fix: 修复callback 时机不一致的问题

build(deps): bump fonttools from 4.59.0 to 4.61.0

.github/workflows/spell-check.yml

@@ -0,0 +1,17 @@
+name: Spell Check
+on: [push, pull_request]
+
+permissions:
+  contents: read
+
+jobs:
+  spellcheck:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Check spelling in specific files
+        uses: codespell-project/actions-codespell@v2
+        with:
+          check_filenames: true
+          only_warn: false
+          path: "**/*.{md, py}"

.gitignore

@@ -1,13 +1,12 @@
-# cache
+# Ignore everything
-__pycache__
+*
-.pytest_cache
 
-# params
+# Allow directories to be traversed
-params/*
+!*/
 
-# vscode file
+# Allow specific file types and root files
-.vscode
+!*.py
-
+!*.md
-# build file
+!*.png
-build
+!LICENSE
-*.egg-info
+!pyproject.toml

LICENSE

@@ -1,201 +1,674 @@
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+<https://www.gnu.org/licenses/>.
+
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+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -12,39 +12,39 @@
 
 <h2 id="english">English Version</h2>
 
-This is a Chinese-English bilingual Transformer model supporting both languages. It contains model configurations and training workflows, completing training by loading parameters defined in `param_path/config.json`. The training script `train.py` parses command-line arguments, including dataset root directory, number of training epochs, batch size, checkpoint interval, and checkpoint directory.
+A training and inference framework for autoregressive Transformer language models.
 
-**Model Download Options (Choose One):**
+**Model Download Options (choose one):**
 
-1. Visit [HuggingFace](https://huggingface.co/ViperEk/KHAOSZ) to access **Files and versions**
+1. Visit [HuggingFace](https://huggingface.co/ViperEk/KHAOSZ) and check **Files and versions**
-2. Run `scripts/download.py` to download parameters
+2. Run `scripts/download.py` to download model parameters
 
 **Demo Video:** [bilibili](https://www.bilibili.com/video/BV1z5RPYHEkd)
 
-Training dataset sources are listed in the **Model Card** section of the HuggingFace download link.
+For training data sources, please refer to the **Model Card** section on the HuggingFace download page.
 
-**License:** Code follows Apache-2.0 protocol. Please credit the source code when used.
+**License:** The code follows the GPL-3.0 license. Please provide attribution when using it.
+
+- **📊 Device Selection:** Uses CUDA for training by default
+- **🌐 Performance Optimization:** Enable `dtype=torch.bfloat16` to accelerate training and reduce memory usage. Ensure your hardware supports this feature
+- **🤖 Language Support:** The model supports training in Chinese and English. Since the BBPE tokenizer hasn't been trained on multilingual text, OOV (Out-of-Vocabulary) issues are minimal for Chinese and English, but may exist for other languages
 
-- **📊 Device Selection:** Code defaults to CUDA training
-- **🌐 Performance Optimization:** `dtype=torch.bfloat16` is enabled to accelerate training and reduce memory usage. Ensure hardware supports this feature.
-- **🤖 Language Support:** Model supports Chinese and English training. The BBPE tokenizer was trained without multilingual text, so OOV (out-of-vocabulary) issues are minimized for these languages but may exist for others.
 
 ### 📌 Training Guide
 
 To train this Transformer model, follow these steps:
 
-**(1). Prepare Dataset:**
+**(1). Prepare the Dataset:**
 
-Place datasets in the designated root directory. Files should be text documents in Chinese, English, or mixed. Format should align with model input requirements - preferably pre-tokenized token_ids stored as `torch.Tensor` (using `torch.Tensor` saves memory compared to Python lists, which default to 64-bit precision).
+Place the dataset in the specified root directory. This system uses the BBPE tokenizer for tokenization and requires training with pre-tokenized segments (stored as *.h5 format files).
 
 **(2). Install Dependencies:**
 
 ```bash
-pip install -r requirements.txt
+pip install -e .
-pip install .
 ```
 
-**(3). Run Training Script:**
+**(3). Run the Training Script:**
 
 ```bash
 python train.py \
@@ -55,29 +55,29 @@ python train.py \
 --batch_size=8 \
 --max_lr=2e-4 \
 --checkpoint_interval=10000 \
---checkpoint_dir=checkpoints
+--checkpoint_dir=checkpoints 
 ```
 
-**Parameters Explanation:**
+**Parameter Explanation:**
 - `--train_type`: Training type (seq, sft, dpo)
-- `--data_root_path`: Root directory of the dataset
+- `--data_root_path`: Dataset root directory
-- `--param_path`: Path to the model training parameters
+- `--param_path`: Path to model training parameters
 - `--n_epoch`: Total number of training epochs
 - `--batch_size`: Batch size
 - `--accumulation_steps`: Number of batches per training step
-- `--warmup_steps`: Number of warmup steps
+- `--warmup_steps`: Warmup steps
 - `--max_lr`: Maximum learning rate (using warmup + cosine decay)
 - `--checkpoint_interval`: Checkpoint saving interval
-- `--checkpoint_dir`: Directory to save checkpoints
+- `--checkpoint_dir`: Checkpoint saving directory
-- `--resume_dir`: Resume training from the specified path
+- `--resume_dir`: Resume training from specified path
+
 
-Training logs will be saved in `train_log.txt`. Checkpoints will be saved in the specified directory for resuming training or evaluation.
 
 ### 👉 Usage Guide
 
-**(1). Chatting with the Model:**
+**(1). Chat with the Model:**
 
-Open `chat.py` or use streaming/non-streaming interfaces:
+Open `chat.py` or use the streaming/non-streaming interfaces:
 
 **Streaming Output:**
 ```python
@@ -129,7 +129,7 @@ while True:
     print(response)
 ```
 
-**(2) Retrieval-Augmented Generation (RAG):**
+**(2). Retrieval-Augmented Generation (RAG):**
 
 ```python
 import torch
@@ -148,29 +148,8 @@ retrieved_content = model.retrieve_generate(
 print(retrieved_content)
 ```
 
-### 📌 Model Specifications
-
-This model is based on a 24-layer Transformer with parameters defined in `config.json`, totaling approximately 1.0 billion (1.0B) parameters.
-
-**Key Design Choices:**
-- Weight tying between embedding and final linear layers (standard for small models to save parameters)
-- Embedding layer optimization: Without weight tying, a 10,000-word vocabulary would consume ~102M parameters (0.1B)
-
-**Limitations:**
-- May struggle with complex language phenomena due to smaller parameter size
-- Prone to overfitting on specialized datasets
-- Limited multilingual capabilities
-
-**Advantages:**
-- Runs efficiently on lower-spec hardware
-- Shorter training time compared to larger models
-
-**Training Pipeline:** 
-The model has completed pre-training + SFT (Supervised Fine-Tuning) + DPO (Direct Preference Optimization) workflows. All corresponding training code is included in the repository.
-
-
 <h2 id="chinese">中文版本</h2>
-这是一个支持中英文双语的 Transformer 模型，能够处理两种语言。模型包含配置文件和训练流程，通过加载 `param_path/config.json` 中定义的参数完成训练。训练脚本 `train.py` 支持命令行参数解析，包括数据集根目录、训练轮数（epochs）、批量大小（batch size）、检查点保存间隔、检查点目录等。
+这是一个支持基于自回归模式的 Transfomer 语言模型训练以及推理框架
 
 **模型下载选项（任选其一）：**
 
@@ -181,30 +160,28 @@ The model has completed pre-training + SFT (Supervised Fine-Tuning) + DPO (Direc
 
 训练数据来源请参见 HuggingFace 下载页面中的 **Model Card** 部分。
 
-**许可证：** 代码遵循 Apache-2.0 协议，使用时请注明出处。
+**许可证：** 代码遵循 GPL-3.0 协议，使用时请注明出处。
 
 - **📊 设备选择：** 默认使用 CUDA 进行训练
 - **🌐 性能优化：** 启用 `dtype=torch.bfloat16` 以加速训练并减少内存占用，请确保硬件支持该特性
 - **🤖 语言支持：** 模型支持中文和英文训练。由于 BBPE 分词器未使用多语言文本训练，因此中英文的 OOV（未登录词）问题较少，其他语言可能存在 OOV 问题
 
 
-
 ### 📌 训练指南
 
 要训练该 Transformer 模型，请按照以下步骤操作：
 
-#### **(1). 准备数据集：**
+**(1). 准备数据集：**
 
-将数据集放置在指定的根目录下。文件应为包含中文、英文或混合文本的文本文档。格式应符合模型输入要求——建议使用预分词后的 `token_ids` 并以 `torch.Tensor` 格式保存（使用 `torch.Tensor` 相比 Python 列表更节省内存，列表默认为 64 位精度）。
+将数据集放置在指定的根目录下， 本系统采用 BBPE 分词器进行分词，并且要求使用已经经过分词的 token 分段训练（分段存储为 *.h5 格式）
 
-#### **(2). 安装依赖：**
+**(2). 安装依赖：**
 
 ```bash
-pip install -r requirements.txt
+pip install -e .
-pip install .
 ```
 
-#### **(3). 运行训练脚本：**
+**(3). 运行训练脚本：**
 
 ```bash
 python train.py \
@@ -231,13 +208,11 @@ python train.py \
 - `--checkpoint_dir`: 检查点保存目录
 - `--resume_dir`: 从指定路径恢复训练
 
-训练日志将保存在 `train_log.txt` 中。检查点将保存在指定目录，用于恢复训练或评估。
-
 
 
 ### 👉 使用指南
 
-#### **(1). 与模型对话：**
+**(1). 与模型对话：**
 
 打开 `chat.py` 或使用流式/非流式接口：
 
@@ -291,7 +266,7 @@ while True:
     print(response)
 ```
 
-#### **(2). 基于检索的生成（RAG）：**
+**(2). 基于检索的生成（RAG）：**
 
 ```python
 import torch
@@ -308,26 +283,4 @@ retrieved_content = model.retrieve_generate(
     top_p=0.95
 )
 print(retrieved_content)
-```
+```
-
-
-
-### 📌 模型规格说明（重复部分）
-
-该模型基于一个 24 层的 Transformer 架构，参数配置定义在 `config.json` 中，总参数量约为 10 亿（1.0B）。
-
-**关键设计选择：**
-- 在嵌入层（embedding）与最终线性层之间进行权重绑定（weight tying），这是小型模型中常见的节省参数量的做法
-- 嵌入层优化：若不进行权重绑定，一个包含 10,000 个词的词汇表将消耗约 1.02 亿（0.1B）参数
-
-**局限性：**
-- 由于参数规模较小，可能在处理复杂语言现象时表现受限
-- 在特定领域的数据集上容易出现过拟合
-- 多语言能力有限
-
-**优势：**
-- 可在低配置硬件上高效运行
-- 相较于大型模型，训练时间更短
-
-**训练流程：**  
-该模型已完成预训练（pre-training）+ 监督微调（SFT, Supervised Fine-Tuning）+ 直接偏好优化（DPO, Direct Preference Optimization）的全流程。所有相关的训练代码均已包含在代码库中。

assets/docs/architecture.md

@@ -0,0 +1,220 @@
+## 1. 为什么我要做这个项目？
+
+现在市面上有很多大模型，比如GPT、LLaMA这些，动不动就是几十亿甚至上千亿参数。但说实话，这些模型对硬件要求太高了，普通开发者根本玩不起。我就想：**能不能做一个既好用又能在普通电脑上跑起来的模型呢？** 这其实也是目前大部分人的期望， 能有一个可以本地部署的ai小型项目，实现完全私有化并且有一定的智能能力。
+
+于是就有了这个KHAOSZ项目，1B参数，中英双语，支持对话、文本生成、RAG检索，而且训练代码都是开源的！
+
+## 2. 系统架构
+
+系统分为以下板块
+
+```mermaid
+graph LR
+    %% 样式定义
+    classDef config fill:#e1f5fe,stroke:#01579b;
+    classDef trainer fill:#f3e5f5,stroke:#4a148c;
+    classDef data fill:#e8f5e8,stroke:#1b5e20;
+    classDef model fill:#fff3e0,stroke:#e65100;
+    classDef inference fill:#fce4ec,stroke:#880e4f;
+    classDef parallel fill:#e0f2f1,stroke:#004d40;
+    
+    %% 配置模块
+    subgraph Config["Config(配置模块)"]
+        C1[model_config.py]
+        C2[train_config.py]
+        C3[scheduler_config.py]
+    end
+    class Config config;
+
+    %% 训练器模块
+    subgraph Trainer["Trainer(训练器模块)"]
+        T1[trainer.py]
+        T2[train_content.py]
+        T3[schedule.py]
+        T4[strategy.py]
+        T5[train_callback.py]
+    end
+    class Trainer trainer;
+
+    %% 数据模块
+    subgraph Data["Data(数据模块)"]
+        D1[dataset.py]
+        D2[sampler.py]
+        D3[mmap.py]
+        D4[tokenizer.py]
+        D5[checkpoint.py]
+    end
+    class Data data;
+
+    %% 模型模块
+    subgraph Model["Model(模型模块)"]
+        M1[transformer.py]
+        M2[module.py]
+    end
+    class Model model;
+
+    %% 推理模块
+    subgraph Inference["Inference(推理模块)"]
+        I1[generator.py]
+        I2[core.py]
+    end
+    class Inference inference;
+
+    %% 并行模块
+    subgraph Parallel["Parallel(并行模块)"]
+        P1[setup.py]
+        P2[module.py]
+    end
+    class Parallel parallel;
+
+    %% 配置依赖
+    C2 -.-> T1
+    C1 -.-> M1
+    C3 -.-> T3
+
+    %% 训练器内部依赖
+    T1 --> T5
+    T1 --> T2
+    T2 --> T3
+    T2 --> T4
+
+    %% 数据流
+    D1 --> D2
+    D1 --> D3
+    D1 --> D4
+    D1 --> D5
+
+    %% 模型依赖
+    M1 --> M2
+
+    %% 推理依赖
+    I1 --> I2
+
+    %% 跨模块依赖
+    T2 -.-> M1
+    I1 -.-> M1
+    T2 -.-> D1
+    T1 -.-> P1
+```
+
+
+### 1. 配置管理（/config/）
+- **模型配置**：定义模型结构参数（如层数、头数、维度等），通过 `ModelConfig` 统一管理。
+- **训练配置**：设置训练参数（如批次大小、训练阶段 PT/SFT/DPO、优化器等），由 `TrainConfig` 加载。
+- **调度配置**：控制学习率策略（如余弦退火）和训练进度。
+
+### 2. 硬件与并行（/parallel/）
+- **分布式初始化**：通过 `setup_parallel` 函数，根据配置初始化多卡/多机训练环境。
+
+### 3. 数据处理（/data/）
+- **高效加载**：使用内存映射（mmap）技术加载超大语料，避免内存溢出，实现零拷贝读取。
+
+### 4. 模型与训练（/model/, /trainer/）
+- **统一模型架构**：基于 Transformer，支持灵活配置不同规模（如7B、13B）。
+- **策略化训练器**：`Trainer` 根据训练阶段（PT/SFT/DPO）自动切换训练策略，复用同一训练循环。
+- **训练上下文管理**：统一管理模型、优化器、调度器和指标，支持多阶段无缝衔接。
+
+### 5. 推理服务（/inference/, /utils/）
+- **统一生成接口**：提供同步、批量、流式生成方法，适配所有训练阶段。
+- **KV缓存优化**：在自回归生成中缓存 Key/Value，昇腾XPU下利用高速片上内存加速。
+- **RAG支持**：结合检索器和嵌入模型，从外部知识库注入相关信息，提升回答质量。
+- **智能文本分割**：
+  - **结构优先分割**：按标题、段落等切分；
+  - **语义分割**：基于句子嵌入相似度，确保片段语义完整，提升微调效果。
+
+
+## 3. 训练流程
+
+常见大语言模型（Large Language Model, LLM）的训练流程通常包含三个阶段：**预训练（Pre-training, PT）**、**监督微调（Supervised Fine-Tuning, SFT）** 以及 **基于人类反馈的强化学习（Reinforcement Learning from Human Feedback, RLHF）**。本系统设计支持全流程无缝衔接，通过模块化策略实现不同训练阶段的高效切换与状态管理，确保模型能力从通用语言理解逐步对齐至符合人类偏好的对话与指令执行。
+
+### **2.1 预训练阶段**
+
+预训练阶段旨在构建模型的基础语言能力与通用知识表示。该阶段在大规模、无标注的语料库（通常涵盖数百GB至数TB的文本数据）上进行自监督学习。模型架构基于标准的Transformer Decoder，通过掩码语言建模（如因果语言建模）目标进行训练，使模型能够学习词汇、语法、语义及蕴含于文本中的世界知识。
+
+**核心公式：因果语言建模（Causal Language Modeling）**
+
+$$
+L_{\text{PT}} = - \sum_{t=1}^{T} \log P(x_t \mid x_{\lt t}; \theta)
+$$
+
+**符号说明：**
+
+- $T$：序列长度
+- $x_t$：序列中第 $ t $ 个词元（token）
+- $x_{<t}$：位置 $ t $ 之前的所有词元
+- $\theta$：模型参数
+- $P(x_t \mid x_{<t}; \theta)$：模型在给定上文条件下预测下一个词元的概率
+
+
+
+本阶段的核心在于利用分布式的并行计算资源，实现模型参数的稳定优化。训练器模块中的`PTStrategy`策略，专门负责管理预训练特有的数据采样、长序列分段与梯度累积逻辑。同时，硬件适配模块会根据运行环境（如华为昇腾NPU集群或标准GPU集群）自动选择最优的并行通信后端（如HCCL或NCCL），并进行计算图优化，以最大化硬件利用率和训练吞吐量。
+
+另外系统通过数据模块中的高效内存映射加载器（`MmapFileHandler`），实现海量数据的零拷贝读取，以克服传统IO瓶颈。
+
+
+
+### **2.2 监督微调阶段**
+
+预训练模型虽具备强大的语言生成能力，但尚未对齐至遵循人类指令、进行安全有益对话的行为模式。监督微调阶段旨在弥合这一差距。该阶段使用由人工精心编写的、高质量的“指令-响应”配对数据集。
+
+
+
+**核心公式：序列到序列条件语言建模**
+
+设完整序列 $S = [s_1, s_2, \ldots, s_{P+L}]$，其中：
+
+- 前 $P$ 个token是prompt 以及对应控制token： $X = [s_1, \ldots, s_P]$
+- 后 $L$ 个token是response以及对应控制token： $Y = [s_{P+1}, \ldots, s_{P+L}]$
+
+损失函数为：
+
+$$
+L_{\text{SFT}} = - \sum_{t=P+1}^{P+L} \log P(s_t \mid s_{\lt t}; \theta)
+$$
+
+训练器模块将动态切换到`SFTStrategy`策略。此策略的核心是引入序列级的监督学习目标，例如预测给定指令下完整、正确的响应序列。训练上下文管理器（`TrainContext`）负责平滑地从PT阶段检查点加载模型状态，并初始化新的优化器和学习率调度器。本阶段不仅优化模型参数，更重要的是引导模型学习“对话”这一特定任务范式，使其输出风格、内容与格式均符合人类期望。
+
+
+
+### **2.3 基于人类反馈的强化学习阶段**
+
+为生成更具帮助性、无害性且符合人类偏好的高质量输出，系统进一步集成强化学习阶段。传统的RLHF流程包括**奖励模型训练**与**策略模型微调**两个核心步骤。系统支持以直接偏好优化(Direct Preference Optimization，DPO)算法为代表的策略微调，并针对稳定性与收敛性进行了多项工程优化。
+
+
+
+#### **2.3.1 传统 RLHF（奖励模型训练）**
+
+$$
+L_{\text{RM}} = -\mathbb{E}_{(x, y_w, y_l) \sim D} \left[ \log \sigma\left( r_\phi(x, y_w) - r_\phi(x, y_l) \right) \right]
+$$
+
+**符号说明：**
+
+- $r_\phi(x, y)$：参数为 $phi$ 的奖励模型给出的标量分数
+- $y_w, y_l $：同一提示 $ x $ 下的优选和劣选回答
+- $\sigma $：sigmoid 函数
+- $\mathcal{D} $：人类偏好数据集
+
+
+#### **2.3.2 DPO 直接偏好优化**（推荐）
+
+$$
+L_{\text{DPO}} = -E_{(x, y_w, y_l) \sim D} \left[ \log \sigma\left( \beta \log \frac{\pi_\theta(y_w \mid x)}{\pi_{\text{ref}}(y_w \mid x)} - \beta \log \frac{\pi_\theta(y_l \mid x)}{\pi_{\text{ref}}(y_l \mid x)} \right) \right]
+$$
+
+**符号说明：**
+
+- $\pi_\theta(y \mid x) $：当前策略模型生成回答的概率
+- $\pi_{\text{ref}}(y \mid x) $：参考模型生成回答的概率
+- $\beta $：温度参数（通常设为 0.1-0.5）
+- 注意：隐式学习奖励函数 $r(x, y) = \beta \log \frac{\pi_\theta(y \mid x)}{\pi_{\text{ref}}(y \mid x)} $
+
+
+在本阶段，训练器模块启用`RLHFStrategy`策略（或类似的`DPOStrategy`直接偏好优化策略）。该策略管理一个复杂的训练循环，其中包含策略模型（待优化的LLM）、参考模型（通常为SFT后的模型快照）和奖励模型。系统流程如下：
+
+1. **偏好数据收集与奖励建模**：首先，通过收集人类标注员对同一提示词下多个模型生成结果的排序偏好数据，训练一个独立的奖励模型（Reward Model, RM）。该模型学习为生成文本输出一个标量奖励分数，以量化其符合人类偏好的程度。
+2. **策略优化**：随后，使用奖励模型作为优化信号，通过强化学习算法对SFT模型（作为策略）进行微调。策略优化的目标是最大化从奖励模型获得的期望累计奖励，同时通过KL散度惩罚项约束策略模型与参考模型的输出分布不过度偏离，以防止模式崩溃并保持生成多样性。训练上下文管理器在此阶段同时维护策略模型、参考模型和奖励模型（或价值函数模型）的状态，并协调复杂的多阶段梯度计算。
+
+
+
+通过上述三阶段的递进式训练，模型完成了从通用语言基座到专业化、高对齐度对话智能体的进化。系统通过统一的`Trainer`接口和策略模式设计，使得各阶段训练在代码层面高度复用，在流程层面清晰解耦，为大规模语言模型的研发与迭代提供了高效、灵活且可扩展的工程基础。

assets/docs/kvcache.md

@@ -4,8 +4,8 @@
 
 $$
 \begin{align*}
-o_i &= \sum_j s_{ij} v_{j} \\
+o_i &= \sum_j s_{ij} v_{j} \newline
-s_{ij} &= \text{softmax}\left( \sum_n \frac{q_{i,n} k_{j,n}}{\sqrt{d_k}} \right)
+s_{ij} &= \text{softmax}\left( \frac{q_{i} k_{j}}{\sqrt{d_k}} \right)
 \end{align*}
 $$
 
@@ -13,15 +13,15 @@ $$
 
 $$
 \begin{align*}
-o_n &= \sum_j s_{j}v_{j,n} \\
+o_n &= \sum_j s_{j}v_{j} \newline
-s_j &= \text{softmax}\left(\sum_n\frac{q_n k_{j,n}}{\sqrt{d_k}} \right)
+s_j &= \text{softmax}\left(\frac{q_n k_{j}}{\sqrt{d_k}} \right)
 \end{align*}
 $$
 
 如果我们把式子展开
 
 $$
-o_n = \sum_j \sum_n \text{softmax}\left(\frac{q_n k_{j,n}}{\sqrt{d_k}}\right)v_{j,n}
+o_n = \sum_j \text{softmax}\left(\frac{q_n k_{j}}{\sqrt{d_k}}\right)v_{j}
 $$
 
 以上表达式只有k和v存在长度下标, 而 $q$ 没有， 所以计算过程中 $q$ 的输入是确定的上次输入的最后一个token, 而 $k,  v$ 是需要对不同长度的部分进行缓存的，同时缓存的时候应该注意位置编码的计算应该在kvcache的计算之前进行，否则会存在位置编码的计算错误

demo/generate_retrieve.py

@@ -35,7 +35,7 @@ if __name__ == "__main__":
         top_k=50
     )
 
-    print("retrive content:")
+    print("retrieve content:")
     print("\n".join([f"{idx + 1}. " + text for idx, (text, _) in enumerate(retrieved)]))
 
     print("\n\nretrive generate:")

khaosz/__init__.py

@@ -1,10 +1,9 @@
-__version__ = "1.3.1"
+__version__ = "1.3.2"
 __author__ = "ViperEkura"
 
 from khaosz.api import Khaosz
 from khaosz.config import (
     ModelConfig,
-    ParameterLoader,
     TrainConfig,
 )
 from khaosz.model.transformer import Transformer
@@ -42,7 +41,6 @@ __all__ = [
     "PriorityTextSplitter",
     
     "ModelConfig",
-    "ParameterLoader",
     "TrainConfig",
     
     "DatasetLoader",

khaosz/api.py

@@ -9,12 +9,13 @@ from khaosz.inference.generator import (
     RetrievalGenerator, 
     EmbeddingEncoder
 )
-from khaosz.config.param_config import ParameterLoader
+from khaosz.config.param_config import ModelParameter
 
 
 class Khaosz:
     def __init__(self, model_dir: str):
-        self.parameter = ParameterLoader.load(model_dir)
+        self.parameter = ModelParameter()
+        self.parameter.load(model_dir)
     
     def to(self, *args, **kwargs):
         self.parameter.to(*args, **kwargs)

khaosz/config/__init__.py

@@ -1,5 +1,5 @@
 from khaosz.config.model_config import ModelConfig
-from khaosz.config.param_config import BaseModelIO, ModelParameter, Checkpoint, ParameterLoader
+from khaosz.config.param_config import BaseModelIO, ModelParameter
 from khaosz.config.schedule_config import ScheduleConfig, CosineScheduleConfig, SGDRScheduleConfig
 from khaosz.config.train_config import TrainConfig
 
@@ -7,8 +7,6 @@ from khaosz.config.train_config import TrainConfig
 __all__ = [
     "BaseModelIO",
     "ModelParameter",
-    "Checkpoint",
-    "ParameterLoader",
     "ModelConfig",
     "TrainConfig",
     

khaosz/config/model_config.py

@@ -1,37 +1,43 @@
 import json
 
 from dataclasses import asdict, dataclass
-from typing import Any, Dict, Optional, Self
+from typing import Optional, Self
+
 
 @dataclass
 class ModelConfig:
     # basic config
     vocab_size: Optional[int] = None
-    n_dim: Optional[int] = None
+    dim: Optional[int] = None
-    n_head: Optional[int] = None
+
-    n_layer: Optional[int] = None
+    n_layers: Optional[int] = None
-    m_len: Optional[int] = None
     norm_eps: Optional[float] = None
-    d_ffn: Optional[int] = None
+    dim_ffn: Optional[int] = None
     tie_weight: Optional[bool] = None
     
+    # RoPE
+    max_len: Optional[int] = None
+    rope_theta: Optional[float] = None
+    
     # GQA
-    n_kvhead: Optional[int] = None
+    n_heads: Optional[int] = None
+    n_kv_heads: Optional[int] = None
+    use_qk_norm: Optional[bool] = None
+    use_gated_attention: Optional[bool] = None
     
     
     def load(self, config_path: str) -> Self:
+        config = {}
         with open(config_path, 'r') as f:
-            config: Dict[str, Any] = json.load(f)
+            config.update(json.load(f)) 
+            
         for key, value in config.items():
             if hasattr(self, key):
                 setattr(self, key, value)
     
         return self
                     
-    def save(self, config_path: str) -> None:
+    def save(self, config_path: str):
-        config_dict = asdict(self)
+        config_dict = {k: v for k, v in asdict(self).items() if v is not None}
-        config_dict = {k: v for k, v in config_dict.items() if v is not None}
         with open(config_path, 'w') as f:
             json.dump(config_dict, f, indent=4)
-
-

khaosz/config/param_config.py

@@ -1,30 +1,30 @@
-import pickle as pkl
-import matplotlib.pyplot as plt
-import safetensors.torch as st
 import torch.nn as nn
-import torch.optim as optim
+import safetensors.torch as st
 
 from dataclasses import dataclass, field
-from typing import Any, Dict, List, Optional, Self, Union
+from typing import Optional, Self, Union
 from pathlib import Path
 
 from khaosz.data.tokenizer import BpeTokenizer
 from khaosz.config.model_config import ModelConfig
 from khaosz.model.transformer import Transformer
 
-
+@dataclass
 class BaseModelIO:
     """Base class for model I/O operations."""
     
-    def __init__(
+    model: Optional[nn.Module] = field(
-        self, 
+        default=None,
-        model: Optional[nn.Module] = None, 
+        metadata={"help": "Transformer model."}
-        tokenizer: Optional[BpeTokenizer] = None,
+    )
-        config: Optional[ModelConfig] = None
+    tokenizer: BpeTokenizer = field(
-    ):
+        default_factory=BpeTokenizer,
-        self.model = model
+        metadata={"help": "Tokenizer for the model."}
-        self.tokenizer = tokenizer or BpeTokenizer()
+    )
-        self.config = config or ModelConfig()
+    config: ModelConfig = field(
+        default_factory=ModelConfig,
+        metadata={"help": "Transformer model configuration."}
+    )
     
     def _get_file_paths(self, directory: Union[str, Path]) -> dict[str, Path]:
         """Get standardized file paths for model components."""
@@ -52,15 +52,16 @@ class BaseModelIO:
         self.config.load(str(paths["config"]))
         self.tokenizer.load(str(paths["tokenizer"]))
         
+        if self.model is None:
+            self.model = Transformer(self.config)
+        
         if paths["model"].exists():
             state_dict = st.load_file(str(paths["model"]))
-            if self.model is None:
-                self.model = Transformer(self.config)
             self.model.load_state_dict(state_dict)
         
         return self
     
-    def to(self, *args, **kwargs) -> Self:
+    def to(self, *args, **kwargs) -> "BaseModelIO":
         """Move model to device."""
         if self.model is not None:
             self.model.to(*args, **kwargs)
@@ -71,174 +72,9 @@ class BaseModelIO:
 class ModelParameter(BaseModelIO):
     """Container for model parameters with serialization capabilities."""
     
-    model: Optional[nn.Module] = field(
-        default=None,
-        metadata={"help": "Transformer model."}
-    )
-    tokenizer: BpeTokenizer = field(
-        default_factory=BpeTokenizer,
-        metadata={"help": "Tokenizer for the model."}
-    )
-    config: ModelConfig = field(
-        default_factory=ModelConfig,
-        metadata={"help": "Transformer model configuration."}
-    )
-    
     def save(self, save_dir: Union[str, Path]):
         self.save_components(save_dir)
     
-    def load(self, load_dir: Union[str, Path]) -> Self:
+    def load(self, load_dir: Union[str, Path]) -> "ModelParameter":
         return self.load_components(load_dir)
 
-
-@dataclass
-class Checkpoint(BaseModelIO):
-    """Extended model parameters with training state."""
-    
-    model: Optional[nn.Module] = field(
-        default=None,
-        metadata={"help": "Transformer model."}
-    )
-    tokenizer: BpeTokenizer = field(
-        default_factory=BpeTokenizer,
-        metadata={"help": "Tokenizer for the model."}
-    )
-    config: ModelConfig = field(
-        default_factory=ModelConfig,
-        metadata={"help": "Transformer model configuration."}
-    )
-    optimizer_state: Dict[str, Any] = field(
-        default=None,
-        metadata={"help": "Optimizer state."}
-    )
-    scheduler_state: Dict[str, Any] = field(
-        default=None,
-        metadata={"help": "Sampler state."}
-    )
-    loss_list: List[float] = field(
-        default_factory=list,
-        metadata={"help": "List of training losses."}
-    )
-    epoch: int = field(
-        default=0,
-        metadata={"help": "Current epoch."}
-    )
-    batch_iter: int = field(
-        default=0,
-        metadata={"help": "Current iteration."}
-    )
-    
-    def _get_training_paths(self, directory: Union[str, Path]) -> dict[str, Path]:
-        paths = self._get_file_paths(directory)
-        paths.update({
-            "loss_list": paths["model"].parent / "loss.pkl",
-            "loss_plot": paths["model"].parent / "loss.png",
-            "optimizer_state": paths["model"].parent / "optimizer_state.pkl",
-            "sampler_state": paths["model"].parent / "sampler_state.pkl"
-        })
-        return paths
-    
-    def save_training_state(self, save_dir: Union[str, Path]):
-        paths = self._get_training_paths(save_dir)
-        
-        # Save loss plot
-        self._plot_loss(str(paths["loss_plot"]))
-        
-        # Save loss list
-        with open(str(paths["loss_list"]), "wb") as f:
-            pkl.dump(self.loss_list, f)
-        
-        # Save optimizer state
-        with open(str(paths["optimizer_state"]), "wb") as f:
-            pkl.dump(self.optimizer_state, f)
-        
-        # Save sampler state
-        with open(str(paths["sampler_state"]), "wb") as f:
-            pkl.dump(self.scheduler_state, f)
-    
-    def load_training_state(self, load_dir: Union[str, Path]) -> Self:
-        paths = self._get_training_paths(load_dir)
-        
-        # Load loss list
-        if paths["loss_list"].exists():
-            with open(str(paths["loss_list"]), "rb") as f:
-                self.loss_list = pkl.load(f)
-        
-        # Load optimizer state
-        if paths["optimizer_state"].exists():
-            with open(str(paths["optimizer_state"]), "rb") as f:
-                self.optimizer_state = pkl.load(f)
-        
-        # Load sampler state
-        if paths["sampler_state"].exists():
-            with open(str(paths["sampler_state"]), "rb") as f:
-                self.scheduler_state = pkl.load(f)
-        
-        return self
-    
-    def _plot_loss(self, save_path: str): 
-        """Plot and save loss curve."""
-        if not self.loss_list:
-            return
-        
-        batch_iter = len(self.loss_list)
-        
-        plt.figure(figsize=(10, 6))
-        plt.plot(self.loss_list)
-        plt.title(f"Training Loss - Iteration {batch_iter}")
-        plt.xlabel("Batch")
-        plt.ylabel("Loss")
-        plt.grid(True)
-        plt.savefig(save_path, dpi=300, bbox_inches="tight")
-        plt.close()
-    
-    def save(self, save_dir: Union[str, Path]):
-        """Save complete checkpoint."""
-        self.save_components(save_dir)
-        self.save_training_state(save_dir)
-    
-    def load(self, load_dir: Union[str, Path]) -> Self:
-        """Load complete checkpoint."""
-        self.load_components(load_dir)
-        self.load_training_state(load_dir)
-        return self
-
-
-class ParameterLoader:
-    """Factory class for loading model parameters or checkpoints."""
-    
-    @staticmethod
-    def load(load_dir: Union[str, Path]) -> Union[ModelParameter, Checkpoint]:
-        """Load either ModelParameter or Checkpoint based on directory contents."""
-        load_dir = Path(load_dir)
-        
-        # Check for training-specific files
-        loss_file = load_dir / "loss.pkl"
-        has_training_data = loss_file.exists()
-        
-        # Create appropriate instance
-        if has_training_data:
-            checkpoint = Checkpoint()
-            checkpoint.load(str(load_dir))
-            return checkpoint
-        else:
-            params = ModelParameter()
-            params.load(str(load_dir))
-            return params
-    
-    @staticmethod
-    def create_checkpoint(
-        model: nn.Module, 
-        tokenizer: BpeTokenizer,
-        config: ModelConfig,
-        loss_list: Optional[list[float]] = None,
-        optimizer: Optional[optim.Optimizer] = None,
-    ) -> Checkpoint:
-        """Convenience method to create a training checkpoint."""
-        return Checkpoint(
-            model=model,
-            tokenizer=tokenizer,
-            config=config,
-            loss_list=loss_list or [],
-            optimizer_state=optimizer
-        )

khaosz/config/schedule_config.py

@@ -1,4 +1,4 @@
-from typing import Any, Literal, Dict
+from typing import Any, Dict
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, field
 
@@ -39,7 +39,10 @@ class CosineScheduleConfig(ScheduleConfig):
         default=None,
         metadata={"help": "Total training steps for cosine schedule."}
     )
-    schedule_type: Literal["cosine"] = "cosine"
+    
+    def __post_init__(self) -> None:
+        self.schedule_type = "cosine"
+        self.validate()
     
     def get_kwargs(self) -> Dict[str, Any]:
         if self.total_steps is None:
@@ -68,7 +71,10 @@ class SGDRScheduleConfig(ScheduleConfig):
         default=2,
         metadata={"help": "Multiplier for cycle length growth."}
     )
-    schedule_type: Literal["sgdr"] = "sgdr"
+
+    def __post_init__(self) -> None:
+        self.schedule_type = "sgdr"
+        self.validate()
 
     def get_kwargs(self) -> Dict[str, Any]:
         return {

khaosz/config/train_config.py

@@ -1,16 +1,20 @@
-from dataclasses import dataclass, field
+import torch.nn as nn
-from typing import Optional, TYPE_CHECKING
 from torch.utils.data import Dataset
 from torch.optim import Optimizer
+from torch.optim.lr_scheduler import LRScheduler
 
-if TYPE_CHECKING:
+from dataclasses import dataclass, field
-    from khaosz.trainer.strategy import BaseStrategy
+from typing import Callable, List, Optional
 
 
 @dataclass
 class TrainConfig:
-    
+    # basic setting
-    strategy: "BaseStrategy" = field(
+    model: nn.Module = field(
+        default=None,
+        metadata={"help": "Model for training."}
+    )
+    strategy: str = field(
         default=None,
         metadata={"help": "Training strategy."}
     )
@@ -18,13 +22,13 @@ class TrainConfig:
         default=None,
         metadata={"help": "Dataset for training."}
     )
-    optimizer: Optimizer = field(
+    optimizer_fn: Callable[[nn.Module], Optimizer] = field(
         default=None,
-        metadata={"help": "Optimizer for training."}
+        metadata={"help": "Optimizer factory for training."}
     )
-    checkpoint_dir: str = field(
+    scheduler_fn: Callable[[Optimizer], LRScheduler] = field(
-        default="./checkpoint",
+        default=None,
-        metadata={"help": "Checkpoint directory."}
+        metadata={"help": "Scheduler factory for training."}
     )
     n_epoch: int = field(
         default=1,
@@ -34,18 +38,6 @@ class TrainConfig:
         default=4,
         metadata={"help": "Batch size for training."}
     )
-    start_epoch: int = field(
-        default=0,
-        metadata={"help": "Start epoch for training."}
-    )
-    start_batch: int = field(
-        default=0,
-        metadata={"help": "Start batch iteration for training."}
-    )
-    checkpoint_interval: int = field(
-        default=5000,
-        metadata={"help": "Number of iterations between checkpoints."}
-    )
     accumulation_steps: int = field(
         default=1,
         metadata={"help": "Number of iterations between steps."}
@@ -54,6 +46,26 @@ class TrainConfig:
         default=1.0,
         metadata={"help": "Maximum gradient norm."}
     )
+    
+    # checkpoint setting
+    start_epoch: int = field(
+        default=0,
+        metadata={"help": "Start epoch for training."}
+    )
+    start_batch: int = field(
+        default=0,
+        metadata={"help": "Start batch iteration for training."}
+    )
+    checkpoint_dir: str = field(
+        default="./checkpoint",
+        metadata={"help": "Checkpoint directory."}
+    )
+    checkpoint_interval: int = field(
+        default=5000,
+        metadata={"help": "Number of iterations between checkpoints."}
+    )
+    
+    # dataloader setting
     random_seed: int = field(
         default=3407,
         metadata={"help": "Random seed."}
@@ -69,4 +81,56 @@ class TrainConfig:
     pin_memory: bool = field(
         default=False,
         metadata={"help": "Pin memory for dataloader."}
-    )
+    )
+    
+    # distributed training
+    nprocs: int = field(
+        default=1,
+        metadata={"help": "Number of processes for distributed training."}
+    )
+    backend: str = field(
+        default="nccl",
+        metadata={"help": "Distributed training backend."}
+    )
+    master_addr: str = field(
+        default="localhost",
+        metadata={"help": "Master address for distributed training."}
+    )
+    master_port: str = field(
+        default="29500",
+        metadata={"help": "Master port for distributed training."}
+    )
+    parallel_wrapper: Optional[Callable] = field(
+        default=None,
+        metadata={"help": "Parallel function for training."}
+    )
+    state_dict_fn: Optional[Callable] = field(
+         default=None,
+         metadata={"help": "Parallel function for state  dict saving."}
+    )
+
+    # others
+    device_ids: Optional[List[int]] = field(
+        default=None,
+        metadata={"help": "Device ids for distributed training."}
+    )
+    device_type: str = field(
+        default="cuda",
+        metadata={"help": "Device type for distributed training."}
+    )
+    extra_kwargs: dict = field(
+        default_factory=dict,
+        metadata={"help": "Other arguments."}
+    )
+    
+    def __post_init__(self):
+        self.validate()
+    
+    def validate(self):
+        required_fields = ["model", "strategy", "dataset", "optimizer_fn", "scheduler_fn"]
+        
+        for field_name in required_fields:
+            if getattr(self, field_name) is None:
+                raise ValueError(f"{field_name} is required.")
+        
+        

khaosz/data/__init__.py

@@ -1,16 +1,15 @@
-from khaosz.data.data_util import (
+from khaosz.data.dataset import (
     BaseDataset, 
     SeqDataset, 
     DpoDataset, 
     SftDataset, 
     PpoDataset, 
-    MutiSegmentFetcher,
+    MultiSegmentFetcher,
-    ResumeableRandomSampler,
+    DatasetLoader
-    DatasetLoader,
-    load_pkl_files, 
 )
 
 from khaosz.data.tokenizer import BpeTokenizer
+from khaosz.data.sampler import ResumableDistributedSampler
 
 __all__ = [
     "BaseDataset",
@@ -18,9 +17,8 @@ __all__ = [
     "DpoDataset",
     "SftDataset",
     "PpoDataset",
-    "MutiSegmentFetcher",
+    "MultiSegmentFetcher",
-    "ResumeableRandomSampler",
     "DatasetLoader",
-    "load_pkl_files",
+    "BpeTokenizer",
-    "BpeTokenizer"
+    "ResumableDistributedSampler"
 ]

khaosz/data/checkpoint.py

@@ -0,0 +1,67 @@
+import json
+import torch
+import torch.distributed as dist
+
+from pathlib import Path
+from typing import Dict, Any
+from khaosz.parallel.setup import get_rank
+
+
+class Checkpoint:
+    def __init__(
+        self,
+        state_dict: Dict[str, Any],
+        epoch: int = 0,
+        iteration: int = 0,
+    ):
+        self.state_dict = state_dict
+        self.epoch = epoch
+        self.iteration = iteration
+
+    def save(
+        self,
+        save_dir: str,
+    ) -> None:
+        
+        save_path = Path(save_dir)
+        save_path.mkdir(parents=True, exist_ok=True)
+
+        rank = get_rank()
+        if rank == 0:
+            meta = {
+                "epoch": self.epoch,
+                "iteration": self.iteration,
+            }
+            with open(save_path / "meta.json", "w") as f:
+                json.dump(meta, f, indent=2)
+
+            with open(save_path / f"state_dict.pt", "wb") as f:
+                torch.save(self.state_dict, f)
+
+    @classmethod
+    def load(
+        cls,
+        save_dir: str,
+    ) -> "Checkpoint":
+
+        rank = get_rank()
+        save_path = Path(save_dir)
+        
+        meta = {}
+        if rank == 0:
+            with open(Path(save_dir) / "meta.json", "r") as f:
+                meta = json.load(f)
+
+        if dist.is_initialized():
+            meta_list = [meta]
+            dist.broadcast_object_list(meta_list, src=0)
+            meta = meta_list[0]
+
+        with open(save_path / f"state_dict.pt", "rb") as f:
+            state_dict = torch.load(f)
+
+        return cls(
+            state_dict=state_dict,
+            epoch=meta["epoch"],
+            iteration=meta["iteration"],
+        )

khaosz/data/dataset.py

@@ -1,40 +1,28 @@
 import torch
 import bisect
-import pickle as pkl
+
 from abc import ABC, abstractmethod
 from torch import Tensor
-from torch.utils.data import Dataset, Sampler
+from torch.utils.data import Dataset
+from khaosz.data.file import load_h5
 from typing import Callable, List, Dict, Literal, Optional, Union
-  
-MutiSeg = Dict[str, List[Tensor]]
-Seg = Dict[str, Tensor]
 
-def load_pkl_files(paths: List[str]):
-    segments: MutiSeg = {}
-    total_samples = 0
-
-    for path in paths:
-        with open(path, "rb") as f:
-            pkl_file: Seg = pkl.load(f)
-        for key, value in pkl_file.items():
-            if key not in segments:
-                segments[key] = []
-            segments[key].append(value)
-        first_key = list(pkl_file.keys())[0]
-        total_samples += pkl_file[first_key].numel()
-    
-    return segments, total_samples
 
 
 class BaseSegmentFetcher:
     def __init__(self, segments: List[Tensor]):
         self.segments = segments
         self.cum_lengths = []
+        
         total = 0
         for seg in segments:
-            total += len(seg)
+            total += torch.numel(seg)
             self.cum_lengths.append(total)
-        self.total_length = total if segments else 0
+            
+        self.total_length = total
+        
+    def __len__(self) -> int:
+        return self.total_length
 
     def fetch_data(self, begin_idx: int, end_idx: int) -> Tensor:
         if not (0 <= begin_idx < self.total_length and 0 <= end_idx <= self.total_length):
@@ -52,20 +40,25 @@ class BaseSegmentFetcher:
             prev_cum = self.cum_lengths[i - 1] if i > 0 else 0
             start = max(begin_idx - prev_cum, 0)
             end = min(end_idx - prev_cum, len(self.segments[i]))
-            result_segments.append(self.segments[i][start:end])
+            data = self.segments[i][start:end]
+            result_segments.append(data)
 
         return torch.cat(result_segments, dim=0)
     
 
-class MutiSegmentFetcher:
+class MultiSegmentFetcher:
-    def __init__(self, muti_segments: MutiSeg):
+    def __init__(self, muti_segments: Dict):
         self.muti_keys = list(muti_segments.keys())
         self.muti_fetchers = {
             key: BaseSegmentFetcher(segments)
             for key, segments in muti_segments.items()
         }
+    
+    def __len__(self) -> int:
+        len_list = [len(seg) for seg in self.muti_fetchers.values()]
+        return min(len_list)
         
-    def key_fetch(self, begin_idx: int, end_idx: int, keys: Union[str, List[str]]) -> Union[Tensor, Seg]:
+    def key_fetch(self, begin_idx: int, end_idx: int, keys: Union[str, List[str]]) -> Dict:
         fetch_dict = {} 
         keys = [keys] if isinstance(keys, str) else keys
         
@@ -76,38 +69,28 @@ class MutiSegmentFetcher:
 
         return fetch_dict if len(keys) > 1 else fetch_dict[keys[0]]
     
-    def fetch_data(self, begin_idx: int, end_idx: int) -> Union[Tensor, Seg]:
+    def fetch_data(self, begin_idx: int, end_idx: int) -> Dict:
         return self.key_fetch(begin_idx, end_idx, self.muti_keys)
 
 
 class BaseDataset(Dataset, ABC):
     def __init__(self, window_size: int, stride: int):
         super().__init__()
-        self.segments: MutiSeg = {}
+        self.segments = {}
         self.window_size = window_size
         self.stride = stride
         self.total_samples = None
 
-    def save(self, save_path: str):
+    def load(self, load_path: str):
-        keys = list(self.segments.keys())
+        self.segments = load_h5(load_path)
-        if not keys:
+        self.fetcher = MultiSegmentFetcher(self.segments)
-            return
+        self.total_samples = len(self.fetcher)
-        
-        first_item = self.segments[keys[0]]
-        segment_size = len(first_item)
-        
-        for i in range(segment_size):
-            formated_segment = {key: self.segments[key][i] for key in keys}
-            pkl.dump(formated_segment, open(f"{save_path}_{i}.pkl", "wb"))
-    
-    def load(self, load_path: Union[str, List[str]]):
-        paths = [load_path] if isinstance(load_path, str) else load_path
-        self.segments, self.total_samples = load_pkl_files(paths)
-        self.fetcher = MutiSegmentFetcher(self.segments)
         
     def get_index(self, index: int) -> int:
-        begin_idx = min(index * self.stride, self.total_samples - self.window_size - 1)
+        assert self.total_samples > self.window_size
-        end_idx = begin_idx + self.window_size
+        
+        begin_idx = min(index * self.stride, self.total_samples - 1 - self.window_size)
+        end_idx = min(begin_idx + self.window_size, self.total_samples - 1)
         
         return begin_idx, end_idx
         
@@ -119,13 +102,13 @@ class BaseDataset(Dataset, ABC):
         assert self.total_samples is not None
         if self.total_samples <= self.window_size:
             return 0
-        return self.total_samples // self.stride + 1
+        return (self.total_samples - 1 - self.window_size) // self.stride + 1
     
 
 class SeqDataset(BaseDataset):
     def __init__(self, window_size: int, stride: int):
         super().__init__(window_size, stride)
-        self.fetcher = MutiSegmentFetcher(self.segments)
+        self.fetcher = MultiSegmentFetcher(self.segments)
 
     def _fetch_data(self, begin_idx: int, end_idx: int) -> Tensor:
         return self.fetcher.key_fetch(begin_idx, end_idx, "sequence")
@@ -143,7 +126,7 @@ class SeqDataset(BaseDataset):
 class SftDataset(BaseDataset):
     def __init__(self, window_size: int, stride: int):
         super().__init__(window_size, stride)
-        self.fetcher = MutiSegmentFetcher(self.segments)
+        self.fetcher = MultiSegmentFetcher(self.segments)
 
     def _fetch_data(self, begin_idx: int, end_idx: int, key: str) -> Tensor:
         return self.fetcher.key_fetch(begin_idx, end_idx, key)
@@ -161,7 +144,7 @@ class SftDataset(BaseDataset):
 class DpoDataset(BaseDataset):
     def __init__(self, window_size: int, stride: int):
         super().__init__(window_size, stride)
-        self.fetcher = MutiSegmentFetcher(self.segments)
+        self.fetcher = MultiSegmentFetcher(self.segments)
 
     def _fetch_data(self, begin_idx: int, end_idx: int, key: str) -> Tensor:
         return self.fetcher.key_fetch(begin_idx, end_idx, key)
@@ -180,7 +163,7 @@ class DpoDataset(BaseDataset):
 class PpoDataset(BaseDataset):
     def __init__(self, window_size: int, stride: int):
         super().__init__(window_size, stride)
-        self.fetcher = MutiSegmentFetcher(self.segments)
+        self.fetcher = MultiSegmentFetcher(self.segments)
 
     def _fetch_data(self, begin_idx: int, end_idx: int, key: str) -> Tensor:
         return self.fetcher.key_fetch(begin_idx, end_idx, key)
@@ -200,10 +183,9 @@ class DatasetLoader:
     @staticmethod       
     def load(
         train_type: Literal["seq", "sft", "dpo"],
-        load_path: Union[str, List[str]],
+        load_path: str,
         window_size: int, 
         stride: Optional[int] = None,
-        **kwargs
         ) -> BaseDataset:
         if stride is None:
             stride = window_size
@@ -217,40 +199,3 @@ class DatasetLoader:
         dataset.load(load_path)
         
         return dataset
-
-
-class ResumeableRandomSampler(Sampler[int]):
-    def __init__(self, data_source, start_epoch=0, start_iter=0, seed=42):
-        self.num_samples = len(data_source)
-        self.epoch = start_epoch
-        self.iter = start_iter
-        
-        generator = torch.Generator()
-        generator.manual_seed(seed)
-
-        # consume  previous epochs
-        for _ in range(start_epoch):
-            torch.randperm(self.num_samples, generator=generator)
-        
-        self.generator = generator
-        self._indices = None
-    
-    def _get_indices(self):
-        current_epoch_indices = torch.randperm(self.num_samples, generator=self.generator).tolist()
-        self._indices = current_epoch_indices[self.iter % self.num_samples:]
-    
-    def __iter__(self):
-        if self._indices is None:
-            self._get_indices()
-        
-        for i in self._indices:
-            self.iter += 1
-            yield i
-        
-        self.epoch += 1
-        self._indices = None
-    
-    def __len__(self):
-        if self._indices is None:
-            self._get_indices()
-        return len(self._indices)

khaosz/data/file.py

@@ -0,0 +1,42 @@
+import os
+import h5py
+import torch
+
+from pathlib import Path
+from torch import Tensor
+from typing import Dict, List
+
+
+def save_h5(file_path: str, file_name: str, tensor_group: Dict[str, List[Tensor]]):
+    os.makedirs(file_path, exist_ok=True)
+    full_file_path = os.path.join(file_path, f"{file_name}.h5")
+    with h5py.File(full_file_path, 'w') as f:
+        for key, tensors in tensor_group.items():
+            grp = f.create_group(key)
+            for idx, tensor in enumerate(tensors):
+                arr = tensor.cpu().numpy()
+                grp.create_dataset(f'data_{idx}', data=arr)
+
+def load_h5(file_path: str, share_memory=True) -> Dict[str, List[Tensor]]:
+    tensor_group: Dict[str, List[Tensor]] = {}
+
+    root_path = Path(file_path)
+    h5_files = list(root_path.rglob("*.h5")) + list(root_path.rglob("*.hdf5"))
+    
+    for h5_file in h5_files:
+        with h5py.File(h5_file, 'r') as f:
+            for key in f.keys():
+                grp = f[key]
+                dsets = []
+                for dset_name in grp.keys():
+                    dset = grp[dset_name]
+                    tensor = torch.from_numpy(dset[:])
+                    if share_memory:
+                        tensor = tensor.share_memory_()
+                    dsets.append(tensor)
+            
+                if tensor_group.get(key) is None:
+                    tensor_group[key] = []
+                tensor_group[key].extend(dsets)
+
+    return tensor_group

khaosz/data/sampler.py

@@ -0,0 +1,78 @@
+import torch
+import torch.distributed as dist
+
+from torch.utils.data import Dataset, Sampler
+from typing import Optional
+
+
+class ResumableDistributedSampler(Sampler[int]):
+    def __init__(
+        self, 
+        data_source: Dataset,
+        start_epoch: int=0, 
+        start_iter: int=0, 
+        seed: int=42,
+        drop_last: bool=False,
+        shuffle: bool=True,
+        process_group: Optional[dist.ProcessGroup]=None,
+    ):
+        self.epoch = start_epoch
+        self.iter = start_iter
+        self.seed = seed
+        self.num_samples = len(data_source)
+        
+        if process_group is not None:
+            # input process group
+            self.rank = dist.get_rank(process_group)
+            self.num_replicas = dist.get_world_size(process_group)
+            
+        elif dist.is_available() and dist.is_initialized():
+            # use default process group
+            process_group = dist.group.WORLD
+            self.rank = dist.get_rank()
+            self.num_replicas = dist.get_world_size()
+            
+        else:
+            # single process
+            self.rank = 0
+            self.num_replicas = 1
+        
+        self.drop_last = drop_last
+        self.shuffle = shuffle
+        
+        offset = 0 if drop_last else  self.num_replicas - 1
+        self.num_samples_per_replica = (self.num_samples + offset) // self.num_replicas
+        self.total_size = self.num_samples_per_replica * self.num_replicas
+        
+        self._indices = None
+    
+    def _get_indices(self):
+        if self.shuffle:
+            generator = torch.Generator()
+            generator.manual_seed(self.seed + self.epoch)
+            indices = torch.randperm(self.num_samples, generator=generator).tolist()
+        else:
+            indices = torch.arange(self.num_samples).tolist()
+        
+        if not self.drop_last and self.num_samples < self.total_size:
+            padding_size = self.total_size - len(indices)
+            indices += indices[:padding_size]
+        
+        local_indices = indices[self.rank:self.total_size:self.num_replicas]
+        
+        self.iter = self.iter % self.num_samples_per_replica
+        self._indices = local_indices[self.iter:]
+    
+    def __iter__(self):
+        if self._indices is None:
+            self._get_indices()
+        
+        for i in self._indices:
+            self.iter += 1
+            yield i
+        
+        self.epoch += 1
+        self._indices = None
+    
+    def __len__(self):
+        return self.num_samples_per_replica

khaosz/data/tokenizer.py

@@ -9,32 +9,28 @@ class BpeTokenizer:
     def __init__(self, path=None):
         self._control_tokens = ["<bos>", "<eos>", "<pad>"]
         self._special_tokens = ["<|im_start|>", "<|im_end|>"]
+        
         model = BPE()
-        tokenizer = Tokenizer(model)
+        self._tokenizer = Tokenizer(model)
-        tokenizer.normalizer = normalizers.Sequence([
+        self._tokenizer.normalizer = normalizers.Sequence([
-            normalizers.NFC()
+            normalizers.NFC(),
+            normalizers.Strip() 
         ])
-        tokenizer.pre_tokenizer = pre_tokenizers.Sequence([
+        
-            pre_tokenizers.Punctuation(behavior="isolated"),
+        self._tokenizer.pre_tokenizer = pre_tokenizers.Sequence([
-            pre_tokenizers.Metaspace(prepend_scheme="never"),
+            pre_tokenizers.UnicodeScripts(),
-            pre_tokenizers.Split(pattern=r"(\d+|[a-zA-Z]+|(?:'s|'t|'re|'ve|'m|'ll|'d))", behavior="isolated"),
+            pre_tokenizers.ByteLevel(add_prefix_space=False, use_regex=True)
-            pre_tokenizers.ByteLevel(add_prefix_space=False, use_regex=False)
         ])
-        tokenizer.decoder = decoders.Sequence([
+        
-            decoders.ByteLevel(),
+        self._tokenizer.decoder = decoders.ByteLevel()
-            decoders.Metaspace(prepend_scheme="never")
+        self._tokenizer.post_processor = processors.ByteLevel(trim_offsets=True)
-        ])
-        tokenizer.post_processor = processors.Sequence([
-            processors.ByteLevel(trim_offsets=False)
-        ])
-        self._tokenizer = tokenizer
         
         if path is not None:
             self._tokenizer = Tokenizer.from_file(path)
         
-    def _prepare_trainer(self, vocab_size: int, min_freq: int, reserved_token_size: int) -> tuple:
+    def _prepare_trainer(self, vocab_size: int, min_freq: int, reserved_token_size: int, max_token_length=18) -> tuple:
         assert reserved_token_size > len(self._special_tokens)
-        reserved_tokens = [f"<|rsv{i:02d}|>" for i in range(reserved_token_size - len(self._special_tokens))]
+        reserved_tokens = [f"<|reserve{i:02d}|>" for i in range(reserved_token_size - len(self._special_tokens))]
         detail_vocab_size = vocab_size - (len(reserved_tokens) + len(self._special_tokens))
         
         alphabet = pre_tokenizers.ByteLevel.alphabet()
@@ -44,11 +40,11 @@ class BpeTokenizer:
         trainer = BpeTrainer(
             vocab_size=detail_vocab_size,
             min_frequency=min_freq,
-            limit_alphabet=detail_vocab_size // 4,
+            limit_alphabet=detail_vocab_size // 6,
-            max_token_length=18,
+            max_token_length=max_token_length,
             special_tokens=self._control_tokens,
-            show_progress=True,
             initial_alphabet=alphabet,
+            show_progress=True,
         )
         
         return trainer, detail_vocab_size, reserved_tokens

khaosz/inference/__init__.py

@@ -0,0 +1 @@
+# init file

khaosz/inference/core.py

@@ -100,7 +100,7 @@ class GeneratorCore:
     ) -> List[int]:
         cur_cache_pos = start_pos
         
-        for _ in range(len(ids), self.config.m_len):
+        for _ in range(len(ids), self.config.max_len):
             next_token_id, cache_increase = self.generate_iterator(
                 input_ids, temperature, top_k, top_p, attn_mask, kv_caches, cur_cache_pos)
             
@@ -127,7 +127,7 @@ class EmbeddingEncoderCore:
         with_batch = isinstance(sentence, list)
         ids = self.tokenizer.encode(sentence)
         batch_ids = ids if with_batch else [ids]
-        max_model_len = self.config.m_len
+        max_model_len = self.config.max_len
         
         all_fragments = []
         fragment_origin_idx = []
@@ -195,10 +195,10 @@ class KVCacheManager:
         self.batch_size = batch_size
         self.device = device
         self.dtype = dtype
-        self.num_layers = config.n_layer
+        self.num_layers = config.n_layers
-        self.max_len = config.m_len
+        self.max_len = config.max_len
-        self.num_heads = config.n_kvhead
+        self.num_heads = config.n_kv_heads
-        self.head_dim = config.n_dim //config.n_head
+        self.head_dim = config.dim //config.n_heads
         
         self._kv_cache: Tuple[Tensor, Tensor] = None
         self._seq_mask: Tensor = None
@@ -206,11 +206,11 @@ class KVCacheManager:
 
     def _initialize(self):
         k_cache = torch.zeros(
-            (self.batch_size, self.num_layers, self.max_len, self.num_heads, self.head_dim),
+            (self.batch_size, self.max_len, self.num_layers, self.num_heads, self.head_dim),
             device=self.device, dtype=self.dtype
         )
         v_cache = torch.zeros(
-            (self.batch_size, self.num_layers, self.max_len, self.num_heads, self.head_dim),
+            (self.batch_size, self.max_len, self.num_layers, self.num_heads, self.head_dim),
             device=self.device, dtype=self.dtype
         )
         self._kv_cache = (k_cache, v_cache)

khaosz/inference/cuda_graph.py

@@ -0,0 +1,98 @@
+import torch
+from torch import Tensor
+from functools import wraps
+from inspect import signature
+
+
+class CudaGraphWrapper:
+    def __init__(self, function, device="cuda", cast=False):
+        self.function = function
+        self.cast = cast
+        self.device = device
+        self.static_input = None
+        self.static_output = None
+        self.graph = None
+        self.signature = signature(function)
+        
+    def _update_inplace(self, lhs, rhs):
+        if isinstance(lhs, Tensor) and isinstance(rhs, Tensor):
+            if lhs.shape != rhs.shape:
+                raise ValueError(
+                    f"Tensor shape mismatch! "
+                    f"Expected: {lhs.shape}, Got: {rhs.shape}. "
+                    f"Function: {self.function}"
+                )
+            if self.cast:
+                if lhs.device != rhs.device:
+                    rhs = rhs.to(device=lhs.device)
+                
+                if lhs.dtype != rhs.dtype:
+                    rhs = rhs.to(dtype=lhs.dtype)
+            else: 
+                if lhs.device != rhs.device:
+                    raise ValueError(
+                        f"Tensor device mismatch! "
+                        f"Expected: {lhs.device}, Got: {rhs.device}. "
+                        f"Function: {self.function}"
+                    )
+                if lhs.dtype != rhs.dtype:
+                    raise ValueError(
+                        f"Tensor dtype mismatch! "
+                        f"Expected: {lhs.dtype}, Got: {rhs.dtype}. "
+                        f"Function: {self.function}"
+                    )
+                lhs.copy_(rhs)
+        elif isinstance(lhs, dict):
+            for k in lhs:
+                if k in rhs:
+                    self._update_inplace(lhs[k], rhs[k])
+        elif isinstance(lhs, (list, tuple)):
+            for i in range(len(lhs)):
+                if i < len(rhs):
+                    self._update_inplace(lhs[i], rhs[i])
+        elif isinstance(lhs, (int, float, bool, str, type(None))):
+            if lhs != rhs:
+                raise ValueError("Does not support changing control parameters.")   
+
+    def _update_args(self, input_args, input_kwargs):
+        bound_args = self.signature.bind(*input_args, **input_kwargs)
+        bound_args.apply_defaults()
+        args_dict = bound_args.arguments
+        
+        if self.static_input is None:
+            self.static_input = args_dict
+        else:
+            self._update_inplace(self.static_input, args_dict)
+    
+    def run(self, *args, **kwargs):
+        self._update_args(args, kwargs)
+        
+        if self.graph is None:
+            # warmup
+            _ = torch.matmul(
+                torch.randn(100, 100, device=self.device),
+                torch.randn(100, 100, device=self.device)
+            )
+            torch.cuda.synchronize()
+            
+            # capture graph
+            self.graph = torch.cuda.CUDAGraph()
+            with torch.cuda.graph(self.graph):
+                self.static_output = self.function(**self.static_input)
+        
+        self.graph.replay()
+        
+        return self.static_output
+
+
+def cuda_graph(device="cuda", cast=False):
+    def decorator(func):
+        wrapper = CudaGraphWrapper(func, device, cast)
+        
+        @wraps(func)
+        def wrapped(*args, **kwargs):
+            return wrapper.run(*args, **kwargs)
+        
+        return wrapped 
+    
+    return decorator

khaosz/inference/generator.py

@@ -167,7 +167,7 @@ class StreamGenerator(GeneratorCore):
         self.model.eval()
         kv_caches = cache_manager.get_kvcache()
         
-        for _ in range(len(ids), self.config.m_len):
+        for _ in range(len(ids), self.config.max_len):
             next_token_id, cache_increase = self.generate_iterator(
                 input_ids, temperature, top_k, top_p, kv_caches=kv_caches, start_pos=cur_cache_pos)
             
@@ -219,7 +219,7 @@ class BatchGenerator(GeneratorCore):
         start_cache_pos = max_ids_len
         cur_cache_pos = 0
         
-        while max_ids_len < self.config.m_len and sum(activate_task_mask) != 0:
+        while max_ids_len < self.config.max_len and sum(activate_task_mask) != 0:
             kv_caches = cache_manager.get_kvcache()
             attn_mask =cache_manager.get_seq_mask()
             

khaosz/model/module.py

@@ -102,70 +102,92 @@ class RotaryEmbedding(nn.Module):
 
 
 class Linear(nn.Module):
-    def __init__(self, in_dim: int, out_dim: int, bias: bool = False, weight_param=None, bias_param=None):
+    def __init__(self, in_dim: int, out_dim: int, bias: bool = False):
         super().__init__()
-        weight_param = torch.empty((out_dim, in_dim)) if weight_param is None else weight_param
+        self.weight = nn.Parameter(torch.empty((out_dim, in_dim)))
-        bias_param = torch.zeros(out_dim) if bias_param is None else bias_param
+        self.bias = nn.Parameter(torch.zeros(out_dim)) if bias else None
-        
-        self.weight = nn.Parameter(weight_param)
-        self.bias = nn.Parameter(bias_param) if bias else None
 
     def forward(self, x: Tensor) -> Tensor:
         return F.linear(x, self.weight, self.bias)
 
 
 class RMSNorm(nn.Module):
-    def __init__(self, n_dim, norm_eps):
+    def __init__(self, dim, norm_eps):
         super().__init__()
-        self.weight = nn.Parameter(torch.ones(n_dim))
+        self.weight = nn.Parameter(torch.ones(dim))
+        self.normalized_shape = (dim, )
         self.norm_eps = norm_eps
 
     def forward(self, x: Tensor) -> Tensor:
-        dtype = x.dtype
+        rms =  F.rms_norm(x.float(), self.normalized_shape, self.weight, self.norm_eps)
-        x = x.float()
+        return rms.to(x.dtype)
-        mean_square = torch.mean(torch.pow(x, 2), dim=-1, keepdim=True)
-        norm = x * torch.rsqrt(mean_square + self.norm_eps)
-        norm = norm.to(dtype)
-        out = norm * self.weight
-        return out
     
     
 class MLP(nn.Module):
-    def __init__(self, n_dim: int, d_ffn: int):
+    def __init__(self, dim: int, dim_feed_forward: int):
         super().__init__()
-        self.up = Linear(n_dim, d_ffn)
+        self.up = Linear(dim, dim_feed_forward)
-        self.gate = Linear(n_dim, d_ffn)
+        self.gate = Linear(dim, dim_feed_forward)
-        self.down = Linear(d_ffn, n_dim)
+        self.down = Linear(dim_feed_forward, dim)
     
     def forward(self, x: Tensor) -> Tensor:
         gated = self.up(x) * F.silu(self.gate(x))
         out = self.down(gated)
         return out
 
+class Attention(nn.Module):
+    
+    def forward(self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None, is_causal: bool= False):
+        # (bsz, seq_len, n_heads, head_dim) -> (bsz, n_heads, seq_len, head_dim)
+        q, k, v = q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3)
+        # (bsz, n_heads, seq_len, head_dim) - > (bsz, seq_len, n_heads*head_dim)
+        sdqa_out = F.scaled_dot_product_attention(q, k, v, mask, is_causal=is_causal).permute(0, 2, 1, 3).contiguous().flatten(2)
+        
+        return sdqa_out
+
 
 class GQA(nn.Module):
     def __init__(
         self, 
-        n_dim: int, 
+        dim: int, 
-        n_head: int, 
+        n_heads: int, 
-        n_kvhead: int, 
+        n_kv_heads: int, 
+        use_qk_norm: bool,
+        norm_eps: float,
+        use_gated_attention: bool,
         layer_id: int
     ):
         super().__init__()
-        assert n_dim % n_head == 0
+        assert dim % n_heads == 0
-        assert n_head % n_kvhead == 0
+        assert n_heads % n_kv_heads == 0
         
-        self.head_dim = n_dim // n_head
+        self.head_dim = dim // n_heads
         self.layer_id = layer_id
-        self.n_dim = n_dim
+        self.dim = dim
-        self.n_heads = n_head
+        self.n_heads = n_heads
-        self.n_kvheads = n_kvhead
+        self.n_kv_heads = n_kv_heads
-        self.n_rep = n_head // n_kvhead
+        self.n_rep = n_heads // n_kv_heads
+        self.use_qk_norm = use_qk_norm
+        self.use_gated_attention = use_gated_attention
         
-        self.q_proj = Linear(n_dim, n_head * self.head_dim)
+        self.attention = Attention()
-        self.k_proj = Linear(n_dim, n_kvhead * self.head_dim)
+
-        self.v_proj = Linear(n_dim, n_kvhead * self.head_dim)
+        self.q_proj = Linear(dim, n_heads * self.head_dim)
-        self.o_proj = Linear(n_dim, n_dim)
+        self.k_proj = Linear(dim, n_kv_heads * self.head_dim)
+        self.v_proj = Linear(dim, n_kv_heads * self.head_dim)
+        self.o_proj = Linear(dim, dim)
+        
+        if self.use_qk_norm:
+            self.q_norm = RMSNorm(self.head_dim, norm_eps)
+            self.k_norm = RMSNorm(self.head_dim, norm_eps)
+        
+        if self.use_gated_attention:
+            self.gate = Linear(dim, dim)
+
+    def _split_heads(self, x: Tensor, n_heads) -> Tensor:
+        batch_size, seq_len, _ = x.shape
+        x = x.reshape(batch_size, seq_len, n_heads, self.head_dim)
+        return x
     
     def forward(
         self,
@@ -178,43 +200,53 @@ class GQA(nn.Module):
         bsz, seq_len, _ = x.size()
         # x(bsz, seq_len, n_heads * head_dim) -> (bsz, seq_len, n_heads, head_dim)
         q = self._split_heads(self.q_proj(x), self.n_heads)
-        k = self._split_heads(self.k_proj(x), self.n_kvheads)
+        k = self._split_heads(self.k_proj(x), self.n_kv_heads)
-        v = self._split_heads(self.v_proj(x), self.n_kvheads)
+        v = self._split_heads(self.v_proj(x), self.n_kv_heads)
         q, k = apply_rotary_emb(q, rotary_emb), apply_rotary_emb(k, rotary_emb)
         
+        if self.use_qk_norm:
+            q, k = self.q_norm(q), self.k_norm(k)
+        
         if kv_cache is not None:
             k_cache, v_cache = kv_cache
             
             # copy to cache
-            k_cache[:bsz, self.layer_id, start_pos:start_pos + seq_len] = k
+            k_cache[:bsz, start_pos:start_pos + seq_len, self.layer_id] = k
-            v_cache[:bsz, self.layer_id, start_pos:start_pos + seq_len] = v
+            v_cache[:bsz, start_pos:start_pos + seq_len, self.layer_id] = v
             
             # get cache
-            k = k_cache[:bsz, self.layer_id, :start_pos + seq_len]
+            k = k_cache[:bsz, :start_pos + seq_len, self.layer_id]
-            v = v_cache[:bsz, self.layer_id, :start_pos + seq_len]
+            v = v_cache[:bsz, :start_pos + seq_len, self.layer_id]
         
         k, v = repeat_kv(k, self.n_rep), repeat_kv(v, self.n_rep)
+        sdqa_out = self.attention(q, k, v, mask, is_causal=(mask == None))
         
-        # (bsz, seq_len, n_heads, head_dim) -> (bsz, n_heads, seq_len, head_dim)
+        if self.use_gated_attention:
-        q, k, v = q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3)
+            sdqa_out = sdqa_out * F.sigmoid(self.gate(x))
-        sdqa_out = F.scaled_dot_product_attention(q, k, v, mask, is_causal=(mask == None)).permute(0, 2, 1, 3)
+        
-        out = self.o_proj(sdqa_out.contiguous().view(bsz, seq_len, -1))
+        out = self.o_proj(sdqa_out)
 
         return out
-    
+
-    def _split_heads(self, x: Tensor, n_heads) -> Tensor:
-        batch_size, seq_len, _ = x.shape
-        x = x.reshape(batch_size, seq_len, n_heads, self.head_dim)
-        return x
-    
 
 class DecoderBlock(nn.Module):
-    def __init__(self, n_dim, n_head, d_ffn, n_kvhead, norm_eps, layer_id):
+    def __init__(
+        self, 
+        dim: int, 
+        n_heads: int, 
+        dim_ffn: int, 
+        n_kv_heads: int, 
+        norm_eps: int, 
+        use_qk_norm: bool, 
+        use_gated_attention: bool, 
+        layer_id: int
+    ):
         super().__init__()
-        self.attention = GQA(n_dim, n_head, n_kvhead, layer_id)
+        self.attention = GQA(dim, n_heads, n_kv_heads, 
-        self.norm_attn = RMSNorm(n_dim, norm_eps)
+                             use_qk_norm, norm_eps, use_gated_attention, layer_id)
-        self.ffn = MLP(n_dim, d_ffn)
+        self.input_norm = RMSNorm(dim, norm_eps)
-        self.norm_ffn = RMSNorm(n_dim, norm_eps)
+        self.mlp = MLP(dim, dim_ffn)
+        self.post_attention_norm = RMSNorm(dim, norm_eps)
 
     def forward(
         self,
@@ -226,7 +258,7 @@ class DecoderBlock(nn.Module):
     ) -> Tensor:
         # attention
         attn_output = self.attention(
-            self.norm_attn(x), 
+            self.input_norm(x), 
             rotary_emb, 
             attention_mask, 
             kv_cache, 
@@ -235,16 +267,15 @@ class DecoderBlock(nn.Module):
         x = attn_output + x
         
         # feed forward
-        x = self.ffn(self.norm_ffn(x)) + x
+        x = self.mlp(self.post_attention_norm(x)) + x
         
         return x
 
 
 class Embedding(nn.Module):
-    def __init__(self, vocab_size: int, embedding_dim: int, weight_param=None):
+    def __init__(self, vocab_size: int, embedding_dim: int):
         super().__init__()
-        weight_param = torch.empty((vocab_size, embedding_dim)) if weight_param is None else weight_param
+        self.weight = nn.Parameter(torch.empty((vocab_size, embedding_dim)))
-        self.weight = nn.Parameter(weight_param)
     
     def forward(self, x: Tensor) -> Tensor:
         return F.embedding(x, self.weight)

khaosz/model/transformer.py

@@ -59,16 +59,17 @@ class Transformer(nn.Module):
     def __init__(self, config: ModelConfig):
         super().__init__()
         self.config = config
-        self.rotary_embeding = RotaryEmbedding(config.n_dim // config.n_head, config.m_len)
+        self.rotary_embeding = RotaryEmbedding(config.dim // config.n_heads, config.max_len)
-        self.embed_tokens = Embedding(config.vocab_size, config.n_dim)
+        self.embed_tokens = Embedding(config.vocab_size, config.dim)
         
         self.layers = nn.ModuleList([
-            DecoderBlock(config.n_dim, config.n_head, config.d_ffn, config.n_kvhead, config.norm_eps, layer_id)
+            DecoderBlock(config.dim, config.n_heads, config.dim_ffn, config.n_kv_heads, 
-            for layer_id in range(config.n_layer)
+                         config.norm_eps, config.use_qk_norm, config.use_gated_attention, layer_id)
+            for layer_id in range(config.n_layers)
         ])
         
-        self.norm = RMSNorm(config.n_dim, config.norm_eps)
+        self.norm = RMSNorm(config.dim, config.norm_eps)
-        self.lm_head = Linear(config.n_dim, config.vocab_size)
+        self.lm_head = Linear(config.dim, config.vocab_size)
         
         if self.config.tie_weight == True:
             self.lm_head.weight = self.embed_tokens.weight
@@ -83,8 +84,9 @@ class Transformer(nn.Module):
             # same tensor
             state_dict[lm_head_key] = state_dict[embed_key]
         else:
-            # use clone to avoid sharing the same tensor
+            if lm_head_key not in state_dict and embed_key in state_dict:
-            state_dict[lm_head_key] = torch.clone(state_dict[embed_key])
+                # use clone to avoid sharing the same tensor
+                state_dict[lm_head_key] = torch.clone(state_dict[embed_key])
         
         return super().load_state_dict(state_dict, strict, assign)
     

khaosz/parallel/__init__.py

@@ -0,0 +1,27 @@
+from khaosz.parallel.setup import (
+    get_world_size, 
+    get_rank,
+    get_current_device,
+    
+    only_on_rank,
+    setup_parallel, 
+    spawn_parallel_fn
+)
+
+from khaosz.parallel.module import (
+    RowParallelLinear,
+    ColumnParallelLinear
+)
+
+__all__ = [
+    "get_world_size",
+    "get_rank",
+    "get_current_device",
+    
+    "only_on_rank",
+    "setup_parallel",
+    "spawn_parallel_fn",
+    
+    "RowParallelLinear",
+    "ColumnParallelLinear"
+]

khaosz/parallel/module.py

@@ -0,0 +1,107 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.distributed as dist
+
+from torch import Tensor
+from typing import Dict
+
+
+class ParallelModel(nn.Module):
+    def __init__(self, process_group: dist.ProcessGroup):
+        super().__init__()
+        self.process_group = process_group
+        self.rank = dist.get_rank(self.process_group)
+        self.world_size = dist.get_world_size(self.process_group)
+
+
+class RowParallelLinear(ParallelModel):
+    def __init__(
+        self, 
+        process_group: dist.ProcessGroup,
+        in_features: int, 
+        out_features: int, 
+        bias: bool = True,
+        reduce_results: bool = True
+    ):
+        super().__init__(process_group)
+        
+        self.in_features = in_features
+        self.out_features = out_features
+        self.in_features_per_rank = in_features // self.world_size
+        self.reduce_results = reduce_results
+        
+        if in_features % self.world_size != 0:
+            raise ValueError(f"in_features must be divisible by world_size. Got {in_features} and {self.world_size}")
+        
+        self.weight = nn.Parameter(torch.empty(out_features, self.in_features_per_rank))
+        self.bias = nn.Parameter(torch.zeros(out_features)) if bias else None
+        
+    def forward(self, input: Tensor) -> Tensor:
+        output = F.linear(input, self.weight)
+        
+        if self.reduce_results:
+            dist.all_reduce(output, op=dist.ReduceOp.SUM, group=self.process_group)
+        
+        if self.bias is not None:
+            output += self.bias
+        
+        return output
+    
+    def load_state_dict(self, state_dict: Dict[str, Tensor]):        
+        full_weight = state_dict.get('weight')
+        full_bias = state_dict.get('bias')
+        
+        start_idx = self.rank * self.in_features_per_rank
+        end_idx = start_idx + self.in_features_per_rank
+        weight_slice = full_weight[:, start_idx:end_idx]
+        self.weight.data.copy_(weight_slice)
+        
+        if self.bias is not None:
+            self.bias.data.copy_(full_bias)
+
+
+class ColumnParallelLinear(ParallelModel):
+    def __init__(
+        self, 
+        process_group: dist.ProcessGroup,
+        in_features: int, 
+        out_features: int, 
+        bias: bool = True,
+        gather_results: bool = True
+    ):
+        super().__init__(process_group)
+        
+        self.in_features = in_features
+        self.out_features = out_features
+        self.out_features_per_rank = out_features // self.world_size
+        self.gather_results = gather_results
+
+        if out_features % self.world_size != 0:
+            raise ValueError(f"out_features must be divisible by world_size. Got {out_features} and {self.world_size}")
+
+        self.weight = nn.Parameter(torch.empty(self.out_features_per_rank, self.in_features))
+        self.bias = nn.Parameter(torch.zeros(self.out_features_per_rank)) if bias else None
+        
+    def forward(self, input: Tensor) -> Tensor:
+        output = F.linear(input, self.weight, self.bias)
+        
+        if self.gather_results:
+            output_list = [torch.empty_like(output) for _ in range(self.world_size)]
+            dist.all_gather(output_list, output, group=self.process_group)
+            output = torch.cat(output_list, dim=-1)
+        
+        return output
+    
+    def load_state_dict(self, state_dict: Dict[str, Tensor]):        
+        full_weight = state_dict.get('weight')
+        full_bias = state_dict.get('bias')
+        
+        start_idx = self.rank * self.out_features_per_rank
+        end_idx = start_idx + self.out_features_per_rank
+        weight_slice = full_weight[start_idx:end_idx, :]
+        self.weight.data.copy_(weight_slice)
+        
+        if self.bias is not None:
+            bias_slice = full_bias[start_idx:end_idx]
+            self.bias.data.copy_(bias_slice)

khaosz/parallel/setup.py

@@ -0,0 +1,151 @@
+import os
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+from functools import wraps
+from contextlib import contextmanager
+from typing import Callable, List, Optional
+
+
+def get_current_device():
+    return os.environ["LOCAL_DEVICE"]
+
+def get_world_size() -> int:
+    if dist.is_available() and dist.is_initialized():
+        return dist.get_world_size()
+    else:
+        return 1
+
+def get_rank() -> int:
+    if dist.is_available() and dist.is_initialized():
+        return dist.get_rank()
+    else:
+        return 0
+
+@contextmanager
+def setup_parallel(
+    rank: int, 
+    world_size: int,
+    backend: str = "nccl",
+    master_addr: str = "localhost",
+    master_port: str = "29500",
+    device_type: str = "cuda",
+    device_ids: Optional[List[int]] = None
+):
+
+    if dist.is_available() and dist.is_initialized():
+        yield dist.group.WORLD
+        return 
+
+    if world_size <= 1:
+        yield None
+        return
+    
+    if device_ids is None:
+        device_ids = [i for i in range(world_size)]
+    
+    rank = device_ids[rank % len(device_ids)]
+    device_id = torch.device(device_type, device_ids[rank])
+    
+    os.environ['MASTER_ADDR'] = master_addr
+    os.environ['MASTER_PORT'] = master_port
+    
+    os.environ['LOCAL_RANK'] = str(rank)
+    os.environ['WORLD_SIZE'] = str(world_size)
+    os.environ["LOCAL_DEVICE"] = str(device_id)
+    
+    dist.init_process_group(
+        rank=rank,
+        world_size=world_size,
+        backend=backend,
+        device_id=device_id
+    )
+    
+    try:
+        if backend == "nccl" and torch.cuda.is_available():
+            torch.cuda.set_device(device_id)
+        elif backend == "ccl" and hasattr(torch, 'xpu') and torch.xpu.is_available():
+            torch.xpu.set_device(device_id)
+        
+        yield dist.group.WORLD
+    finally:
+        if dist.is_initialized():
+            dist.destroy_process_group()
+
+def only_on_rank(rank, sync=False):
+    """
+    decorator to run a function only on a specific rank.
+    """
+    
+    def decorator(func):
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            if get_rank() == rank:
+                return func(*args, **kwargs)
+            if sync:
+                dist.barrier()
+        
+        return wrapper
+    
+    return decorator
+
+def wrapper_spawn_func(
+    rank: int, 
+    world_size: int, 
+    backend: str, 
+    master_addr: str, 
+    master_port: str,
+    device_type: str,
+    device_ids: List[int], 
+    func: Callable, 
+    kwargs: dict
+):
+    try:
+        with setup_parallel(
+            rank=rank, 
+            world_size=world_size, 
+            backend=backend, 
+            master_addr=master_addr, 
+            master_port=master_port,
+            device_type=device_type,
+            device_ids=device_ids
+        ):
+            func(**kwargs)
+        
+    except Exception as e:
+        print(f"Error in rank {rank}: {e}")
+        raise
+
+def spawn_parallel_fn(
+    func: Callable, 
+    world_size: int, 
+    backend: str = "nccl",
+    master_addr: str = "localhost",
+    master_port: str = "29500",
+    device_type: str = "cuda",
+    device_ids: Optional[List[int]] = None,
+    **kwargs
+):
+    # clear environment variables
+    for key in ['MASTER_ADDR', 'MASTER_PORT', 'RANK', 'WORLD_SIZE', 'LOCAL_RANK', 'LOCAL_DEVICE']:
+        if key in os.environ:
+            del os.environ[key]
+    
+    if world_size == 1:
+        device_ids = device_ids or [0]
+        deice_id = torch.device(device_type, device_ids[0])
+        os.environ["LOCAL_DEVICE"] = str(deice_id)
+        
+        func(**kwargs)
+        return
+
+    wrapper_spawn_func_args = (world_size, backend, master_addr, master_port, 
+                               device_type, device_ids, func, kwargs)
+
+    mp.spawn(
+        wrapper_spawn_func, 
+        nprocs=world_size, 
+        args=wrapper_spawn_func_args, 
+        join=True
+    )

khaosz/trainer/__init__.py

@@ -8,7 +8,7 @@ from khaosz.trainer.train_callback import (
     CheckpointCallback,
     TrainCallback,
     SchedulerCallback,
-    StepMonitorCallback
+    MetricLoggerCallback
 )
 
 __all__ = [
@@ -25,5 +25,5 @@ __all__ = [
     "CheckpointCallback",
     "TrainCallback",
     "SchedulerCallback",
-    "StepMonitorCallback"
+    "MetricLoggerCallback"
 ]

khaosz/trainer/metric_util.py

@@ -62,4 +62,28 @@ def grad_nan_num(model: nn.Module) -> Dict[str, int]:
         if param.grad:
             nan_num = param.grad.isnan().sum().item()
             nan_nums[name] = nan_num
-    return nan_nums
+    return nan_nums
+
+def ctx_get_loss(ctx):
+    return ctx.loss
+
+def ctx_get_lr(ctx):
+    return ctx.optimizer.param_groups[-1]['lr']
+
+def ctx_get_grad_norm(ctx):
+    return grad_norm(ctx.model)
+
+def ctx_get_grad_std(ctx):
+    return grad_std(ctx.model)
+
+def ctx_get_grad_max(ctx):
+    return grad_max(ctx.model)
+
+def ctx_get_grad_min(ctx):
+    return grad_min(ctx.model)
+
+def ctx_get_grad_mean(ctx):
+    return grad_mean(ctx.model)
+
+def ctx_get_grad_nan_num(ctx):
+    return grad_nan_num(ctx.model)

khaosz/trainer/schedule.py

@@ -151,8 +151,8 @@ class SchedulerFactory:
     """
 
     @staticmethod
-    def load_scheduler(optimizer, scedule_config: ScheduleConfig) -> BaseScheduler:
+    def load(optimizer, schedule_config: ScheduleConfig) -> BaseScheduler:
-        kwargs = scedule_config.get_kwargs()
+        kwargs = schedule_config.get_kwargs()
         schedule_type = kwargs.pop("schedule_type")
         
         if schedule_type == "cosine":

khaosz/trainer/strategy.py

@@ -4,14 +4,19 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 from torch import Tensor
-from typing import Any, Tuple, Callable, Dict, Union
+from typing import Any, Callable, Dict, Union
 from abc import ABC, abstractmethod
 
 
-def get_logprobs(model:nn.Module, input_ids: Tensor, mask: Tensor, pad_token_id: int):
+def get_logprobs(
+    model: Union[nn.Module, Callable[..., Dict[str, Tensor]]], 
+    input_ids: Tensor, 
+    mask: Tensor, 
+    pad_token_id: int
+):
     input_mask =  input_ids.ne(pad_token_id)
     logits = model(input_ids, input_mask)["logits"]
-    log_probs = torch.log_softmax(logits, dim=-1)
+    log_probs = torch.log_softmax(logits.float(), dim=-1)
     
     shifted_log_probs = log_probs[:, :-1, :] 
     shifted_input_ids = input_ids[:, 1:]
@@ -41,7 +46,7 @@ class BaseStrategy(ABC):
     def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
         raise NotImplementedError
     
-    def __call__(self, batch: Tuple[Tensor, ...]) -> Tensor:
+    def __call__(self, batch: Dict[str, Tensor]) -> Tensor:
         return self.compute_loss(batch)
 
 
@@ -55,7 +60,7 @@ class SeqStrategy(BaseStrategy):
         logits = self.model(input_ids=input_ids)["logits"]
         
         loss = F.cross_entropy(
-            input=logits.flatten(0, 1),
+           input=logits.flatten(0, 1).float(),
             target=target_ids.flatten()
         )
         
@@ -75,7 +80,7 @@ class SftStrategy(BaseStrategy):
         target_ids = target_ids.masked_fill(loss_mask == 0, ignore_index)
         
         loss = F.cross_entropy(
-            input=logits.flatten(0, 1),
+            input=logits.flatten(0, 1).float(),
             target=target_ids.flatten(),
             ignore_index=ignore_index
         )
@@ -94,7 +99,7 @@ class DpoStrategy(BaseStrategy):
         self.pad_token_id = pad_token_id
         self.beta = beta
         
-    def compute_loss(self, batch: Tuple[Tensor, ...]) -> Tensor:
+    def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
         batch = move_to_device(batch, self.device)
         good_ids, bad_ids = batch["chosen"], batch["rejected"]
         good_mask, bad_mask = batch["chosen_mask"], batch["rejected_mask"]
@@ -115,41 +120,6 @@ class DpoStrategy(BaseStrategy):
         return dpo_loss
 
 
-class PpoStrategy(BaseStrategy):
-    def __init__(self, model, pad_token_id, epsilon):
-        super().__init__(model)
-        ref_model = copy.deepcopy(self.model)
-        ref_model.requires_grad_(False)
-        ref_model.eval()
-        
-        self.ref_model = ref_model
-        self.pad_token_id = pad_token_id
-        self.epsilon = epsilon
-        
-    def ppo_clip_loss_masked(
-        self,
-        log_probs: Tensor, 
-        old_log_probs: Tensor, 
-        advantages: Tensor, 
-        values: Tensor, 
-        returns: Tensor,
-        mask: Tensor, 
-        clip_eps: float=0.2, 
-    ):
-        ratio = torch.exp(log_probs - old_log_probs)
-        surr1 = ratio * advantages
-        surr2 = torch.clamp(ratio, 1 - clip_eps, 1 + clip_eps) * advantages
-        policy_loss = -torch.min(surr1, surr2).masked_select(mask).mean()
-
-        value_loss = F.mse_loss(values.masked_select(mask),
-                                returns.masked_select(mask))
-
-        entropy = -(log_probs.exp() * log_probs).masked_select(mask).mean()
-        entropy_loss = -entropy
-        return policy_loss, value_loss, entropy_loss
-
-
-
 class StrategyFactory:
     
     def load(model, train_type, device, **kwargs):
@@ -157,7 +127,7 @@ class StrategyFactory:
             "seq": lambda: SeqStrategy(model, device),
             "sft": lambda: SftStrategy(model, device),
             "dpo": lambda: DpoStrategy(
-                model, 
+                model,
                 device,
                 kwargs.get("pad_token_id"), 
                 kwargs.get("dpo_beta")

khaosz/trainer/train_callback.py

@@ -1,26 +1,27 @@
 import os
 import json
 import time
+import torch.nn as nn
 
 from pathlib import Path
 from tqdm import tqdm
 from torch.nn.utils import clip_grad_norm_
-from torch.optim.lr_scheduler import LambdaLR
+from torch.optim.lr_scheduler import LRScheduler
-from typing import List, Optional, Protocol, TYPE_CHECKING
+from typing import Callable, List, Optional, Protocol
 
-from khaosz.config import ScheduleConfig
+from khaosz.parallel import only_on_rank
 from khaosz.trainer.metric_util import (
-    grad_max,
+    ctx_get_loss,
-    grad_min,
+    ctx_get_lr,
-    grad_norm,
+    ctx_get_grad_max,
-    grad_mean,
+    ctx_get_grad_min,
-    grad_std,
+    ctx_get_grad_norm,
-    grad_nan_num
+    ctx_get_grad_mean,
+    ctx_get_grad_std,
+    ctx_get_grad_nan_num
 )
-
+from khaosz.data.checkpoint import Checkpoint
-if TYPE_CHECKING:
+from khaosz.trainer.train_context import TrainContext
-    from khaosz.trainer.trainer import Trainer
-    from khaosz.trainer.train_context import TrainContext
 
 
 class TrainCallback(Protocol):
@@ -28,31 +29,31 @@ class TrainCallback(Protocol):
     Callback interface for trainer.
     """
 
-    def on_train_begin(self, trainer: 'Trainer', context: 'TrainContext'):
+    def on_train_begin(self, context: TrainContext):
         """ Called at the beginning of training. """
     
-    def on_train_end(self, trainer: 'Trainer', context: 'TrainContext'):
+    def on_train_end(self, context: TrainContext):
         """ Called at the end of training. """
 
-    def on_epoch_begin(self, trainer: 'Trainer', context: 'TrainContext'):
+    def on_epoch_begin(self, context: TrainContext):
         """ Called at the beginning of each epoch. """
 
-    def on_epoch_end(self, trainer: 'Trainer', context: 'TrainContext'):
+    def on_epoch_end(self, context: TrainContext):
         """  Called at the end of each epoch. """
     
-    def on_step_begin(self, trainer: 'Trainer', context: 'TrainContext'):
+    def on_step_begin(self, context: TrainContext):
         """ Called at the beginning of each step. """
 
-    def on_step_end(self, trainer: 'Trainer', context: 'TrainContext'):
+    def on_step_end(self, context: TrainContext):
         """ Called at the end of each step."""
 
-    def on_batch_begin(self, trainer: 'Trainer', context: 'TrainContext'):
+    def on_batch_begin(self, context: TrainContext):
         """ Called at the beginning of each batch. """
 
-    def on_batch_end(self, trainer: 'Trainer', context: 'TrainContext'):
+    def on_batch_end(self, context: TrainContext):
         """ Called at the end of each batch. """
     
-    def on_error(self, trainer: 'Trainer', context: 'TrainContext'):
+    def on_error(self, context: TrainContext):
         """ Called when an error occurs during training. """
 
 
@@ -63,29 +64,27 @@ class GradientClippingCallback(TrainCallback):
     def __init__(self, max_grad_norm: float):
         self.max_grad_norm = max_grad_norm
     
-    def on_step_begin(self, trainer: 'Trainer', context: 'TrainContext'):
+    def on_step_begin(self, context: TrainContext):
         _ = context
-        clip_grad_norm_(trainer.parameter.model.parameters(), self.max_grad_norm)
+        clip_grad_norm_(context.model.parameters(), self.max_grad_norm)
 
 
 class SchedulerCallback(TrainCallback):
     """
     Scheduler callback for trainer.
     """
-    def __init__(self, schedule_config: ScheduleConfig):
+    def __init__(self):
-        self.schedule_config = schedule_config
+        self.scheduler: LRScheduler = None
-        self.scheduler: Optional[LambdaLR] = None
     
-    def on_train_begin(self, trainer: 'Trainer', context: 'TrainContext'):
+    def on_train_begin(self, context: TrainContext):
-
+        for group in context.optimizer.param_groups:
-        for group in trainer.train_config.optimizer.param_groups:
             if "initial_lr" not in group:
                 group["initial_lr"] = group["lr"] 
         
         self.scheduler = context.scheduler
     
-    def on_batch_end(self, trainer: 'Trainer', context: 'TrainContext'):
+    def on_batch_end(self, context: TrainContext):
-        _ = trainer, context
+        _ = context
         if self.scheduler:
             self.scheduler.step()
 
@@ -94,133 +93,140 @@ class CheckpointCallback(TrainCallback):
     """ 
     Checkpoint callback for trainer.
     """
-    def __init__(self, checkpoint_interval: int):
+    def __init__(
-        self.checkpoint_interval = checkpoint_interval
+        self, 
+        save_dir: str,  
+        interval: int,
+        weight_only: bool = False,
+        state_dict_fn: Optional[Callable[[nn.Module], dict]] = None
+    ):
+        self.save_dir = save_dir
+        self.interval = interval
+        self.weight_only = weight_only
+        self.state_dict_fn = state_dict_fn
         self.last_ckpt_iter = 0
     
-    def _save_checkpoint(self, trainer: 'Trainer', context: 'TrainContext'):
+    @only_on_rank(0)
-        save_path = os.path.join(trainer.train_config.checkpoint_dir, f"iter_{context.batch_iter}")
+    def _save_checkpoint(self, context: TrainContext):
-        context.checkpoint.optimizer_state = context.optimizer.state_dict()
+        save_path = os.path.join(self.save_dir, f"epoch_{context.epoch}_iter_{context.iteration}")
-        context.checkpoint.scheduler_state = context.scheduler.state_dict()
+        state_dict = self.state_dict_fn(context.model) if self.state_dict_fn else context.model.state_dict()
-        context.checkpoint.epoch = context.epoch
-        context.checkpoint.batch_iter = context.batch_iter
-        context.checkpoint.save(save_path)
-        self.last_ckpt_iter = context.batch_iter
-    
-    def on_batch_end(self, trainer: 'Trainer', context: 'TrainContext'):
-        context.checkpoint.loss_list.append(context.loss)
         
-        if context.batch_iter - self.last_ckpt_iter >= self.checkpoint_interval:
+        context.checkpoint = Checkpoint(
-            self._save_checkpoint(trainer, context)
+            state_dict=state_dict,
-            
+            epoch=context.epoch,
-    def on_train_end(self, trainer: 'Trainer', context: 'TrainContext'):
+            iteration=context.iteration
-        if context.batch_iter != self.last_ckpt_iter:
+        )
-            self._save_checkpoint(trainer, context)
+
+        context.checkpoint.save(save_path)
+        self.last_ckpt_iter = context.iteration
+    
+    def on_batch_end(self, context: TrainContext):
+        if context.iteration - self.last_ckpt_iter >= self.interval:
+            self._save_checkpoint(context)
+    
+    def on_train_end(self, context: TrainContext):
+        if context.iteration != self.last_ckpt_iter:
+            self._save_checkpoint(context)
+    
+    def on_error(self, context: TrainContext):
+        self._save_checkpoint(context)
 
 
 class ProgressBarCallback(TrainCallback):
     """ 
     Progress bar callback for trainer.
     """
-    def __init__(self):
+    def __init__(self, num_epoch: int):
+        self.num_epoch = num_epoch
         self.progress_bar: tqdm = None
     
-    def on_epoch_begin(self, trainer: 'Trainer', context: 'TrainContext'):
+    @only_on_rank(0)
+    def on_epoch_begin(self, context: TrainContext):
         self.progress_bar = tqdm(
             context.dataloader, 
-            desc=f"Epoch {context.epoch+1}/{trainer.train_config.n_epoch}", 
+            desc=f"Epoch {context.epoch+1}/{self.num_epoch}", 
             dynamic_ncols=True
         )
     
-    def on_batch_end(self, trainer: 'Trainer', context: 'TrainContext'):
+    @only_on_rank(0)
-        _ = trainer
+    def on_batch_end(self, context: TrainContext):
         self.progress_bar.set_postfix({
             "loss": f"{context.loss:.4f}",
             "lr": f"{context.optimizer.param_groups[-1]['lr']:.2e}"
         })
         self.progress_bar.update(1)
     
-    def on_epoch_end(self, trainer: 'Trainer', context: 'TrainContext'):
+    @only_on_rank(0)
-        _ = trainer, context
+    def on_epoch_end(self, context: TrainContext):
+        _ = context
         if self.progress_bar:
             self.progress_bar.close()
 
 
-class StepMonitorCallback(TrainCallback):
+class MetricLoggerCallback(TrainCallback):
-    """
-    Customizable logger callback for trainer.
-    
-    This callback provides flexible logging capabilities for training metrics,
-    supporting multiple log formats and custom log handlers.
-    """
-    
     def __init__(
-        self,
+        self, 
-        log_dir: Optional[str] = None,
+        log_dir:str,
-        log_interval: int = 100,
+        save_interval:int,
-        metrics: Optional[List[str]] = None
+        log_interval:int=10,
+        metrics:List[str]=None
     ):
-        """
+        self.step_num = 0
-        Args:
+        self.last_save_step = 0
-            log_dir: Directory to save log files. If None, logs won't be saved to file.
+        self.save_interval = save_interval
-            log_interval: Log every N steps
-            metrics: List of metrics to log. Supported: ['loss', 'lr', 'grad_norm', 'grad_std',
-                    grad_max', 'grad_min', 'grad_mean', 'grad_nan_num']
-            custom_handlers: List of custom log handler functions
-            json_log: Whether to save logs in JSON format
-        """
-        
-        self.log_dir = Path(log_dir) if log_dir else Path(os.getcwd()) / "logs"
         self.log_interval = log_interval
         self.metrics = metrics or ['loss', 'lr']
-        self.step_num = 0
         
+        self.log_dir = Path(log_dir) if log_dir else Path.cwd() / "logs"
         self.log_dir.mkdir(parents=True, exist_ok=True)
-    
-    def _handle_info(self, trainer: 'Trainer', context: 'TrainContext'):
-        """ Logs training information to console and file. """
         
-        log_data = {
+        self.log_cache = []
+        
+        self._metric_funcs = {
+            'loss': ctx_get_loss,
+            'lr': ctx_get_lr,
+            'grad_norm': ctx_get_grad_norm,
+            'grad_std': ctx_get_grad_std,
+            'grad_max': ctx_get_grad_max,
+            'grad_min': ctx_get_grad_min,
+            'grad_mean': ctx_get_grad_mean,
+            'grad_nan_num': ctx_get_grad_nan_num
+        }
+
+    def _get_log_data(self, context: TrainContext):
+        return {
             "timestamp": time.strftime('%Y-%m-%d %H:%M:%S'),
             "epoch": context.epoch,
-            "iter": context.batch_iter,
+            "iter": context.iteration,
-            "metrics": self.metrics,
+            **{m: self._metric_funcs[m](context) for m in self.metrics}
         }
-        
-        for metric in self.metrics:
-            if metric == 'loss':
-                log_data[metric] = context.loss
-            elif metric == 'lr':
-                log_data[metric] = context.optimizer.param_groups[-1]['lr']
-            elif metric == 'grad_norm':
-                log_data[metric] = grad_norm(trainer.parameter.model)
-            elif metric == 'grad_std':
-                log_data[metric] = grad_std(trainer.parameter.model)
-            elif metric == 'grad_max':
-                log_data[metric] = grad_max(trainer.parameter.model)
-            elif metric == 'grad_min':
-                log_data[metric] = grad_min(trainer.parameter.model)
-            elif metric == 'grad_mean':
-                log_data[metric] = grad_mean(trainer.parameter.model)
-            elif metric == 'grad_nan_num':
-                log_data[metric] = grad_nan_num(trainer.parameter.model)
-            else:
-                raise ValueError(f"Invalid metric: {metric}")
-        
-        return log_data
     
-    def _handle_log(self, trainer: 'Trainer', context: 'TrainContext'):
+    @only_on_rank(0)
-        """ Logs training information to console and file. """
+    def _add_log(self, log_data):
-        log_data = self._handle_info(trainer, context)
+        self.log_cache.append(log_data)
-        try:
-            log_file = self.log_dir / f"log_epoch_{context.epoch}_iter_{context.batch_iter}.json"
-            with open(log_file, 'a') as f:
-                json.dump(log_data, f, indent=4)
-        except Exception:
-            raise
     
-    def on_step_end(self, trainer: 'Trainer', context: 'TrainContext'):
+    @only_on_rank(0)
+    def _save_log(self, epoch, iter):
+        log_file = self.log_dir / f"epoch_{epoch}_iter_{iter}_metric.jsonl"
+        
+        with open(log_file, 'w') as f:
+            for log in self.log_cache:
+                f.write(json.dumps(log) + '\n')
+    
+    def on_batch_end(self, context):
         if self.step_num % self.log_interval == 0:
-            self._handle_log(trainer, context)
+            log_data = self._get_log_data(context)
+            self._add_log(log_data)
         
-        self.step_num += 1
+        if self.step_num - self.last_save_step >= self.save_interval:
+            self._save_log(context.epoch, context.iteration)
+            self.last_save_step = self.step_num
+        
+        self.step_num += 1
+    
+    def on_train_end(self, context):
+        self._save_log(context.epoch, context.iteration)
+    
+    def on_error(self, context):
+        self._save_log(context.epoch, context.iteration)
+    

khaosz/trainer/train_context.py

@@ -1,105 +1,99 @@
-from dataclasses import dataclass, field, fields
+import torch.nn as nn
-from typing import Optional, Self, TYPE_CHECKING
 from torch.optim import Optimizer
+from torch.optim.lr_scheduler import LRScheduler
 from torch.utils.data import DataLoader
-from khaosz.config import Checkpoint
-from khaosz.data import ResumeableRandomSampler
-from khaosz.trainer.schedule import BaseScheduler, SchedulerFactory
 
-if TYPE_CHECKING:
+from khaosz.data import ResumableDistributedSampler
-    from khaosz.trainer.trainer import Trainer
+from khaosz.data.checkpoint import Checkpoint
+from khaosz.trainer.strategy import StrategyFactory, BaseStrategy
+from khaosz.config.train_config import TrainConfig
+from khaosz.parallel.setup import get_current_device, get_world_size, get_rank
+
+from dataclasses import dataclass, field
+from typing import Optional, Self
 
 
 @dataclass
 class TrainContext:
+    model: nn.Module = field(default=None)
+    strategy: BaseStrategy = field(default=None)
     dataloader: DataLoader = field(default=None)
     optimizer: Optimizer = field(default=None)
-    scheduler: BaseScheduler = field(default=None)
+    scheduler: LRScheduler = field(default=None)
     checkpoint: Checkpoint = field(default=None)
+    
     epoch: int = field(default=0)
-    batch_iter: int = field(default=0)
+    iteration: int = field(default=0)
     loss: float = field(default=0.0)
     
-    def asdict(self) -> dict:
+    world_size: int = field(default=1)
-        return {field.name: getattr(self, field.name) 
+    rank: int = field(default=0)
-                for field in fields(self)}
+    kwargs: dict = field(default_factory=dict)
 
 
 class TrainContextBuilder:
-    def __init__(self, trainer: 'Trainer'):
+    def __init__(self, config: TrainConfig):
-        self.trainer = trainer
+        self.config = config
-        self._context: TrainContext = None
+        self._context = TrainContext(
+            model=config.model,
+            world_size=get_world_size(),
+            rank=get_rank(),
+        )
+        
+        device = get_current_device()
+        self._context.model = self._context.model.to(device=device)
+        
+        if self.config.nprocs > 1:
+            fn = self.config.parallel_wrapper
+            self._context.model = fn(self._context.model)
+        
+        self._context.optimizer = self.config.optimizer_fn(self._context.model)
+        self._context.scheduler = self.config.scheduler_fn(self._context.optimizer)
     
     def with_checkpoint(self, checkpoint: Optional[Checkpoint]) -> Self:
-        self._context = TrainContext()
         if checkpoint is None:
             checkpoint = Checkpoint(
-                model=self.trainer.parameter.model,
+                state_dict=self._context.model.state_dict(),
-                tokenizer=self.trainer.parameter.tokenizer,
-                config=self.trainer.parameter.config,
             )
         else:
             # resume from the assigned checkpoint or assigned iteration
-            self._context.epoch = max(checkpoint.epoch, self.trainer.train_config.start_epoch)
+            self._context.epoch = max(checkpoint.epoch, self.config.start_epoch)
-            self._context.batch_iter = max(checkpoint.batch_iter, self.trainer.train_config.start_batch)
+            self._context.iteration = max(checkpoint.iteration, self.config.start_batch)
+            self._context.model.load_state_dict(checkpoint.state_dict)
         
         self._context.checkpoint = checkpoint
         return self
     
-    def with_optimizer(self) -> Self:
-        if self._context is None:
-            raise RuntimeError("Must call with_checkpoint() before with_optimizer()")
-        
-        optimizer = self.trainer.train_config.optimizer
-        
-        if self._context.checkpoint and self._context.checkpoint.optimizer_state:
-            optimizer.load_state_dict(self._context.checkpoint.optimizer_state)
-        
-        self._context.optimizer = optimizer
-        
-        if self._context.checkpoint:
-            self._context.checkpoint.optimizer_state = optimizer.state_dict()
-        
-        return self
-    
-    def with_scheduler(self) -> Self:
-        if not hasattr(self._context, 'optimizer') or self._context.optimizer is None:
-            raise RuntimeError("Must call with_optimizer() before with_scheduler()")
-
-        optimizer = self.trainer.train_config.optimizer
-        schedule_config = self.trainer.schedule_config
-        scheduler = SchedulerFactory.load_scheduler(optimizer, schedule_config)
-        
-        if self._context.checkpoint and self._context.checkpoint.scheduler_state:
-            scheduler.load_state_dict(self._context.checkpoint.scheduler_state)
-        
-        self._context.scheduler = scheduler
-        
-        if self._context.checkpoint:
-            self._context.checkpoint.scheduler_state = scheduler.state_dict()
-        
-        return self
-    
     def with_dataloader(self) -> Self:
-        # fix: change batch level batch_iter to sample level offset
+        # fix: change batch level iteration to sample level offset
-        sampler_offset = self._context.batch_iter * self.trainer.train_config.batch_size
+        config = self.config
-        resumeable_sampler = ResumeableRandomSampler(
+        sampler_offset = self._context.iteration * config.batch_size
-            data_source=self.trainer.train_config.dataset,
+        resumeable_sampler = ResumableDistributedSampler(
+            data_source=config.dataset,
             start_epoch=self._context.epoch,
             start_iter=sampler_offset,
-            seed=self.trainer.train_config.random_seed
+            seed=config.random_seed
         )
         
         dataloader = DataLoader(
-            self.trainer.train_config.dataset, 
+            config.dataset, 
-            batch_size=self.trainer.train_config.batch_size, 
+            batch_size=config.batch_size, 
             sampler=resumeable_sampler,
-            num_workers=self.trainer.train_config.num_workers,
+            num_workers=config.num_workers,
-            pin_memory=self.trainer.train_config.pin_memory,
+            pin_memory=config.pin_memory,
-            prefetch_factor=self.trainer.train_config.prefetch_factor
+            prefetch_factor=config.prefetch_factor
         )
         self._context.dataloader = dataloader
         return self
     
+    def with_strategy(self) -> Self:
+        self._context.strategy = StrategyFactory.load(
+            model=self.config.model,
+            train_type=self.config.strategy,
+            device=get_current_device(),
+            **self.config.extra_kwargs
+        )
+        return self
+    
     def build(self) -> TrainContext:
         return self._context

khaosz/trainer/trainer.py

@@ -1,19 +1,17 @@
 import logging
 from typing import Optional, List
-from khaosz.config import (
+from khaosz.config import TrainConfig
-    ModelParameter, 
-    Checkpoint, 
-    ScheduleConfig,
-    TrainConfig
-)
 from khaosz.trainer.train_callback import (
     TrainCallback, 
     ProgressBarCallback, 
-    CheckpointCallback, 
+    CheckpointCallback,
+    MetricLoggerCallback,
     GradientClippingCallback,
     SchedulerCallback
 )
 from khaosz.trainer.train_context import TrainContext, TrainContextBuilder
+from khaosz.data.checkpoint import Checkpoint
+from khaosz.parallel.setup import spawn_parallel_fn
 
 logger = logging.getLogger(__name__)
 
@@ -21,66 +19,76 @@ logger = logging.getLogger(__name__)
 class Trainer:
     def __init__(
         self,
-        parameter: ModelParameter,
         train_config: TrainConfig,
-        schedule_config: ScheduleConfig,
         callbacks: Optional[List[TrainCallback]] = None
     ):
-        self.parameter = parameter
         self.train_config = train_config
-        self.schedule_config = schedule_config
+        default_callbacks = self._get_default_callbacks()
-        self.callbacks = callbacks or self._get_default_callbacks()
+        self.callbacks = default_callbacks + callbacks if callbacks else default_callbacks
 
     def _get_default_callbacks(self) -> List[TrainCallback]:
+        train_config = self.train_config
         return [
-            ProgressBarCallback(),
+            ProgressBarCallback(train_config.n_epoch),
-            CheckpointCallback(self.train_config.checkpoint_interval),
+            CheckpointCallback(train_config.checkpoint_dir, train_config.checkpoint_interval),
-            GradientClippingCallback(self.train_config.max_grad_norm),
+            MetricLoggerCallback(train_config.checkpoint_dir, train_config.checkpoint_interval),
-            SchedulerCallback(self.schedule_config),
+            GradientClippingCallback(train_config.max_grad_norm),
+            SchedulerCallback(),
         ]
-        
+    
     def _build_context(self, checkpoint: Optional[Checkpoint]) -> TrainContext:
-        return (TrainContextBuilder(self)
+        return (TrainContextBuilder(self.train_config)
                 .with_checkpoint(checkpoint)
-                .with_optimizer()
-                .with_scheduler()
                 .with_dataloader()
+                .with_strategy()
                 .build())
     
     def _call_callbacks(self, method_name: str, context: TrainContext):
         for callback in self.callbacks:
             method = getattr(callback, method_name, None)
             if method:
-                method(self, context)
+                method(context)
+    
+    def train(self, checkpoint: Optional[Checkpoint] = None):
+        config = self.train_config
+        spawn_parallel_fn(
+            self._train_impl,
+            backend=config.backend,
+            world_size=config.nprocs,
+            master_addr=config.master_addr,
+            master_port=config.master_port,
+            checkpoint=checkpoint
+        )
 
-    def train(self, checkpoint: Optional[Checkpoint] = None) -> Checkpoint:
+    def _train_impl(self, checkpoint: Optional[Checkpoint] = None) -> Checkpoint:
         context = self._build_context(checkpoint)
         self._call_callbacks('on_train_begin', context)
         
         try:
-            self.parameter.model.train()
+            context.model.train()
             # 1.epoch
             for epoch in range(context.epoch, self.train_config.n_epoch):
                 context.epoch = epoch
                 self._call_callbacks('on_epoch_begin', context)
                 
                 for batch in context.dataloader:
-                    if context.batch_iter % self.train_config.accumulation_steps == 0:
+                    if context.iteration % self.train_config.accumulation_steps == 0:
                         # 2. step
                         self._call_callbacks('on_step_begin', context)
-                        self.train_config.optimizer.step()
+                        context.optimizer.step()
-                        self.train_config.optimizer.zero_grad()
+                        context.optimizer.zero_grad()
                         self._call_callbacks('on_step_end', context)
                     
                     # 3. batch
                     self._call_callbacks('on_batch_begin', context)
-                    loss = self.train_config.strategy(batch)
+                    loss = context.strategy(batch)
                     context.loss = loss.item()
-                    context.batch_iter += 1
+                    context.iteration += 1
                     
                     # to make the loss normalized by accumulation steps
-                    normalized_loss = loss / self.train_config.accumulation_steps
+                    stand_batch = self.train_config.accumulation_steps * self.train_config.nprocs
-                    normalized_loss.backward()
+                    stand_loss = loss / stand_batch
+                    stand_loss.backward()
                     
                     self._call_callbacks('on_batch_end', context)
 
@@ -91,5 +99,4 @@ class Trainer:
             self._call_callbacks('on_error', context)
             raise
         finally:
-            self._call_callbacks('on_train_end', context)
+            self._call_callbacks('on_train_end', context)
-            return context.checkpoint

khaosz/utils/__init__.py

@@ -0,0 +1 @@
+# init file

pyproject.toml

@@ -0,0 +1,36 @@
+[build-system]
+requires = ["setuptools>=64", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+dynamic = ["version"]
+name = "khaosz"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "h5py==3.15.1",
+    "numpy==2.3.2",
+    "torch==2.7.1",
+    "tokenizers==0.21.4",
+    "tqdm==4.67.1",
+    "safetensors==0.5.3",
+    "huggingface-hub==0.34.3",
+    "pytest==9.0.2"
+]
+keywords = ["nlp", "datasets", "language-models", "machine-learning"]
+license = { text = "GPL-3.0" }
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "License :: OSI Approved :: GPL-3.0",
+    "Operating System :: OS Independent",
+]
+urls = { Homepage = "https://github.com/khaosz/khaosz" }
+
+[tool.setuptools.packages.find]
+where = ["."]
+
+[tool.pip]
+extra-index-url = "https://download.pytorch.org/whl/cu126" 
+
+[tool.setuptools.dynamic]
+version = { attr = "khaosz.__version__" }

requirements.txt

@@ -1,36 +0,0 @@
-# python=3.12
---extra-index-url https://download.pytorch.org/whl/cu126
-
-certifi==2025.8.3
-charset-normalizer==3.4.2
-colorama==0.4.6
-contourpy==1.3.3
-cycler==0.12.1
-filelock==3.13.1
-fonttools==4.59.0
-fsspec==2024.6.1
-huggingface-hub==0.34.3
-idna==3.10
-Jinja2==3.1.6
-kiwisolver==1.4.8
-MarkupSafe==2.1.5
-matplotlib==3.10.5
-mpmath==1.3.0
-networkx==3.3
-numpy==2.3.2
-packaging==25.0
-pillow==11.3.0
-pyparsing==3.2.3
-python-dateutil==2.9.0.post0
-PyYAML==6.0.2
-requests==2.32.4
-safetensors==0.5.3
-setuptools==78.1.1
-six==1.17.0
-sympy==1.13.3
-tokenizers==0.21.4
-torch==2.7.1+cu126
-tqdm==4.67.1
-typing_extensions==4.12.2
-urllib3==2.5.0
-wheel==0.45.1

setup.py

@@ -1,19 +0,0 @@
-import re
-import khaosz
-from setuptools import find_packages, setup
-
-
-with open("requirements.txt") as f:
-    required = [line for line in f.read().splitlines() 
-                if line and re.match(r'^[^=]+==[^=]+$', line.strip())]
-
-setup(
-    name="khaosz", 
-    version=khaosz.__version__, 
-    packages=find_packages(),
-    install_requires=required,
-    dependency_links=[
-        "https://download.pytorch.org/whl/cu126",
-    ],
-    python_requires=">=3.12",
-)

tests/conftest.py

@@ -4,19 +4,14 @@ import numpy as np
 import tempfile
 import shutil
 import torch
-
 import pytest
-import matplotlib
-from torch.utils.data import Dataset
 
+from torch.utils.data import Dataset
 from khaosz.config.model_config import ModelConfig
 from khaosz.data.tokenizer import BpeTokenizer
 from khaosz.model.transformer import Transformer
 
 
-matplotlib.use("Agg")
-
-
 class RandomDataset(Dataset):
     def __init__(self, length=None, max_length=64, vocab_size=1000):
         self.length = length or int(np.random.randint(100, 200))
@@ -83,19 +78,19 @@ def base_test_env(request: pytest.FixtureRequest):
     n_dim_choices = [8, 16, 32]
     n_head_choices = [2, 4]
     
-    n_dim = int(np.random.choice(n_dim_choices))
+    dim = int(np.random.choice(n_dim_choices))
-    n_head = int(np.random.choice(n_head_choices))
+    n_heads = int(np.random.choice(n_head_choices))
-    n_kvhead = n_head // 2
+    n_kv_heads = n_heads // 2
-    d_ffn = n_dim * 2
+    dim_ffn = dim * 2
 
     config = {
         "vocab_size": 1000,
-        "n_dim": n_dim,
+        "dim": dim,
-        "n_head": n_head,
+        "n_heads": n_heads,
-        "n_kvhead": n_kvhead,
+        "n_kv_heads": n_kv_heads,
-        "d_ffn": d_ffn,
+        "dim_ffn": dim_ffn,
-        "m_len": 1024,
+        "max_len": 1024,
-        "n_layer": 4,
+        "n_layers": 4,
         "norm_eps": 1e-5
     }
     
@@ -108,7 +103,7 @@ def base_test_env(request: pytest.FixtureRequest):
     
     yield {
         "device": device,
-        "test_dir": test_dir,
+        "test_dir": str(test_dir),
         "config_path": config_path,
         "transformer_config": transformer_config,
         "model": model,

tests/data/test_checkpoint.py

@@ -0,0 +1,84 @@
+import torch
+import tempfile
+import torch.distributed as dist
+
+from torch.optim import AdamW
+from torch.optim.lr_scheduler import CosineAnnealingLR
+from khaosz.data.checkpoint import Checkpoint
+from khaosz.parallel.setup import get_rank, spawn_parallel_fn
+
+def test_single_process():
+    model = torch.nn.Linear(10, 5)
+    optimizer = AdamW(model.parameters(), lr=1e-3)
+    scheduler = CosineAnnealingLR(optimizer, T_max=10)
+
+    for epoch in range(3):
+        for iteration in range(10):
+
+            x = torch.randn(32, 10)
+            y = torch.randn(32, 5)
+            loss = model(x).mean()
+            loss.backward()
+            optimizer.step()
+            optimizer.zero_grad()
+        
+        scheduler.step()
+    
+    checkpoint = Checkpoint(
+        state_dict=model.state_dict(),
+        epoch=3,
+        iteration=30
+    )
+    
+    with tempfile.TemporaryDirectory() as tmpdir:
+        checkpoint.save(tmpdir)
+        
+        loaded_checkpoint = Checkpoint.load(tmpdir)
+        
+        assert loaded_checkpoint.epoch == 3
+        assert loaded_checkpoint.iteration == 30
+def simple_training():
+    model = torch.nn.Linear(10, 5)
+    optimizer = AdamW(model.parameters(), lr=1e-3)
+    scheduler = CosineAnnealingLR(optimizer, T_max=10)
+    
+    for epoch in range(2):
+        for iteration in range(5):
+            x = torch.randn(16, 10)
+            y = torch.randn(16, 5)
+            loss = model(x).mean()
+            loss.backward()
+            optimizer.step()
+            optimizer.zero_grad()
+        scheduler.step()
+
+    checkpoint = Checkpoint(
+        state_dict=model.state_dict(),
+        epoch=2,
+        iteration=10,
+    )
+    
+    rank = get_rank()
+    
+    if rank == 0:
+        shared_dir = tempfile.mkdtemp()
+        checkpoint.save(shared_dir)
+    else:
+        shared_dir = None
+
+    
+    if dist.is_initialized():
+        dir_list = [shared_dir]
+        dist.broadcast_object_list(dir_list, src=0)
+        shared_dir = dir_list[0]
+    
+ 
+    loaded = Checkpoint.load(shared_dir)
+    assert loaded.epoch == 2
+
+def test_multi_process():
+    spawn_parallel_fn(
+        simple_training,
+        world_size=2,
+        backend="gloo"
+    )

tests/data/test_dataset.py

@@ -0,0 +1,146 @@
+import torch
+import numpy as np
+
+from khaosz.data.file import save_h5
+from khaosz.data.dataset import *
+
+
+
+def test_dataset_loader_random_paths(base_test_env):
+    """Test dataset loader with multiple random paths"""
+    test_dir = base_test_env["test_dir"]
+    
+    # Create multiple mmap dataset directories with random data
+    num_files = np.random.randint(2, 5)
+    
+    for i in range(num_files):
+        seq_length = np.random.randint(200, 400)
+        dummy_data = {
+            "sequence": [torch.randint(0, 1000, (seq_length,), dtype=torch.int64) for _ in range(10)],
+        }
+        save_h5(test_dir, f"data_{i}", dummy_data)
+    
+        # Test loading with multiple paths
+        loaded_dataset = DatasetLoader.load(
+            train_type="seq", 
+            load_path=test_dir, 
+            window_size=64, 
+        )
+        assert loaded_dataset is not None
+        assert len(loaded_dataset) > 0
+    
+    # Test that we can get items without errors
+    for i in range(len(loaded_dataset)):
+        item = loaded_dataset[i]
+        assert "input_ids" in item
+        assert "target_ids" in item
+        assert item["input_ids"].shape == item["target_ids"].shape
+        assert item["input_ids"].shape[0] == 64
+
+
+def test_dpo_strategy_with_random_data(base_test_env):
+    """Test DPO strategy with randomized preference data"""
+    test_dir = base_test_env["test_dir"]
+    
+    # Create DPO-style data with memory mapping format
+    seq_length = np.random.randint(100, 200)
+    
+    dummy_data = {
+        "chosen": [torch.randint(0, 1000, (seq_length,), dtype=torch.int64)],
+        "rejected": [torch.randint(0, 1000, (seq_length,), dtype=torch.int64)],
+        "chosen_mask": [torch.ones(seq_length, dtype=torch.bool)],
+        "rejected_mask": [torch.ones(seq_length, dtype=torch.bool)]
+    }
+    
+    save_h5(test_dir, "dpo_data", dummy_data)
+    
+    # Load DPO dataset
+    dpo_dataset = DatasetLoader.load(
+        train_type="dpo", 
+        load_path=test_dir, 
+        window_size=64, 
+    )
+    
+    assert dpo_dataset is not None
+    assert hasattr(dpo_dataset, 'fetcher')
+    assert len(dpo_dataset) > 0
+    
+    # Test that we can get DPO items without errors
+    for i in range(min(3, len(dpo_dataset))):
+        item = dpo_dataset[i]
+        assert "chosen" in item
+        assert "rejected" in item
+        assert "chosen_mask" in item
+        assert "rejected_mask" in item
+        assert item["chosen"].shape == item["rejected"].shape
+        assert item["chosen_mask"].shape == item["rejected_mask"].shape
+
+
+def test_sft_dataset_with_random_data(base_test_env):
+    """Test SFT dataset with random data"""
+    test_dir = base_test_env["test_dir"]
+    
+    # Create SFT-style data with memory mapping format
+    seq_length = np.random.randint(100, 200)
+    
+    dummy_data = {
+        "sequence": [torch.randint(0, 1000, (seq_length,), dtype=torch.int64)],
+        "loss_mask": [torch.ones(seq_length, dtype=torch.bool)]
+    }
+    
+    save_h5(test_dir, "sft_data", dummy_data)
+    
+    # Load SFT dataset
+    sft_dataset = DatasetLoader.load(
+        train_type="sft", 
+        load_path=test_dir, 
+        window_size=64, 
+    )
+    
+    assert sft_dataset is not None
+    assert hasattr(sft_dataset, 'fetcher')
+    assert len(sft_dataset) > 0
+    
+    # Test that we can get SFT items without errors
+    for i in range(min(3, len(sft_dataset))):
+        item = sft_dataset[i]
+        assert "input_ids" in item
+        assert "target_ids" in item
+        assert "loss_mask" in item
+        assert item["input_ids"].shape == item["target_ids"].shape
+        assert item["loss_mask"].shape[0] == 64
+
+
+def test_dataset_with_custom_stride(base_test_env):
+    """Test dataset with custom stride parameter"""
+    test_dir = base_test_env["test_dir"]
+    
+    # Create test data
+    seq_length = 200
+    dummy_data = {
+        "sequence": [torch.randint(0, 1000, (seq_length,), dtype=torch.int64)],
+    }
+    
+    save_h5(test_dir,"stride_test_data", dummy_data)
+    
+    # Test with custom stride
+    custom_stride = 32
+    dataset = DatasetLoader.load(
+        train_type="seq", 
+        load_path=test_dir, 
+        window_size=64, 
+        stride=custom_stride
+    )
+    
+    assert dataset is not None
+    assert len(dataset) > 0
+    
+    # With stride 32 and window 64 on 200 length data, we should get more samples
+    # than with default stride (which equals window size)
+    default_stride_dataset = DatasetLoader.load(
+        train_type="seq", 
+        load_path=test_dir, 
+        window_size=64, 
+    )
+    
+    assert len(dataset) > len(default_stride_dataset)

tests/data/test_sampler.py

@@ -1,13 +1,13 @@
 from khaosz.trainer import *
-from khaosz.data.data_util import *
+from khaosz.data import *
 
 def test_random_sampler_consistency(random_dataset):
     """Test RandomSampler produces consistent results with same seed"""
     dataset = random_dataset
     
     # Create two samplers with same seed
-    sampler1 = ResumeableRandomSampler(dataset, seed=42)
+    sampler1 = ResumableDistributedSampler(dataset, seed=42)
-    sampler2 = ResumeableRandomSampler(dataset, seed=42)
+    sampler2 = ResumableDistributedSampler(dataset, seed=42)
     
     indices1 = list(iter(sampler1))
     indices2 = list(iter(sampler2))
@@ -19,8 +19,8 @@ def test_random_sampler_different_seeds(random_dataset):
     dataset = random_dataset
     
     # Create two samplers with different seeds
-    sampler1 = ResumeableRandomSampler(dataset, seed=42)
+    sampler1 = ResumableDistributedSampler(dataset, seed=42)
-    sampler2 = ResumeableRandomSampler(dataset, seed=123)
+    sampler2 = ResumableDistributedSampler(dataset, seed=123)
     
     indices1 = list(iter(sampler1))
     indices2 = list(iter(sampler2))
@@ -34,7 +34,7 @@ def test_sampler_across_epochs(random_dataset):
     dataset = random_dataset
     n = len(dataset)
     
-    sampler = ResumeableRandomSampler(dataset, seed=42)
+    sampler = ResumableDistributedSampler(dataset, seed=42)
     
     # Get indices for first epoch
     epoch1_indices = list(iter(sampler))

tests/module/test_module.py

@@ -22,12 +22,12 @@ def test_env(request: pytest.FixtureRequest):
     
     config = {
         "vocab_size": 1000,
-        "n_dim": 128,
+        "dim": 128,
-        "n_head": 4,
+        "n_heads": 4,
-        "n_kvhead": 2,
+        "n_kv_heads": 2,
-        "d_ffn": 256,
+        "dim_ffn": 256,
-        "m_len": 64,
+        "max_len": 64,
-        "n_layer": 2,
+        "n_layers": 2,
         "norm_eps": 1e-5
     }
     with open(config_path, 'w') as f:
@@ -51,13 +51,6 @@ def test_env(request: pytest.FixtureRequest):
 
     shutil.rmtree(test_dir)
 
-# parameter loader
-def test_parameter_loader(test_env):
-    loaded_param = ParameterLoader.load(test_env["test_dir"])
-    assert loaded_param.model is not None
-    assert loaded_param.tokenizer is not None
-    assert loaded_param.config == test_env["transformer_config"]
-
 def test_model_parameter(test_env):
     save_dir = os.path.join(test_env["test_dir"], "save")
     model_param = ModelParameter(test_env["model"],test_env["tokenizer"] , test_env["transformer_config"])
@@ -71,9 +64,9 @@ def test_model_parameter(test_env):
 def test_transformer(test_env):
     model = test_env["model"]
     input_ids = torch.randint(0, test_env["transformer_config"].vocab_size, 
-                              (4, test_env["transformer_config"].m_len))
+                              (4, test_env["transformer_config"].max_len))
     output_logits = model(input_ids)["logits"]
-    target_shape = (4, test_env["transformer_config"].m_len, test_env["transformer_config"].vocab_size)
+    target_shape = (4, test_env["transformer_config"].max_len, test_env["transformer_config"].vocab_size)
     assert output_logits.shape == target_shape
     
 # generator
@@ -87,7 +80,7 @@ def test_embedding_encoder_core(test_env):
     
     single_emb = encoder.encode("测试文本")
     assert isinstance(single_emb, torch.Tensor)
-    assert single_emb.shape[-1] == test_env["transformer_config"].n_dim
+    assert single_emb.shape[-1] == test_env["transformer_config"].dim
     
 
     batch_emb = encoder.encode(["测试1", "测试2"])

tests/module/test_tie_weight.py

@@ -16,12 +16,12 @@ def transformer_test_env():
     
     config = {
         "vocab_size": 1000,
-        "n_dim": 128,
+        "dim": 128,
-        "n_head": 4,
+        "n_heads": 4,
-        "n_kvhead": 2,
+        "n_kv_heads": 2,
-        "d_ffn": 256,
+        "dim_ffn": 256,
-        "m_len": 64,
+        "max_len": 64,
-        "n_layer": 2,
+        "n_layers": 2,
         "norm_eps": 1e-5
     }
     
@@ -77,6 +77,11 @@ def test_tie_weight_init(transformer_test_env):
     assert not torch.equal(model.lm_head.weight, model.embed_tokens.weight)
     assert model.lm_head.weight.data_ptr() != model.embed_tokens.weight.data_ptr()
 
+    original_weight = model.embed_tokens.weight.clone()
+    model.embed_tokens.weight.data[0, 0] = 100.0
+    
+    assert not torch.equal(model.lm_head.weight, model.embed_tokens.weight)
+    assert not torch.equal(model.lm_head.weight, original_weight)
 
 def test_model_save_load_with_tie_weight(transformer_test_env):
     test_dir = transformer_test_env["test_dir"]
@@ -104,16 +109,11 @@ def test_model_save_load_with_tie_weight(transformer_test_env):
     assert model.lm_head.weight.data_ptr() == model.embed_tokens.weight.data_ptr()
     assert "lm_head.weight" not in model.state_dict()
 
-    # case 2: not tie weight
+    # case 2: not tie weight (form tie-weight state dict load)
     config_data["tie_weight"] = False
     with open(config_path, 'w') as f:
         json.dump(config_data, f)
     
-    config = ModelConfig().load(config_path)
-    original_model = Transformer(config)
-
-    st.save_file(original_model.state_dict(), model_path)
-    
     loaded_config = ModelConfig().load(config_path)
     model = Transformer(loaded_config)
     model.load_state_dict(st.load_file(model_path))

tests/test_dataset_loader.py

@@ -1,67 +0,0 @@
-import os
-import torch
-import pickle
-import numpy as np
-
-from khaosz.trainer import *
-from khaosz.data.data_util import *
-
-
-def test_dataset_loader_random_paths(base_test_env):
-    """Test dataset loader with multiple random paths"""
-    test_dir = base_test_env["test_dir"]
-    
-    # Create multiple pkl files with random data
-    num_files = np.random.randint(2, 5)
-    pkl_paths = []
-    
-    for i in range(num_files):
-        pkl_path = os.path.join(test_dir, f"test_data_{i}.pkl")
-        seq_length = np.random.randint(50, 100)
-        dummy_data = {
-            "sequence": torch.randint(0, 1000, (seq_length,)),
-            "chosen": torch.randint(0, 1000, (seq_length,)),
-            "rejected": torch.randint(0, 1000, (seq_length,)),
-            "chosen_mask": torch.ones(seq_length, dtype=torch.bool),
-            "rejected_mask": torch.ones(seq_length, dtype=torch.bool)
-        }
-        with open(pkl_path, "wb") as f:
-            pickle.dump(dummy_data, f)
-        pkl_paths.append(pkl_path)
-    
-    # Test loading with multiple paths
-    loaded_dataset = DatasetLoader.load(
-        train_type="seq", 
-        load_path=pkl_paths, 
-        window_size=64, 
-    )
-    assert loaded_dataset is not None
-    assert len(loaded_dataset) > 0
-
-def test_dpo_strategy_with_random_data(base_test_env):
-    """Test DPO strategy with randomized preference data"""
-    test_dir = base_test_env["test_dir"]
-    
-    # Create DPO-style data
-    pkl_path = os.path.join(test_dir, "dpo_data.pkl")
-    seq_length = np.random.randint(40, 80)
-    
-    dummy_data = {
-        "chosen": torch.randint(0, 1000, (seq_length,)),
-        "rejected": torch.randint(0, 1000, (seq_length,)),
-        "chosen_mask": torch.ones(seq_length, dtype=torch.bool),
-        "rejected_mask": torch.ones(seq_length, dtype=torch.bool)
-    }
-    
-    with open(pkl_path, "wb") as f:
-        pickle.dump(dummy_data, f)
-    
-    # Load DPO dataset
-    dpo_dataset = DatasetLoader.load(
-        train_type="dpo", 
-        load_path=pkl_path, 
-        window_size=64, 
-    )
-    
-    assert dpo_dataset is not None
-    assert hasattr(dpo_dataset, 'fetcher')

tests/test_parallel.py

@@ -0,0 +1,39 @@
+import torch
+import torch.distributed as dist
+
+from khaosz.parallel import (
+    get_rank,
+    only_on_rank,
+    spawn_parallel_fn
+)
+
+@only_on_rank(0)
+def _test_only_on_rank_helper():
+    return True
+
+def only_on_rank():
+    result = _test_only_on_rank_helper()
+    if get_rank() == 0:
+        assert result is True
+    else:
+        assert result is None
+
+def all_reduce():
+    x = torch.tensor([get_rank()], dtype=torch.int)
+    dist.all_reduce(x, op=dist.ReduceOp.SUM)
+    expected_sum = sum(range(dist.get_world_size()))
+    assert x.item() == expected_sum
+
+def test_spawn_only_on_rank():
+    spawn_parallel_fn(
+        only_on_rank,
+        world_size=2,
+        backend="gloo"
+    )
+
+def test_spawn_all_reduce():
+    spawn_parallel_fn(
+        all_reduce,
+        world_size=2,
+        backend="gloo"
+    )

tests/trainer/test_callbacks.py

@@ -5,10 +5,20 @@ from khaosz.trainer import *
 
 def test_callback_integration(base_test_env, random_dataset):
     """Test that all callbacks are properly integrated"""
-    optimizer = torch.optim.AdamW(base_test_env["model"].parameters())
+    schedule_config = CosineScheduleConfig(
+        warmup_steps=10,
+        total_steps=20
+    )
+    
+    optimizer_fn = lambda model: torch.optim.AdamW(model.parameters())
+    scheduler_fn = lambda optim: SchedulerFactory.load(optim, schedule_config)
+    
     train_config = TrainConfig(
+        model=base_test_env["model"],
+        strategy='seq',
         dataset=random_dataset,
-        optimizer=optimizer,
+        optimizer_fn=optimizer_fn,
+        scheduler_fn=scheduler_fn,
         checkpoint_dir=base_test_env["test_dir"],
         n_epoch=1,
         batch_size=2,
@@ -18,36 +28,26 @@ def test_callback_integration(base_test_env, random_dataset):
         random_seed=42
     )
     
-    schedule_config = CosineScheduleConfig(
+
-        warmup_steps=10,
-        total_steps=20
-    )
     
     # Create custom callbacks to track calls
     callback_calls = []
     
     class TrackingCallback(TrainCallback):
-        def on_train_begin(self, trainer, context):
+        def on_train_begin(self, context):
             callback_calls.append('on_train_begin')
         
-        def on_batch_end(self, trainer, context):
+        def on_batch_end(self, context):
             callback_calls.append('on_batch_end')
         
-        def on_epoch_end(self, trainer, context):
+        def on_epoch_end(self, context):
             callback_calls.append('on_epoch_end')
     
-    train_config.strategy = StrategyFactory.load(base_test_env["model"], "seq", base_test_env["device"])
+
-    model_parameter = ModelParameter(
-        base_test_env["model"], 
-        base_test_env["tokenizer"], 
-        base_test_env["transformer_config"]
-    )
     
     trainer = Trainer(
-        model_parameter, 
         train_config, 
-        schedule_config,
+        callbacks=[TrackingCallback()]
-        callbacks=[TrackingCallback(), ProgressBarCallback()]
     )
     
     trainer.train()

tests/trainer/test_early_stopping.py

@@ -1,41 +1,46 @@
+import os
 import torch
 import numpy as np
 from khaosz.config import *
 from khaosz.trainer import *
-
+from khaosz.data.checkpoint import Checkpoint
 
 def test_early_stopping_simulation(base_test_env, early_stopping_dataset):
     """Simulate early stopping behavior"""
     
-    optimizer = torch.optim.AdamW(base_test_env["model"].parameters())
+    schedule_config = CosineScheduleConfig(warmup_steps=10, total_steps=20)
+    
+    optimizer_fn = lambda model: torch.optim.AdamW(model.parameters())
+    scheduler_fn = lambda optim: SchedulerFactory.load(optim, schedule_config)
+    
     train_config = TrainConfig(
+        strategy="seq",
+        optimizer_fn=optimizer_fn,
+        scheduler_fn=scheduler_fn,
+        model=base_test_env["model"],
         dataset=early_stopping_dataset,
-        optimizer=optimizer,
         checkpoint_dir=base_test_env["test_dir"],
         n_epoch=2,
         batch_size=2,
-        checkpoint_interval=2,
+        checkpoint_interval=1,
         accumulation_steps=2,
         random_seed=np.random.randint(1e4),
     )
-    
+
-    train_config.strategy = StrategyFactory.load(base_test_env["model"], "seq", base_test_env["device"])
+    trainer = Trainer(train_config)
-    model_parameter = ModelParameter(
-        base_test_env["model"], 
-        base_test_env["tokenizer"], 
-        base_test_env["transformer_config"]
-    )
-    schedule_config = CosineScheduleConfig(warmup_steps=10, total_steps=20)
-    trainer = Trainer(model_parameter, train_config, schedule_config)
     
     # Should handle early stopping gracefully
     checkpoint = None
     try:
         checkpoint = trainer.train()
-        assert len(checkpoint.loss_list) == 2
     except Exception:
         # Handle any exceptions
         pass
     
-    checkpoint = trainer.train(checkpoint)
+    load_dir = os.path.join(base_test_env["test_dir"], "epoch_0_iter_2")
-    assert len(checkpoint.loss_list) == 10
+    checkpoint = Checkpoint.load(load_dir)
+    trainer.train(checkpoint)
+    
+    load_dir = os.path.join(base_test_env["test_dir"], "epoch_1_iter_10")
+    checkpoint = Checkpoint.load(load_dir)
+    assert checkpoint.iteration == 10

tests/trainer/test_train_strategy.py

@@ -4,7 +4,7 @@ import pytest
 
 from khaosz.config import *
 from khaosz.trainer.schedule import *
-from khaosz.data.data_util import *
+from khaosz.data.dataset import *
 
 
 def test_schedule_factory_random_configs():
@@ -35,7 +35,7 @@ def test_schedule_factory_random_configs():
             config.validate()
             
             # Create scheduler using factory
-            scheduler = SchedulerFactory.load_scheduler(optimizer, config)
+            scheduler = SchedulerFactory.load(optimizer, config)
             
             # Verify scheduler type
             if isinstance(config, CosineScheduleConfig):
@@ -83,7 +83,7 @@ def test_schedule_factory_edge_cases():
     
     for config in edge_cases:
         config.validate()
-        scheduler = SchedulerFactory.load_scheduler(optimizer, config)
+        scheduler = SchedulerFactory.load(optimizer, config)
         assert scheduler is not None
         
         # Test multiple steps
@@ -97,16 +97,17 @@ def test_schedule_factory_invalid_configs():
     # Test invalid configurations that should raise errors
     invalid_configs = [
         # Negative warmup steps
-        CosineScheduleConfig(warmup_steps=-10, total_steps=1000, min_rate=0.1),
+        {"warmup_steps": -10, "total_steps": 1000, "min_rate": 0.1},
         # Total steps less than warmup steps
-        CosineScheduleConfig(warmup_steps=500, total_steps=400, min_rate=0.1),
+        {"warmup_steps": 500, "total_steps": 400, "min_rate": 0.1},
         # Invalid min_rate
-        CosineScheduleConfig(warmup_steps=100, total_steps=1000, min_rate=-0.1),
+        {"warmup_steps": 100, "total_steps": 1000, "min_rate": -0.1},
-        CosineScheduleConfig(warmup_steps=100, total_steps=1000, min_rate=1.1),
+        {"warmup_steps": 100, "total_steps": 1000, "min_rate": 1.1},
     ]
     
-    for config in invalid_configs:
+    for kwargs in invalid_configs:
         with pytest.raises(ValueError):
+            config = CosineScheduleConfig(**kwargs)
             config.validate()
 
 
@@ -117,7 +118,7 @@ def test_schedule_factory_state_persistence():
     optimizer = torch.optim.AdamW(model.parameters(), lr=0.001)
     
     config = CosineScheduleConfig(warmup_steps=100, total_steps=1000, min_rate=0.1)
-    scheduler = SchedulerFactory.load_scheduler(optimizer, config)
+    scheduler = SchedulerFactory.load(optimizer, config)
     
     # Take a few steps
     for _ in range(5):
@@ -127,7 +128,7 @@ def test_schedule_factory_state_persistence():
     state_dict = scheduler.state_dict()
     
     # Create new scheduler and load state
-    new_scheduler = SchedulerFactory.load_scheduler(optimizer, config)
+    new_scheduler = SchedulerFactory.load(optimizer, config)
     new_scheduler.load_state_dict(state_dict)
     
     # Verify states match

tests/trainer/test_trainer.py

@@ -4,17 +4,26 @@ import numpy as np
 
 from khaosz.config import *
 from khaosz.trainer import *
-from khaosz.data.data_util import *
+from khaosz.data.dataset import *
 
 def test_different_batch_sizes(base_test_env, random_dataset):
     """Test training with different batch sizes"""
     batch_sizes = [1, 2, 4, 8]
     
     for batch_size in batch_sizes:
-        optimizer = torch.optim.AdamW(base_test_env["model"].parameters())
+        schedule_config = CosineScheduleConfig(
+            warmup_steps=10,
+            total_steps=20
+        )
+        optimizer_fn = lambda model: torch.optim.AdamW(model.parameters())
+        scheduler_fn = lambda optim: SchedulerFactory.load(optim, schedule_config)
+        
         train_config = TrainConfig(
+            strategy="seq",
+            model=base_test_env["model"],
             dataset=random_dataset,
-            optimizer=optimizer,
+            optimizer_fn=optimizer_fn,
+            scheduler_fn=scheduler_fn,
             checkpoint_dir=base_test_env["test_dir"],
             n_epoch=1,
             batch_size=batch_size,
@@ -31,10 +40,19 @@ def test_gradient_accumulation(base_test_env, random_dataset):
     accumulation_steps_list = [1, 2, 4]
     
     for accumulation_steps in accumulation_steps_list:
-        optimizer = torch.optim.AdamW(base_test_env["model"].parameters())
+        schedule_config = CosineScheduleConfig(
+            warmup_steps=10,
+            total_steps=20
+        )
+        optimizer_fn = lambda model: torch.optim.AdamW(model.parameters())
+        scheduler_fn = lambda optim: SchedulerFactory.load(optim, schedule_config)
+        
         train_config = TrainConfig(
+            strategy="seq",
+            model=base_test_env["model"],
+            optimizer_fn=optimizer_fn,
+            scheduler_fn=scheduler_fn,
             dataset=random_dataset,
-            optimizer=optimizer,
             checkpoint_dir=base_test_env["test_dir"],
             n_epoch=1,
             batch_size=2,
@@ -44,18 +62,7 @@ def test_gradient_accumulation(base_test_env, random_dataset):
             random_seed=42
         )
         
-        schedule_config = CosineScheduleConfig(
+        trainer = Trainer(train_config)
-            warmup_steps=10,
-            total_steps=20
-        )
-        train_config.strategy = StrategyFactory.load(base_test_env["model"], "seq", base_test_env["device"])
-        model_parameter = ModelParameter(
-            base_test_env["model"], 
-            base_test_env["tokenizer"], 
-            base_test_env["transformer_config"]
-        )
-        
-        trainer = Trainer(model_parameter, train_config, schedule_config)
         trainer.train()
         
         assert train_config.accumulation_steps == accumulation_steps
@@ -70,10 +77,19 @@ def test_memory_efficient_training(base_test_env, random_dataset):
     ]
     
     for config in small_batch_configs:
-        optimizer = torch.optim.AdamW(base_test_env["model"].parameters())
+        schedule_config = CosineScheduleConfig(
+            warmup_steps=10,
+            total_steps=20
+        )
+        optimizer_fn = lambda model: torch.optim.AdamW(model.parameters())
+        scheduler_fn = lambda optim: SchedulerFactory.load(optim, schedule_config)
+        
         train_config = TrainConfig(
+            strategy="seq",
+            model=base_test_env["model"],
             dataset=random_dataset,
-            optimizer=optimizer,
+            optimizer_fn=optimizer_fn,
+            scheduler_fn=scheduler_fn,
             checkpoint_dir=base_test_env["test_dir"],
             n_epoch=1,
             batch_size=config["batch_size"],

tools/benchmark.py

@@ -28,7 +28,7 @@ class GenerationBenchmark:
     def _initialize_kv_cache(self, batch_size: int) -> list:
         """初始化KV缓存"""
         config = self.config
-        shape = (batch_size, config.n_layer, config.m_len, config.n_kvhead, config.n_dim // config.n_head)
+        shape = (batch_size, config.max_len, config.n_layers, config.n_kv_heads, config.dim // config.n_heads)
         k_cache = torch.zeros(shape, device=self.device, dtype=self.dtype)
         v_cache = torch.zeros(shape, device=self.device, dtype=self.dtype)
         return (k_cache, v_cache)
@@ -175,12 +175,12 @@ def print_benchmark_result(result: BenchmarkResult):
 if __name__ == "__main__":
     config = ModelConfig(
         vocab_size=10000,
-        n_dim=1536,
+        dim=1536,
-        n_head=24,
+        n_heads=24,
-        n_kvhead=4,
+        n_kv_heads=4,
-        d_ffn=6912,
+        dim_ffn=6912,
-        m_len=2048,
+        max_len=2048,
-        n_layer=24,
+        n_layers=24,
         norm_eps=1e-5,
     )
     

tools/train.py

@@ -1,21 +1,87 @@
 import os
 import argparse
 import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.nn.parallel import DistributedDataParallel as DDP
 
-from torch.optim import AdamW
+from typing import List, Optional
-from khaosz.config import ParameterLoader, Checkpoint, TrainConfig, CosineScheduleConfig
+from functools import partial
-from khaosz.trainer import Trainer, StrategyFactory
 from khaosz.data import DatasetLoader
+from khaosz.config import ModelParameter, TrainConfig, CosineScheduleConfig
+from khaosz.trainer import Trainer, SchedulerFactory
+from khaosz.parallel import get_rank
 
 
-PROJECT_ROOT = os.path.dirname(os.path.abspath(__file__))
+def parse_args() -> argparse.Namespace:
+    def parse_device_ids(s: Optional[str]) -> Optional[List[int]]:
+        if s is None or s.strip() == "":
+            return None
+        try:
+            return [int(x.strip()) for x in s.split(",") if x.strip()]
+        except ValueError as e:
+            raise argparse.ArgumentTypeError(f"Invalid device_ids format: {s}. Expected comma-separated integers like '0,1,2'.")
 
-def get_files(root_path: str) -> list[str]:
-    paths = []
-    for root, _, files in os.walk(root_path):
-        paths.extend([os.path.join(root, file) for file in files])
 
-    return paths
+    parser = argparse.ArgumentParser(description="Train the Transformer model.")
+    
+    parser.add_argument("--train_type",choices=["seq", "sft", "dpo"], help="Train type.")
+    parser.add_argument("--data_root_path", type=str, required=True, help="Path to the root directory of the dataset.")
+    parser.add_argument("--param_path", type=str, required=True, help="Path to the model parameters or resume checkpoint.")
+    
+    parser.add_argument("--n_epoch", type=int, default=1, help="Number of epochs to train.")
+    parser.add_argument("--batch_size", type=int, default=1, help="Batch size for training.")
+    parser.add_argument("--accumulation_steps", type=int, default=1, help="Number of iterations between each optimizer step.")
+    parser.add_argument("--warmup_steps", type=int, default=1000, help="Number of iters between warnings.")
+    parser.add_argument("--max_lr", type=float, default=3e-4, help="Max learning rate for training.")
+    parser.add_argument("--max_grad_norm", type=float, default=1.0, help="Max gradient norm for clipping.")
+    parser.add_argument("--adamw_beta1", type=float, default=0.9, help="Beta values for AdamW optimizer.")
+    parser.add_argument("--adamw_beta2", type=float, default=0.95, help="Beta values for AdamW optimizer.")
+    parser.add_argument("--adamw_weight_decay", type=float, default=0.01, help="Weight decay for AdamW optimizer.")
+    parser.add_argument("--random_seed", type=int, default=3407, help="Random seed for reproducibility.")
+    parser.add_argument("--num_workers", type=int, default=4, help="Number of workers for data loading.")
+    parser.add_argument("--no_pin_memory", action="store_false", dest="pin_memory", help="Disable pin memory")
+    parser.add_argument("--window_size", type=int, default=None, help="the max length of the input sequence.")
+    parser.add_argument("--stride", type=int, default=None, help="the step size of the input sequence.")
+    parser.add_argument("--dpo_beta", type=float, default=0.1, help="DPO beta value.")
+    
+    parser.add_argument("--checkpoint_interval", type=int, default=5000, help="Number of iters between checkpoints.")
+    parser.add_argument("--checkpoint_dir", type=str, default="checkpoint", help="Directory to save checkpoints.")
+    parser.add_argument("--start_epoch", type=int, default=0, help="Start epoch for training.")
+    parser.add_argument("--start_batch", type=int, default=0, help="Start batch for training.")
+    
+    parser.add_argument("--nprocs", type=int, default=1, help="Number of GPUs to use.")
+    parser.add_argument("--device_ids", type=parse_device_ids, default=None, help="Device IDs to use.")
+    parser.add_argument("--device_type", type=str, default="cuda", help="Device type to use.")
+    
+    args = parser.parse_args()
+
+    return args
+
+def ddp_wrap(model: nn.Module):
+    local_rank = get_rank()
+    model = model.to(device=f"cuda:{local_rank}", dtype=torch.bfloat16)
+    ddp_model = DDP(
+        model,
+        device_ids=[local_rank],
+        output_device=local_rank,
+        find_unused_parameters=False
+    )
+    return ddp_model
+
+def create_optimizer(model: nn.Module, **kwargs) -> optim.Optimizer:
+    return optim.AdamW(model.parameters(), **kwargs)
+
+def create_scheduler(optimizer: optim.Optimizer, **kwargs) -> optim.lr_scheduler.LRScheduler:
+    return SchedulerFactory.load(optimizer, **kwargs)
+
+def prepare_checkpoint(model: nn.Module) -> dict:
+    if isinstance(model, torch.nn.parallel.DistributedDataParallel):
+        state_dict = model.module.state_dict()
+    else:
+        state_dict = model.state_dict()
+    return state_dict
+
 
 def train(
     train_type: str,
@@ -31,31 +97,29 @@ def train(
     checkpoint_interval: int,
     checkpoint_dir: str,
     dpo_beta: float,
-    adamw_betas: tuple,
+    adamw_beta1: float,
+    adamw_beta2: float,
     adamw_weight_decay: float,
     max_grad_norm: float,
-    embdeding_lr_rate: int,
     random_seed: int,
+    num_workers: int,
+    pin_memory: bool,
     window_size: int,
     stride: int,
-    resume_from_checkpoint: bool
+    nprocs: int,
+    device_ids: List[int],
+    device_type: str,
 ):
     assert train_type in ["seq", "sft", "dpo"]
     assert os.path.exists(param_path)
     
-    parameter = ParameterLoader.load(param_path)
+    parameter = ModelParameter()
-    checkpoint = None
+    parameter.load(param_path)
-    
-    if isinstance(parameter, Checkpoint) and resume_from_checkpoint:
-        checkpoint = parameter
 
     if window_size is None:
-        window_size = parameter.config.m_len
+        window_size = parameter.config.max_len
 
     model = parameter.model
-    device = torch.device("cuda")
-    model = model.to(device=device, dtype=torch.bfloat16)
-    cache_files = get_files(data_root_path)
     
     kwargs = {
         "dpo_beta": dpo_beta,
@@ -63,37 +127,30 @@ def train(
         "eos_token_id": parameter.tokenizer.eos_id,
         "pad_token_id": parameter.tokenizer.pad_id,
     }
-    
-    strategy = StrategyFactory.load(
-        model, 
-        train_type,
-        device,
-        **kwargs
-    )
 
     dataset = DatasetLoader.load(
         train_type=train_type,
-        load_path=cache_files,
+        load_path=data_root_path,
         window_size=window_size,
-        stride=stride,
+        stride=stride
-        **kwargs
     )
     
-    param_groups = [
+    schedule_config = CosineScheduleConfig(
-        {"params": [p for n, p in model.named_parameters() if "embedding" in n], "lr": max_lr * embdeding_lr_rate},
+        warmup_steps=warmup_steps,
-        {"params": [p for n, p in model.named_parameters() if "embedding" not in n], "lr": max_lr}
+        total_steps=len(dataset) * n_epoch // (batch_size * nprocs), 
-    ]
-
-    optim = AdamW(
-        param_groups,
-        betas=adamw_betas,
-        weight_decay=adamw_weight_decay
     )
+
+    optimizer_fn = partial(create_optimizer, 
+                           **{"lr": max_lr, "betas": (adamw_beta1, adamw_beta2), "weight_decay": adamw_weight_decay})
+    scheduler_fn = partial(create_scheduler, 
+                           **{"schedule_config": schedule_config})
     
     train_config = TrainConfig(
-        strategy=strategy,
+        model=model,
+        strategy=train_type,
         dataset=dataset,
-        optimizer=optim,
+        optimizer_fn=optimizer_fn,
+        scheduler_fn=scheduler_fn,
         checkpoint_dir=checkpoint_dir,
         n_epoch=n_epoch,
         batch_size=batch_size,
@@ -103,50 +160,20 @@ def train(
         accumulation_steps=accumulation_steps,
         max_grad_norm=max_grad_norm,
         random_seed=random_seed,
-        num_workers=4,
+        num_workers=num_workers,
-        pin_memory=True
+        pin_memory=pin_memory,
+        nprocs=nprocs,
+        parallel_wrapper=ddp_wrap,
+        state_dict_fn=prepare_checkpoint,
+        device_ids=device_ids,
+        device_type=device_type,
+        extra_kwargs=kwargs,
     )
     
-    schedule_config = CosineScheduleConfig(
+    trainer = Trainer(train_config)
-        warmup_steps=warmup_steps,
+    trainer.train()
-        total_steps=len(dataset) * n_epoch // batch_size, 
-    )
-    
-    trainer = Trainer(
-        parameter=parameter,
-        train_config=train_config,
-        schedule_config=schedule_config,
-    )
-    trainer.train(checkpoint)
     
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Train the Transformer model.")
+    args = parse_args()
-    # train args
-    parser.add_argument("--train_type",choices=["seq", "sft", "dpo"], help="Train type.")
-    parser.add_argument("--data_root_path", type=str, required=True, help="Path to the root directory of the dataset.")
-    parser.add_argument("--param_path", type=str, required=True, help="Path to the model parameters or resume checkpoint.")
-    parser.add_argument("--n_epoch", type=int, default=1, help="Number of epochs to train.")
-    parser.add_argument("--batch_size", type=int, default=1, help="Batch size for training.")
-    parser.add_argument("--accumulation_steps", type=int, default=1, help="Number of iterations between each optimizer step.")
-    parser.add_argument("--warmup_steps", type=int, default=1000, help="Number of iters between warnings.")
-    parser.add_argument("--max_lr", type=float, default=3e-4, help="Max learning rate for training.")
-    parser.add_argument("--checkpoint_interval", type=int, default=5000, help="Number of iters between checkpoints.")
-    parser.add_argument("--checkpoint_dir", type=str, default="checkpoint", help="Directory to save checkpoints.")
-    parser.add_argument("--max_grad_norm", type=float, default=1.0, help="Max gradient norm for clipping.")
-    parser.add_argument("--adamw_betas", type=tuple, default=(0.9, 0.95), help="Beta values for AdamW optimizer.")
-    parser.add_argument("--adamw_weight_decay", type=float, default=0.01, help="Weight decay for AdamW optimizer.")
-    parser.add_argument("--embdeding_lr_rate", type=float, default=1.0, help="The rate between the embedding layers lr rate and the max lr rate.")
-    parser.add_argument("--random_seed", type=int, default=3407, help="Random seed for reproducibility.")
-    
-    # other configs
-    parser.add_argument("--window_size", type=int, default=None, help="the max length of the input sequence.")
-    parser.add_argument("--stride", type=int, default=None, help="the step size of the input sequence.")
-    parser.add_argument("--start_epoch", type=int, default=0, help="Start epoch for training.")
-    parser.add_argument("--start_batch", type=int, default=0, help="Start batch for training.")
-    parser.add_argument("--resume_from_checkpoint", type=bool, default=False, help="train from checkpoint or not.")
-    parser.add_argument("--dpo_beta", type=float, default=0.1, help="DPO beta value.")
-    
-    args = parser.parse_args()
-
     train(**vars(args))