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LICENSE

@@ -0,0 +1,34 @@
+OPENPANGU MODEL LICENSE AGREEMENT VERSION 1.0
+
+This OPENPANGU MODEL LICENSE AGREEMENT VERSION 1.0 (the "Agreement") is a legal agreement between You and Huawei Technologies Co., Ltd. ("Huawei", "We" or "Us"), and it governs Your reproducing, use, modification, and distribution of openPangu as made available by Huawei under this Agreement. 
+
+By using, reproducing, modifying, distributing, performing or displaying any portion or element of openPangu, or otherwise accepting the terms of this Agreement, You agree to be bound by this Agreement.
+
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+END OF THE TERMS AND CONDITIONS

README.md

@@ -1,3 +1,172 @@
----
-license: unknown
----
+# Open-Source Pangu openPangu-7B-Diffusion-DeepDiver
+
+[中文](README_CN.md) | English
+
+## 1. Introduction
+
+openPangu-7B-Diffusion-DeepDiver is a 7B-parameter language model based on block diffusion large language models (Diffusion LLM), specifically trained and fine-tuned for multi-agent scenarios (including tool invocation, information retrieval, and multi-step decision-making). Its underlying architecture and inference pipeline follow the design of [openPangu-R-7B-Diffusion](https://ai.gitcode.com/ascend-tribe/openPangu-R-7B-Diffusion/blob/main/README.md) (including block-wise denoising and bidirectional attention within blocks), thus maintaining consistent structures and interfaces for single-pass generation and parallel decoding.
+
+For complete evaluations and training details, please refer to the technical report *“DLLM Agent: See Farther, Run Faster”* ([arXiv:2602.07451v2](https://arxiv.org/html/2602.07451v2)).
+
+- openPangu-7B-Diffusion-DeepDiver: Agent model with a context length of 32k.
+
+### Key Features
+- Uses the same DLLM architecture and iterative inference pipeline as openPangu-R-7B-Diffusion.
+- Trained and fine-tuned with data and objectives tailored for in-depth agent research, making it more robust in multi-round tool invocation and planning tasks.
+- Introduces context-clean corruption and span-aware attention alignment to reduce noise propagation in multi-round agent dialogues and improve the reliability of tool invocation formats.
+
+#### Inference
+
+![Context_Causal_Block_Diffusion_LLM](images/Context_Causal_Block_Diffusion_LLM.png)
+
+openPangu-7B-Diffusion-DeepDiver adopts **context-causal block diffusion decoding**, performing diffusion decoding block by block. Within each block, full attention is applied, while causal attention is used for preceding context. Once all tokens in a block are decoded, the entire block is stored in the historical KV cache with causal masking, and decoding proceeds to the first token of the next block.
+
+- Supports variable-length inference and KV caching.
+- Flexible context length, not limited by block size.
+- Supports both autoregressive and block diffusion decoding.
+- Uses confidence-threshold sampling, achieving up to 2.5× throughput improvement over standard autoregressive decoding.
+- Similar to Fast dLLMv2, small blocks can be configured within each block to balance throughput and performance, with optimal results typically at small block sizes of 4 or 8.
+
+##### Inference in Agent Workflows
+
+Integrated into the [DeepDiver v2 Agent](https://ai.gitcode.com/ascend-tribe/openPangu-Embedded-7B-DeepDiver/tree/main/deepdiver_v2) workflow, the model applies DLLM’s iterative denoising inference strategy for each round of tool generation.
+
+Deepdiver v2 is a planner-centered MAS (Multi-Agent System) architecture that coordinates multiple executors.
+
+For detailed information, refer to its [technical report](https://ai.gitcode.com/ascend-tribe/openPangu-Embedded-7B-DeepDiver).
+
+#### Training
+
+![alt text](images/image.png)
+
+##### Training Corpus
+
+The model is trained on 11k specially collected or synthesized agent trajectory datasets (including planner → seeker multi-agent interactions, real tool calls, and tool-return traces). These data aim to help the model learn to generate semantically consistent and format-compliant tool invocation instructions in multi-round interactions. See the “Agent-oriented Fine-tuning” section in the technical report for details.
+
+##### Supervision Method
+
+Cross-entropy losses for both diffusion and autoregressive models are jointly optimized during training, ensuring stability and preserving reliable left-to-right generation.
+
+##### Masking and Attention Alignment
+
+To address information contamination caused by diffusion when multi-round contexts and tool outputs are mixed, training applies context-clean corruption to mask irrelevant context segments and span-aware attention alignment within generation ranges. Experiments on agent datasets show that both techniques improve final information retrieval scores.
+
+## 2. Model Architecture
+
+|                                | openPangu-7B-Diffusion-DeepDiver               |
+| :----------------------------: | :-------------------------: |
+|        **Architecture**        |            Dense            |
+| **Parameters (Non-Embedding)** |             7B              |
+|      **Number of Layers**      |             34              |
+|      **Hidden Dimension**      |            12800            |
+|    **Attention Mechanism**     |             GQA             |
+| **Number of Attention Heads**  |     32 for Q, 8 for KV       |
+|      **Vocabulary Size**       |            153k             |
+|      **Context Length**        |             32k             |
+| **Continued Training Tokens**  |            700B             |
+
+## 3. Evaluation Results
+
+Table 1. Comparison results on a 110-question subset of BrowseComp-zh.
+
+| Method                             | Accuracy (%) | Tool Calls | Agent Rounds | Tool Failure Rate |
+| ---------------------------------- | -----------: | ---------: | -----------: | ----------------: |
+| AR Agent (autoregressive backbone) |         15.5 |        7.5 |       14.8   |              1.9% |
+| DLLM Agent (diffusion backbone)    |         15.5 |        6.7 |       13.0   |              6.4% |
+
+Although the final accuracy is comparable to AR on this subset, DLLM requires fewer tool calls and sub-agent rounds, and achieves about 30% average end-to-end latency reduction. However, DLLM shows a higher tool failure rate, indicating that it is still less stable than AR models.
+
+## 4. Deployment and Usage
+
+### 4.1 Environment Setup
+
+##### Hardware Requirements
+
+Atlas 800T A2 (64GB). For drivers and firmware, see:
+[[Atlas 800T A2](https://www.hiascend.com/hardware/firmware-drivers/community?product=4&model=26&cann=8.2.RC1.alpha003&driver=Ascend+HDK+25.0.RC1)].
+
+##### Software Environment
+
+- OS: Linux (openEuler ≥ 24.03 recommended)
+- CANN == 8.1.RC1. See [[CANN Install]](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/82RC1alpha002/softwareinst/instg/instg_0001.html?Mode=PmIns&OS=Ubuntu&Software=cannToolKit)
+- python == 3.10
+- torch == 2.6.0
+- torch-npu == 2.6.0
+- transformers == 4.53.2
+
+The above configurations have been verified. Higher versions may be supported. Please submit an issue if you have questions.
+
+### 4.2 Inference Examples
+
+Below is a simple example of using openPangu-7B-Diffusion-DeepDiver with the `transformers` framework and the Deepdiver v2 Agent framework.
+
+#### Loading and Running
+
+Before running, modify `generate.py` to specify the model path.
+
+```bash
+cd inference
+python generate.py
+````
+
+For optimal throughput, set sampling parameters to `alg="confidence_threshold", threshold=0.9, num_small_blocks=1`, and choose an appropriate batch size based on hardware.
+
+#### Service Deployment
+
+Download the [lightweight service script](https://github.com/LinWeizheDragon/dllm-agent/blob/main/launch_server.py), place it in the model directory, and start the service:
+
+```bash
+python launch_server.py --load /path/to/model --port 9999
+```
+
+#### Deepdiver v2
+
+Download the [Deepdiver v2 package](https://ai.gitcode.com/ascend-tribe/openPangu-Embedded-7B-DeepDiver/tree/main/deepdiver_v2) (no model weights required) and install it following official documentation.
+
+Copy `env.template` to `config/.env`, set `MODEL_REQUEST_URL` to the model service URL, and modify `MODEL_NAME` to match the deployed model name (default: `local-diffusion-llm`).
+
+Start the MCP service:
+
+```bash
+python src/tools/mcp_server_standard.py
+```
+
+Send a query to Deepdiver v2:
+
+```bash
+python cli/demo.py -q "今天北京的天气怎么样？"
+```
+
+For more usage details, refer to the official repository.
+
+Currently, openPangu-7B-Diffusion-DeepDiver has only been trained and tested within the Deepdiver v2 framework. It has not been adapted for other agent frameworks or tasks, and performance on other setups is not guaranteed.
+
+## 5. License
+
+When using the model or its outputs, please cite the technical report:
+“DLLM Agent: See Farther, Run Faster” (arXiv:2602.07451v2).
+
+Unless otherwise specified, openPangu-7B-Diffusion-DeepDiver is licensed under the OPENPANGU MODEL LICENSE AGREEMENT VERSION 1.0, which aims to promote the development of AI technologies. See the [LICENSE](LICENSE) file in the repository root for details.
+
+## 6. Disclaimer
+
+Due to inherent technical limitations and the nature of AI-generated content, Huawei makes no guarantees regarding the following:
+
+* The generated outputs may contain defects, inaccuracies, or inappropriate content, and do not represent Huawei’s views.
+* The model is not guaranteed to be 100% accurate, reliable, complete, timely, secure, error-free, uninterrupted, or stable.
+* The outputs do not constitute advice or decisions and do not guarantee authenticity, completeness, accuracy, legality, or usefulness. They cannot replace professional advice in medical, legal, or other domains. Users must make independent judgments, and Huawei assumes no responsibility.
+
+## 7. Feedback
+
+For suggestions or feedback, please submit an issue or contact: [openPangu@huawei.com](mailto:openPangu@huawei.com).
+
+## 7. Citation
+
+```bibtex
+@article{zhen2026dllm,
+  title={DLLM Agent: See Farther, Run Faster},
+  author={Zhen, Huiling and Lin, Weizhe and Liu, Renxi and Han, Kai and Li, Yiming and Tian, Yuchuan and Chen, Hanting and Li, Xiaoguang and Li, Xiaosong and Chen, Chen and others},
+  journal={arXiv preprint arXiv:2602.07451},
+  year={2026}
+}
+```

README_CN.md

@@ -0,0 +1,162 @@
+# 开源盘古openPangu-7B-Diffusion-DeepDiver
+
+中文 | [English](README.md)
+
+## 1. 简介
+
+开源盘古openPangu-7B-Diffusion-DeepDiver 是一个基于block diffusion大语言模型（Diffusion LLM）的7B语言模型，专门针对多agent场景（包含工具调用、信息检索和多步决策）进行了训练与微调。模型的底层架构与推理流程沿用了[openPangu-R-7B-Diffusion](https://ai.gitcode.com/ascend-tribe/openPangu-R-7B-Diffusion/blob/main/README.md)的设计（包括 block-wise denoising、块内双向注意力），因此在单次生成与并行解码能力上保持一致的结构与接口体验。
+
+该模型的完整评测、训练细节请参考技术报告《DLLM Agent: See Farther, Run Faster》（[arXiv:2602.07451v2](https://arxiv.org/html/2602.07451v2)）。
+
+- 开源盘古openPangu-7B-Diffusion-DeepDiver：Agent模型，上下文长度为32k。
+
+### 主要特点：
+- 与 openPangu-R-7B-Diffusion采用相同的 DLLM 架构与迭代推理流程。
+- 专门面向 agent 深度研究场景的训练数据与微调目标，使模型在多轮工具调用与规划任务上更稳健。
+- 引入 context-clean corruption和span-aware attention alignment的训练策略，以降低 diffusion在多轮agent对话时的噪声传播并提升工具调用格式的可靠性。
+
+#### 推理
+![Context_Causal_Block_Diffusion_LLM](images/Context_Causal_Block_Diffusion_LLM.png)
+
+开源盘古openPangu-7B-Diffusion-DeepDiver采用**前文因果块扩散解码**，逐块进行扩散解码。解码过程中块内为全注意力，前文为因果注意力。当块内的token全部完成解码时，将整块token存入前文KV缓存，缓存采用因果注意力掩码，同时解码下一个block的首token。
+
+- 支持变长推理和KV缓存。
+- 灵活的上下文长度，不受块长度的限制。
+- 支持自回归和块扩散两种解码方式。
+- 使用confidence threshold采样，相比标准自回归解码，吞吐量最高可提升2.5倍。
+- 类似于Fast dLLMv2在block内设置small block，可实现吞吐和效果的权衡，通常在small block长为4或8时表现最优。
+
+##### 在Agent流程中的推理
+集成于[DeepDiver v2 Agent](https://ai.gitcode.com/ascend-tribe/openPangu-Embedded-7B-DeepDiver/tree/main/deepdiver_v2)工作流，模型在每轮生成工具调用内容时采用DLLM 的迭代去噪方式做推理。
+Deepdiver v2是以Planner（规划器）为中心, 协调多个Executor（执行器）的MAS（Multi-Agent System，多Agent系统）架构。
+Deepdiver v2的详细说明参考其[技术报告](https://ai.gitcode.com/ascend-tribe/openPangu-Embedded-7B-DeepDiver)。
+
+
+#### 训练
+
+![alt text](images/image.png)
+
+##### 训练语料
+
+使用11k专门采集或合成的的agent轨迹数据（含 planner → seeker的多agent交互、真实工具调用、工具返回结果的轨迹数据）。这些数据的目的是让模型学习在多轮交互中产生语义一致且符合调用格式的工具调用指令。详见技术报告关于“Agent-oriented Fine-tuning” 的讨论。
+
+##### 监督方式
+
+在训练中同步训练扩散模型和自回归模型的交叉熵损失，从而保证训练的稳定，并且保持模型可以稳定地从左到右生成。
+
+##### Masking 与注意力对齐
+
+为了解决多轮对话上下文与模型输出的工具调用合在一起时扩散造成的信息污染，训练时采用屏蔽无关context片段的做法（context-clean corruption）并对生成token的注意力做了生成范围内的对齐（span-aware attention alignment）；在agent数据集上的测试表明这两项修改均能提升信息检索的最终得分。
+
+## 2. 模型架构
+
+|                                | openPangu-7B-Diffusion-DeepDiver               |
+| :----------------------------: | :-------------------------: |
+|        **Architecture**        |            Dense            |
+| **Parameters (Non-Embedding)** |             7B              |
+|      **Number of Layers**      |             34              |
+|      **Hidden Dimension**      |            12800            |
+|    **Attention Mechanism**     |             GQA             |
+| **Number of Attention Heads**  |     32 for Q，8 for KV      |
+|      **Vocabulary Size**       |            153k             |
+| **Context Length**  |             32k              |
+| **Continued training Tokens**  |            700B             |
+
+## 3. 测评结果
+
+表 1. BrowseComp-zh的110问题子集的模型结果对比。
+
+| Method                             | 正确率 (%) | 工具调用次数 | Agent轮数 | 工具调用失败率 |
+| ---------------------------------- | -----------: | ---------: | ---------: | ------------------: |
+| AR Agent (autoregressive backbone) |         15.5 |        7.5 |       14.8 |                1.9% |
+| DLLM Agent (diffusion backbone)    |         15.5 |        6.7 |       13.0 |                6.4% |
+
+虽然最终准确率在该子集上与 AR 相当，但DLLM在工具调用次数与sub-Agent轮数上更节省，且在端到端延迟上显示约30%的平均加速，但 DLLM 也显示出更高的工具调用失败率，显示出DLLM相比AR模型依然不足够稳定。
+
+## 4. 部署和使用
+
+### 4.1 环境准备
+
+##### 硬件规格
+
+Atlas 800T A2 (64GB)，驱动与固件安装包获取请参照 [[Atlas 800T A2](https://www.hiascend.com/hardware/firmware-drivers/community?product=4&model=26&cann=8.2.RC1.alpha003&driver=Ascend+HDK+25.0.RC1)]。
+
+##### 软件环境
+
+- 操作系统：Linux（推荐 openEuler>=24.03）
+- CANN==8.1.RC1，安装准备及流程请参照 [[CANN Install]](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/82RC1alpha002/softwareinst/instg/instg_0001.html?Mode=PmIns&OS=Ubuntu&Software=cannToolKit)
+- python==3.10
+- torch==2.6.0
+- torch-npu==2.6.0
+- transformers==4.53.2
+
+以上软件配套经过验证，理论可以支持更高版本，如有疑问，可以提交 issue。
+
+### 4.2 推理样例
+
+下述内容提供 开源盘古openPangu-7B-Diffusion-DeepDiver 在 `transformers` 框架上结合deepdiver v2 Agent框架进行推理的一个简单示例：
+
+#### 测试加载和运行
+
+运行前请修改 generate.py，添加模型路径。
+
+```bash
+cd inference
+python generate.py
+```
+
+与基准测试不同，为了实现最佳吞吐量，采样参数应设置为 `alg="confidence_threshold", threshold=0.9, num_small_blocks=1`，并根据设备选择合适的batch size。
+
+#### 服务化部署
+下载[简易服务化脚本](https://github.com/LinWeizheDragon/dllm-agent/blob/main/launch_server.py)，放入模型文件夹中，执行下面命令启动简易服务化部署：
+
+```bash
+python launch_server.py --load /path/to/model --port 9999
+```
+
+#### Deepdiver v2
+从Deepdiver v2官方仓库下载[Deepdiver v2包](https://ai.gitcode.com/ascend-tribe/openPangu-Embedded-7B-DeepDiver/tree/main/deepdiver_v2)（不需要下载模型权重），根据官方文档进行安装。将其中的`env.template`复制粘贴到`config/.env`后，在其中指定模型服务的url为`MODEL_REQUEST_URL`，将`MODEL_NAME`修改为模型服务对应的模型名称（默认为`local-diffusion-llm`）。
+
+启动MCP服务：
+```bash
+python src/tools/mcp_server_standard.py
+```
+
+向Deepdiver v2发出query：
+
+```bash
+python cli/demo.py -q "今天北京的天气怎么样？"
+```
+
+Deepdiver v2的其他用法可以参考其官方仓库。
+
+目前开源盘古openPangu-7B-Diffusion-DeepDiver模型仅在Deepdiver v2框架上做过训练和测试，没有对其他Agent框架或者任务做适配。我们不能保证开源盘古openPangu-7B-Diffusion-DeepDiver模型在其他框架或者任务上的表现。
+
+## 5. 模型许可证
+
+请在使用模型或结果时引用技术报告：“DLLM Agent: See Farther, Run Faster” (arXiv:2602.07451v2)。
+
+除文件中对开源许可证另有约定外，开源盘古openPangu-7B-Diffusion-DeepDiver模型根据 OPENPANGU MODEL LICENSE AGREEMENT VERSION 1.0 授权，旨在允许使用并促进人工智能技术的进一步发展。有关详细信息，请参阅模型存储库根目录中的 [LICENSE](LICENSE) 文件。
+
+## 6. 免责声明
+
+由于 开源盘古openPangu-7B-Diffusion-DeepDiver（“模型”）所依赖的技术固有的技术限制，以及人工智能生成的内容是由开源盘古自动生成的，华为无法对以下事项做出任何保证：
+
+- 尽管该模型的输出由 AI 算法生成，但不能排除某些信息可能存在缺陷、不合理或引起不适的可能性，生成的内容不代表华为的态度或立场；
+- 无法保证该模型 100% 准确、可靠、功能齐全、及时、安全、无错误、不间断、持续稳定或无任何故障；
+- 该模型的输出内容不构成任何建议或决策，也不保证生成的内容的真实性、完整性、准确性、及时性、合法性、功能性或实用性。生成的内容不能替代医疗、法律等领域的专业人士回答您的问题。生成的内容仅供参考，不代表华为的任何态度、立场或观点。您需要根据实际情况做出独立判断，华为不承担任何责任。
+
+## 7. 反馈
+
+如果有任何意见和建议，请提交issue或联系 openPangu@huawei.com。
+
+## 7. 引用
+
+```bibtex
+@article{zhen2026dllm,
+  title={DLLM Agent: See Farther, Run Faster},
+  author={Zhen, Huiling and Lin, Weizhe and Liu, Renxi and Han, Kai and Li, Yiming and Tian, Yuchuan and Chen, Hanting and Li, Xiaoguang and Li, Xiaosong and Chen, Chen and others},
+  journal={arXiv preprint arXiv:2602.07451},
+  year={2026}
+}
+```

config.json

@@ -0,0 +1,31 @@
+{
+  "architectures": [
+    "PanguEmbeddedForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_pangu_embedded.PanguEmbeddedConfig",
+    "AutoModel": "modeling_pangu_embedded.PanguEmbeddedModel",
+    "AutoModelForCausalLM": "modeling_pangu_embedded.PanguEmbeddedForCausalLM"
+  },
+  "bias": true,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "pad_token_id": 0,
+  "eos_token_id": 45892,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12800,
+  "max_position_embeddings": 32768,
+  "model_type": "PanguEmbedded",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 34,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 16000000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.53.2",
+  "use_cache": true,
+  "vocab_size": 153376
+}

generation_config.json

@@ -0,0 +1,11 @@
+{
+  "_from_model_config": true,
+  "do_sample": true,
+  "bos_token_id": 1,
+  "pad_token_id": 0,
+  "eos_token_id": 45892,
+  "temperature": 1.0,
+  "top_k": 0,
+  "top_p": 0.8,
+  "transformers_version": "4.53.2"
+}

inference/generate.py

@@ -0,0 +1,56 @@
+# coding=utf-8
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+import types
+import torch
+try:
+    import torch_npu
+except ImportError as e:
+    pass
+from transformers import AutoTokenizer
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from generation_utils import diffusion_generate
+
+model_local_path = "path_to_openPangu-7B-Diffusion-Base"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(
+    model_local_path, 
+    use_fast=False, 
+    trust_remote_code=True,
+    local_files_only=True
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_local_path,
+    trust_remote_code=True,
+    torch_dtype="auto",
+    device_map="npu",
+    local_files_only=True
+)
+
+model.diffusion_generate = types.MethodType(diffusion_generate, model)
+
+mask_token_id = 45830
+eos_token_id = tokenizer.eos_token_id
+
+prompts = ["introduce the china", "hello",
+        "Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. "
+        "How many clips did Natalia sell altogether in April and May?"]
+input_ids = tokenizer(prompts, return_tensors="pt", padding=True, padding_side="left").input_ids.to(model.device)
+# Create attention mask: Mark positions with non-padding tokens as True(attended), and padding tokens as False(ignored).
+attention_mask = input_ids.ne(tokenizer.pad_token_id)
+
+output = model.diffusion_generate(
+    input_ids,
+    block_length=32,
+    attention_mask=attention_mask,
+    temperature=0.0,
+    max_new_tokens=128,
+    alg="entropy",
+    mask_token_id=mask_token_id,
+    eos_token_id=eos_token_id,
+    num_small_blocks=4
+)
+generation = tokenizer.batch_decode(output[:, input_ids.shape[1]:].tolist())
+generation = [x.split(tokenizer.eos_token)[0].strip() for x in generation]
+print(generation)

inference/generation_utils.py

@@ -0,0 +1,313 @@
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2025 NVIDIA CORPORATION & AFFILIATES
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+# Modified from Dream repos: https://github.com/HKUNLP/Dream
+
+
+
+from dataclasses import dataclass
+from collections.abc import Iterable
+from typing import Any, Dict, Optional, Tuple, Union
+
+import torch
+try:
+    import torch_npu
+except ImportError as e:
+    pass
+import torch.distributions as dists
+from torch.nn import functional as F
+
+from transformers.cache_utils import Cache, DynamicCache
+
+
+def top_p_logits(logits, top_p=None):
+    sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+    cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+    sorted_indices_to_remove = cumulative_probs > top_p
+    # Shift the indices to the right to keep the first token above the threshold
+    sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+    sorted_indices_to_remove[..., 0] = 0
+
+    mask = torch.zeros_like(logits, dtype=torch.bool, device=logits.device)
+    mask = mask.scatter_(-1, sorted_indices, sorted_indices_to_remove)
+    logits = logits.masked_fill(mask, torch.finfo(logits.dtype).min)
+    return logits
+
+
+def top_k_logits(logits, top_k=None):
+    top_k = min(top_k, logits.size(-1))  # Safety check
+    # 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 = logits.masked_fill(indices_to_remove, torch.finfo(logits.dtype).min)
+    return logits
+
+
+def sample_tokens(logits, temperature=0.0, top_p=None, top_k=None, margin_confidence=False, neg_entropy=False):
+
+    if temperature > 0:
+        logits = logits / temperature
+    if top_p is not None and top_p < 1:
+        logits = top_p_logits(logits, top_p)
+    if top_k is not None:
+        logits = top_k_logits(logits, top_k)
+    probs = torch.softmax(logits, dim=-1)
+
+    if temperature > 0:
+        try:
+            x0 = dists.Categorical(probs=probs).sample()
+            confidence = torch.gather(probs, -1, x0.unsqueeze(-1)).squeeze(-1)
+        except:
+            confidence, x0 = probs.max(dim=-1)
+    else:
+        confidence, x0 = probs.max(dim=-1)
+    
+    if margin_confidence:
+        sorted_probs, _ = torch.sort(probs, dim=-1, descending=True)
+        # Extract top1 and top2 probabilities
+        top1_probs = sorted_probs[:, 0] 
+        top2_probs = sorted_probs[:, 1] 
+        # Calculate confidence as top1 - top2
+        confidence = top1_probs - top2_probs 
+    
+    if neg_entropy:
+        epsilon = 1e-10
+        log_probs = torch.log(probs + epsilon)
+        confidence = torch.sum(probs * log_probs, dim=-1)
+    
+    return confidence, x0
+
+
+class BlockDynamicCache(DynamicCache):
+    """
+    When `skip_cache_update` is True, this class does NOT update the cached key and value states.
+    Instead, it concatenates the current states with the original cached states along the sequence dimension
+    and returns the result. 
+
+    Example:
+
+        ```python
+        >>> past_key_values = BlockDynamicCache()
+        >>> past_key_values.skip_cache_update = True
+        >>> outputs.past_key_values
+        ```
+    """
+    def __init__(self, _distributed_cache_data: Optional[Iterable] = None) -> None:
+        """
+        Initialize a BlockDynamicCache instance.
+
+        skip_cache_update is False by default.
+        """
+        super().__init__(_distributed_cache_data)
+        self.skip_cache_update = False
+        
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        layer_idx: int,
+        cache_kwargs: Optional[dict[str, Any]] = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
+
+        Behavior depends on the `skip_cache_update` flag:
+        - If `skip_cache_update` is True:
+            * Does NOT update the stored cache.
+            * Concatenates the current `key_states` and `value_states` 
+              with the original cached states along the sequence dimension.
+            * Returns the concatenated result.
+        - If `skip_cache_update` is False:
+            * Uses the parent class update logic to update the cache.
+
+        Parameters:
+            key_states (`torch.Tensor`):
+                The new key states to cache.
+            value_states (`torch.Tensor`):
+                The new value states to cache.
+            layer_idx (`int`):
+                The index of the layer to cache the states for.
+            cache_kwargs (`dict[str, Any]`, `optional`):
+                Additional arguments for the cache subclass. No additional arguments are used in `DynamicCache`.
+
+        Returns:
+            Tuple[torch.Tensor, torch.Tensor]:
+                The updated key and value states after concatenation or update.
+                When `skip_cache_update=True`, returns the concatenated tensor without modifying cache.
+                When `skip_cache_update=False`, returns the result from the parent class.
+        """
+        if self.skip_cache_update:
+            key_cache = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
+            value_cache = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
+            return key_cache, value_cache
+        return super().update(key_states, value_states, layer_idx, cache_kwargs)
+
+    
+
+@torch.no_grad()
+def diffusion_generate(
+    model,
+    inputs: Optional[torch.Tensor] = None,
+    top_p: Optional[int] = None,
+    top_k: Optional[int] = None,
+    threshold: Optional[float] = 0.9,
+    num_small_blocks: Optional[int] = 1,
+    **kwargs,
+):
+    block_length=kwargs.pop("block_length", 32)
+    attention_mask = kwargs.pop("attention_mask", None)
+    alg = kwargs.get("alg", 'origin')
+    temperature = kwargs.get("temperature", 0.0)
+    mask_token_id = kwargs.get("mask_token_id", None)
+    eos_token_id = kwargs.get("eos_token_id", None)
+
+    if mask_token_id is None:
+        raise ValueError("mask_token_id must be provided")
+
+    if eos_token_id is None:
+        raise ValueError("eos_token_id must be provided")
+
+    if inputs is None:
+        raise ValueError("inputs must be provided")
+
+    if attention_mask is None:
+        raise ValueError("attention_mask must be provided")
+
+
+    input_ids = inputs
+
+    if type(kwargs.get('max_new_tokens', None)) is int:
+        max_length = kwargs.get('max_new_tokens') + input_ids.shape[-1]
+    elif kwargs.get('max_length', None) is None:
+        raise ValueError("Pass max_new_tokens or max_length")
+
+    prompt_length = input_ids.shape[1]
+    if (max_length - prompt_length) % block_length != 0:
+        raise ValueError(
+            f"The token length ({max_length - prompt_length}) "
+            f"cannot be evenly divided by the block length ({block_length})."
+        )
+
+    num_blocks = (max_length - prompt_length) // block_length
+    device = model.device
+    position_ids = torch.arange(max_length, device=device).unsqueeze(0)
+    # pad input_ids to max_length
+    x = F.pad(input_ids, (0, max_length - prompt_length), value=mask_token_id)
+
+    # Initialize cache for the prompt
+    past_key_values = BlockDynamicCache()
+
+    causal_mask = torch.tril(torch.ones(max_length, max_length, device=device, dtype=torch.bool))[None, None, :, :]
+    
+    padding_mask = F.pad(attention_mask, (0, max_length - attention_mask.shape[1]), value=1.0)
+    position_ids = padding_mask.long().cumsum(-1) - 1
+    position_ids.masked_fill_(padding_mask == 0, 1)
+    # [B, N] --> [B, 1, N, N]
+    padding_mask = torch.logical_and(
+        padding_mask.unsqueeze(1).unsqueeze(-2),
+        padding_mask.unsqueeze(1).unsqueeze(-1),
+    )
+    attention_mask = padding_mask & causal_mask
+
+    
+    # Prefill stage
+    if prompt_length > 0:
+        cur_x = x[:, :prompt_length]
+        cur_attn_mask = attention_mask[:, :, :prompt_length, :prompt_length]
+        cur_position_ids = position_ids[:, :prompt_length]
+        output = model(cur_x,
+                attention_mask=cur_attn_mask,
+                position_ids=cur_position_ids,
+                past_key_values=past_key_values,
+                use_cache=True
+                        )
+        past_key_values = output.past_key_values
+    
+        logits = output.logits[:, -1:]
+        confidence, x0 = sample_tokens(logits, temperature=temperature, top_p=top_p, top_k=top_k)
+        x[:, prompt_length:prompt_length + 1] = x0
+        
+    # Process each block
+    for num_block in range(num_blocks):
+        block_start = prompt_length + num_block * block_length
+        block_end = prompt_length + (num_block + 1) * block_length
+        cur_x = x[:, block_start:block_end]
+        cur_attn_mask = attention_mask[:, :, block_start:block_end, :block_end]
+        cur_padding_mask = padding_mask[:, :, block_start:block_end, :block_end]
+        cur_position_ids = position_ids[:, block_start:block_end]    
+        # Use cache for generation
+        small_block_length = block_length // num_small_blocks
+
+        if block_length % num_small_blocks != 0:
+            raise ValueError(
+                f"block_length ({block_length}) must be divisible by num_small_blocks ({num_small_blocks})."
+            )
+
+        # Just concatenates current key value states, do not update key value cache
+        past_key_values.skip_cache_update = True
+        for small_block_idx in range(num_small_blocks):
+            small_block_start = small_block_idx * small_block_length
+            small_block_end = small_block_start + small_block_length
+
+            while True:
+                sub_mask_index = (cur_x[:, small_block_start:small_block_end] == mask_token_id)
+                if sub_mask_index.sum() == 0:
+                    break
+                
+                output = model(cur_x,
+                        attention_mask=cur_padding_mask,
+                        position_ids=cur_position_ids,
+                        past_key_values=past_key_values,
+                        use_cache=True)
+                logits = output.logits
+                logits = torch.cat([logits[:, :1], logits[:, :-1]], dim=1)
+                logits = logits[:, small_block_start:small_block_end]
+
+                confidence, x0 = sample_tokens(logits, temperature=temperature, top_p=top_p, top_k=top_k,
+                                            neg_entropy=(alg == 'entropy'), margin_confidence=(alg == 'topk_margin'))
+                confidence = torch.where(sub_mask_index, confidence, -torch.inf)
+                transfer_index = (F.one_hot(torch.max(confidence, dim=1)[1], num_classes=small_block_length) == 1)
+                if alg == 'confidence_threshold':
+                    transfer_index |= (confidence > threshold)
+                cur_x[:, small_block_start:small_block_end][transfer_index] = x0[transfer_index]
+
+            if eos_token_id and (x[:, prompt_length:] == eos_token_id).any(dim=1).all():
+                return x
+            
+        # Store kv cache
+        past_key_values.skip_cache_update = False
+        output = model(cur_x, 
+                        attention_mask=cur_attn_mask,
+                position_ids=cur_position_ids,
+                past_key_values=past_key_values,
+                use_cache=True,
+                        )
+        past_key_values = output.past_key_values
+        if num_block < num_blocks - 1:
+            logits = output.logits[:, -1:]
+            confidence, x0 = sample_tokens(logits, temperature=temperature, top_p=top_p, top_k=top_k)
+            x[:, block_end:block_end + 1] = x0
+
+    return x
+
+
+
+
+
+
+
+
+
+

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modeling_openpangu_dense.py

@@ -0,0 +1,576 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#                       This file was automatically generated from modular_openpangu_dense.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_openpangu_dense.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+
+# coding=utf-8
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All Rights Reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Callable, Optional, Union
+
+import torch
+from torch import nn
+try:
+    import torch_npu
+    from torch_npu.contrib import transfer_to_npu
+    if "910" in torch.npu.get_device_name():
+        NPU_ATTN_INFR = True
+        print("[INFO] torch_npu detected. Using NPU fused infer attention.")
+    else:
+        NPU_ATTN_INFR = False
+except ImportError as e:
+    NPU_ATTN_INFR = False
+
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.generation import GenerationMixin
+from transformers.masking_utils import create_causal_mask
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+from transformers.modeling_layers import GradientCheckpointingLayer
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    SequenceClassifierOutputWithPast,
+)
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from transformers.processing_utils import Unpack
+from transformers.utils import LossKwargs, auto_docstring, can_return_tuple, logging
+from .configuration_openpangu_dense import PanguEmbeddedConfig
+
+logger = logging.get_logger(__name__)
+
+
+class PanguEmbeddedRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        PanguEmbeddedRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class PanguEmbeddedRotaryEmbedding(nn.Module):
+    def __init__(self, config: PanguEmbeddedConfig, device=None):
+        super().__init__()
+        # BC: "rope_type" was originally "type"
+        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
+            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+        else:
+            self.rope_type = "default"
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+    @torch.no_grad()
+    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):  # Force float32
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class PanguEmbeddedMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor],
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs,
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+        attn_weights = attn_weights + causal_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+class PanguEmbeddedAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: PanguEmbeddedConfig, layer_idx: int):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        self.num_heads = config.num_attention_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        self.scaling = self.head_dim**-0.5
+        self.attention_dropout = config.attention_dropout
+        self.is_causal = True
+
+        self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.bias)
+        self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.bias)
+        self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.bias)
+        self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.bias)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor],
+        past_key_value: Optional[Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        attention_interface: Callable = eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+        
+        if not self.training and NPU_ATTN_INFR:
+            q_len = input_shape[1]
+            if attention_mask is not None:
+                attention_mask = ~attention_mask.bool()
+
+            attn_output, _ = torch_npu.npu_fused_infer_attention_score(
+                query_states, key_states, value_states,
+                num_heads=self.num_heads, num_key_value_heads=self.num_key_value_heads,
+                input_layout="BNSD", atten_mask=attention_mask, scale=self.scaling)
+            attn_output = attn_output.transpose(1, 2)
+            attn_weights = None
+        else:
+            attn_output, attn_weights = attention_interface(
+                self,
+                query_states,
+                key_states,
+                value_states,
+                attention_mask,
+                dropout=0.0 if not self.training else self.attention_dropout,
+                scaling=self.scaling,
+                **kwargs,
+            )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class PanguEmbeddedDecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: PanguEmbeddedConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = PanguEmbeddedAttention(config=config, layer_idx=layer_idx)
+        self.mlp = PanguEmbeddedMLP(config)
+        self.input_layernorm = PanguEmbeddedRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = PanguEmbeddedRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        return outputs
+
+
+@auto_docstring
+class PanguEmbeddedPreTrainedModel(PreTrainedModel):
+    config_class = PanguEmbeddedConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["PanguEmbeddedDecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn_3 = True
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+    _supports_cache_class = True
+    _supports_quantized_cache = True
+    _supports_static_cache = True
+    _supports_attention_backend = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, PanguEmbeddedRMSNorm):
+            module.weight.data.fill_(1.0)
+
+
+@auto_docstring
+class PanguEmbeddedModel(PanguEmbeddedPreTrainedModel):
+    def __init__(self, config: PanguEmbeddedConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [PanguEmbeddedDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = PanguEmbeddedRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = PanguEmbeddedRotaryEmbedding(config=config)
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
+    ) -> BaseModelOutputWithPast:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+            )
+            use_cache = False
+
+        # TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache
+        if not isinstance(past_key_values, (type(None), Cache)):
+            raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache()
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+
+        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_values,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                cache_position=cache_position,
+                position_embeddings=position_embeddings,
+                **flash_attn_kwargs,
+            )
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values if use_cache else None,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
+
+
+@auto_docstring
+class PanguEmbeddedForCausalLM(PanguEmbeddedPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+    _tp_plan = {"lm_head": "colwise_rep"}
+    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = PanguEmbeddedModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        **kwargs: Unpack[KwargsForCausalLM],
+    ) -> CausalLMOutputWithPast:
+ 
+        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
+        )
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs: BaseModelOutputWithPast = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+__all__ = [
+    "PanguEmbeddedForCausalLM",
+    "PanguEmbeddedModel",
+    "PanguEmbeddedPreTrainedModel",
+]

modular_openpangu_dense.py

@@ -0,0 +1,149 @@
+# coding=utf-8
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All Rights Reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Callable, Optional, Tuple
+
+import torch
+from torch import nn
+
+import torch_npu
+from torch_npu.contrib import transfer_to_npu
+if "910" in torch.npu.get_device_name():
+    NPU_ATTN_INFR = True
+    print("[INFO] torch_npu detected. Using NPU fused infer attention.")
+else:
+    NPU_ATTN_INFR = False
+
+from transformers.cache_utils import Cache
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
+from transformers.processing_utils import Unpack
+from transformers.utils import logging
+from transformers.models.llama.modeling_llama import (
+    LlamaAttention,
+    LlamaDecoderLayer,
+    LlamaForCausalLM,
+    LlamaForSequenceClassification,
+    LlamaMLP,
+    LlamaModel,
+    apply_rotary_pos_emb,
+    eager_attention_forward,
+)
+from .configuration_openpangu_dense import PanguEmbeddedConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class PanguEmbeddedMLP(LlamaMLP):
+    def __init__(self, config):
+        super().__init__(config)
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+
+
+class PanguEmbeddedAttention(LlamaAttention):
+    def __init__(self, config: PanguEmbeddedConfig, layer_idx: int):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        self.num_heads = config.num_attention_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        self.scaling = self.head_dim**-0.5
+        self.attention_dropout = config.attention_dropout
+        self.is_causal = True
+
+        self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.bias)
+        self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.bias)
+        self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.bias)
+        self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.bias)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor],
+        past_key_value: Optional[Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        attention_interface: Callable = eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+        
+        if not self.training and NPU_ATTN_INFR:
+            q_len = input_shape[1]
+            if attention_mask is not None:
+                attention_mask = ~attention_mask.bool()
+            elif q_len > 1:
+                attention_mask = torch.triu(torch.ones([q_len, q_len]), diagonal=1).bool().unsqueeze(0).unsqueeze(0).to(query_states.device)
+
+            attn_output, _ = torch_npu.npu_fused_infer_attention_score(
+                query_states, key_states, value_states,
+                num_heads=self.num_heads, num_key_value_heads=self.num_key_value_heads,
+                input_layout="BNSD", atten_mask=attention_mask, scale=self.scaling)
+            attn_output = attn_output.transpose(1, 2)
+            attn_weights = None
+        else:
+            attn_output, attn_weights = attention_interface(
+                self,
+                query_states,
+                key_states,
+                value_states,
+                attention_mask,
+                dropout=0.0 if not self.training else self.attention_dropout,
+                scaling=self.scaling,
+                **kwargs,
+            )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+            
+
+class PanguEmbeddedDecoderLayer(LlamaDecoderLayer):
+    pass
+    
+
+class PanguEmbeddedModel(LlamaModel):
+    pass
+
+
+class PanguEmbeddedForCausalLM(LlamaForCausalLM):
+    pass

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenization_openpangu.py

@@ -0,0 +1,273 @@
+# coding=utf-8
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All Rights Reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from shutil import copyfile
+from typing import Any, Dict, List, Optional, Tuple
+
+import sentencepiece as spm
+
+from transformers.tokenization_utils import PreTrainedTokenizer
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
+
+PRETRAINED_VOCAB_FILES_MAP = {}
+
+
+def convert_bool(string):
+    if isinstance(string, str):
+        if string.lower() == "true":
+            return True
+        elif string.lower() == "false":
+            return False
+        else:
+            return string
+    else:
+        return string
+
+
+class PanguTokenizer(PreTrainedTokenizer):
+    """
+    Construct a  tokenizer. Based on byte-level Byte-Pair-Encoding.
+
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    model_input_names = ["input_ids", "attention_mask"]
+    _auto_class = "AutoTokenizer"
+
+    def __init__(
+        self,
+        vocab_file,
+        unk_token="<unk>",
+        bos_token="<s>",
+        eos_token="</s>",
+        pad_token="</s>",
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        add_bos_token=True,
+        add_eos_token=False,
+        decode_with_prefix_space=False,
+        clean_up_tokenization_spaces=False,
+        **kwargs,
+    ):
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(vocab_file)
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+        self.vocab_file = vocab_file
+        self.add_bos_token = convert_bool(add_bos_token)
+        self.add_eos_token = add_eos_token
+        self.decode_with_prefix_space = decode_with_prefix_space
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(vocab_file)
+        self._no_prefix_space_tokens = None
+
+        """ Initialisation"""
+
+    @property
+    def no_prefix_space_tokens(self):
+        if self._no_prefix_space_tokens is None:
+            vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
+            self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")}
+        return self._no_prefix_space_tokens
+
+    @property
+    def vocab_size(self):
+        """Returns vocab size"""
+        return self.sp_model.get_piece_size()
+
+    @property
+    def bos_token_id(self) -> Optional[int]:
+        return self.sp_model.bos_id()
+
+    @property
+    def eos_token_id(self) -> Optional[int]:
+        return super().eos_token_id
+
+    def get_vocab(self):
+        """Returns vocab as a dict"""
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def _tokenize(self, text):
+        """Returns a tokenized string."""
+        return self.sp_model.encode(text, out_type=str)
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.sp_model.piece_to_id(token)
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        token = self.sp_model.IdToPiece(index)
+        return token
+
+    def _maybe_add_prefix_space(self, tokens, decoded):
+        if tokens and tokens[0] not in self.no_prefix_space_tokens:
+            return " " + decoded
+        else:
+            return decoded
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        current_sub_tokens = []
+        out_string = ""
+        prev_is_special = False
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                # Decode the current sub-tokens first
+                if current_sub_tokens:
+                    out_string += self.sp_model.decode(current_sub_tokens)
+                    current_sub_tokens = []
+                # Append the special token without adding extra spaces
+                out_string += token
+                prev_is_special = True
+            else:
+                current_sub_tokens.append(token)
+                prev_is_special = False
+        # Decode any remaining sub-tokens
+        if current_sub_tokens:
+            out_string += self.sp_model.decode(current_sub_tokens)
+        # Clean up leading and trailing spaces
+        if self.clean_up_tokenization_spaces:
+            out_string = self.clean_up_tokenization(out_string)
+        out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
+        return out_string[1:]
+
+    # Override decode to set spaces_between_special_tokens to True as default 
+    def decode(self,
+               token_ids,
+               spaces_between_special_tokens: bool = False,
+               **kwargs):
+        return super().decode(
+            token_ids=token_ids,
+            spaces_between_special_tokens=spaces_between_special_tokens,
+            **kwargs,
+        )
+
+    def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        """
+        Save the vocabulary and special tokens file to a directory.
+
+        Args:
+            save_directory (`str`):
+                The directory in which to save the vocabulary.
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return ("",)
+        out_vocab_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+        elif not os.path.isfile(self.vocab_file):
+            with open(out_vocab_file, "wb") as fi:
+                content_spiece_model = self.sp_model.serialized_model_proto()
+                fi.write(content_spiece_model)
+
+        return (out_vocab_file,)
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        if self.add_bos_token:
+            bos_token_ids = [self.bos_token_id]
+        else:
+            bos_token_ids = []
+
+        output = bos_token_ids + token_ids_0
+
+        if token_ids_1 is not None:
+            output = output + token_ids_1
+
+        if self.add_eos_token:
+            output = output + [self.eos_token_id]
+
+        return output
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        if token_ids_1 is None:
+            return [1] + ([0] * len(token_ids_0)) + [1]
+        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
+        use of token type ids, therefore a list of zeros is returned.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of zeros.
+        """
+        eos = [self.eos_token_id]
+
+        if token_ids_1 is None:
+            return len(token_ids_0 + eos) * [0]
+        return len(token_ids_0 + eos + token_ids_1 + eos) * [0]

tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b16f1558c0cd4ae6ef1a2c605713be0a514f50e1ce2d2c878979ce988c148ec
+size 2477809

tokenizer_config.json

@@ -0,0 +1 @@
+{"add_bos_token": true, "add_eos_token": false, "add_prefix_space": true, "added_tokens_decoder": {"0": {"content": "<unk>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true}, "1": {"content": "<s>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true}, "2": {"content": "</s>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true}, "45806": {"content": "<|User|>:", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true}, "45813": {"content": "<|Bot|>:", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true}, "45830": {"content": "[unused0]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true}, "45840": {"content": "[unused1]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true}, "45846": {"content": "[unused2]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true}, "45849": {"content": "[unused3]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true}, "45861": {"content": "[unused4]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true}, "45866": {"content": "[unused5]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true}, "45874": {"content": "[unused6]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true}, "45883": {"content": "[unused7]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true}, "45884": {"content": "[unused8]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true}, "45887": {"content": "[unused9]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, 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