
+        }  # type: dict[int, bytes|str]
+        self.decoder.update({v: k for k, v in self.special_tokens.items()})
+
+        self.tokenizer = enc  # type: tiktoken.Encoding
+
+        self.eod_id = self.tokenizer.eot_token
+        self.im_start_id = self.special_tokens[IMSTART]
+        self.im_end_id = self.special_tokens[IMEND]
+
+    def __getstate__(self):
+        # for pickle lovers
+        state = self.__dict__.copy()
+        del state["tokenizer"]
+        return state
+
+    def __setstate__(self, state):
+        # tokenizer is not python native; don't pass it; rebuild it
+        self.__dict__.update(state)
+        enc = tiktoken.Encoding(
+            "Qwen",
+            pat_str=PAT_STR,
+            mergeable_ranks=self.mergeable_ranks,
+            special_tokens=self.special_tokens,
+        )
+        self.tokenizer = enc
+
+    def __len__(self) -> int:
+        return self.tokenizer.n_vocab
+
+    def get_vocab(self) -> Dict[bytes, int]:
+        return self.mergeable_ranks
+
+    def convert_tokens_to_ids(
+        self, tokens: Union[bytes, str, List[Union[bytes, str]]]
+    ) -> List[int]:
+        ids = []
+        if isinstance(tokens, (str, bytes)):
+            if tokens in self.special_tokens:
+                return self.special_tokens[tokens]
+            else:
+                return self.mergeable_ranks.get(tokens)
+        for token in tokens:
+            if token in self.special_tokens:
+                ids.append(self.special_tokens[token])
+            else:
+                ids.append(self.mergeable_ranks.get(token))
+        return ids
+
+    def _add_tokens(
+        self,
+        new_tokens: Union[List[str], List[AddedToken]],
+        special_tokens: bool = False,
+    ) -> int:
+        if not special_tokens and new_tokens:
+            raise ValueError("Adding regular tokens is not supported")
+        for token in new_tokens:
+            surface_form = token.content if isinstance(token, AddedToken) else token
+            if surface_form not in SPECIAL_TOKENS_SET:
+                raise ValueError("Adding unknown special tokens is not supported")
+        return 0
+
+    def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
+        """
+        Save only the vocabulary of the tokenizer (vocabulary).
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        file_path = os.path.join(save_directory, "qwen.tiktoken")
+        with open(file_path, "w", encoding="utf8") as w:
+            for k, v in self.mergeable_ranks.items():
+                line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
+                w.write(line)
+        return (file_path,)
+
+    def tokenize(
+        self,
+        text: str,
+        allowed_special: Union[Set, str] = "all",
+        disallowed_special: Union[Collection, str] = (),
+        **kwargs,
+    ) -> List[Union[bytes, str]]:
+        """
+        Converts a string in a sequence of tokens.
+
+        Args:
+            text (`str`):
+                The sequence to be encoded.
+            allowed_special (`Literal["all"]` or `set`):
+                The surface forms of the tokens to be encoded as special tokens in regular texts.
+                Default to "all".
+            disallowed_special (`Literal["all"]` or `Collection`):
+                The surface forms of the tokens that should not be in regular texts and trigger errors.
+                Default to an empty tuple.
+
+            kwargs (additional keyword arguments, *optional*):
+                Will be passed to the underlying model specific encode method.
+
+        Returns:
+            `List[bytes|str]`: The list of tokens.
+        """
+        tokens = []
+        text = unicodedata.normalize("NFC", text)
+
+        # this implementation takes a detour: text -> token id -> token surface forms
+        for t in self.tokenizer.encode(
+            text, allowed_special=allowed_special, disallowed_special=disallowed_special
+        ):
+            tokens.append(self.decoder[t])
+        return tokens
+
+    def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
+        """
+        Converts a sequence of tokens in a single string.
+        """
+        text = ""
+        temp = b""
+        for t in tokens:
+            if isinstance(t, str):
+                if temp:
+                    text += temp.decode("utf-8", errors=self.errors)
+                    temp = b""
+                text += t
+            elif isinstance(t, bytes):
+                temp += t
+            else:
+                raise TypeError("token should only be of type types or str")
+        if temp:
+            text += temp.decode("utf-8", errors=self.errors)
+        return text
+
+    @property
+    def vocab_size(self):
+        return self.tokenizer.n_vocab
+
+    def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
+        """Converts an id to a token, special tokens included"""
+        if index in self.decoder:
+            return self.decoder[index]
+        raise ValueError("unknown ids")
+
+    def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
+        """Converts a token to an id using the vocab, special tokens included"""
+        if token in self.special_tokens:
+            return self.special_tokens[token]
+        if token in self.mergeable_ranks:
+            return self.mergeable_ranks[token]
+        raise ValueError("unknown token")
+
+    def _tokenize(self, text: str, **kwargs):
+        """
+        Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
+        vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
+
+        Do NOT take care of added tokens.
+        """
+        raise NotImplementedError
+
+    def _decode(
+        self,
+        token_ids: Union[int, List[int]],
+        skip_special_tokens: bool = False,
+        errors: str = None,
+        **kwargs,
+    ) -> str:
+        if isinstance(token_ids, int):
+            token_ids = [token_ids]
+        if skip_special_tokens:
+            token_ids = [i for i in token_ids if i < self.eod_id]
+        return self.tokenizer.decode(token_ids, errors=errors or self.errors)

Qwen/Qwen-1_8B-Chat/tokenizer_config.json

@@ -0,0 +1,10 @@
+{
+  "model_max_length": 8192,
+  "tokenizer_class": "QWenTokenizer",
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_qwen.QWenTokenizer",
+      null
+      ]
+  }
+}

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