refactor : Pipeline 去除去重，ids 重命名为 sequence，泛型透传

- 移除 Pipeline 内置去重逻辑及 dedup_signature 工具函数
- 删除 ProcessingConfig.deduplicate 字段
- builder 返回 'sequence' 替代 'ids'，与 dataset 层统一
- pipeline 纯透传，泛型处理任意 key 补齐默认值

.dockerignore

@@ -0,0 +1,9 @@
+# Ignore everything
+*
+
+# Allow necessary files
+!astrai/
+!scripts/
+!assets/
+!pyproject.toml
+!README.md

.gitattributes

@@ -0,0 +1,19 @@
+# Auto detect text files
+* text=auto
+
+# Files that MUST use LF (Unix/Linux execution)
+*.sh text eol=lf
+*.py text eol=lf
+*.md text eol=lf
+*.yml text eol=lf
+
+Dockerfile text eol=lf
+.dockerignore text eol=lf
+
+.gitignore text eol=lf
+.gitattributes text eol=lf
+
+# Windows scripts - use CRLF
+*.bat text eol=crlf
+*.cmd text eol=crlf
+*.ps1 text eol=crlf

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,27 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: "[BUG]"
+labels: bug
+assignees: ''
+
+---
+
+## Description
+A clear and concise description of what the bug is.
+## Steps to Reproduce
+1.  ...
+2.  ...
+3.  ...
+## Expected Behavior
+What you expected to happen.
+## Actual Behavior
+What actually happened.
+## Environment
+- Python version:
+- AstrAI version (or commit hash):
+- Operating System:
+- GPU (if applicable):
+- CUDA/cuDNN version (if applicable):
+## Additional Context
+Add any other context, screenshots, or logs here.

.github/ISSUE_TEMPLATE/custom.md

@@ -0,0 +1,10 @@
+---
+name: Custom issue template
+about: Describe this issue template's purpose here.
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,19 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: "[FEAT]"
+labels: ''
+assignees: ''
+
+---
+
+## Description
+A clear and concise description of the feature you'd like to see.
+## Problem Statement
+What problem does this feature solve? Why is it needed?
+## Proposed Solution
+Describe the solution you'd like. Include any design ideas, API changes, or implementation details.
+## Alternatives Considered
+Describe any alternative solutions or features you've considered.
+## Additional Context
+Add any other context, screenshots, or references here.

.github/PULL_REQUEST_TEMPLATE.md

@@ -0,0 +1,26 @@
+## Description
+Please include a summary of the change and which issue is fixed. Please also include relevant motivation and context.
+
+Fixes # (issue number)
+
+## Type of Change
+Please delete options that are not relevant.
+
+- [ ] Bug fix (non-breaking change which fixes an issue)
+- [ ] New feature (non-breaking change which adds functionality)
+- [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
+- [ ] Documentation update
+- [ ] Other (please describe):
+
+## How Has This Been Tested?
+Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce.
+
+## Checklist:
+- [ ] My code follows the style guidelines of this project (run `ruff format .` and `ruff check . --select I`)
+- [ ] I have performed a self-review of my own code
+- [ ] Code is self-documenting (no unnecessary comments)
+- [ ] I have made corresponding changes to the documentation
+- [ ] My changes generate no new warnings
+- [ ] I have added tests that prove my fix is effective or that my feature works
+- [ ] New and existing unit tests pass locally with my changes
+- [ ] Any dependent changes have been merged and published in downstream modules

.github/workflows/docker.yml

@@ -0,0 +1,50 @@
+name: Build and Push Docker Image
+
+on:
+  push:
+    tags:
+      - 'v*'
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v3
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Login to GitHub Container Registry
+        uses: docker/login-action@v3
+        with:
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Extract metadata
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: ghcr.io/${{ github.repository }}
+          tags: |
+            type=ref,event=tag
+            type=raw,value=latest
+
+      - name: Build and push
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          platforms: linux/amd64
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max

.github/workflows/lint.yml

@@ -0,0 +1,31 @@
+name: Lint
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python 3.12
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+
+      - name: Install dependencies
+        run: |
+          pip install --upgrade pip
+          pip install .[dev]
+
+      - name: Check formatting with ruff
+        run: |
+          ruff format --check .
+
+      - name: Check import sorting
+        run: |
+          ruff check . --select I

.github/workflows/tests.yml

@@ -0,0 +1,31 @@
+name: Tests
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.12"]
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: |
+          pip install --upgrade pip
+          pip install .[dev]
+
+      - name: Run tests with pytest
+        run: |
+          python -m pytest tests/ -v

.gitignore

@@ -1,13 +1,23 @@
-# cache
+# Ignore everything
-__pycache__
+*
-.pytest_cache
 
-# params
+# Allow directories to be traversed
-params/*
+!*/
 
-# vscode file
+# Allow specific file types and root files
-.vscode
+!*.py
+!*.sh
 
-# build file
+# Allow GitHub files
-build
+!/.github/**
-*.egg-info
+
+# Allow root files
+!/.gitattributes
+!/.dockerignore
+!/Dockerfile
+!/docker-compose.yml
+!/assets/**
+!/CONTRIBUTING.md
+!/LICENSE
+!/pyproject.toml
+!/README.md

CONTRIBUTING.md

@@ -0,0 +1,100 @@
+# Contributing to AstrAI
+
+Thank you for your interest in contributing! This document provides step-by-step guidelines.
+
+## Quick Start
+
+```bash
+git clone https://github.com/your-username/AstrAI.git
+cd AstrAI
+pip install -e ".[dev]"     # install with dev dependencies (pytest, ruff)
+```
+
+## Before You Commit
+
+Run the following checks **in order** — CI will reject if any fail.
+
+### 1. Format
+
+```bash
+ruff format .
+```
+
+> **Note**: `ruff format` may rename parameters (e.g. `mask` → `attn_mask`).  
+> Always review the diff after formatting.
+
+### 2. Import sorting
+
+```bash
+ruff check . --select I
+```
+
+If this fails, **manually fix** import ordering (ruff does not auto-fix in this project's CI):
+
+```bash
+ruff check . --select I --fix .
+ruff format .    # re-format after fix
+```
+
+### 3. Run tests
+
+```bash
+python -u -m pytest tests/ -v
+```
+
+> Failed tests may leave orphan tempdirs under `%TEMP%`. Clean them manually if needed.
+
+### 4. (Optional) Full pre-commit check
+
+If you have Git Bash available:
+
+```bash
+bash scripts/pre_commit.sh
+```
+
+This runs format check, import sort check, and tests in one go.
+
+## Commit Style
+
+```
+fix/feat/chore/docs/refactor/perf/test/style/ci/build/revert : short description (~50 chars)
+
+- bullet point body (each ~60 chars)
+```
+
+- **Type** must be one of: `fix`, `feat`, `chore`, `docs`, `refactor`, `perf`, `test`, `style`, `ci`, `build`, `revert`.
+- **Subject line** ends with no period.
+- **Body** uses bullet points starting with `-`.
+- No `(scope)` parentheses.
+
+## Common Issues
+
+| Problem | Cause | Fix |
+|---------|-------|-----|
+| `ruff check --select I` fails | Wrong import order | `ruff check . --select I --fix .` then `ruff format .` |
+| `ruff format` changed many files | Not formatted before commit | Review diff carefully before staging |
+| Pre-commit hook rejects | Tests or lint failed | Fix individually, do not `--no-verify` |
+| Tests fail with tempdir left | Test crash | Clean `%TEMP%` manually |
+
+## Submitting Changes
+
+1. Fork the repo.
+2. Create a feature branch: `git checkout -b feat/my-feature`
+3. Make changes following the steps above.
+4. Commit with the commit style above.
+5. Push: `git push origin feat/my-feature`
+6. Open a Pull Request against `main`.
+
+## Code Review
+
+- All PRs are reviewed. We may request changes.
+- CI runs `ruff format --check .` then `ruff check . --select I` (no `--fix` in CI).
+- Ensure all tests pass.
+
+## License
+
+By contributing, you agree that your contributions will be licensed under the [GPL-3.0 License](LICENSE).
+
+---
+
+Questions? Ask in [GitHub Discussions](https://github.com/ViperEkura/AstrAI/discussions) or open an issue.

Dockerfile

@@ -0,0 +1,55 @@
+# AstrAI Dockerfile - Multi-stage Build (Optimized)
+
+# Build stage - use base image with minimal build tools
+FROM ubuntu:24.04 AS builder
+
+WORKDIR /app
+
+# Install Python 3.12 and minimal build dependencies
+RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
+    python3.12 \
+    python3.12-dev \
+    python3.12-venv \
+    gcc \
+    g++ \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create isolated virtual environment
+RUN python3.12 -m venv --copies /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# Copy source code and install (deps read from pyproject.toml)
+COPY astrai/ ./astrai/
+COPY pyproject.toml .
+RUN pip install --no-cache-dir --upgrade pip \
+    && pip install --no-cache-dir . \
+    --extra-index-url https://download.pytorch.org/whl/cu126
+
+# Production stage
+FROM ubuntu:24.04 AS production
+
+WORKDIR /app
+
+# Install Python 3.12 runtime and healthcheck dependency
+RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
+    python3.12 \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy virtual environment from builder
+COPY --from=builder /opt/venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# Copy application code
+COPY astrai/ ./astrai/
+COPY scripts/ ./scripts/
+COPY assets/ ./assets/
+COPY pyproject.toml .
+COPY README.md .
+
+# Create non-root user
+RUN useradd -m astrai && chown -R astrai:astrai /app
+USER astrai
+
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1

LICENSE

@@ -1,201 +1,674 @@
-                                 Apache License
+                    GNU GENERAL PUBLIC LICENSE
-                           Version 2.0, January 2004
+                       Version 3, 29 June 2007
-                        http://www.apache.org/licenses/
 
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
 
-   1. Definitions.
+                            Preamble
 
-      "License" shall mean the terms and conditions for use, reproduction,
+  The GNU General Public License is a free, copyleft license for
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README.md

@@ -1,333 +1,255 @@
-![image-20250306182014120](/assets/images/project_logo_clipped.png)
+<div align="center">
-
-<div style="display: flex; flex-direction: column; align-items: center; justify-content: center; text-align: center; font-size: 16px; font-weight: bold; margin-top: 50px;">
   
-  <div>
+  <img src="assets/images/logo.png" width="auto" alt="Logo">
-    <a href="#english" style="text-decoration: none; margin: 0 10px; color: blue;">English</a> | 
+  <p>
-    <a href="#chinese" style="text-decoration: none; margin: 0 10px; color: blue;">中文</a>
+    <strong>A lightweight Transformer training & inference framework</strong>
-  </div>
+  </p>
-
-  <h1 style="margin: 20px 0 0 0; font-size: 2.5em; font-weight: bold;">KHAOSZ </h1>
 </div>
 
-<h2 id="english">English Version</h2>
+<div align="center">
+  <img src="https://img.shields.io/badge/python-3.12+-blue.svg" alt="python">
+  <img src="https://img.shields.io/badge/license-GPL--3.0-blue.svg" alt="license">
+  <img src="https://img.shields.io/github/v/release/ViperEkura/AstrAI?color=76bad9" alt="release">
+  <img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.github.com%2Frepos%2FViperEkura%2FAstrAI&query=%24.stargazers_count&label=stars&suffix=%20stars&color=76bad9" alt="stars">
+  <img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.github.com%2Frepos%2FViperEkura%2FAstrAI&query=%24.forks_count&label=forks&suffix=%20forks&color=76bad9" alt="forks">
+</div>
+<br>
 
-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.
+<div align="center">
+  <a href="#english">English</a> •
+  <a href="assets/docs/README-zh-CN.md">中文</a> •
+  <a href="https://github.com/ViperEkura/AstrAI/issues">Issue Tracker</a> •
+  <a href="https://github.com/ViperEkura/AstrAI/discussions">Discussions</a> •
+  <a href="https://huggingface.co/ViperEk/">HuggingFace</a>
+</div>
 
-**Model Download Options (Choose One):**
+<br>
 
-1. Visit [HuggingFace](https://huggingface.co/ViperEk/KHAOSZ) to access **Files and versions**
+## 📖 Table of Contents
-2. Run `scripts/download.py` to download parameters
 
-**Demo Video:** [bilibili](https://www.bilibili.com/video/BV1z5RPYHEkd)
+- [Features](#features)
+- [Quick Start](#quick-start)
+- [Documentation](#documentation)
+- [Contributing](#contributing)
+- [Community](#community)
+- [License](#license)
 
-Training dataset sources are listed in the **Model Card** section of the HuggingFace download link.
+---
 
-**License:** Code follows Apache-2.0 protocol. Please credit the source code when used.
+<a id="english"></a>
+## English
 
-- **📊 Device Selection:** Code defaults to CUDA training
+### Features
-- **🌐 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
+- 🚀 **High Performance**: Optimized for both training and inference with efficient parallelization.
+- 🔧 **Flexible**: Support for seq/sft/dpo/grpo training, customizable model architectures.
+- 💡 **Easy to Use**: Simple API with comprehensive examples and demos.
+- 📦 **Lightweight**: Minimal dependencies, easy to deploy.
+- 🔬 **Research‑Friendly**: Modular design, easy to experiment with new ideas.
+- 🤗 **HuggingFace-Style API**: AutoModel/AutoTokenizer APIs inspired by HuggingFace for easy model and tokenizer loading.
+- 🔌 **Dual API Compatibility**: Supports both OpenAI and Anthropic chat completion APIs out of the box.
 
-To train this Transformer model, follow these steps:
+### Quick Start
 
-**(1). Prepare Dataset:**
+#### Installation
-
-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).
-
-**(2). Install Dependencies:**
 
 ```bash
-pip install -r requirements.txt
+git clone https://github.com/ViperEkura/AstrAI.git
-pip install .
+cd AstrAI
+pip install -e .
 ```
 
-**(3). Run Training Script:**
+For development dependencies:
 
 ```bash
-python train.py \
+pip install -e ".[dev]"
---train_type=train_type[seq, sft, dpo] \
---data_root_path=/path/to/dataset \
---param_path=/path/to/param_path \
---n_epoch=5 \
---batch_size=8 \
---max_lr=2e-4 \
---checkpoint_interval=10000 \
---checkpoint_dir=checkpoints
 ```
 
-**Parameters Explanation:**
+#### Download Pre-trained Model
-- `--train_type`: Training type (seq, sft, dpo)
-- `--data_root_path`: Root directory of the dataset
-- `--param_path`: Path to the 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
-- `--max_lr`: Maximum learning rate (using warmup + cosine decay)
-- `--checkpoint_interval`: Checkpoint saving interval
-- `--checkpoint_dir`: Directory to save checkpoints
-- `--resume_dir`: Resume training from the specified path
 
-Training logs will be saved in `train_log.txt`. Checkpoints will be saved in the specified directory for resuming training or evaluation.
+Download pre-trained model weights (1B bilingual checkpoint) to `params/`:
-
-### 👉 Usage Guide
-
-**(1). Chatting with the Model:**
-
-Open `chat.py` or use streaming/non-streaming interfaces:
-
-**Streaming Output:**
-```python
-import torch
-from khaosz import Khaosz
-
-model_dir = "your_model_parameter_dir"
-model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
-history = []
-
-while True:
-    query = input(">> ")
-    if query == "!exit":
-        break
-    
-    response_size = 0
-    for response, history in model.stream_generate(
-        query=query, 
-        history=history,
-        temperature=0.85,
-        top_p=0.95,
-        top_k=50
-    ):
-        print(response[response_size:], end="")
-        response_size = len(response)       
-```
-
-**Non-streaming Output:**
-```python
-import torch
-from khaosz import Khaosz
-
-model_dir = "your_model_parameter_dir"
-model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
-history = []
-
-while True:
-    query = input(">> ")
-    if query == "!exit":
-        break
-    
-    response = model.generate(
-        query=query, 
-        history=history,
-        temperature=0.85,
-        top_p=0.95,
-        top_k=50
-    )
-    print(response)
-```
-
-**(2) Retrieval-Augmented Generation (RAG):**
-
-```python
-import torch
-from khaosz import Khaosz
-
-model_dir = "your_model_parameter_dir"
-model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
-
-retrieved_content = model.retrieve_generate(
-    query=query,
-    retrieve_top_k=5,
-    temperature=0.6,
-    top_k=30,
-    top_p=0.95
-)
-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。
-
-**模型下载选项（任选其一）：**
-
-1. 访问 [HuggingFace](https://huggingface.co/ViperEk/KHAOSZ) 查看 **Files and versions**
-2. 运行 `scripts/download.py` 下载模型参数
-
-**演示视频：** [bilibili](https://www.bilibili.com/video/BV1z5RPYHEkd)
-
-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。
-
-- **📊 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：**
-
-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）。
-
-#### **(2). 安装依赖：**
 
 ```bash
-pip install -r requirements.txt
+python scripts/demo/download.py
-pip install .
 ```
 
-#### **(3). 运行训练脚本：**
+Or download manually from [HuggingFace](https://huggingface.co/ViperEk/KHAOSZ) into `params/`.
+
+#### Train a Model
 
 ```bash
-python train.py \
+export CUDA_VISIBLE_DEVICES=0,1,2,3
---train_type=train_type[seq, sft, dpo] \
+
---data_root_path=/path/to/dataset \
+nohup python scripts/tools/train.py \
---param_path=/path/to/param_path \
+    --nprocs=4 \
---n_epoch=5 \
+    --parallel_mode=ddp \
---batch_size=8 \
+    --train_type=seq \
---max_lr=2e-4 \
+    --data_root_path=/path/to/dataset \
---checkpoint_interval=10000 \
+    --param_path=/path/to/model \
---checkpoint_dir=checkpoints 
+    --batch_per_device=4 \
+    --grad_accum_steps=8 \
+    --warmup_ratio=0.05 \
+    --max_lr=1e-4 \
+    --max_grad_norm=1.0 \
+    --adamw_beta1=0.9 \
+    --adamw_beta2=0.95 \
+    --adamw_weight_decay=0.01 \
+    --window_size=2048 \
+    --ckpt_interval=10000 \
+    --ckpt_dir=./checkpoint \
+    --random_seed=3407 \
+    --label_smoothing=0.05 \
+    > out.log 2> err.log &
 ```
 
-**参数说明：**
+Full reference at [Parameter Guide](assets/docs/params.md).
-- `--train_type`: 训练类型（seq, sft, dpo）
-- `--data_root_path`: 数据集根目录
-- `--param_path`: 模型训练参数路径
-- `--n_epoch`: 总训练轮数
-- `--batch_size`: 批量大小
-- `--accumulation_steps`: 每个训练步骤的 batch 数量
-- `--warmup_steps`: 预热步数（warmup steps）
-- `--max_lr`: 最大学习率（使用预热 + 余弦衰减）
-- `--checkpoint_interval`: 检查点保存间隔
-- `--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。
+#### Generate Text
 
-
+```bash
-
+python scripts/tools/generate.py \
-### 👉 使用指南
+    --param_path /path/to/model \
-
+    --input_json_file /path/to/input.jsonl \
-#### **(1). 与模型对话：**
+    --output_json_file /path/to/output.jsonl
-
-打开 `chat.py` 或使用流式/非流式接口：
-
-**流式输出：**
-```python
-import torch
-from khaosz import Khaosz
-
-model_dir = "your_model_parameter_dir"
-model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
-history = []
-
-while True:
-    query = input(">> ")
-    if query == "!exit":
-        break
-    
-    response_size = 0
-    for response, history in model.stream_generate(
-        query=query, 
-        history=history,
-        temperature=0.85,
-        top_p=0.95,
-        top_k=50
-    ):
-        print(response[response_size:], end="")
-        response_size = len(response)       
 ```
 
-**非流式输出：**
+#### Docker
-```python
-import torch
-from khaosz import Khaosz
 
-model_dir = "your_model_parameter_dir"
+Build and run with Docker (recommended for GPU environments):
-model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
-history = []
 
-while True:
+```bash
-    query = input(">> ")
+# Build image
-    if query == "!exit":
+docker build -t astrai:latest .
-        break
+
-    
+# Run with GPU support
-    response = model.generate(
+docker run --gpus all -it astrai:latest
-        query=query, 
+
-        history=history,
+# Run with specific GPUs
-        temperature=0.85,
+docker run --gpus '"device=0,1"' -it astrai:latest
-        top_p=0.95,
+
-        top_k=50
+# Run inference server
-    )
+docker run --gpus all -p 8000:8000 astrai:latest \
-    print(response)
+  python -m scripts.tools.server --port 8000 --device cuda
+
+# Run with volume mount for data
+docker run --gpus all -v /path/to/data:/data -it astrai:latest
+
+# Docker Compose (GPU, default)
+docker compose up -d
+
+# Docker Compose (CPU only)
+docker compose --profile cpu up -d
 ```
 
-#### **(2). 基于检索的生成（RAG）：**
+> **Note**: `--gpus all` is required for CUDA support. Without it, `torch.cuda.is_available()` will return `False`.
 
-```python
+#### Start HTTP Server
-import torch
-from khaosz import Khaosz
 
-model_dir = "your_model_parameter_dir"
+Start the inference server with OpenAI and Anthropic-compatible HTTP API:
-model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
 
-retrieved_content = model.retrieve_generate(
+```bash
-    query=query,
+python -m scripts.tools.server --port 8000 --device cuda
-    retrieve_top_k=5,
-    temperature=0.6,
-    top_k=30,
-    top_p=0.95
-)
-print(retrieved_content)
 ```
 
+Make requests:
 
+```bash
+# OpenAI-compatible
+curl -X POST http://localhost:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "messages": [{"role": "user", "content": "Hello"}],
+    "max_tokens": 512
+  }'
 
-### 📌 模型规格说明（重复部分）
+# OpenAI-compatible streaming
+curl -X POST http://localhost:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "messages": [{"role": "user", "content": "Tell a story"}],
+    "stream": true,
+    "max_tokens": 500
+  }'
 
-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）。
+# Anthropic-compatible
+curl -X POST http://localhost:8000/v1/messages \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "astrai",
+    "system": "You are a helpful assistant.",
+    "messages": [{"role": "user", "content": "Hello"}],
+    "max_tokens": 512
+  }'
 
-**关键设计选择：**
+# Anthropic-compatible streaming with stop sequences
-- 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
+curl -X POST http://localhost:8000/v1/messages \
-- 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
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "astrai",
+    "messages": [{"role": "user", "content": "Write a story"}],
+    "max_tokens": 500,
+    "stream": true,
+    "stop_sequences": ["The end"]
+  }'
 
-**局限性：**
+# Health check
-- 由于参数规模较小，可能在处理复杂语言现象时表现受限
+curl http://localhost:8000/health
-- 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：**
+#### Demo
-- 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：**  
+Check out the demos in the `scripts/demo/` folder:
-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。
+
+```bash
+# Download pre‑processed data (required before running demos)
+python scripts/demo/download.py
+
+# Interactive streaming chat
+python scripts/demo/stream_chat.py
+
+# Batch generation
+python scripts/demo/generate_batch.py
+
+# Auto‑regressive generation
+python scripts/demo/generate_ar.py
+```
+
+Watch a video walkthrough on [bilibili](https://www.bilibili.com/video/BV1fuLB6yEj6).
+
+### Documentation
+
+| Document | Description |
+|----------|-------------|
+| [Parameter Guide](./assets/docs/params.md) | Training & inference parameters |
+| [Architecture](./assets/docs/architecture.md) | System architecture, class diagram & design patterns |
+| [Training](./assets/docs/training.md) | Training loop, strategies & formulas |
+| [Inference](./assets/docs/inference.md) | KVCache, continuous batching, sampling & HTTP API |
+| [Data Flow](./assets/docs/dataflow.md) | Data pipeline, storage backends & dataset architecture |
+| [Preprocessing](./assets/docs/preprocessing.md) | Declarative JSON-driven data preprocessing |
+
+### Contributing
+
+We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.
+
+1. Fork the repository.
+2. Create a feature branch.
+3. Commit your changes.
+4. Open a Pull Request.
+
+For major changes, please open an issue first to discuss what you would like to change.
+
+### Community
+
+- **GitHub Issues**: [Issue Tracker](https://github.com/ViperEkura/AstrAI/issues)
+- **Discussions**: [GitHub Discussions](https://github.com/ViperEkura/AstrAI/discussions)
+- **HuggingFace**: [Model Hub](https://huggingface.co/ViperEk)
+
+### License
+
+This project is licensed under the [GPL-3.0 License](LICENSE).
+
+---
+
+<div align="center">
+  <em>A lightweight Transformer framework designed for both high performance and ease of use.</em>
+</div>

assets/docs/README-zh-CN.md

@@ -0,0 +1,261 @@
+<div align="center">
+  
+  <img src="../images/logo.png" width="auto" alt="Logo">
+  
+  <div>
+    <a href="../../README.md">English</a> • 
+    <a href="#chinese">中文</a>
+  </div>
+  
+  <p>
+    <strong>轻量级 Transformer 训练与推理框架</strong>
+  </p>
+</div>
+
+<div align="center">
+  <img src="https://img.shields.io/badge/python-3.12+-blue.svg" alt="python">
+  <img src="https://img.shields.io/badge/license-GPL--3.0-blue.svg" alt="license">
+  <img src="https://img.shields.io/github/v/release/ViperEkura/AstrAI?color=76bad9" alt="release">
+  <img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.github.com%2Frepos%2FViperEkura%2FAstrAI&query=%24.stargazers_count&label=stars&suffix=%20stars&color=76bad9" alt="stars">
+  <img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.github.com%2Frepos%2FViperEkura%2FAstrAI&query=%24.forks_count&label=forks&suffix=%20forks&color=76bad9" alt="forks">
+</div>
+
+<br>
+
+<div align="center">
+  <a href="../../README.md">English</a> •
+  <a href="#chinese">中文</a> •
+  <a href="https://github.com/ViperEkura/AstrAI/issues">问题追踪</a> •
+  <a href="https://github.com/ViperEkura/AstrAI/discussions">讨论区</a> •
+  <a href="https://huggingface.co/ViperEk">HuggingFace</a>
+</div>
+<br>
+
+## 📖 目录
+
+- [特性](#特性)
+- [快速开始](#快速开始)
+- [文档](#文档)
+- [贡献](#贡献)
+- [社区](#社区)
+- [许可证](#许可证)
+
+---
+
+<a id="chinese"></a>
+## 中文
+
+### 特性
+
+- 🚀 **高性能**: 训练与推理双向优化，高效并行。
+- 🔧 **灵活**: 支持 seq/sft/dpo/grpo 多种训练方式，可定制模型架构。
+- 💡 **易用**: 简洁的 API 与丰富的示例、演示。
+- 📦 **轻量**: 依赖少，部署简单。
+- 🔬 **研究友好**: 模块化设计，便于实验新想法。
+- 🤗 **HuggingFace 风格 API**: 类 HuggingFace 的 AutoModel/AutoTokenizer 接口，方便加载模型和分词器。
+- 🔌 **双 API 兼容**: 同时支持 OpenAI 和 Anthropic 聊天补全 API，开箱即用。
+
+### 快速开始
+
+#### 安装
+
+```bash
+git clone https://github.com/ViperEkura/AstrAI.git
+cd AstrAI
+pip install -e .
+```
+
+安装开发依赖：
+
+```bash
+pip install -e ".[dev]"
+```
+
+#### 下载预训练模型
+
+下载预训练模型权重（1B 双语检查点）到 `params/` 目录：
+
+```bash
+python scripts/demo/download.py
+```
+
+或从 [HuggingFace](https://huggingface.co/ViperEk/KHAOSZ) 手动下载放入 `params/`。
+
+#### 训练模型
+
+```bash
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+
+nohup python scripts/tools/train.py \
+    --nprocs=4 \
+    --parallel_mode=ddp \
+    --train_type=seq \
+    --data_root_path=/path/to/dataset \
+    --param_path=/path/to/model \
+    --batch_per_device=4 \
+    --grad_accum_steps=8 \
+    --warmup_ratio=0.05 \
+    --max_lr=1e-4 \
+    --max_grad_norm=1.0 \
+    --adamw_beta1=0.9 \
+    --adamw_beta2=0.95 \
+    --adamw_weight_decay=0.01 \
+    --window_size=2048 \
+    --ckpt_interval=10000 \
+    --ckpt_dir=./checkpoint \
+    --random_seed=3407 \
+    --label_smoothing=0.05 \
+    > out.log 2> err.log &
+```
+
+完整参数列表见[参数说明](./params.md)。
+
+#### 文本生成
+
+```bash
+python scripts/tools/generate.py \
+    --param_path /path/to/model \
+    --input_json_file /path/to/input.jsonl \
+    --output_json_file /path/to/output.jsonl
+```
+
+#### Docker
+
+使用 Docker 构建和运行（推荐用于 GPU 环境）：
+
+```bash
+# 构建镜像
+docker build -t astrai:latest .
+
+# 启用 GPU 运行
+docker run --gpus all -it astrai:latest
+
+# 指定特定 GPU
+docker run --gpus '"device=0,1"' -it astrai:latest
+
+# 运行推理服务
+docker run --gpus all -p 8000:8000 astrai:latest \
+  python -m scripts.tools.server --port 8000 --device cuda
+
+# 挂载数据卷
+docker run --gpus all -v /path/to/data:/data -it astrai:latest
+
+# Docker Compose（GPU，默认）
+docker compose up -d
+
+# Docker Compose（仅 CPU）
+docker compose --profile cpu up -d
+```
+
+> **注意**: 必须使用 `--gpus all` 才能启用 CUDA 支持，否则 `torch.cuda.is_available()` 将返回 `False`。
+
+#### 启动 HTTP 服务
+
+启动推理服务器，支持 OpenAI 和 Anthropic 兼容的 HTTP API：
+
+```bash
+python -m scripts.tools.server --port 8000 --device cuda
+```
+
+发起请求：
+
+```bash
+# OpenAI 兼容
+curl -X POST http://localhost:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "messages": [{"role": "user", "content": "你好"}],
+    "max_tokens": 512
+  }'
+
+# OpenAI 兼容流式
+curl -X POST http://localhost:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "messages": [{"role": "user", "content": "讲个故事"}],
+    "stream": true,
+    "max_tokens": 500
+  }'
+
+# Anthropic 兼容
+curl -X POST http://localhost:8000/v1/messages \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "astrai",
+    "system": "你是一个乐于助人的助手。",
+    "messages": [{"role": "user", "content": "你好"}],
+    "max_tokens": 512
+  }'
+
+# Anthropic 兼容流式并设置停止序列
+curl -X POST http://localhost:8000/v1/messages \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "astrai",
+    "messages": [{"role": "user", "content": "写个故事"}],
+    "max_tokens": 500,
+    "stream": true,
+    "stop_sequences": ["结束"]
+  }'
+
+# 健康检查
+curl http://localhost:8000/health
+```
+
+#### 演示
+
+查看 `scripts/demo/` 文件夹中的演示：
+
+```bash
+# 下载预处理数据（运行演示前必需）
+python scripts/demo/download.py
+
+# 交互式流式聊天
+python scripts/demo/stream_chat.py
+
+# 批量生成
+python scripts/demo/generate_batch.py
+
+# 自回归生成
+python scripts/demo/generate_ar.py
+```
+
+观看 [bilibili](https://www.bilibili.com/video/BV1fuLB6yEj6) 上的视频演示。
+
+### 文档
+
+| 文档 | 说明 |
+|------|------|
+| [参数说明](./params.md) | 训练与推理参数配置 |
+| [架构文档](./architecture.md) | 系统架构、类图与设计模式 |
+| [训练文档](./training.md) | 训练循环、策略与公式 |
+| [推理文档](./inference.md) | KVCache、连续批处理、采样与 HTTP API |
+| [数据流程](./dataflow.md) | 数据管道、存储后端与数据集架构 |
+| [数据预处理](./preprocessing.md) | 声明式 JSON 驱动数据预处理 |
+
+### 贡献
+
+我们欢迎贡献！请参阅[贡献指南](../../CONTRIBUTING.md)了解详情。
+
+1. Fork 本仓库。
+2. 创建功能分支。
+3. 提交更改。
+4. 发起 Pull Request。
+
+重大更改请先开 issue 讨论。
+
+### 社区
+
+- **GitHub Issues**: [问题追踪](https://github.com/ViperEkura/AstrAI/issues)
+- **Discussions**: [GitHub 讨论区](https://github.com/ViperEkura/AstrAI/discussions)
+- **HuggingFace**: [模型中心](https://huggingface.co/ViperEk)
+
+### 许可证
+
+本项目采用 [GPL-3.0 许可证](../../LICENSE)。
+
+---
+
+<div align="center">
+  <em>专为高性能与易用性设计的轻量级 Transformer 框架。</em>
+</div>

assets/docs/dataflow.md

@@ -0,0 +1,64 @@
+# Data Flow
+
+This document describes the data pipeline: from raw text to model input tensors.
+
+## Overview
+
+```
+Raw Text → AutoTokenizer → Token IDs → .h5/.bin → Store.load() → Store.fetch() → Dataset → Sampler → DataLoader → Training/Inference
+```
+
+## Data Preparation
+
+Raw text is tokenized via `AutoTokenizer.encode()` and saved as HDF5 (`.h5`) or binary (`.bin` + `meta.json`) files with keyed tensor groups.
+
+Storage format is auto-detected by `detect_format()`; backends are dispatched via registry:
+
+```
+StoreFactory.create("h5")  → H5Store
+StoreFactory.create("bin") → MmapStore
+```
+
+H5 backend supports shared memory via `.share_memory_()`. Bin (mmap) uses OS page-cache sharing natively.
+
+## Data Keys by Training Type
+
+| Type | Storage Keys |
+|------|-------------|
+| `seq` | `sequence` (→ input_ids, target_ids via offset-by-1) |
+| `sft` | `sequence`, `loss_mask` |
+| `dpo` | `chosen`, `rejected`, `chosen_mask`, `rejected_mask` |
+| `grpo` | `prompts`, `responses`, `masks`, `rewards` |
+
+## Dataset Architecture
+
+```
+DatasetFactory.load(train_type, load_path, window_size, stride=None, storage_type=None)
+  → BaseDataset.load(load_path, storage_type=None)
+    → detect_format(load_path)
+    → StoreFactory.create(storage_type)
+    → Store.load(load_path)
+      → H5Store._normalize() / MmapStore._normalize()
+        → Store._data[Dict[str, List[Tensor]]] + _cum[Dict[str, List[int]]]
+          → BaseDataset.__getitem__(idx)
+            → get_index(idx) → [begin, end)
+            → Store.fetch(begin, end, keys) → Tensor / Dict[str, Tensor]
+```
+
+`window_size` = max input length, `stride` = step between consecutive samples (defaults to `window_size`, optional). `storage_type` defaults to `None` (auto-detect via `detect_format`).
+
+`Store.fetch(begin, end, keys)` accepts a single key (`str`) returning a `Tensor`, or a list of keys returning `Dict[str, Tensor]`. Internally uses `bisect` across multi-segment tensors. Raises `RuntimeError("Store not loaded")` if called before `load()`.
+
+## Sampler
+
+`ResumableDistributedSampler` supports checkpoint-aware distributed sampling:
+
+- Tracks `start_epoch` / `start_iter` for resume
+- Shuffle via `torch.Generator(seed + epoch)`
+- Per-replica index slicing for DDP
+
+## DataLoader
+
+Standard PyTorch `DataLoader` with configurable `batch_size`, `num_workers`, `pin_memory`, `prefetch_factor`. Sampler produces indices; dataloader fetches tensor batches via `__getitem__`.
+
+> Document Update Time: 2026-05-30

assets/docs/inference.md

@@ -0,0 +1,152 @@
+# Inference
+
+## KV Cache
+
+At decode time, only the last query token matters. All previous K/V are cached to avoid recomputation:
+
+$$
+o_n = \sum_j \text{softmax}\left(\frac{q_n k_j}{\sqrt{d_k}}\right) v_j
+$$
+
+RoPE is applied **before** KV cache write, not after — otherwise position encoding drift occurs.
+
+## KVCache System
+
+Six classes (plus two helpers) working together:
+
+```
+KVCache (facade)
+  ├── PagePool         orchestrates page allocation + prefix matching
+  │     ├── Allocator   bitmask-based page allocator + ref-count + LRU eviction (inside PagePool)
+  │     └── PrefixCache hash-based prefix matching (page_hash via polynomial hash) (inside PagePool)
+  ├── TaskTable        maps task_id → page_table + cached token count
+  ├── Storage          k_cache / v_cache tensors (n_layers × n_pages × page_size × n_kv_heads × head_dim)
+  └── KvcacheView      bundles Storage + page_table + total_len for attention layers (returned by bind())
+```
+
+`KVCache.bind(page_table, total_len)` returns a `KvcacheView` used by attention layers via `write()` / `gather()`.
+
+## Continuous Batching
+
+`InferenceScheduler` runs a daemon thread with a 4-phase loop:
+
+```
+1. Cleanup → Remove finished tasks, free KV pages
+2. Refill  → Pop from waiting_queue, task_alloc pages, activate
+3. Prefill → Group by (prompt_len, start_pos), run full forward
+4. Decode  → Pick largest same-position group, single-token forward
+```
+
+## Sampling (Strategy Pattern)
+
+```
+BaseSamplingStrategy (ABC)
+  ├── TemperatureStrategy
+  ├── TopKStrategy
+  ├── TopPStrategy
+  └── SamplingPipeline
+```
+
+`SamplingPipeline` composes them: Temperature → Top-K → Top-P → softmax → multinomial.  
+`sample()` is a convenience shortcut for one-shot usage.
+
+## Protocol Handlers (Strategy Pattern)
+
+```python
+class ProtocolHandler:  # concrete orchestrator
+    def __init__(self, request, engine, builder): ...
+    async def handle(self):
+        prompt, ctx, stops = builder.prepare(request, engine)
+        agen = engine.generate_async(prompt, ...)
+        if stream: self._handle_stream(agen, ctx, stops)
+        else:      return await self._handle_non_stream(agen, ctx, stops)
+```
+
+`ResponseBuilder` (ABC): `prepare()`, `format_stream_start()`, `format_chunk()`, `format_stream_end()`, `format_response()`.
+
+`OpenAIResponseBuilder` → `/v1/chat/completions`, `AnthropicResponseBuilder` → `/v1/messages`.
+
+Adding a protocol = one builder file, no handler subclassing needed.
+
+## Engine & GenerateResult
+
+```
+InferenceEngine
+  ├── generate(prompt, stream, ...) → str | List[str] | Generator
+  ├── generate_with_request(req)    → same
+  ├── generate_async(prompt, ...)   → AsyncGenerator
+  ├── get_stats()                   → Dict
+  └── shutdown()
+```
+
+`GenerateResult` uses `Condition` for non-streaming (`wait_completion()`) and `Event` for streaming (`wait()`). Stream callback is `cb(token)`.
+
+## HTTP API
+
+```
+POST /v1/chat/completions   OpenAI
+POST /v1/messages            Anthropic
+GET  /health                 {"status":"ok","model_loaded":true}
+GET  /stats                  scheduler statistics
+```
+
+### OpenAI
+
+```bash
+curl -X POST http://localhost:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{"messages":[{"role":"user","content":"Hello"}],"max_tokens":512}'
+```
+
+Response:
+```json
+{
+  "id": "chatcmpl-abc123",
+  "object": "chat.completion",
+  "created": 1717000000,
+  "model": "astrai",
+  "choices": [{"index": 0, "message": {"role": "assistant", "content": "Hello!"}, "finish_reason": "stop"}],
+  "usage": {"prompt_tokens": 5, "completion_tokens": 10, "total_tokens": 15}
+}
+```
+
+Streaming SSE: `object: "chat.completion.chunk"` — starts with role delta, then token chunks, ends with finish chunk + usage stats, then `data: [DONE]`.
+
+### Anthropic
+
+```bash
+curl -X POST http://localhost:8000/v1/messages \
+  -H "Content-Type: application/json" \
+  -d '{"model":"astrai","system":"You are helpful.","messages":[{"role":"user","content":"Hello"}],"max_tokens":512}'
+```
+
+Supports `stop_sequences` and streaming via `event: content_block_delta`.
+
+### GenerationRequest Parameters
+
+| Param | Type | Default | Description |
+|-------|------|---------|-------------|
+| `messages` | List[dict] | required | Chat messages (role, content) |
+| `top_k` | int | 50 | Top-k count |
+| `top_p` | float | 1.0 | Nucleus threshold |
+| `temperature` | float | 1.0 | Sampling temperature (> 0.0) |
+| `max_tokens` | Optional[int] | None | Max generation length |
+| `stream` | bool | False | Stream output |
+
+## Engine API
+
+```python
+# Non-streaming
+engine.generate("Hello", stream=False)          # -> str
+engine.generate(["A", "B"], stream=False)       # -> List[str]
+
+# Streaming
+engine.generate("Hello", stream=True)           # -> Generator[str]
+engine.generate(["A", "B"], stream=True)        # -> Generator[Tuple[int, str]]
+
+# Async
+async for token in engine.generate_async("Hello", ...):    # -> AsyncGenerator[str]
+    print(token)
+```
+
+> Document Update Time: 2026-05-30

assets/docs/introduction.md

@@ -1,89 +0,0 @@
-## 模型介绍
-
-
-
-### 1. 模型搭建
-
-本模型采用Transformer架构， 使用GQA（q_head=24, kv_head=4） 机制，相较于传统的MHA可以节省KV cache 的显存占用（但是目前没有做KV cache），通过堆叠24层Transformer实现模型的搭建， 参数量为1.0b。Transformer 是自回归模型， 是通过计算前面所有的token的关系得到下一个token的概率分布
-
-![structure](../images/structure.png)
-
-什么是自回归模型呢， 在把句子拆分成token之后, 模型会预测下一个token的概率分布。这意味着模型会根据给定的上下文（即已经出现的tokens序列），计算出下一个可能的token及其对应的概率。
-
-
-
-#### 1. 自回归
-
-假设我们有一个句子被拆分成如下tokens列表：
-
-```
-["你好", "，" "今天", "天气"]
-```
-
-接下来，模型会基于这个序列预测下一个可能出现的token。这通常以概率分布的形式给出，比如：
-
-```
--> {"token": "不错", "probability": 0.4}
--> {"token": "晴朗", "probability": 0.2}
--> ......
-```
-
-这里，“不错”和“晴朗”是两个可能跟随在“天气”之后的tokens，并且给出了每个token成为下一个token的可能性大小。
-
-之后，我们通过采样（通过top_k, top_p, temperature参数调整采样后的结果）得到下一个token并且将下一个token加入序列作为输入
-
-```
-["你好", "，" "今天", "天气", "不错"]
-```
-
-之后都是在重复这个流程， 直到遇到控制流程结束的token（<|end_of_seqence|>）模型停止处理（一般模型都会设置控制token， 不然模型会一直输出到显存爆炸）。 
-
-
-
-
-
-#### 2. 因果掩码
-
-transformer 中采用注意力机制，输入的形状一般为[bsz, seq_len]， 输出为[bsz, seq_len，n_dim]， 为了实现预测下一个token， 模型的输入和输出必须错开来一个位置。模型预测的target必须错开一个位置， 在训练的时候我们也采用错开一个位置的方法
-
-```
-sequence : [[1, 2, 3, 4, 5, 6]]
-input_ids: [[1, 2, 3, 4, 5]]
-target_ids: [[2, 3, 4, 5, 6]]
-```
-
-
-
-注意力得分计算的公式为
-
-
-$$ s_{ij} = softmax(\frac{q_i^Tk_j}{\sqrt{d_k}}) $$
-$$ s_{ij} := s_{ij} + mask_{ij} $$
-
-
-其中注意力得分代表了模型对两个token之间相似程度的关注程度
-
-对于decoder only结构的模型， 为了防止模型从未来的位置偷到信息， 在注意力的计算过程中需要增加掩码，我们需要在注意力得分计算之前应用一个掩码。这个掩码通常是一个下三角矩阵，对于长度为n的序列，它的形状是[n, n]。下面以一个长度为5的序列为例，展示如何创建这样的因果掩码矩阵：
-
-```
-[[0, -inf, -inf, -inf, -inf],
- [0,    0, -inf, -inf, -inf],
- [0,    0,    0, -inf, -inf],
- [0,    0,    0,    0, -inf],
- [0,    0,    0,    0,    0]]
-```
-
-在这个矩阵中，0表示可以注意到的位置，而-inf表示应该被掩盖（即不应注意到）的位置。因为这个句子保证了注意力得分中 $j > i$  的部分通过softmax 之后由`inf` 变成0， 也就是模型不能看到未来的信息
-
-
-
-#### 3. 旋转位置编码
-
-旋转位置编码（Rotary Position Embedding, RoPE）是一种为了解决Transformer模型中缺乏对序列位置信息直接建模的问题而设计的位置编码方法。与传统的位置编码（如正弦和余弦函数的位置编码）不同，RoPE通过将位置信息直接嵌入到查询（Query, Q）和键（Key, K）向量中来实现，使得模型能够更自然地处理序列中的相对位置关系。
-
-
-$$ q_i = R_i W_q x_i $$
-$$ k_j = R_j W_k x_j $$
-$$ q_i^T k_j = (R_i W_q x_i)^T( R_j W_k x_j) = x_i^T W_q^T R_{i-j} W_k x_j $$
-
-其中的 $R_{i-j}$ 控制了模型的不同token 在不同相对距离上注意力的衰减，在 $i - j$ 绝对值越大的时候， 衰减的程度越强， 通过这种方式能让模型学习到相对位置关系， 从而使得模型可以扩展和适应长序列

assets/docs/kvcache.md

@@ -1,27 +0,0 @@
-## kv_cache 实现
-
-根据注意力的计算公式
-
-$$
-\begin{align*}
-o_i &= \sum_j s_{ij} v_{j} \\
-s_{ij} &= \text{softmax}\left( \sum_n \frac{q_{i,n} k_{j,n}}{\sqrt{d_k}} \right)
-\end{align*}
-$$
-
-由于模型是自回归模型, 我们只用求序列最后一个部分，也就是说 $ i $ 的下标是确定的, 是序列最后一个元素, 我们求的是 $o_{n} $ 
-
-$$
-\begin{align*}
-o_n &= \sum_j s_{j}v_{j,n} \\
-s_j &= \text{softmax}\left(\sum_n\frac{q_n k_{j,n}}{\sqrt{d_k}} \right)
-\end{align*}
-$$
-
-如果我们把式子展开
-
-$$
-o_n = \sum_j \sum_n \text{softmax}\left(\frac{q_n k_{j,n}}{\sqrt{d_k}}\right)v_{j,n}
-$$
-
-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

assets/docs/params.md

@@ -0,0 +1,100 @@
+# Parameter Documentation
+
+## Training Parameters
+
+### Basic Parameters
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--train_type` | Training type (`seq`, `sft`, `dpo`, `grpo`) | required |
+| `--data_root_path` | Dataset root directory | required |
+| `--param_path` | Model parameters or checkpoint path | required |
+| `--n_epoch` | Total training epochs | 1 |
+| `--batch_per_device` | Batch size per device | 1 |
+| `--grad_accum_steps` | Gradient accumulation steps between optimizer steps | 1 |
+
+### Learning Rate Scheduling
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--warmup_ratio` | Fraction of total steps used for LR warmup | 0.05 |
+| `--max_lr` | Maximum learning rate (cosine decay after warmup) | 3e-4 |
+| `--max_grad_norm` | Maximum gradient norm for clipping | 1.0 |
+
+### Optimizer (AdamW)
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--adamw_beta1` | AdamW beta1 | 0.9 |
+| `--adamw_beta2` | AdamW beta2 | 0.95 |
+| `--adamw_weight_decay` | AdamW weight decay | 0.01 |
+
+### Data Loading
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--window_size` | Max input sequence length | model config `max_len` |
+| `--stride` | Stride for sliding window over sequences | None |
+| `--random_seed` | Random seed for reproducibility | 3407 |
+| `--num_workers` | DataLoader worker processes | 4 |
+| `--no_pin_memory` | Disable pin_memory (enabled by default) | (flag) |
+
+### Checkpoint & Resume
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--ckpt_interval` | Iterations between checkpoints | 5000 |
+| `--ckpt_dir` | Checkpoint save directory | checkpoint |
+| `--start_epoch` | Resume from epoch (0 = from scratch) | 0 |
+| `--start_batch` | Resume from batch iteration | 0 |
+
+### Distributed Training
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--nprocs` | Number of GPUs / processes | 1 |
+| `--parallel_mode` | Parallel strategy (`none`, `ddp`, or `fsdp`) | none |
+| `--device_type` | Device type | cuda |
+| `--start_method` | Multiprocessing start method (`spawn`, `fork`, `forkserver`) | spawn |
+
+### Strategy-specific
+
+| Parameter | Description | Default | Used by |
+|-----------|-------------|---------|---------|
+| `--dpo_beta` | DPO beta value | 0.1 | `dpo` |
+| `--label_smoothing` | Label smoothing for cross-entropy loss | 0.05 | `seq`, `sft` |
+| `--group_size` | GRPO group size | 4 | `grpo` |
+| `--grpo_clip_eps` | GRPO clipping epsilon | 0.2 | `grpo` |
+| `--grpo_kl_coef` | GRPO KL penalty coefficient | 0.01 | `grpo` |
+| `--grpo_sync_interval` | GRPO ref_model sync interval (steps) | 200 | `grpo` |
+
+### Usage Example
+
+```bash
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+
+nohup python scripts/tools/train.py \
+    --nprocs=4 \
+    --parallel_mode=ddp \
+    --train_type=seq \
+    --data_root_path=/path/to/dataset \
+    --param_path=/path/to/model \
+    --batch_per_device=4 \
+    --grad_accum_steps=8 \
+    --warmup_ratio=0.05 \
+    --max_lr=1e-4 \
+    --max_grad_norm=1.0 \
+    --adamw_beta1=0.9 \
+    --adamw_beta2=0.95 \
+    --adamw_weight_decay=0.01 \
+    --window_size=2048 \
+    --ckpt_interval=10000 \
+    --ckpt_dir=./checkpoint \
+    --random_seed=3407 \
+    --label_smoothing=0.05 \
+    > out.log 2> err.log &
+```
+
+---
+
+> Document Update Time: 2026-05-24

assets/docs/preprocessing.md

@@ -0,0 +1,283 @@
+# Preprocessing Pipeline
+
+Declarative JSON-driven data preprocessing. No code needed -- describe your input format and mask rules in a config file, the engine does the rest.
+
+## Philosophy
+
+| Component | Responsibility |
+|-----------|---------------|
+| `tokenizer_config.json` (`chat_template`) | Formatting -- how roles become tokens |
+| `pipeline.json` (`mask`) | Masking -- which roles participate in training |
+
+The two are fully decoupled. A single config file captures the entire pipeline, reusable and version-controllable. Extension is via factory registration (`@MaskBuilderFactory.register`) -- no need to touch existing code.
+
+## Quick Start
+
+### SFT Chat
+
+```json
+{
+  "version": 1,
+  "input": {
+    "type": "chat",
+    "messages_key": "messages"
+  },
+  "mask": {
+    "system": "mask",
+    "user": "mask",
+    "assistant": "train"
+  },
+  "mask_default": "mask",
+  "preprocessing": {
+    "max_seq_len": 2048,
+    "deduplicate": true
+  },
+  "output": {
+    "domain_key": "source",
+    "storage_format": "bin",
+    "max_tokens_per_shard": 100000000
+  }
+}
+```
+
+Three lines of mask rules cover the most common SFT case: train on assistant turns, mask everything else.
+
+### Instruction Tuning
+
+```json
+{
+  "version": 1,
+  "input": {
+    "type": "instruction",
+    "prompt_key": "instruction",
+    "response_key": "output"
+  },
+  "mask": {
+    "prompt": "mask",
+    "response": "train"
+  },
+  "mask_default": "mask",
+  "preprocessing": {
+    "max_seq_len": 2048
+  },
+  "output": {
+    "storage_format": "bin"
+  }
+}
+```
+
+Mask splits at the prompt/response field boundary.
+
+### Pretraining
+
+```json
+{
+  "version": 1,
+  "input": {
+    "type": "text",
+    "text_key": "content"
+  },
+  "mask": {},
+  "preprocessing": {
+    "max_seq_len": 2048,
+    "min_chars": 50
+  },
+  "output": {
+    "storage_format": "bin"
+  }
+}
+```
+
+No mask -- train on all tokens.
+
+### Run
+
+```bash
+python scripts/tools/preprocess.py data/*.jsonl -o output/ -c sft.json
+```
+
+## Configuration Reference
+
+### `input`
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `type` | string | yes | `"chat"` | Format: `"chat"`, `"instruction"`, or `"text"` |
+| `messages_key` | string | no | `"messages"` | JSON key for messages array (chat) |
+| `prompt_key` | string | no | `"prompt"` | JSON key for prompt field (instruction) |
+| `response_key` | string | no | `"response"` | JSON key for response field (instruction) |
+| `text_key` | string | no | `"text"` | JSON key for text field |
+
+### `mask`
+
+A map of `{role_or_field: "mask" | "train"}`. The engine uses this to build `loss_mask`:
+
+- `"mask"` -- tokens in this span are ignored during training (`loss_mask=0`)
+- `"train"` -- tokens in this span contribute to the loss (`loss_mask=1`)
+
+For chat mode, keys are role names (`system`, `user`, `assistant`, ...).
+For instruction mode, keys are `"prompt"` and `"response"`.
+
+| Field | Type | Default | Description |
+|-------|------|---------|-------------|
+| `mask` | dict | `{}` | Role/field to action mapping |
+| `mask_default` | string | `"mask"` | Default action for unlisted roles |
+
+### `preprocessing`
+
+| Field | Type | Default | Description |
+|-------|------|---------|-------------|
+| `max_seq_len` | int | `2048` | Maximum token length; truncated if exceeded |
+| `min_chars` | int | `50` | Minimum character length; dropped if shorter (text mode only) |
+| `max_chars` | int | `2000000` | Maximum character length; dropped if longer (text mode only) |
+| `deduplicate` | bool | `true` | Remove exact duplicates via MD5 of first 200 chars |
+| `max_items` | int or null | `null` | Maximum items to process; `null` = unlimited |
+
+### `output`
+
+| Field | Type | Default | Description |
+|-------|------|---------|-------------|
+| `domain_key` | string or null | `null` | JSON key for domain grouping; `null` = all output to `__default__` |
+| `storage_format` | string | `"bin"` | `"bin"` (mmap, zero-copy) or `"h5"` (HDF5) |
+| `max_tokens_per_shard` | int | `100000000` | Max tokens per output shard |
+
+## Mask Algorithm
+
+### Chat Mode (role-span tracking)
+
+For each message in the `messages` array:
+
+1. Prepend BOS token (position 0, always masked)
+2. Render through the chat template for that single message
+3. Encode the rendered text, record token span `(start, end, role)`
+4. Concatenate all spans — special tokens from the chat template naturally prevent BPE merging across message boundaries
+5. Fill `loss_mask` from the mask rules
+
+**Multi-turn example**:
+
+```
+Data:
+  [system: "You are helpful."]
+  [user: "What is 2+2?"]
+  [assistant: "4"]
+  [user: "What is 3+3?"]
+  [assistant: "6"]
+
+Config:
+  "mask": {"system": "mask", "user": "mask", "assistant": "train"}
+
+Result:
+  tokens:  <bos> [system span] [user span] [assistant:4 span] [user span] [assistant:6 span]
+  mask:      0       0            0              1               0             1
+```
+
+Both assistant turns are trained. All system and user tokens are masked.
+
+### Instruction Mode (field boundary)
+
+Encode the prompt and response fields independently, then split the mask at the field boundary.
+
+- `"prompt": "mask", "response": "train"` -- mask the left half, train the right half
+- `"prompt": "train", "response": "mask"` -- the reverse
+
+### Text Mode (no mask)
+
+Pure tokenization. No `loss_mask` is produced. Used for pretraining.
+
+## Output Layout
+
+### Single-Shard (`bin`)
+
+```
+output_dir/
+  __default__/              # when domain_key is null
+    meta.json               # {"sequence": {"shape": [N], "dtype": "int64"}, ...}
+    sequence.bin            # int64 raw bytes, mmap-able for zero-copy reads
+    loss_mask.bin           # int64 raw bytes
+  wiki/                     # when domain_key="source" and item["source"]="wiki"
+    meta.json
+    sequence.bin
+    loss_mask.bin
+```
+
+### Multi-Shard (`bin`)
+
+When `max_tokens_per_shard` is exceeded, bin output is split into numbered shard subdirectories:
+
+```
+output_dir/
+  __default__/
+    shard_0000/
+      meta.json
+      sequence.bin
+      loss_mask.bin
+    shard_0001/
+      meta.json
+      sequence.bin
+      loss_mask.bin
+```
+
+`MmapStore` automatically discovers and merges all shards under the domain directory.
+
+### H5 Output
+
+HDF5 files are always named with a shard index, avoiding overwrite regardless of `max_tokens_per_shard`:
+
+```
+output_dir/
+  __default__/
+    data_0000.h5            # each H5 contains key→dataset groups
+    data_0001.h5
+  wiki/
+    data_0000.h5
+```
+
+## Python API Usage
+
+```python
+from astrai.preprocessing.pipeline import Pipeline
+from astrai.config.preprocess_config import PipelineConfig
+
+config = PipelineConfig.from_json("sft_pipeline.json")
+Pipeline(
+    config,
+    ["data_part1.jsonl", "data_part2.jsonl"],
+    output_dir="output/",
+    tokenizer_path="params"
+).run()
+```
+
+Or from the CLI:
+
+```bash
+python scripts/tools/preprocess.py data/*.jsonl -o output/ -c sft.json
+```
+
+## Extension
+
+Register a custom builder for new formats:
+
+```python
+from astrai.preprocessing.builder import BaseMaskBuilder, MaskBuilderFactory
+
+@MaskBuilderFactory.register("my_format")
+class MyFormatBuilder(BaseMaskBuilder):
+    def build(self, item: dict, config, tokenizer) -> dict | None:
+        # Return {"ids": [...], "loss_mask": [...], "domain": "..."}
+        # Return None to skip this item
+        ...
+```
+
+Then set `"input": {"type": "my_format"}` in your config.
+
+## Compared to Old Pipeline
+
+| Old (`astrai.preprocess.Pipeline`) | New (`astrai.preprocessing.pipeline.Pipeline`) |
+|---|---|
+| Configured via constructor arguments | Configured via JSON file |
+| Hardcoded `_transform_chat` / `_transform_text` | Factory-registered `Builder` with declarative mask rules |
+| Auto-detects format via magic key lists | Explicit `input.type` declaration |
+| Double-encodes (full + prompt), uses length diff for mask | Single-encode with role-span tracking |
+| Only trains the last assistant turn | Configurable: multi-turn, single-turn, or no mask |
+
+> Document Update Time: 2026-05-30

assets/docs/training.md

@@ -0,0 +1,201 @@
+# Training
+
+### Autoregression
+
+Given a token sequence, the model predicts the probability of the next token. Each generated token is appended to the input and fed back, repeating until an end-of-sequence token or max length.
+
+### Causal Mask
+
+```
+sequence : [[1, 2, 3, 4, 5, 6]]
+input_ids: [[1, 2, 3, 4, 5]]
+target_ids: [[2, 3, 4, 5, 6]]
+```
+
+Lower-triangular mask prevents attending to future positions:
+
+```
+[[0, -inf, -inf, -inf, -inf],
+ [0,    0, -inf, -inf, -inf],
+ [0,    0,    0, -inf, -inf],
+ [0,    0,    0,    0, -inf],
+ [0,    0,    0,    0,    0]]
+```
+
+### Rotary Position Embedding (RoPE)
+
+RoPE embeds position into Q/K vectors via complex rotation:
+
+$$ q_i = R_i W_q x_i, \quad k_j = R_j W_k x_j, \quad q_i^T k_j = x_i^T W_q^T R_{i-j} W_k x_j $$
+
+The complex rotation `freqs_cis` is pre-computed once (`cos, sin` pairs per position). `apply_rotary_emb` multiplies Q/K as complex numbers.
+
+## Training Loop
+
+Two-level loop: **epoch** → **batch**. Optimizer step fires every `grad_accum_steps` batches.
+
+```
+on_train_begin
+  model.train()
+  on_epoch_begin
+    for batch in dataloader:
+      on_batch_begin
+      with executor.accumulate(model):
+        loss = strategy.compute_loss(batch)
+        context.loss = loss.item()
+        stand_loss = loss / executor.grad_accum_steps
+        executor.backward(stand_loss)
+        context.iteration += 1
+        on_batch_end
+
+        if executor.sync_gradients:
+          on_optimizer_step
+          optimizer.step()
+          optimizer.zero_grad()
+          if scheduler:
+            scheduler.step()
+    on_epoch_end
+on_train_end
+```
+
+### Callback Lifecycle
+
+| Hook | Fires | Default callback |
+|------|-------|-----------------|
+| `on_train_begin` | Before training starts | `GradientCheckpointingCallback` |
+| `on_epoch_begin` | Start of each epoch | `ProgressBarCallback` |
+| `on_batch_begin` | Every batch | — |
+| `on_optimizer_step` | Every accumulation window | `GradientClippingCallback`, `ValidationCallback` |
+| `on_batch_end` | Every batch | `CheckpointCallback`, `MetricLoggerCallback`, `ProgressBarCallback` |
+| `on_epoch_end` | End of each epoch | `ProgressBarCallback` |
+| `on_error` | On exception during training | `CheckpointCallback`, `MetricLoggerCallback` |
+| `on_train_end` | Training ends (always via finally) | `CheckpointCallback`, `MetricLoggerCallback`, `GradientCheckpointingCallback` |
+
+Default callbacks (in order): `gradient_checkpointing` (activation checkpointing, optional), `checkpoint` (safetensors, rank-0), `metric_logger` (JSONL, rank-0), `progress_bar` (tqdm), `gradient_clipping`, `validation` (periodic validation on val_dataset).
+
+## Strategies
+
+### SEQ (Pre-training)
+
+Next-token cross-entropy with optional label smoothing:
+
+$$
+L_{\text{PT}} = -\sum_{t=1}^{T} \log P(x_t \mid x_{\lt t}; \theta)
+$$
+
+Keys: `input_ids`, `target_ids`. Optional: `label_smoothing`.
+
+### SFT (Supervised Fine-Tuning)
+
+Masked cross-entropy (`ignore_index=-100`) over response tokens:
+
+$$
+L_{\text{SFT}} = -\sum_{t=P+1}^{P+L} \log P(s_t \mid s_{\lt t}; \theta)
+$$
+
+Keys: `input_ids`, `target_ids`, `loss_mask`. Optional: `label_smoothing`.
+
+### DPO (Direct Preference Optimization)
+
+Frozen reference model, preference margin via log-ratio:
+
+$$
+L_{\text{DPO}} = -\mathbb{E}\left[\log\sigma\left(\beta\log\frac{\pi_\theta(y_w\mid x)}{\pi_{\text{ref}}(y_w\mid x)} - \beta\log\frac{\pi_\theta(y_l\mid x)}{\pi_{\text{ref}}(y_l\mid x)}\right)\right]
+$$
+
+Parameters: `beta=0.1`, `reduction="mean"`. Keys: `chosen`, `rejected`, `chosen_mask`, `rejected_mask`.
+
+### GRPO (Group Relative Policy Optimization)
+
+On-policy PPO with group-normalized advantages:
+
+$$
+\text{Advantage}_i = \frac{r_i - \mu}{\sigma + \epsilon}
+$$
+
+$$
+L_{\text{GRPO}} = -\mathbb{E}\left[\min\left(\frac{\pi_\theta}{\pi_{\text{ref}}}A,\; \text{clip}\left(\frac{\pi_\theta}{\pi_{\text{ref}}}, 1-\epsilon, 1+\epsilon\right)A\right)\right] + \lambda \cdot \mathbb{E}\left[(\log\pi_\theta - \log\pi_{\text{ref}})^2\right]
+$$
+
+Parameters: `group_size=4`, `clip_eps=0.2`, `kl_coef=0.01`, `sync_interval=200`, `reduction="mean"`.
+
+Keys: `prompts`, `responses`, `masks`, `rewards`.
+
+## LR Schedulers
+
+| Type | Class | Description |
+|------|-------|-------------|
+| Cosine | `CosineScheduler` | Linear warmup → cosine decay to `min_rate` |
+| SGDR | `SGDRScheduler` | Cosine annealing with warm restarts (`t_mult=2`) |
+
+Created by `SchedulerFactory.create(optimizer, schedule_type, **kwargs)`. Valid types: `"cosine"`, `"sgdr"`. Omit to use no scheduler.
+
+## Gradient Checkpointing
+
+Trades compute for memory by recomputing activations during backward pass. Specify module types via `gradient_checkpointing_modules`:
+
+```python
+from astrai.model.components.decoder_block import DecoderBlock
+config = TrainConfig(..., gradient_checkpointing_modules=[DecoderBlock])
+```
+
+Callback wraps each `DecoderBlock.forward` with `torch.utils.checkpoint.checkpoint(use_reentrant=False)`, compatible with `torch.compile`. Uses `nn.Module.apply()` for traversal — works through DDP wrappers without manual unwrap. Empty list (default) means no-op.
+
+## Checkpoint
+
+```
+Checkpoint(state_dict, epoch, iteration, extra, meta, config)
+  ├── save(save_dir)    rank-0 only: meta.json (epoch/iteration/timestamp) + config.json (model config) + model.safetensors + optional {key}.pt (optimizer.pt, scheduler.pt)
+  └── load(save_dir, broadcast=False)    loads from local disk; set broadcast=True to broadcast metadata from rank-0
+```
+
+Optimizer/scheduler state persisted by default via `Checkpoint.extra`.  
+Model config (`context.model_config`) saved into `config.json` during training via `CheckpointCallback`.
+
+## TrainContextBuilder (Builder Pattern)
+
+```python
+context = (
+    TrainContextBuilder(config)
+        .with_resume_dir(resume_dir)
+        .build()
+)
+# Returns TrainContext with model, strategy, optimizer, scheduler, dataloader, checkpoint
+```
+
+- Loads checkpoint weights if provided
+- Creates executor via `ExecutorFactory.create(cfg.parallel_mode, grad_accum_steps=cfg.grad_accum_steps, **cfg.executor_kwargs)`
+- Calls `executor.prepare(model, optimizer, dataloader, scheduler)` for model distribution (e.g. DDP) + gradient accumulation wrappers
+- Creates `ResumableDistributedSampler` for shuffle+resume
+- Builds strategy via `StrategyFactory.create(train_type, model, device, **kwargs)`
+
+## Training CLI
+
+```bash
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+
+nohup python scripts/tools/train.py \
+    --nprocs=4 \
+    --parallel_mode=ddp \
+    --train_type=seq \
+    --data_root_path=/path/to/dataset \
+    --param_path=/path/to/model \
+    --batch_per_device=4 \
+    --grad_accum_steps=8 \
+    --warmup_ratio=0.05 \
+    --max_lr=1e-4 \
+    --max_grad_norm=1.0 \
+    --adamw_beta1=0.9 \
+    --adamw_beta2=0.95 \
+    --adamw_weight_decay=0.01 \
+    --window_size=2048 \
+    --ckpt_interval=10000 \
+    --ckpt_dir=./checkpoint \
+    --random_seed=3407 \
+    --label_smoothing=0.05 \
+    > out.log 2> err.log &
+```
+
+Full parameter reference at [params.md](params.md).
+
+> Document Update Time: 2026-05-30

astrai/__init__.py

@@ -0,0 +1,34 @@
+__version__ = "1.3.7"
+__author__ = "ViperEkura"
+
+from astrai.config import (
+    AutoRegressiveLMConfig,
+    EncoderConfig,
+    TrainConfig,
+)
+from astrai.dataset import DatasetFactory
+from astrai.factory import BaseFactory
+from astrai.inference import (
+    GenerationRequest,
+    InferenceEngine,
+)
+from astrai.model import AutoModel, AutoRegressiveLM
+from astrai.tokenize import AutoTokenizer
+from astrai.trainer import CallbackFactory, SchedulerFactory, StrategyFactory, Trainer
+
+__all__ = [
+    "AutoRegressiveLM",
+    "AutoRegressiveLMConfig",
+    "EncoderConfig",
+    "TrainConfig",
+    "DatasetFactory",
+    "AutoTokenizer",
+    "GenerationRequest",
+    "InferenceEngine",
+    "Trainer",
+    "CallbackFactory",
+    "StrategyFactory",
+    "SchedulerFactory",
+    "BaseFactory",
+    "AutoModel",
+]

astrai/config/__init__.py

@@ -0,0 +1,25 @@
+from astrai.config.model_config import (
+    AutoRegressiveLMConfig,
+    BaseModelConfig,
+    ConfigFactory,
+    EncoderConfig,
+)
+from astrai.config.preprocess_config import (
+    InputConfig,
+    OutputConfig,
+    PipelineConfig,
+    ProcessingConfig,
+)
+from astrai.config.train_config import TrainConfig
+
+__all__ = [
+    "BaseModelConfig",
+    "AutoRegressiveLMConfig",
+    "EncoderConfig",
+    "ConfigFactory",
+    "TrainConfig",
+    "InputConfig",
+    "OutputConfig",
+    "PipelineConfig",
+    "ProcessingConfig",
+]

astrai/config/base.py

@@ -0,0 +1,98 @@
+import json
+from dataclasses import MISSING, dataclass, fields
+from pathlib import Path
+from typing import Any, Dict, Optional, Self, Union, get_type_hints
+
+
+@dataclass
+class BaseConfig:
+    def to_dict(self) -> Dict[str, Any]:
+        d = {}
+        for fld in fields(self):
+            v = getattr(self, fld.name)
+            if isinstance(v, (str, int, float, bool)):
+                d[fld.name] = v
+            elif v is None:
+                d[fld.name] = None
+            elif isinstance(v, (dict, list, tuple)):
+                try:
+                    val = list(v) if isinstance(v, tuple) else v
+                    json.dumps(val)
+                    d[fld.name] = val
+                except (TypeError, ValueError):
+                    pass
+            elif isinstance(v, BaseConfig):
+                d[fld.name] = v.to_dict()
+            elif hasattr(v, "__dataclass_fields__"):
+                sub = {}
+                for f in fields(v):
+                    a = getattr(v, f.name)
+                    sub[f.name] = list(a) if isinstance(a, tuple) else a
+                d[fld.name] = sub
+        return d
+
+    @classmethod
+    def from_dict(cls, d: Dict[str, Any]) -> Self:
+        hints = get_type_hints(cls)
+        inst = cls.__new__(cls)
+        for fld in fields(cls):
+            if fld.name in d:
+                v = d[fld.name]
+                target = cls._unwrap_optional(hints.get(fld.name))
+                if target is not None:
+                    try:
+                        v = cls._coerce(v, target)
+                    except (TypeError, ValueError):
+                        pass
+                object.__setattr__(inst, fld.name, v)
+            elif fld.default is not MISSING:
+                object.__setattr__(inst, fld.name, fld.default)
+            elif fld.default_factory is not MISSING:
+                object.__setattr__(inst, fld.name, fld.default_factory())
+            else:
+                object.__setattr__(inst, fld.name, None)
+        return inst
+
+    @staticmethod
+    def _unwrap_optional(tp) -> Optional[type]:
+        if tp is None:
+            return None
+        origin = getattr(tp, "__origin__", None)
+        if origin is not None:
+            args = getattr(tp, "__args__", ())
+            non_none = [a for a in args if a is not type(None)]
+            return non_none[0] if non_none else None
+        return tp
+
+    @staticmethod
+    def _coerce(value: Any, target_type: type) -> Any:
+        if target_type is bool and isinstance(value, bool):
+            return value
+        if (
+            target_type is int
+            and isinstance(value, (int, float))
+            and not isinstance(value, bool)
+        ):
+            return int(value)
+        if (
+            target_type is float
+            and isinstance(value, (int, float))
+            and not isinstance(value, bool)
+        ):
+            return float(value)
+        if target_type is str and isinstance(value, str):
+            return value
+        if isinstance(value, target_type):
+            return value
+        if isinstance(value, dict) and issubclass(target_type, BaseConfig):
+            return target_type.from_dict(value)
+        raise TypeError
+
+    @classmethod
+    def from_json(cls, path: Union[str, Path]) -> Self:
+        with open(path, "r", encoding="utf-8") as f:
+            return cls.from_dict(json.load(f))
+
+    def to_json(self, path: Union[str, Path]):
+        with open(path, "w", encoding="utf-8") as f:
+            json.dump(self.to_dict(), f, indent=2, ensure_ascii=False)

astrai/config/model_config.py

@@ -0,0 +1,92 @@
+import json
+from dataclasses import dataclass
+from typing import Any, Dict, Optional, Self
+
+from astrai.config.base import BaseConfig
+from astrai.factory import BaseFactory
+
+
+class ConfigFactory(BaseFactory[BaseConfig]):
+    """Factory that dispatches config classes by ``model_type``."""
+
+    @classmethod
+    def load(cls, raw: Dict[str, Any]) -> BaseConfig:
+        model_type = raw.get("model_type") or "autoregressive_lm"
+        config_cls = cls.get_component_class(model_type)
+        return config_cls.from_dict(raw)
+
+
+@dataclass
+class BaseModelConfig(BaseConfig):
+    """Base config with ``model_type`` dispatch and file I/O."""
+
+    model_type: Optional[str] = None
+
+    @classmethod
+    def from_file(cls, config_path: str) -> Self:
+        with open(config_path, "r") as f:
+            raw: Dict[str, Any] = json.load(f)
+        return cls.from_dict(raw)
+
+    def to_file(self, config_path: str):
+        d = self.to_dict()
+        config_dict = {k: v for k, v in d.items() if v is not None}
+        with open(config_path, "w") as f:
+            json.dump(config_dict, f, indent=4)
+
+
+@dataclass
+@ConfigFactory.register("autoregressive_lm")
+class AutoRegressiveLMConfig(BaseModelConfig):
+    """Configuration for autoregressive language model."""
+
+    vocab_size: Optional[int] = None
+    dim: Optional[int] = None
+    n_layers: Optional[int] = None
+    norm_eps: Optional[float] = None
+    dim_ffn: Optional[int] = None
+    tie_weight: Optional[bool] = None
+
+    max_len: Optional[int] = None
+    rope_theta: Optional[float] = None
+    rope_scaling: Optional[dict] = None
+
+    attn_type: str = "gqa"
+    n_heads: Optional[int] = None
+    n_kv_heads: Optional[int] = None
+    use_qk_norm: Optional[bool] = None
+    use_gated_attention: Optional[bool] = None
+
+    kv_lora_rank: Optional[int] = None
+    qk_nope_head_dim: Optional[int] = None
+    qk_rope_head_dim: Optional[int] = None
+
+    ffn_type: str = "mlp"
+    n_routed_experts: Optional[int] = None
+    n_shared_experts: Optional[int] = None
+    n_activated_experts: Optional[int] = None
+    topk_method: Optional[str] = None
+
+
+@dataclass
+@ConfigFactory.register("embedding")
+class EncoderConfig(BaseModelConfig):
+    """Configuration for embedding encoder model."""
+
+    vocab_size: Optional[int] = None
+    dim: Optional[int] = None
+    n_layers: Optional[int] = None
+    norm_eps: Optional[float] = None
+    dim_ffn: Optional[int] = None
+
+    max_len: Optional[int] = None
+    rope_theta: Optional[float] = None
+    rope_scaling: Optional[dict] = None
+
+    n_heads: Optional[int] = None
+    n_kv_heads: Optional[int] = None
+    use_qk_norm: Optional[bool] = None
+    use_gated_attention: Optional[bool] = None
+
+    pooling_type: Optional[str] = None
+    normalize_embeddings: Optional[bool] = None

astrai/config/preprocess_config.py

@@ -0,0 +1,37 @@
+"""Pipeline configuration for JSONL preprocessing."""
+
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional
+
+from astrai.config.base import BaseConfig
+
+
+@dataclass
+class InputConfig(BaseConfig):
+    sections: Optional[List[Dict]] = None
+
+
+@dataclass
+class ProcessingConfig(BaseConfig):
+    max_seq_len: int = 2048
+    min_chars: int = 50
+    max_chars: int = 2_000_000
+    max_items: Optional[int] = None
+
+
+@dataclass
+class OutputConfig(BaseConfig):
+    domain_key: Optional[str] = None
+    storage_format: str = "bin"
+    max_tokens_per_shard: int = 100_000_000
+    dtype: Dict[str, str] = field(default_factory=dict)
+
+
+@dataclass
+class PipelineConfig(BaseConfig):
+    version: int = 1
+    input: InputConfig = field(default_factory=InputConfig)
+    mask: Dict[str, str] = field(default_factory=dict)
+    mask_default: str = "mask"
+    preprocessing: ProcessingConfig = field(default_factory=ProcessingConfig)
+    output: OutputConfig = field(default_factory=OutputConfig)

astrai/config/train_config.py

@@ -0,0 +1,140 @@
+from dataclasses import dataclass, field, fields
+from typing import Callable, List, Optional
+
+import torch.nn as nn
+from torch.optim import Optimizer
+from torch.optim.lr_scheduler import LRScheduler
+from torch.utils.data import Dataset
+
+from astrai.config.base import BaseConfig
+from astrai.model.components.lora import LoRAConfig
+
+
+def required(**kw):
+    return {"required": True, **kw}
+
+
+@dataclass
+class TrainConfig(BaseConfig):
+    # basic setting
+    model_fn: Callable[[], nn.Module] = field(
+        default=None, metadata=required(help="Model factory for training.")
+    )
+    strategy: str = field(default=None, metadata=required(help="Training strategy."))
+    dataset: Dataset = field(
+        default=None, metadata=required(help="Dataset for training.")
+    )
+    optimizer_fn: Callable[[nn.Module], Optimizer] = field(
+        default=None, metadata=required(help="Optimizer factory for training.")
+    )
+    scheduler_fn: Callable[[Optimizer], LRScheduler] = field(
+        default=None, metadata=required(help="Scheduler factory for training.")
+    )
+    n_epoch: int = field(default=1, metadata={"help": "Number of epochs for training."})
+    batch_per_device: int = field(
+        default=4, metadata={"help": "Batch size per device."}
+    )
+    grad_accum_steps: int = field(
+        default=1, metadata={"help": "Number of iterations between steps."}
+    )
+    max_grad_norm: float = field(
+        default=1.0, metadata={"help": "Maximum gradient norm."}
+    )
+    gradient_checkpointing_modules: list = field(
+        default_factory=list,
+        metadata={"help": "Module types to enable activation checkpointing for."},
+    )
+
+    # 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."}
+    )
+    ckpt_dir: str = field(
+        default="./checkpoint", metadata={"help": "Checkpoint directory."}
+    )
+    ckpt_interval: int = field(
+        default=5000, metadata={"help": "Number of iterations between checkpoints."}
+    )
+
+    # lora setting
+    lora: Optional[LoRAConfig] = field(
+        default=None,
+        metadata={"help": "LoRA config. None means full fine-tuning."},
+    )
+
+    # metric setting
+    log_dir: str = field(
+        default="./checkpoint/logs", metadata={"help": "Directory for metric logs."}
+    )
+    log_interval: int = field(
+        default=100,
+        metadata={"help": "Number of batch iterations between metric logs."},
+    )
+    metrics: List[str] = field(
+        default_factory=lambda: ["loss", "lr"],
+        metadata={"help": "Metrics to record during training."},
+    )
+
+    # dataloader setting
+    random_seed: int = field(default=3407, metadata={"help": "Random seed."})
+    num_workers: int = field(
+        default=0, metadata={"help": "Number of workers for dataloader."}
+    )
+    prefetch_factor: Optional[int] = field(
+        default=None, metadata={"help": "Prefetch factor for dataloader."}
+    )
+    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_mode: str = field(
+        default="none",
+        metadata={"help": "Parallel strategy: none, ddp, fsdp."},
+    )
+    start_method: str = field(
+        default="spawn",
+        metadata={"help": "Multiprocessing start method (spawn/fork/forkserver)."},
+    )
+
+    # others
+    device_type: str = field(
+        default="cuda", metadata={"help": "Device type for distributed training."}
+    )
+    val_dataset: Optional[Dataset] = field(
+        default=None, metadata={"help": "Dataset for validation."}
+    )
+    val_step: int = field(
+        default=1000,
+        metadata={"help": "Number of optimizer steps between validation runs."},
+    )
+
+    executor_kwargs: dict = field(
+        default_factory=dict,
+        metadata={"help": "Extra kwargs passed to ExecutorFactory.create()."},
+    )
+    extra_kwargs: dict = field(
+        default_factory=dict, metadata={"help": "Other arguments."}
+    )
+
+    def __post_init__(self):
+        self.validate()
+
+    def validate(self):
+        for fld in fields(self):
+            if fld.metadata.get("required") and getattr(self, fld.name) is None:
+                raise ValueError(f"TrainConfig.{fld.name} is required but got None.")

astrai/dataset/__init__.py

@@ -0,0 +1,31 @@
+from astrai.dataset.dataset import (
+    BaseDataset,
+    DatasetFactory,
+)
+from astrai.dataset.sampler import ResumableDistributedSampler
+from astrai.dataset.storage import (
+    H5Store,
+    MmapStore,
+    Store,
+    StoreFactory,
+    detect_format,
+    load_bin,
+    load_h5,
+    save_bin,
+    save_h5,
+)
+
+__all__ = [
+    "BaseDataset",
+    "DatasetFactory",
+    "Store",
+    "StoreFactory",
+    "H5Store",
+    "MmapStore",
+    "detect_format",
+    "save_h5",
+    "load_h5",
+    "save_bin",
+    "load_bin",
+    "ResumableDistributedSampler",
+]

astrai/dataset/dataset.py

@@ -0,0 +1,308 @@
+"""Dataset implementations with factory pattern for training."""
+
+from abc import ABC, abstractmethod
+from typing import Dict, List, Optional
+
+import torch
+from torch import Tensor
+from torch.utils.data import Dataset
+
+from astrai.dataset.storage import (
+    Store,
+    StoreFactory,
+    detect_format,
+)
+from astrai.factory import BaseFactory
+
+
+class BaseDataset(Dataset, ABC):
+    """Abstract base class for all dataset types.
+
+    Implements common functionality for window-based data fetching.
+    Uses a storage abstraction for format-agnostic data loading.
+    """
+
+    def __init__(self, window_size: int, stride: int):
+        super().__init__()
+        self.window_size = window_size
+        self.stride = stride
+        self.storage: Optional[Store] = None
+
+    @property
+    def required_keys(self) -> List[str]:
+        """Return required storage keys for this dataset type.
+
+        Subclasses should override to specify expected keys.
+        """
+        return []
+
+    def _validate_keys(self):
+        if not self.required_keys:
+            return
+        actual_keys = set(self.storage.keys)
+        missing = [k for k in self.required_keys if k not in actual_keys]
+        if missing:
+            raise KeyError(
+                f"Dataset {type(self).__name__} requires keys {self.required_keys}, "
+                f"but storage at {self._load_path} only has {sorted(actual_keys)}. "
+                f"Missing: {missing}"
+            )
+
+    def load(self, load_path: str, storage_type: Optional[str] = None):
+        """Load dataset from the given path.
+
+        Auto-detects the storage format if not specified.
+
+        Args:
+            load_path: Path to the data directory or file
+            storage_type: Force a specific storage type ("h5", "bin"),
+                          or None for auto-detection
+
+        Raises:
+            KeyError: If the loaded storage is missing required keys.
+        """
+        if storage_type is None:
+            storage_type = detect_format(load_path)
+        self.storage = StoreFactory.create(storage_type)
+        self._load_path = load_path
+        self.storage.load(load_path)
+        self._validate_keys()
+
+    @property
+    def count(self) -> int:
+        """Return the total number of raw elements (tokens) in the dataset."""
+        if self.storage is None:
+            return 0
+        return len(self.storage)
+
+    @property
+    def keys(self) -> List[str]:
+        """Return the available data keys."""
+        if self.storage is None:
+            return []
+        return self.storage.keys
+
+    def get_index(self, index: int) -> tuple:
+        """Calculate begin and end indices for a sample.
+
+        Args:
+            index: Sample index
+
+        Returns:
+            Tuple of (begin_idx, end_idx)
+        """
+        if self.storage is None:
+            raise RuntimeError("Dataset not loaded, call load() first")
+        total = len(self.storage)
+        if total <= self.window_size:
+            raise ValueError(
+                f"Data too short: {total} tokens <= window_size {self.window_size}"
+            )
+
+        begin_idx = min(index * self.stride, total - 1 - self.window_size)
+        end_idx = min(begin_idx + self.window_size, total - 1)
+
+        return begin_idx, end_idx
+
+    @abstractmethod
+    def __getitem__(self, index: int) -> Dict[str, Tensor]:
+        """Get a single sample by index.
+
+        Must be implemented by subclasses.
+        """
+        raise NotImplementedError
+
+    def __len__(self) -> int:
+        if self.storage is None:
+            return 0
+        total = len(self.storage)
+        if total <= self.window_size:
+            return 0
+        return (total - 1 - self.window_size) // self.stride + 1
+
+
+class DatasetFactory(BaseFactory["BaseDataset"]):
+    """Factory class for creating dataset instances.
+
+    Supports decorator-based registration for extensible dataset types.
+    All default dataset types (seq, sft, dpo, grpo) are registered automatically
+    when their classes are defined with the decorator.
+
+    Example usage:
+        @DatasetFactory.register("custom")
+        class CustomDataset(BaseDataset):
+            ...
+
+        dataset = DatasetFactory.create("custom", window_size, stride)
+    """
+
+    @classmethod
+    def _validate_component(cls, dataset_cls: type):
+        """Validate that the dataset class inherits from BaseDataset."""
+        if not issubclass(dataset_cls, BaseDataset):
+            raise TypeError(f"{dataset_cls.__name__} must inherit from BaseDataset")
+
+    @classmethod
+    def create(cls, train_type: str, window_size: int, stride: int) -> "BaseDataset":
+        """Create a dataset instance.
+
+        Args:
+            train_type: Type of training ("seq", "sft", "dpo", "grpo")
+            window_size: Window size for data sampling
+            stride: Stride between consecutive samples
+
+        Returns:
+            Dataset instance
+        """
+        return super().create(train_type, window_size, stride)
+
+    @classmethod
+    def load(
+        cls,
+        train_type: str,
+        load_path: str,
+        window_size: int,
+        stride: Optional[int] = None,
+        storage_type: Optional[str] = None,
+    ) -> "BaseDataset":
+        """Create and load a dataset in one step.
+
+        Args:
+            train_type: Type of training dataset
+            load_path: Path to the data file
+            window_size: Window size for data sampling
+            stride: Stride between consecutive samples (default: same as window_size)
+            storage_type: Storage type ("h5", "bin") or None for auto-detection
+
+        Returns:
+            Loaded dataset instance
+        """
+        if stride is None:
+            stride = window_size
+
+        dataset = cls.create(train_type, window_size, stride)
+        dataset.load(load_path, storage_type=storage_type)
+
+        return dataset
+
+    @classmethod
+    def available_types(cls) -> list:
+        """Return list of registered dataset type names."""
+        return cls.list_registered()
+
+
+@DatasetFactory.register("seq")
+class SEQDataset(BaseDataset):
+    """Dataset for sequential next-token prediction training."""
+
+    def __init__(self, window_size: int, stride: int):
+        super().__init__(window_size, stride)
+
+    @property
+    def required_keys(self) -> List[str]:
+        return ["sequence"]
+
+    def _fetch_data(self, begin_idx: int, end_idx: int) -> Tensor:
+        return self.storage.fetch(begin_idx, end_idx, "sequence")
+
+    def __getitem__(self, index):
+        begin_idx, end_idx = self.get_index(index)
+
+        x = self._fetch_data(begin_idx, end_idx).to(dtype=torch.long)
+        y = self._fetch_data(begin_idx + 1, end_idx + 1).to(dtype=torch.long)
+
+        return {"input_ids": x, "target_ids": y}
+
+
+@DatasetFactory.register("sft")
+class SFTDataset(BaseDataset):
+    """Dataset for supervised fine-tuning with loss masking."""
+
+    def __init__(self, window_size: int, stride: int):
+        super().__init__(window_size, stride)
+
+    @property
+    def required_keys(self) -> List[str]:
+        return ["sequence", "loss_mask"]
+
+    def _fetch_data(self, begin_idx: int, end_idx: int, key: str) -> Tensor:
+        return self.storage.fetch(begin_idx, end_idx, key)
+
+    def __getitem__(self, index):
+        begin_idx, end_idx = self.get_index(index)
+
+        x = self._fetch_data(begin_idx, end_idx, "sequence").to(dtype=torch.long)
+        y = self._fetch_data(begin_idx + 1, end_idx + 1, "sequence").to(
+            dtype=torch.long
+        )
+        loss_mask = self._fetch_data(begin_idx + 1, end_idx + 1, "loss_mask").to(
+            dtype=torch.bool
+        )
+
+        return {"input_ids": x, "target_ids": y, "loss_mask": loss_mask}
+
+
+@DatasetFactory.register("dpo")
+class DPODataset(BaseDataset):
+    """Dataset for Direct Preference Optimization training."""
+
+    def __init__(self, window_size: int, stride: int):
+        super().__init__(window_size, stride)
+
+    @property
+    def required_keys(self) -> List[str]:
+        return ["chosen", "rejected", "chosen_mask", "rejected_mask"]
+
+    def _fetch_data(self, begin_idx: int, end_idx: int, key: str) -> Tensor:
+        return self.storage.fetch(begin_idx, end_idx, key)
+
+    def __getitem__(self, index: int):
+        begin_idx, end_idx = self.get_index(index)
+
+        chosen = self._fetch_data(begin_idx, end_idx, "chosen").to(dtype=torch.long)
+        rejected = self._fetch_data(begin_idx, end_idx, "rejected").to(dtype=torch.long)
+        chosen_mask = self._fetch_data(begin_idx, end_idx, "chosen_mask").to(
+            dtype=torch.bool
+        )
+        rejected_mask = self._fetch_data(begin_idx, end_idx, "rejected_mask").to(
+            dtype=torch.bool
+        )
+
+        return {
+            "chosen": chosen,
+            "rejected": rejected,
+            "chosen_mask": chosen_mask,
+            "rejected_mask": rejected_mask,
+        }
+
+
+@DatasetFactory.register("grpo")
+class GRPODataset(BaseDataset):
+    """Dataset for Group Relative Policy Optimization training."""
+
+    def __init__(self, window_size: int, stride: int):
+        super().__init__(window_size, stride)
+
+    @property
+    def required_keys(self) -> List[str]:
+        return ["prompts", "responses", "masks", "rewards"]
+
+    def _fetch_data(self, begin_idx: int, end_idx: int, key: str) -> Tensor:
+        return self.storage.fetch(begin_idx, end_idx, key)
+
+    def __getitem__(self, index: int) -> Dict[str, Tensor]:
+        begin_idx, end_idx = self.get_index(index)
+
+        prompts = self._fetch_data(begin_idx, end_idx, "prompts").to(dtype=torch.long)
+        responses = self._fetch_data(begin_idx, end_idx, "responses").to(
+            dtype=torch.long
+        )
+        masks = self._fetch_data(begin_idx, end_idx, "masks").to(dtype=torch.bool)
+        rewards = self._fetch_data(begin_idx, end_idx, "rewards")
+
+        return {
+            "prompts": prompts,
+            "responses": responses,
+            "masks": masks,
+            "rewards": rewards,
+        }

astrai/dataset/sampler.py

@@ -0,0 +1,84 @@
+from typing import Optional
+
+import torch
+import torch.distributed as dist
+from torch.utils.data import Dataset, Sampler
+
+
+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.iter = self.iter % self.num_samples_per_replica
+
+        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
+
+    @property
+    def _remaining(self):
+        remaining = self.num_samples_per_replica - self.iter
+        return max(remaining, 0)
+
+    def __len__(self):
+        return self._remaining

astrai/dataset/storage.py

@@ -0,0 +1,264 @@
+"""Storage backends for different data formats.
+
+Layers:
+  - I/O layer:       save_* / load_* functions, read/write raw files (HDF5/bin)
+                      return Dict[str, List[Tensor]] — format-specific, no state
+  - Store (ABC):     central abstraction, normalizes multi-segment into
+                      Dict[str, List[Tensor]] per key via _normalize(),
+                      fetch() uses bisect across segments — no forced concat
+  - Dataset layer:   BaseDataset owns a Store, only calls store.fetch(begin, end, key)
+
+Key properties:
+  - Multi-segment:   segments kept as-is, no forced concatenation — safe for
+                      datasets larger than RAM
+  - Explicit length: _length = min(total elements across keys), set at load,
+                      __len__ returns O(1)
+  - Zero-copy mmap:  MmapStore wraps np.memmap(mode="r"), all DataLoader
+                      workers share OS page-cache pages
+"""
+
+import bisect
+import json
+import os
+from abc import ABC, abstractmethod
+from pathlib import Path
+from typing import Dict, List, Union
+
+import h5py
+import numpy as np
+import torch
+from torch import Tensor
+
+from astrai.factory import BaseFactory
+
+
+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
+
+
+def save_bin(file_path: str, tensor_group: Dict[str, List[Tensor]]):
+    os.makedirs(file_path, exist_ok=True)
+    meta = {}
+    for key, tensors in tensor_group.items():
+        cat = torch.cat(tensors, dim=0)
+        meta[key] = {"shape": list(cat.shape), "dtype": str(cat.dtype).split(".")[-1]}
+        np.asarray(cat.cpu().numpy()).tofile(os.path.join(file_path, f"{key}.bin"))
+    with open(os.path.join(file_path, "meta.json"), "w") as f:
+        json.dump(meta, f)
+
+
+def load_bin(file_path: str) -> Dict[str, List[Tensor]]:
+    with open(os.path.join(file_path, "meta.json"), "r") as f:
+        meta = json.load(f)
+    segments: Dict[str, List[Tensor]] = {}
+    for key, info in meta.items():
+        arr = np.memmap(
+            os.path.join(file_path, f"{key}.bin"),
+            dtype=info["dtype"],
+            mode="r+",
+            shape=tuple(info["shape"]),
+        )
+        segments[key] = [torch.from_numpy(arr)]
+    return segments
+
+
+def detect_format(load_path: str) -> str:
+    """Auto-detect storage format from files in the directory.
+
+    Args:
+        load_path: Directory or file path
+
+    Returns:
+        Format string ("h5" or "bin")
+
+    Raises:
+        FileNotFoundError: If no supported data files are found
+    """
+    root = Path(load_path)
+    if root.is_file():
+        suffix = root.suffix.lower()
+        if suffix in (".h5", ".hdf5"):
+            return "h5"
+        raise ValueError(f"Unsupported file format: {suffix}")
+
+    h5_files = list(root.rglob("*.h5")) + list(root.rglob("*.hdf5"))
+    if h5_files:
+        return "h5"
+    bin_files = list(root.rglob("*.bin"))
+    if bin_files:
+        has_meta = (root / "meta.json").exists() or len(
+            list(root.rglob("meta.json"))
+        ) > 0
+        if has_meta:
+            return "bin"
+    raise FileNotFoundError(f"No supported data files found at {load_path}")
+
+
+class Store(ABC):
+    """String keys -> segmented tensors with ``fetch(begin, end, keys)``.
+
+    Each key maps to one or more tensor segments (no forced concatenation).
+    ``len(store)`` returns ``self._length`` (explicit, O(1)), the minimum
+    total element count across all keys.
+
+    Subclasses fill ``self._data`` and ``self._cum`` during ``load()``
+    via ``_normalize()``.
+    """
+
+    def __init__(self):
+        self._data: Dict[str, List[Tensor]] = {}
+        self._cum: Dict[str, List[int]] = {}
+        self._length: int = 0
+
+    @abstractmethod
+    def load(self, path: str) -> None:
+        raise NotImplementedError
+
+    @property
+    def keys(self) -> List[str]:
+        return list(self._data.keys())
+
+    def __len__(self) -> int:
+        return self._length
+
+    def fetch(
+        self,
+        begin: int,
+        end: int,
+        keys: Union[str, List[str]],
+    ):
+        if not self._data:
+            raise RuntimeError("Store not loaded")
+        if not (0 <= begin < self._length and 0 <= end <= self._length):
+            raise ValueError(
+                f"Index out of bounds: begin={begin}, end={end}, length={self._length}"
+            )
+        if isinstance(keys, str):
+            return self._fetch_key(keys, begin, end)
+        return {k: self._fetch_key(k, begin, end) for k in keys}
+
+    def _fetch_key(self, key: str, begin: int, end: int) -> Tensor:
+        """Fetch slice [begin, end) across potentially multiple segments."""
+        segments = self._data[key]
+        cum = self._cum[key]
+        seg_start = bisect.bisect_right(cum, begin)
+        seg_end = bisect.bisect_left(cum, end)
+
+        results = []
+        for i in range(seg_start, seg_end + 1):
+            prev = cum[i - 1] if i > 0 else 0
+            s = max(begin - prev, 0)
+            e = min(end - prev, segments[i].shape[0])
+            results.append(segments[i][s:e])
+
+        return results[0] if len(results) == 1 else torch.cat(results, dim=0)
+
+    def _normalize(self, raw: Dict[str, List[Tensor]]):
+        """Register segments and pre-compute cumulative lengths.
+
+        Does NOT concatenate — segments are kept as-is to avoid OOM on
+        large datasets.  Sets ``self._length`` to the minimum total
+        element count across all keys.
+        """
+        for key, tensors in raw.items():
+            self._data[key] = tensors
+            cum = []
+            total = 0
+            for t in tensors:
+                total += t.shape[0]
+                cum.append(total)
+            self._cum[key] = cum
+        self._length = (
+            min((cum[-1] if cum else 0) for cum in self._cum.values())
+            if self._cum
+            else 0
+        )
+
+
+class StoreFactory(BaseFactory["Store"]):
+    """Factory for creating Store instances by type name.
+
+    Example::
+
+        @StoreFactory.register("custom")
+        class CustomStore(Store):
+            ...
+    """
+
+    @classmethod
+    def _validate_component(cls, store_cls: type):
+        if not issubclass(store_cls, Store):
+            raise TypeError(f"{store_cls.__name__} must inherit from Store")
+
+
+@StoreFactory.register("h5")
+class H5Store(Store):
+    """HDF5-based storage backend (pre-tokenized data)."""
+
+    def load(self, path: str):
+        self._normalize(load_h5(path))
+
+
+@StoreFactory.register("bin")
+class MmapStore(Store):
+    """Memory-mapped binary storage backend.
+
+    Each key is a single .bin file backed by ``np.memmap(mode="r")``.
+    No per-process memory duplication — all DataLoader workers share the
+    same OS page-cache pages.
+
+    Format on disk::
+
+        data_root/
+          meta.json          # {key: {shape, dtype}, ...}
+          <key>.bin          # raw numpy array, one per key
+    """
+
+    def load(self, path: str):
+        self._mmap_refs = []
+        root = Path(path)
+        all_raw: Dict[str, List[Tensor]] = {}
+        meta_paths = list(root.rglob("meta.json"))
+        for meta_path in meta_paths:
+            raw = load_bin(str(meta_path.parent))
+            for key, tensors in raw.items():
+                if key not in all_raw:
+                    all_raw[key] = []
+                all_raw[key].extend(tensors)
+        if not meta_paths:
+            raise FileNotFoundError(f"No meta.json found under {path}")
+        self._normalize(all_raw)
+        for tensors in self._data.values():
+            self._mmap_refs.extend(tensors)

astrai/factory.py

@@ -0,0 +1,226 @@
+"""Base factory class for extensible component registration."""
+
+import inspect
+from abc import ABC
+from typing import Callable, Dict, Generic, List, Optional, Tuple, Type, TypeVar
+
+T = TypeVar("T")
+
+
+class Registry:
+    """Flexible registry for component classes with category and priority support.
+
+    This registry stores component classes with optional metadata (category, priority).
+    It provides methods for registration, retrieval, and listing with filtering.
+    """
+
+    def __init__(self):
+        self._entries = {}  # name -> (component_cls, category, priority)
+
+    def register(
+        self,
+        name: str,
+        component_cls: Type,
+        category: Optional[str] = None,
+        priority: int = 0,
+    ):
+        """Register a component class with optional category and priority."""
+        if name in self._entries:
+            raise ValueError(f"Component '{name}' is already registered")
+        self._entries[name] = (component_cls, category, priority)
+
+    def get(self, name: str) -> Type:
+        """Get component class by name."""
+        if name not in self._entries:
+            raise KeyError(f"Component '{name}' not found in registry")
+        return self._entries[name][0]
+
+    def get_with_metadata(self, name: str) -> Tuple[Type, Optional[str], int]:
+        """Get component class with its metadata."""
+        entry = self._entries.get(name)
+        if entry is None:
+            raise KeyError(f"Component '{name}' not found in registry")
+        return entry
+
+    def contains(self, name: str) -> bool:
+        """Check if a name is registered."""
+        return name in self._entries
+
+    def list_names(self) -> List[str]:
+        """Return list of registered component names."""
+        return sorted(self._entries.keys())
+
+    def list_by_category(self, category: str) -> List[str]:
+        """Return names of components belonging to a specific category."""
+        return sorted(
+            name for name, (_, cat, _) in self._entries.items() if cat == category
+        )
+
+    def list_by_priority(self, reverse: bool = False) -> List[str]:
+        """Return names sorted by priority (default ascending)."""
+        return sorted(
+            self._entries.keys(),
+            key=lambda name: self._entries[name][2],
+            reverse=reverse,
+        )
+
+    def entries(self) -> Dict[str, Tuple[Type, Optional[str], int]]:
+        """Return raw entries dictionary."""
+        return self._entries.copy()
+
+
+class BaseFactory(ABC, Generic[T]):
+    """Generic factory class for component registration and creation.
+
+    This base class provides a decorator-based registration pattern
+    for creating extensible component factories.
+
+    Example usage:
+        class MyFactory(BaseFactory[MyBaseClass]):
+            pass
+
+        @MyFactory.register("custom")
+        class CustomComponent(MyBaseClass):
+            ...
+
+        component = MyFactory.create("custom", *args, **kwargs)
+    """
+
+    _registry: Registry
+
+    def __init_subclass__(cls, **kwargs):
+        super().__init_subclass__(**kwargs)
+        cls._registry = Registry()
+
+    @classmethod
+    def register(
+        cls, name: str, category: Optional[str] = None, priority: int = 0
+    ) -> Callable[[Type[T]], Type[T]]:
+        """Decorator to register a component class with optional category and priority.
+
+        Args:
+            name: Registration name for the component
+            category: Optional category for grouping components
+            priority: Priority for ordering (default 0)
+
+        Returns:
+            Decorator function that registers the component class
+
+        Raises:
+            TypeError: If the decorated class doesn't inherit from the base type
+        """
+
+        def decorator(component_cls: Type[T]) -> Type[T]:
+            cls._validate_component(component_cls)
+            cls._registry.register(
+                name, component_cls, category=category, priority=priority
+            )
+            return component_cls
+
+        return decorator
+
+    @classmethod
+    def create(cls, name: str, *args, **kwargs) -> T:
+        """Create a component instance by name.
+
+        Filters kwargs to match the component's __init__ signature,
+        so components don't need to declare **kwargs just to absorb
+        parameters meant for other components.
+
+        Args:
+            name: Registered name of the component
+            *args: Positional arguments passed to component constructor
+            **kwargs: Keyword arguments passed to component constructor
+
+        Returns:
+            Component instance
+
+        Raises:
+            ValueError: If the component name is not registered
+        """
+        if not cls._registry.contains(name):
+            raise ValueError(
+                f"Unknown component: '{name}'. "
+                f"Supported types: {sorted(cls._registry.list_names())}"
+            )
+        component_cls = cls._registry.get(name)
+        sig = inspect.signature(component_cls.__init__)
+        has_var_kwargs = any(
+            p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values()
+        )
+        if not has_var_kwargs:
+            valid = {
+                p.name
+                for p in sig.parameters.values()
+                if p.name != "self" and p.kind != inspect.Parameter.VAR_KEYWORD
+            }
+            kwargs = {k: v for k, v in kwargs.items() if k in valid}
+        return component_cls(*args, **kwargs)
+
+    @classmethod
+    def _validate_component(cls, component_cls: Type[T]):
+        """Validate that the component class is valid for this factory.
+
+        Override this method in subclasses to add custom validation.
+
+        Args:
+            component_cls: Component class to validate
+
+        Raises:
+            TypeError: If the component class is invalid
+        """
+        pass
+
+    @classmethod
+    def get_component_class(cls, name: str) -> Type[T]:
+        """Get the registered component class by name without instantiating it.
+
+        Args:
+            name: Registered name of the component
+
+        Returns:
+            The component class itself
+
+        Raises:
+            ValueError: If the component name is not registered
+        """
+        if not cls._registry.contains(name):
+            raise ValueError(
+                f"Unknown component: '{name}'. "
+                f"Supported types: {sorted(cls._registry.list_names())}"
+            )
+        return cls._registry.get(name)
+
+    @classmethod
+    def list_registered(cls) -> list:
+        """List all registered component names.
+
+        Returns:
+            List of registered component names
+        """
+        return cls._registry.list_names()
+
+    @classmethod
+    def is_registered(cls, name: str) -> bool:
+        """Check if a component name is registered.
+
+        Args:
+            name: Component name to check
+
+        Returns:
+            True if registered, False otherwise
+        """
+        return cls._registry.contains(name)
+
+    @classmethod
+    def list_by_category(cls, category: str) -> List[str]:
+        """List registered component names in a category."""
+        return cls._registry.list_by_category(category)
+
+    @classmethod
+    def list_by_priority(cls, reverse: bool = False) -> List[str]:
+        """List registered component names sorted by priority."""
+        return cls._registry.list_by_priority(reverse)
+
+
+__all__ = ["Registry", "BaseFactory"]

astrai/inference/__init__.py

@@ -0,0 +1,85 @@
+"""Inference module for continuous batching.
+
+Layers:
+  - core/:        Core inference loop (cache, executor, scheduler, task)
+  - api/:         HTTP orchestration (ProtocolHandler, server)
+  - protocols/:   Response builders (OpenAI, Anthropic)
+  - transport/:   SSE transport utilities
+  - engine.py:    Facade (InferenceEngine), Value Object (GenerationRequest)
+  - sample.py:    Strategy pattern (TemperatureStrategy, TopKStrategy, TopPStrategy)
+"""
+
+from astrai.inference.api import (
+    AnthropicMessage,
+    ChatCompletionRequest,
+    ChatMessage,
+    GenContext,
+    MessagesRequest,
+    ProtocolHandler,
+    StopChecker,
+    app,
+    run_server,
+)
+from astrai.inference.api.anthropic import AnthropicResponseBuilder
+from astrai.inference.api.openai import OpenAIResponseBuilder
+from astrai.inference.core import (
+    STOP,
+    Allocator,
+    Executor,
+    InferenceScheduler,
+    KVCache,
+    KvcacheView,
+    PagePool,
+    PrefixCache,
+    Storage,
+    Task,
+    TaskManager,
+    TaskStatus,
+    TaskTable,
+    page_hash,
+)
+from astrai.inference.engine import GenerationRequest, InferenceEngine
+from astrai.inference.sample import (
+    BaseSamplingStrategy,
+    SamplingPipeline,
+    TemperatureStrategy,
+    TopKStrategy,
+    TopPStrategy,
+    sample,
+)
+
+__all__ = [
+    "InferenceEngine",
+    "GenerationRequest",
+    "InferenceScheduler",
+    "Executor",
+    "STOP",
+    "Task",
+    "TaskManager",
+    "TaskStatus",
+    "Allocator",
+    "KVCache",
+    "KvcacheView",
+    "PagePool",
+    "PrefixCache",
+    "Storage",
+    "TaskTable",
+    "page_hash",
+    "sample",
+    "BaseSamplingStrategy",
+    "TemperatureStrategy",
+    "TopKStrategy",
+    "TopPStrategy",
+    "SamplingPipeline",
+    "ProtocolHandler",
+    "StopChecker",
+    "GenContext",
+    "OpenAIResponseBuilder",
+    "AnthropicResponseBuilder",
+    "ChatMessage",
+    "ChatCompletionRequest",
+    "AnthropicMessage",
+    "MessagesRequest",
+    "app",
+    "run_server",
+]

astrai/inference/api/__init__.py

@@ -0,0 +1,23 @@
+"""Inference API: protocol handler, stop checker, and FastAPI server."""
+
+from astrai.inference.api.protocol import GenContext, ProtocolHandler, StopChecker
+from astrai.inference.api.server import (
+    AnthropicMessage,
+    ChatCompletionRequest,
+    ChatMessage,
+    MessagesRequest,
+    app,
+    run_server,
+)
+
+__all__ = [
+    "ProtocolHandler",
+    "StopChecker",
+    "GenContext",
+    "AnthropicMessage",
+    "ChatCompletionRequest",
+    "ChatMessage",
+    "MessagesRequest",
+    "app",
+    "run_server",
+]

astrai/inference/api/anthropic.py

@@ -0,0 +1,141 @@
+"""Anthropic message completion response builder."""
+
+import time
+import uuid
+from typing import Any, Dict, List, Tuple, Union
+
+from pydantic import BaseModel
+
+from astrai.inference.api.protocol import (
+    GenContext,
+    ResponseBuilder,
+    StopInfo,
+    sse_event,
+)
+from astrai.inference.engine import InferenceEngine
+
+
+def _extract_text(content: Union[str, List[Dict[str, Any]]]) -> str:
+    if isinstance(content, str):
+        return content
+    if isinstance(content, list):
+        for block in content:
+            if isinstance(block, dict) and block.get("type") == "text":
+                return block.get("text", "")
+    return ""
+
+
+class AnthropicResponseBuilder(ResponseBuilder):
+    def prepare(
+        self, request: BaseModel, engine: InferenceEngine
+    ) -> Tuple[str, GenContext, List[str]]:
+        messages: List[Dict[str, str]] = []
+        system = getattr(request, "system", None)
+        if system:
+            messages.append({"role": "system", "content": system})
+        for m in request.messages:
+            text = _extract_text(m.content)
+            if text:
+                messages.append({"role": m.role, "content": text})
+        prompt = engine.tokenizer.apply_chat_template(messages, tokenize=False)
+        ctx = GenContext(
+            resp_id=f"msg_{uuid.uuid4().hex[:24]}",
+            created=int(time.time()),
+            model=request.model,
+            prompt_tokens=0,
+        )
+        stop_sequences = getattr(request, "stop_sequences", None) or []
+        return prompt, ctx, stop_sequences
+
+    def format_stream_start(self, ctx: GenContext) -> List[str]:
+        return [
+            sse_event(
+                {
+                    "type": "message_start",
+                    "message": {
+                        "id": ctx.resp_id,
+                        "type": "message",
+                        "role": "assistant",
+                        "model": ctx.model,
+                        "content": [],
+                        "usage": {"input_tokens": ctx.prompt_tokens},
+                    },
+                },
+                event="message_start",
+            ),
+            sse_event(
+                {
+                    "type": "content_block_start",
+                    "index": 0,
+                    "content_block": {"type": "text", "text": ""},
+                },
+                event="content_block_start",
+            ),
+        ]
+
+    def format_chunk(self, token: str) -> str:
+        return sse_event(
+            {
+                "type": "content_block_delta",
+                "index": 0,
+                "delta": {"type": "text_delta", "text": token},
+            },
+            event="content_block_delta",
+        )
+
+    def format_stream_end(self, ctx: GenContext, stop: StopInfo) -> List[str]:
+        events: List[str] = []
+        if stop.matched:
+            trimmed = stop.body[: stop.body.rfind(stop.matched)]
+            unyielded = trimmed[len(stop.yielded) :]
+            if unyielded:
+                events.append(
+                    sse_event(
+                        {
+                            "type": "content_block_delta",
+                            "index": 0,
+                            "delta": {"type": "text_delta", "text": unyielded},
+                        },
+                        event="content_block_delta",
+                    )
+                )
+        events.append(
+            sse_event(
+                {"type": "content_block_stop", "index": 0},
+                event="content_block_stop",
+            )
+        )
+        events.append(
+            sse_event(
+                {
+                    "type": "message_delta",
+                    "delta": {
+                        "stop_reason": "stop_sequence" if stop.matched else "end_turn",
+                        "stop_sequence": stop.matched,
+                    },
+                    "usage": {"output_tokens": ctx.completion_tokens},
+                },
+                event="message_delta",
+            )
+        )
+        events.append(sse_event({"type": "message_stop"}, event="message_stop"))
+        return events
+
+    def format_response(
+        self, ctx: GenContext, content: str, stop: StopInfo
+    ) -> Dict[str, Any]:
+        if stop.matched:
+            content = content[: content.rfind(stop.matched)]
+        return {
+            "id": ctx.resp_id,
+            "type": "message",
+            "role": "assistant",
+            "model": ctx.model,
+            "content": [{"type": "text", "text": content}],
+            "stop_reason": "stop_sequence" if stop.matched else "end_turn",
+            "stop_sequence": stop.matched,
+            "usage": {
+                "input_tokens": ctx.prompt_tokens,
+                "output_tokens": ctx.completion_tokens,
+            },
+        }

astrai/inference/api/openai.py

@@ -0,0 +1,140 @@
+"""OpenAI chat completion response builder."""
+
+import logging
+import time
+import uuid
+from typing import Any, Dict, List, Tuple
+
+from pydantic import BaseModel
+
+from astrai.inference.api.protocol import (
+    GenContext,
+    ResponseBuilder,
+    StopInfo,
+    sse_event,
+)
+from astrai.inference.engine import InferenceEngine
+
+logger = logging.getLogger(__name__)
+
+_UNSUPPORTED_PARAMS = (
+    "n",
+    "presence_penalty",
+    "frequency_penalty",
+    "logit_bias",
+    "user",
+)
+
+
+class OpenAIResponseBuilder(ResponseBuilder):
+    def prepare(
+        self, request: BaseModel, engine: InferenceEngine
+    ) -> Tuple[str, GenContext, List[str]]:
+        messages = [{"role": m.role, "content": m.content} for m in request.messages]
+        prompt = engine.tokenizer.apply_chat_template(messages, tokenize=False)
+
+        self._resp_id = f"chatcmpl-{uuid.uuid4().hex[:12]}"
+        self._model = request.model
+
+        for param in _UNSUPPORTED_PARAMS:
+            value = getattr(request, param, None)
+            fields = getattr(type(request), "model_fields", {})
+            default = fields[param].default if param in fields else None
+            if value is not None and value != default:
+                logger.warning(
+                    "ChatCompletionRequest param '%s'=%r is not supported and will be ignored",
+                    param,
+                    value,
+                )
+            if value is not None and value != default:
+                logger.warning(
+                    "ChatCompletionRequest param '%s'=%r is not supported and will be ignored",
+                    param,
+                    value,
+                )
+
+        ctx = GenContext(
+            resp_id=self._resp_id,
+            created=int(time.time()),
+            model=self._model,
+            prompt_tokens=0,
+        )
+        stop = request.stop
+        stop_sequences = (
+            [] if stop is None else [stop] if isinstance(stop, str) else stop
+        )
+        return prompt, ctx, stop_sequences
+
+    def format_stream_start(self, ctx: GenContext) -> List[str]:
+        return [
+            sse_event(
+                {
+                    "id": self._resp_id,
+                    "object": "chat.completion.chunk",
+                    "created": ctx.created,
+                    "model": self._model,
+                    "choices": [
+                        {
+                            "index": 0,
+                            "delta": {"role": "assistant"},
+                            "finish_reason": None,
+                        }
+                    ],
+                }
+            )
+        ]
+
+    def format_chunk(self, token: str) -> str:
+        return sse_event(
+            {
+                "id": self._resp_id,
+                "object": "chat.completion.chunk",
+                "created": 0,
+                "model": self._model,
+                "choices": [
+                    {"index": 0, "delta": {"content": token}, "finish_reason": None}
+                ],
+            }
+        )
+
+    def format_stream_end(self, ctx: GenContext, stop: StopInfo) -> List[str]:
+        return [
+            sse_event(
+                {
+                    "id": self._resp_id,
+                    "object": "chat.completion.chunk",
+                    "created": ctx.created,
+                    "model": self._model,
+                    "choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}],
+                }
+            ),
+            sse_event(
+                {
+                    "prompt_tokens": ctx.prompt_tokens,
+                    "completion_tokens": ctx.completion_tokens,
+                    "total_tokens": ctx.prompt_tokens + ctx.completion_tokens,
+                }
+            ),
+        ]
+
+    def format_response(
+        self, ctx: GenContext, content: str, stop: StopInfo
+    ) -> Dict[str, Any]:
+        return {
+            "id": self._resp_id,
+            "object": "chat.completion",
+            "created": ctx.created,
+            "model": self._model,
+            "choices": [
+                {
+                    "index": 0,
+                    "message": {"role": "assistant", "content": content},
+                    "finish_reason": "stop",
+                }
+            ],
+            "usage": {
+                "prompt_tokens": ctx.prompt_tokens,
+                "completion_tokens": ctx.completion_tokens,
+                "total_tokens": ctx.prompt_tokens + ctx.completion_tokens,
+            },
+        }

astrai/inference/api/protocol.py

@@ -0,0 +1,182 @@
+"""Orchestration layer: ProtocolHandler, StopChecker, GenContext, StopInfo, ResponseBuilder, SSE utils.
+
+ProtocolHandler orchestrates the async generation loop and delegates
+protocol-specific formatting to a ResponseBuilder.
+"""
+
+import json
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Any, AsyncGenerator, Dict, List, Optional, Tuple, Union
+
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+
+from astrai.inference.engine import InferenceEngine
+
+
+def sse_event(data: Dict[str, Any], event: Optional[str] = None) -> str:
+    lines: List[str] = []
+    if event:
+        lines.append(f"event: {event}")
+    lines.append(f"data: {json.dumps(data, ensure_ascii=False)}")
+    lines.append("")
+    return "\n".join(lines)
+
+
+def sse_done() -> str:
+    return "data: [DONE]\n\n"
+
+
+@dataclass
+class GenContext:
+    """Per-generation metadata passed to builder format methods."""
+
+    resp_id: str
+    created: int
+    model: str
+    prompt_tokens: int
+    completion_tokens: int = 0
+
+
+@dataclass
+class StopInfo:
+    """Stop-check result passed to format_stream_end / format_response."""
+
+    matched: Optional[str] = None
+    body: str = ""
+    yielded: str = ""
+
+
+class StopChecker:
+    """Scans accumulated text for stop sequence matches."""
+
+    def __init__(self, sequences: List[str]):
+        self._sequences = [s for s in sequences if s]
+
+    def check(self, text: str) -> Optional[str]:
+        for seq in self._sequences:
+            if seq in text:
+                return seq
+        return None
+
+
+class ResponseBuilder(ABC):
+    """Interface for protocol-specific response formatting.
+
+    A new protocol requires one concrete builder implementing 5 methods.
+    """
+
+    @abstractmethod
+    def prepare(
+        self, request: BaseModel, engine: InferenceEngine
+    ) -> Tuple[str, GenContext, List[str]]:
+        """Return (prompt, ctx, stop_sequences) for a generation request."""
+
+    @abstractmethod
+    def format_stream_start(self, ctx: GenContext) -> List[str]:
+        """SSE events that open the stream."""
+
+    @abstractmethod
+    def format_chunk(self, token: str) -> str:
+        """SSE event for a single generated token."""
+
+    @abstractmethod
+    def format_stream_end(self, ctx: GenContext, stop: StopInfo) -> List[str]:
+        """SSE events that close the stream."""
+
+    @abstractmethod
+    def format_response(
+        self, ctx: GenContext, content: str, stop: StopInfo
+    ) -> Dict[str, Any]:
+        """JSON response body for non-streaming mode."""
+
+
+class ProtocolHandler:
+    """Orchestrates the generation loop, delegates formatting to a builder.
+
+    Usage::
+
+        handler = ProtocolHandler(request, engine, OpenAIResponseBuilder())
+        response = await handler.handle()
+    """
+
+    def __init__(
+        self, request: BaseModel, engine: InferenceEngine, builder: ResponseBuilder
+    ):
+        self.request = request
+        self.engine = engine
+        self.builder = builder
+
+    async def handle(self) -> Union[StreamingResponse, Dict[str, Any]]:
+        prompt, ctx, stop_sequences = self.builder.prepare(self.request, self.engine)
+        ctx.prompt_tokens = len(self.engine.tokenizer.encode(prompt))
+
+        agen = self.engine.generate_async(
+            prompt=prompt,
+            max_tokens=self.request.max_tokens,
+            temperature=self.request.temperature,
+            top_p=self.request.top_p,
+            top_k=self.request.top_k,
+        )
+
+        if self.request.stream:
+            return self._handle_stream(agen, ctx, stop_sequences)
+        else:
+            return await self._handle_non_stream(agen, ctx, stop_sequences)
+
+    def _handle_stream(
+        self, agen: AsyncGenerator, ctx: GenContext, stop_sequences: List[str]
+    ) -> StreamingResponse:
+        checker = StopChecker(stop_sequences)
+
+        async def event_stream():
+            for event in self.builder.format_stream_start(ctx):
+                yield event
+
+            body = ""
+            yielded = ""
+            matched = None
+            async for token in agen:
+                body += token
+
+                matched = checker.check(body)
+                if matched:
+                    break
+
+                ctx.completion_tokens += 1
+                yield self.builder.format_chunk(token)
+                yielded += token
+
+            stop = StopInfo(matched=matched, body=body, yielded=yielded)
+            for event in self.builder.format_stream_end(ctx, stop):
+                yield event
+            yield sse_done()
+
+        return StreamingResponse(
+            event_stream(),
+            media_type="text/event-stream",
+            headers={"Cache-Control": "no-cache", "Connection": "keep-alive"},
+        )
+
+    async def _handle_non_stream(
+        self, agen: AsyncGenerator, ctx: GenContext, stop_sequences: List[str]
+    ) -> Dict[str, Any]:
+        checker = StopChecker(stop_sequences)
+        chunks: List[str] = []
+        body = ""
+        matched = None
+
+        async for token in agen:
+            chunks.append(token)
+            body += token
+
+            matched = checker.check(body)
+            if matched:
+                break
+
+            ctx.completion_tokens += 1
+
+        content = "".join(chunks)
+        stop = StopInfo(matched=matched, body=body)
+        return self.builder.format_response(ctx, content, stop)

astrai/inference/api/server.py

@@ -0,0 +1,169 @@
+"""
+OpenAI / Anthropic-compatible chat completion server backed by continuous-batching inference.
+
+Protocol-specific formatting is delegated to ``astrai.inference.protocol``.
+This module owns the FastAPI app, request/response schemas, and dependency wiring.
+"""
+
+import logging
+from contextlib import asynccontextmanager
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Union
+
+import torch
+import uvicorn
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel, Field
+
+from astrai.inference.api.anthropic import AnthropicResponseBuilder
+from astrai.inference.api.openai import OpenAIResponseBuilder
+from astrai.inference.api.protocol import ProtocolHandler
+from astrai.inference.engine import InferenceEngine
+from astrai.model import AutoModel
+from astrai.tokenize import AutoTokenizer
+
+logger = logging.getLogger(__name__)
+
+_project_root = Path(__file__).parent.parent.parent
+
+
+class ChatMessage(BaseModel):
+    role: str
+    content: str
+
+
+class ChatCompletionRequest(BaseModel):
+    """OpenAI Chat Completion API request body."""
+
+    model: str = "astrai"
+    messages: List[ChatMessage]
+    temperature: Optional[float] = Field(default=1.0, ge=0.0, le=2.0)
+    top_p: Optional[float] = Field(default=1.0, ge=0.0, le=1.0)
+    top_k: Optional[int] = Field(default=50, ge=1)
+    stream: Optional[bool] = False
+    stop: Optional[Union[str, List[str]]] = None
+    max_tokens: Optional[int] = Field(default=2048, ge=1)
+    n: Optional[int] = Field(default=1, ge=1)
+    presence_penalty: Optional[float] = Field(default=0.0, ge=-2.0, le=2.0)
+    frequency_penalty: Optional[float] = Field(default=0.0, ge=-2.0, le=2.0)
+    logit_bias: Optional[Dict[int, float]] = None
+    user: Optional[str] = None
+
+
+class AnthropicMessage(BaseModel):
+    role: str
+    content: Union[str, List[Dict[str, Any]]]
+
+
+class MessagesRequest(BaseModel):
+    """Anthropic Messages API request body."""
+
+    model: str = "astrai"
+    max_tokens: int = Field(default=1024, ge=1)
+    messages: List[AnthropicMessage]
+    system: Optional[str] = None
+    temperature: Optional[float] = Field(default=1.0, ge=0.0, le=2.0)
+    top_p: Optional[float] = Field(default=1.0, ge=0.0, le=1.0)
+    top_k: Optional[int] = Field(default=50, ge=1)
+    stream: Optional[bool] = False
+    stop_sequences: Optional[List[str]] = None
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    config = app.state.server_config
+    if not config.get("_test", False):
+        try:
+            app.state.engine = _create_engine(**config)
+        except Exception as e:
+            logger.error(f"Failed to load model: {e}")
+            raise
+    yield
+    if app.state.engine:
+        app.state.engine.shutdown()
+        logger.info("Inference engine shutdown complete")
+
+
+app = FastAPI(title="AstrAI Inference Server", version="0.2.0", lifespan=lifespan)
+
+
+def _create_engine(
+    param_path: Optional[Path] = None,
+    device: str = "cuda",
+    dtype: torch.dtype = torch.bfloat16,
+    max_batch_size: int = 16,
+) -> InferenceEngine:
+    if param_path is None:
+        param_path = _project_root / "params"
+    if not param_path.exists():
+        raise FileNotFoundError(f"Parameter directory not found: {param_path}")
+
+    tokenizer = AutoTokenizer.from_pretrained(param_path)
+    model = AutoModel.from_pretrained(param_path)
+    model.to(device=device, dtype=dtype)
+    logger.info(f"Model loaded on {device} with dtype {dtype}")
+
+    engine = InferenceEngine(
+        model=model,
+        tokenizer=tokenizer,
+        max_batch_size=max_batch_size,
+    )
+    logger.info(f"Inference engine initialized with max_batch_size={max_batch_size}")
+    return engine
+
+
+def _get_engine() -> InferenceEngine:
+    engine = app.state.engine
+    if engine is None:
+        raise HTTPException(status_code=503, detail="Engine not initialized")
+    return engine
+
+
+@app.get("/health")
+async def health():
+    return {
+        "status": "ok",
+        "model_loaded": app.state.engine is not None,
+    }
+
+
+@app.get("/stats")
+async def get_stats():
+    return _get_engine().get_stats()
+
+
+@app.post("/v1/chat/completions")
+async def chat_completion(request: ChatCompletionRequest):
+    engine = _get_engine()
+    handler = ProtocolHandler(request, engine, OpenAIResponseBuilder())
+    return await handler.handle()
+
+
+@app.post("/v1/messages")
+async def create_message(request: MessagesRequest):
+    engine = _get_engine()
+    handler = ProtocolHandler(request, engine, AnthropicResponseBuilder())
+    return await handler.handle()
+
+
+def run_server(
+    host: str = "0.0.0.0",
+    port: int = 8000,
+    reload: bool = False,
+    device: str = "cuda",
+    dtype: torch.dtype = torch.bfloat16,
+    param_path: Optional[Path] = None,
+    max_batch_size: int = 16,
+):
+    app.state.server_config = {
+        "device": device,
+        "dtype": dtype,
+        "param_path": param_path,
+        "max_batch_size": max_batch_size,
+    }
+    uvicorn.run(
+        app,
+        host=host,
+        port=port,
+        reload=reload,
+    )

astrai/inference/core/__init__.py

@@ -0,0 +1,32 @@
+"""Inference core: cache, executor, scheduler, task management."""
+
+from astrai.inference.core.cache import (
+    Allocator,
+    KVCache,
+    KvcacheView,
+    PagePool,
+    PrefixCache,
+    Storage,
+    TaskTable,
+    page_hash,
+)
+from astrai.inference.core.executor import Executor
+from astrai.inference.core.scheduler import InferenceScheduler
+from astrai.inference.core.task import STOP, Task, TaskManager, TaskStatus
+
+__all__ = [
+    "Allocator",
+    "KVCache",
+    "KvcacheView",
+    "PagePool",
+    "PrefixCache",
+    "Storage",
+    "TaskTable",
+    "page_hash",
+    "Executor",
+    "InferenceScheduler",
+    "STOP",
+    "Task",
+    "TaskManager",
+    "TaskStatus",
+]

astrai/inference/core/cache.py

@@ -0,0 +1,368 @@
+import threading
+from collections import OrderedDict
+from typing import Callable, Dict, List, Optional, Tuple
+
+import torch
+from torch import Tensor
+
+
+def page_hash(token_ids: List[int], page_idx: int, page_size: int) -> int:
+    start = page_idx * page_size
+    end = min(start + page_size, len(token_ids))
+    h = 0
+    for i in range(start, end):
+        h = (h * 31 + token_ids[i]) & 0xFFFFFFFFFFFFFFFF
+    return h
+
+
+class Allocator:
+    """Bitmask-based page allocator with ref-counting and LRU eviction."""
+
+    def __init__(self, n_pages: int):
+        self._free_mask = (1 << n_pages) - 1
+        self._refs: List[int] = [0] * n_pages
+        self._lru: OrderedDict[int, None] = OrderedDict()
+        self.on_evict: Optional[Callable[[int], None]] = None
+        self._lock = threading.Lock()
+
+    def alloc(self) -> int:
+        with self._lock:
+            if self._free_mask:
+                lsb = self._free_mask & -self._free_mask
+                idx = lsb.bit_length() - 1
+                self._free_mask ^= lsb
+                self._refs[idx] = 1
+                return idx
+            if self._lru:
+                idx, _ = self._lru.popitem(last=False)
+                if self.on_evict:
+                    self.on_evict(idx)
+                self._refs[idx] = 1
+                self._free_mask &= ~(1 << idx)
+                return idx
+            return -1
+
+    def free(self, idx: int, keep_cached: bool = False):
+        with self._lock:
+            self._refs[idx] -= 1
+            if self._refs[idx] == 0:
+                if keep_cached:
+                    self._lru[idx] = None
+                else:
+                    self._free_mask |= 1 << idx
+
+    def inc_ref(self, idx: int):
+        with self._lock:
+            self._refs[idx] += 1
+            self._lru.pop(idx, None)
+
+    def ref_count(self, idx: int) -> int:
+        with self._lock:
+            return self._refs[idx]
+
+    def touch(self, idx: int):
+        with self._lock:
+            self._lru.move_to_end(idx)
+
+
+class PrefixCache:
+    """Hash-based prefix matching: maps page hashes to physical page indices."""
+
+    def __init__(self, page_size: int):
+        self._page_size = page_size
+        self._page_to_hash: Dict[int, int] = {}
+        self._hash_to_page: Dict[int, int] = {}
+        self._lock = threading.Lock()
+
+    def evict(self, idx: int):
+        with self._lock:
+            h = self._page_to_hash.pop(idx, None)
+            if h is not None:
+                self._hash_to_page.pop(h, None)
+
+    def has_page(self, idx: int) -> bool:
+        with self._lock:
+            return idx in self._page_to_hash
+
+    def lookup(self, token_ids: List[int]) -> List[int]:
+        with self._lock:
+            full_pages = len(token_ids) // self._page_size
+            hits: List[int] = []
+            for i in range(full_pages):
+                h = page_hash(token_ids, i, self._page_size)
+                p = self._hash_to_page.get(h)
+                if p is None:
+                    break
+                hits.append(p)
+            return hits
+
+    def record(self, page_idx: int, token_ids: List[int], logical_page_idx: int):
+        with self._lock:
+            h = page_hash(token_ids, logical_page_idx, self._page_size)
+            old_h = self._page_to_hash.pop(page_idx, None)
+            if old_h is not None:
+                self._hash_to_page.pop(old_h, None)
+            self._page_to_hash[page_idx] = h
+            self._hash_to_page[h] = page_idx
+
+
+class PagePool:
+    """Orchestrates allocator (page management) and PrefixCache (content addressing)."""
+
+    def __init__(self, allocator: Allocator, prefix: PrefixCache):
+        self._alloc = allocator
+        self._prefix = prefix
+        self._alloc.on_evict = prefix.evict
+
+    @property
+    def allocator(self) -> Allocator:
+        return self._alloc
+
+    @property
+    def prefix(self) -> PrefixCache:
+        return self._prefix
+
+    def alloc(self) -> int:
+        return self._alloc.alloc()
+
+    def free(self, idx: int):
+        keep = self._prefix.has_page(idx)
+        self._alloc.free(idx, keep_cached=keep)
+        if not keep:
+            self._prefix.evict(idx)
+
+    def inc_ref(self, idx: int):
+        self._alloc.inc_ref(idx)
+
+    def lookup(self, token_ids: List[int]) -> List[int]:
+        hits = self._prefix.lookup(token_ids)
+        for p in hits:
+            self._alloc.touch(p)
+        return hits
+
+    def record(self, page_idx: int, token_ids: List[int], logical_page_idx: int):
+        self._prefix.record(page_idx, token_ids, logical_page_idx)
+
+
+class TaskTable:
+    """Maps task_ids to page tables and cached token counts."""
+
+    def __init__(self, page_size: int):
+        self._page_size = page_size
+        self._pages: Dict[str, List[int]] = {}
+        self._cached: Dict[str, int] = {}
+        self._lock = threading.Lock()
+
+    def set(self, task_id: str, page_table: List[int], cached: int):
+        with self._lock:
+            self._pages[task_id] = page_table
+            self._cached[task_id] = cached
+
+    def get(self, task_id: str) -> List[int]:
+        with self._lock:
+            return self._pages.get(task_id, [])
+
+    def get_cached(self, task_id: str) -> int:
+        with self._lock:
+            return self._cached.get(task_id, 0)
+
+    def pop(self, task_id: str) -> Tuple[List[int], int]:
+        with self._lock:
+            pages = self._pages.pop(task_id, [])
+            cached = self._cached.pop(task_id, 0)
+            return pages, cached
+
+    def get_ref(self, task_id: str) -> List[int]:
+        with self._lock:
+            return self._pages.setdefault(task_id, [])
+
+    def table_tensor(self, task_ids: List[str], device: torch.device) -> Tensor:
+        with self._lock:
+            states = [self._pages.get(tid, []) for tid in task_ids]
+            max_pages = max((len(s) for s in states), default=0)
+            rows = [s + [-1] * (max_pages - len(s)) for s in states]
+            return torch.tensor(rows, dtype=torch.long, device=device)
+
+
+class Storage:
+    """KV-cache tensor storage with paged write/gather."""
+
+    def __init__(
+        self,
+        n_layers: int,
+        n_pages: int,
+        page_size: int,
+        n_kv_heads: int,
+        head_dim: int,
+        device: torch.device,
+        dtype: torch.dtype,
+    ):
+        self.page_size = page_size
+        self.k_cache = torch.empty(
+            (n_layers, n_pages, page_size, n_kv_heads, head_dim),
+            device=device,
+            dtype=dtype,
+        )
+        self.v_cache = torch.empty(
+            (n_layers, n_pages, page_size, n_kv_heads, head_dim),
+            device=device,
+            dtype=dtype,
+        )
+
+    def write(
+        self,
+        layer_id: int,
+        page_table: Tensor,
+        start_pos: int,
+        k: Tensor,
+        v: Tensor,
+    ):
+        seq_len = k.size(1)
+        if seq_len == 0:
+            return
+        page_size = self.page_size
+        written = 0
+        first_page = start_pos // page_size
+        last_page = (start_pos + seq_len - 1) // page_size
+        for pi in range(first_page, last_page + 1):
+            phys_pages = page_table[:, pi]
+            page_start = pi * page_size
+            write_start = max(page_start, start_pos)
+            write_end = min(page_start + page_size, start_pos + seq_len)
+            offset = write_start - page_start
+            chunk = write_end - write_start
+            valid = phys_pages >= 0
+            if not valid.all():
+                if valid.any():
+                    valid_pages = phys_pages[valid]
+                    self.k_cache[layer_id, valid_pages, offset : offset + chunk] = k[
+                        valid, written : written + chunk
+                    ]
+                    self.v_cache[layer_id, valid_pages, offset : offset + chunk] = v[
+                        valid, written : written + chunk
+                    ]
+                written += chunk
+                continue
+            self.k_cache[layer_id, phys_pages, offset : offset + chunk] = k[
+                :, written : written + chunk
+            ]
+            self.v_cache[layer_id, phys_pages, offset : offset + chunk] = v[
+                :, written : written + chunk
+            ]
+            written += chunk
+
+    def gather(
+        self, layer_id: int, page_table: Tensor, total_len: int
+    ) -> Tuple[Tensor, Tensor]:
+        safe = page_table.clamp(min=0)
+        k = self.k_cache[layer_id, safe]
+        v = self.v_cache[layer_id, safe]
+        k = k.flatten(1, 2)
+        v = v.flatten(1, 2)
+        if (page_table < 0).any():
+            invalid = (
+                (page_table < 0)
+                .unsqueeze(-1)
+                .expand(-1, -1, self.page_size)
+                .flatten(1, 2)
+            )
+            invalid = invalid[:, :, None, None].expand_as(k)
+            k = k.masked_fill(invalid, 0.0)
+            v = v.masked_fill(invalid, 0.0)
+        k = k[:, :total_len]
+        v = v[:, :total_len]
+        return k, v
+
+
+class KvcacheView:
+    """Bundles Storage + page_table + total_len for attention layers."""
+
+    def __init__(self, storage: Storage, page_table: Tensor, total_len: int = 0):
+        self._storage = storage
+        self._page_table = page_table
+        self._total_len = total_len
+
+    def write(self, layer_id: int, k: Tensor, v: Tensor):
+        start_pos = self._total_len - k.size(1)
+        self._storage.write(layer_id, self._page_table, start_pos, k, v)
+
+    def gather(self, layer_id: int) -> Tuple[Tensor, Tensor]:
+        return self._storage.gather(layer_id, self._page_table, self._total_len)
+
+
+class KVCache:
+    """Facade: page management + KV-cache I/O for continuous batching."""
+
+    def __init__(
+        self,
+        n_layers: int,
+        n_pages: int,
+        page_size: int,
+        n_kv_heads: int,
+        head_dim: int,
+        device: torch.device,
+        dtype: torch.dtype,
+    ):
+        self.page_size = page_size
+        self._pool = PagePool(Allocator(n_pages), PrefixCache(page_size))
+        self._table = TaskTable(page_size)
+        self._storage = Storage(
+            n_layers, n_pages, page_size, n_kv_heads, head_dim, device, dtype
+        )
+
+    def task_alloc(self, task_id: str, prompt_ids: List[int]) -> bool:
+        hits = self._pool.lookup(prompt_ids)
+        cached = len(hits) * self.page_size
+        for p in hits:
+            self._pool.inc_ref(p)
+
+        remaining = len(prompt_ids) - cached
+        n_new = (
+            (remaining + self.page_size - 1) // self.page_size if remaining > 0 else 0
+        )
+        new_pages: List[int] = []
+        if n_new > 0:
+            for _ in range(n_new):
+                p = self._pool.alloc()
+                if p < 0:
+                    for hp in hits:
+                        self._pool.free(hp)
+                    for np in new_pages:
+                        self._pool.free(np)
+                    return False
+                new_pages.append(p)
+
+        self._table.set(task_id, hits + new_pages, cached)
+        return True
+
+    def task_free(self, task_id: str):
+        page_table, _ = self._table.pop(task_id)
+        for idx in page_table:
+            self._pool.free(idx)
+
+    def task_extend(self, task_id: str, pos: int) -> bool:
+        page_table = self._table.get(task_id)
+        needed = (pos + 1 + self.page_size - 1) // self.page_size
+        while len(page_table) < needed:
+            p = self._pool.alloc()
+            if p < 0:
+                return False
+            page_table.append(p)
+        return True
+
+    def task_cached(self, task_id: str) -> int:
+        return self._table.get_cached(task_id)
+
+    def task_record_hashes(
+        self, task_id: str, prompt_ids: List[int], start_logical_page: int = 0
+    ):
+        page_table = self._table.get(task_id)
+        full_pages = len(prompt_ids) // self.page_size
+        for i in range(start_logical_page, full_pages):
+            self._pool.record(page_table[i], prompt_ids, i)
+
+    def make_table_tensor(self, task_ids: List[str], device: torch.device) -> Tensor:
+        return self._table.table_tensor(task_ids, device)
+
+    def bind(self, page_table: Tensor, total_len: int = 0) -> KvcacheView:
+        return KvcacheView(self._storage, page_table, total_len)

astrai/inference/core/executor.py

@@ -0,0 +1,94 @@
+import logging
+from typing import List, Optional
+
+import torch
+
+from astrai.inference.core.cache import KVCache
+from astrai.inference.core.task import Task
+from astrai.inference.sample import sample
+from astrai.model.automodel import AutoModel
+from astrai.tokenize.tokenizer import AutoTokenizer
+
+logger = logging.getLogger(__name__)
+
+
+class Executor:
+    """Model forward passes for prefill and decode phases."""
+
+    def __init__(
+        self,
+        model: AutoModel,
+        tokenizer: AutoTokenizer,
+        page_cache: KVCache,
+        device: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        self.model = model
+        self.tokenizer = tokenizer
+        self.page_cache = page_cache
+        self.device = device or next(model.parameters()).device
+        self.dtype = dtype or next(model.parameters()).dtype
+
+    def execute_prefill(self, tasks: List[Task], prompt_len: int, start_pos: int = 0):
+        if start_pos >= prompt_len:
+            return
+
+        tasks = sorted(tasks, key=lambda t: t.task_id)
+        batch_sz = len(tasks)
+
+        input_ids = torch.tensor(
+            [t.prompt_ids[start_pos:prompt_len] for t in tasks],
+            dtype=torch.long,
+            device=self.device,
+        )
+
+        task_ids = [t.task_id for t in tasks]
+        page_tables = self.page_cache.make_table_tensor(task_ids, self.device)
+
+        with torch.inference_mode():
+            self.model(
+                input_ids,
+                position_ids=torch.arange(
+                    start_pos, prompt_len, dtype=torch.long, device=self.device
+                )
+                .unsqueeze(0)
+                .expand(batch_sz, -1),
+                paged_cache=self.page_cache.bind(page_tables, total_len=prompt_len),
+            )
+
+    def execute_decode(self, tasks: List[Task]) -> List[int]:
+        if not tasks:
+            return []
+
+        input_ids = torch.tensor(
+            [t.output_ids[-1] if t.output_ids else t.prompt_ids[-1] for t in tasks],
+            dtype=torch.long,
+            device=self.device,
+        )
+
+        position_ids = torch.tensor(
+            [t.next_pos for t in tasks], dtype=torch.long, device=self.device
+        )
+        total_len = position_ids.max().item() + 1
+
+        task_ids = [t.task_id for t in tasks]
+        page_tables = self.page_cache.make_table_tensor(task_ids, self.device)
+
+        temperatures = torch.tensor([t.temperature for t in tasks], device=self.device)
+        top_ks = torch.tensor([t.top_k for t in tasks], device=self.device)
+        top_ps = torch.tensor([t.top_p for t in tasks], device=self.device)
+
+        with torch.inference_mode():
+            outputs = self.model(
+                input_ids.unsqueeze(1),
+                paged_cache=self.page_cache.bind(page_tables, total_len=total_len),
+                position_ids=position_ids.unsqueeze(1),
+            )
+            logits = outputs["logits"][:, -1, :]
+
+        return sample(
+            logits,
+            temperature=temperatures,
+            top_k=top_ks,
+            top_p=top_ps,
+        ).tolist()

astrai/inference/core/scheduler.py

@@ -0,0 +1,212 @@
+import logging
+import threading
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+
+from astrai.inference.core.cache import KVCache
+from astrai.inference.core.executor import Executor
+from astrai.inference.core.task import STOP, Task, TaskManager, TaskStatus
+from astrai.model.automodel import AutoModel
+from astrai.tokenize.tokenizer import AutoTokenizer
+
+logger = logging.getLogger(__name__)
+
+
+class InferenceScheduler:
+    """Four-phase continuous batching loop: cleanup -> refill -> prefill -> decode."""
+
+    def __init__(
+        self,
+        model: AutoModel,
+        tokenizer: AutoTokenizer,
+        max_batch_size: int = 16,
+        max_seq_len: Optional[int] = None,
+        max_prompt_len: int = 2048,
+        page_size: int = 64,
+        device: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        config = model.config
+
+        if max_seq_len is not None:
+            self.max_seq_len = max_seq_len
+        elif config.max_len is not None:
+            self.max_seq_len = config.max_len
+        else:
+            raise ValueError(
+                "max_seq_len must be provided either as argument "
+                "or in model config (config.max_len)"
+            )
+        self.device = device or next(model.parameters()).device
+        self.dtype = dtype or next(model.parameters()).dtype
+
+        n_pages = (
+            max_batch_size * (self.max_seq_len + page_size) + page_size - 1
+        ) // page_size
+
+        self._page_cache = KVCache(
+            config.n_layers,
+            n_pages,
+            page_size,
+            config.n_kv_heads,
+            config.dim // config.n_heads,
+            self.device,
+            self.dtype,
+        )
+
+        self._task_mgr = TaskManager(
+            tokenizer=tokenizer,
+            max_batch_size=max_batch_size,
+            max_seq_len=self.max_seq_len,
+            max_prompt_len=max_prompt_len,
+        )
+
+        self._executor = Executor(
+            model=model,
+            tokenizer=tokenizer,
+            page_cache=self._page_cache,
+            device=self.device,
+            dtype=self.dtype,
+        )
+
+        self._running = False
+        self._fatal_error: Optional[Exception] = None
+
+    def add_task(self, prompt: str, **kwargs) -> str:
+        return self._task_mgr.add_task(prompt, **kwargs)
+
+    def remove_task(self, task_id: str):
+        for task in self._task_mgr.remove_task(task_id):
+            self._page_cache.task_free(task.task_id)
+
+    def get_stats(self) -> Dict[str, Any]:
+        return self._task_mgr.get_stats()
+
+    def _run_generation_loop(self):
+        stop_ids = self._task_mgr.tokenizer.stop_ids
+        try:
+            while self._running:
+                finished = self._task_mgr.remove_finished_tasks(stop_ids)
+                for task in finished:
+                    self._page_cache.task_free(task.task_id)
+
+                active = self._task_mgr.get_active_tasks()
+                available = self._task_mgr.max_batch_size - len(active)
+                if available > 0:
+                    candidates = self._task_mgr.pull_candidates(available)
+                    failed = []
+                    for task in candidates:
+                        if self._page_cache.task_alloc(task.task_id, task.prompt_ids):
+                            self._task_mgr.activate(task)
+                        else:
+                            failed.append(task)
+                    if failed:
+                        self._task_mgr.return_to_waiting(failed)
+
+                if not self._task_mgr.has_work():
+                    self._task_mgr.wait_for_tasks(timeout=1.0)
+                    continue
+
+                to_prefill = [
+                    t
+                    for t in self._task_mgr.get_active_tasks()
+                    if t.output_tokens == 0
+                    and self._page_cache.task_cached(t.task_id) < len(t.prompt_ids)
+                ]
+                if to_prefill:
+                    for t in to_prefill:
+                        t.input_tokens = len(t.prompt_ids)
+
+                    groups: Dict[Tuple[int, int], List[Task]] = {}
+                    for t in to_prefill:
+                        key = (
+                            len(t.prompt_ids),
+                            self._page_cache.task_cached(t.task_id),
+                        )
+                        groups.setdefault(key, []).append(t)
+
+                    for (prompt_len, start_pos), group in groups.items():
+                        self._executor.execute_prefill(group, prompt_len, start_pos)
+                        start_logical_page = start_pos // self._page_cache.page_size
+                        for t in group:
+                            self._page_cache.task_record_hashes(
+                                t.task_id,
+                                t.prompt_ids,
+                                start_logical_page=start_logical_page,
+                            )
+
+                pos_groups: Dict[int, List[Task]] = {}
+                for t in self._task_mgr.get_active_tasks():
+                    pos_groups.setdefault(t.next_pos, []).append(t)
+
+                if pos_groups:
+                    best_key = max(pos_groups, key=lambda k: len(pos_groups[k]))
+                    group = sorted(pos_groups[best_key], key=lambda t: t.task_id)
+
+                    valid: List[Task] = []
+                    for t in group:
+                        if self._page_cache.task_extend(t.task_id, t.next_pos):
+                            valid.append(t)
+                        else:
+                            t.status = TaskStatus.ABORTED
+                            if t.stream_callback:
+                                t.stream_callback(STOP)
+
+                    if valid:
+                        next_tokens = self._executor.execute_decode(valid)
+
+                        for t, ntok in zip(valid, next_tokens):
+                            t.output_ids.append(ntok)
+                            t.output_tokens += 1
+                            pos = t.input_tokens + t.output_tokens
+                            extend_ok = self._page_cache.task_extend(t.task_id, pos)
+                            if t.stream_callback:
+                                t.stream_callback(
+                                    self._task_mgr.tokenizer.decode([ntok])
+                                )
+                            if not extend_ok:
+                                t.status = TaskStatus.ABORTED
+                                if t.stream_callback:
+                                    t.stream_callback(STOP)
+
+                        for t in valid:
+                            if t.is_finished(stop_ids):
+                                if t.stream_callback:
+                                    t.stream_callback(STOP)
+
+        except Exception as e:
+            self._fatal_error = e
+            self._running = False
+            logger.error(f"Scheduler loop crashed: {e}", exc_info=True)
+            for task in self._task_mgr.get_active_tasks():
+                if task.stream_callback:
+                    task.stream_callback(STOP)
+                self._page_cache.task_free(task.task_id)
+            for task in self._task_mgr.get_waiting_tasks():
+                if task.stream_callback:
+                    task.stream_callback(STOP)
+            self._task_mgr.clear_queues()
+
+    def start(self):
+        if not self._running:
+            self._running = True
+            t = threading.Thread(target=self._run_generation_loop, daemon=True)
+            t.start()
+            self._loop_thread = t
+
+    def stop(self):
+        self._running = False
+        self._task_mgr.wake()
+        if hasattr(self, "_loop_thread"):
+            self._loop_thread.join(timeout=2.0)
+        for task in self._task_mgr.get_active_tasks():
+            if task.stream_callback:
+                task.stream_callback(STOP)
+            self._page_cache.task_free(task.task_id)
+        for task in self._task_mgr.get_waiting_tasks():
+            if task.stream_callback:
+                task.stream_callback(STOP)
+        self._task_mgr.clear_queues()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()

astrai/inference/core/task.py

@@ -0,0 +1,209 @@
+import logging
+import threading
+import time
+import uuid
+from collections import deque
+from enum import Enum
+from typing import Any, Callable, Deque, Dict, List, Optional
+
+from astrai.tokenize.tokenizer import AutoTokenizer
+
+logger = logging.getLogger(__name__)
+
+STOP = object()
+
+
+class TaskStatus(Enum):
+    """Task lifecycle states."""
+
+    PENDING = "pending"
+    RUNNING = "running"
+    FINISHED = "finished"
+    ABORTED = "aborted"
+
+
+class Task:
+    """Single generation request: prompt, sampling params, output state."""
+
+    def __init__(
+        self,
+        task_id: str,
+        prompt_ids: List[int],
+        max_tokens: Optional[int] = None,
+        temperature: float = 1.0,
+        top_p: float = 1.0,
+        top_k: int = 50,
+        stream_callback: Optional[Callable[[str], None]] = None,
+    ):
+        self.task_id = task_id
+        self.prompt_ids = prompt_ids
+        self.max_tokens = max_tokens
+        self.temperature = temperature
+        self.top_p = top_p
+        self.top_k = top_k
+
+        self.status = TaskStatus.PENDING
+        self.output_ids: List[int] = []
+        self.input_tokens: int = 0
+        self.output_tokens: int = 0
+        self.arrival_time = time.time()
+        self.finish_time: Optional[float] = None
+        self.stream_callback = stream_callback
+
+    @property
+    def next_pos(self) -> int:
+        return self.input_tokens + len(self.output_ids)
+
+    def is_finished(self, stop_ids: List[int]) -> bool:
+        if self.max_tokens is not None and self.output_tokens >= self.max_tokens:
+            return True
+        if self.output_ids and self.output_ids[-1] in stop_ids:
+            return True
+        return False
+
+
+class TaskManager:
+    """Thread-safe task queues and lifecycle transitions (no page ops)."""
+
+    def __init__(
+        self,
+        tokenizer: AutoTokenizer,
+        max_batch_size: int = 16,
+        max_seq_len: int = 8192,
+        max_prompt_len: int = 512,
+    ):
+        self.tokenizer = tokenizer
+        self.max_batch_size = max_batch_size
+        self.max_seq_len = max_seq_len
+        self.max_prompt_len = max_prompt_len
+
+        self.waiting_queue: Deque[Task] = deque()
+        self.active_tasks: List[Task] = []
+
+        self._task_event = threading.Event()
+        self._lock = threading.Lock()
+
+        self._total_tasks = 0
+        self._total_tokens = 0
+
+    def add_task(
+        self,
+        prompt: str,
+        max_tokens: Optional[int] = None,
+        temperature: float = 1.0,
+        top_p: float = 1.0,
+        top_k: int = 50,
+        stream_callback: Optional[Callable[[str], None]] = None,
+    ) -> str:
+        task_id = f"task_{int(time.time())}_{uuid.uuid4().hex[:8]}"
+        prompt_ids = self.tokenizer.encode(prompt)
+        if len(prompt_ids) > self.max_prompt_len:
+            prompt_ids = prompt_ids[-self.max_prompt_len :]
+
+        if len(prompt_ids) >= self.max_seq_len:
+            if stream_callback:
+                stream_callback(STOP)
+            return task_id
+
+        if max_tokens is None:
+            max_tokens = self.max_seq_len - len(prompt_ids)
+        else:
+            max_tokens = min(max_tokens, self.max_seq_len - len(prompt_ids))
+
+        task = Task(
+            task_id=task_id,
+            prompt_ids=prompt_ids,
+            max_tokens=max_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+            stream_callback=stream_callback,
+        )
+
+        with self._lock:
+            self.waiting_queue.append(task)
+            self._total_tasks += 1
+
+        self._task_event.set()
+        return task_id
+
+    def remove_task(self, task_id: str) -> List[Task]:
+        with self._lock:
+            removed_active = [t for t in self.active_tasks if t.task_id == task_id]
+            self.waiting_queue = deque(
+                t for t in self.waiting_queue if t.task_id != task_id
+            )
+            self.active_tasks = [t for t in self.active_tasks if t.task_id != task_id]
+        return removed_active
+
+    def get_stats(self) -> Dict[str, Any]:
+        return {
+            "total_tasks": self._total_tasks,
+            "total_tokens": self._total_tokens,
+            "active_tasks": len(self.active_tasks),
+            "waiting_queue": len(self.waiting_queue),
+        }
+
+    def remove_finished_tasks(self, stop_ids: List[int]) -> List[Task]:
+        with self._lock:
+            finished = []
+            for task in self.active_tasks:
+                if task.status == TaskStatus.ABORTED:
+                    task.finish_time = time.time()
+                    finished.append(task)
+                elif task.is_finished(stop_ids):
+                    task.status = TaskStatus.FINISHED
+                    task.finish_time = time.time()
+                    finished.append(task)
+                    self._total_tokens += task.output_tokens
+
+            self.active_tasks = [
+                t
+                for t in self.active_tasks
+                if t.status not in (TaskStatus.FINISHED, TaskStatus.ABORTED)
+            ]
+            return finished
+
+    def pull_candidates(self, n: int) -> List[Task]:
+        to_add: List[Task] = []
+        with self._lock:
+            take = min(n, len(self.waiting_queue))
+            for _ in range(take):
+                to_add.append(self.waiting_queue.popleft())
+        return to_add
+
+    def activate(self, task: Task):
+        task.status = TaskStatus.RUNNING
+        with self._lock:
+            self.active_tasks.append(task)
+
+    def return_to_waiting(self, tasks: List[Task]):
+        with self._lock:
+            for task in reversed(tasks):
+                self.waiting_queue.appendleft(task)
+
+    def has_work(self) -> bool:
+        return bool(self.active_tasks or self.waiting_queue)
+
+    def wait_for_tasks(self, timeout: float = 1.0):
+        with self._lock:
+            if self.waiting_queue or self.active_tasks:
+                return
+            self._task_event.clear()
+        self._task_event.wait(timeout=timeout)
+
+    def get_active_tasks(self) -> List[Task]:
+        with self._lock:
+            return list(self.active_tasks)
+
+    def get_waiting_tasks(self) -> List[Task]:
+        with self._lock:
+            return list(self.waiting_queue)
+
+    def clear_queues(self):
+        with self._lock:
+            self.waiting_queue.clear()
+            self.active_tasks.clear()
+
+    def wake(self):
+        self._task_event.set()

astrai/inference/engine.py

@@ -0,0 +1,288 @@
+"""Unified inference engine for continuous batching."""
+
+import asyncio
+import gc
+import threading
+from typing import Any, AsyncGenerator, Dict, Generator, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+
+from astrai.inference.core.scheduler import InferenceScheduler
+from astrai.inference.core.task import STOP
+from astrai.tokenize import AutoTokenizer
+
+
+class GenerateResult:
+    """Thread-safe token accumulator for streaming and non-streaming modes."""
+
+    def __init__(self, count: int = 1):
+        self._cond = threading.Condition()
+        self._event = threading.Event()
+        self.tokens: List[Tuple[int, str]] = []
+        self.results: List[str] = [""] * count
+        self._done: List[bool] = [False] * count
+        self._completed = 0
+        self._total = count
+
+    def append(self, token: str, idx: int = 0):
+        with self._cond:
+            self.tokens.append((idx, token))
+            if token is not STOP:
+                self.results[idx] += token
+            else:
+                if not self._done[idx]:
+                    self._done[idx] = True
+                    self._completed += 1
+                    self._cond.notify_all()
+            self._event.set()
+
+    def pop_all(self) -> List[Tuple[int, str]]:
+        with self._cond:
+            out = self.tokens.copy()
+            self.tokens.clear()
+            if not out:
+                self._event.clear()
+            return out
+
+    def wait(self, timeout: Optional[float] = None) -> bool:
+        return self._event.wait(timeout=timeout)
+
+    def wait_completion(self, timeout: float = 300.0):
+        with self._cond:
+            if not self._cond.wait_for(
+                lambda: self._completed >= self._total, timeout=timeout
+            ):
+                raise TimeoutError(
+                    f"Generation timeout after {timeout}s "
+                    f"({self._completed}/{self._total} completed)"
+                )
+
+    def get_results(self) -> List[str]:
+        with self._cond:
+            return self.results.copy()
+
+
+class GenerationRequest:
+    """Request parameters for text generation."""
+
+    def __init__(
+        self,
+        messages: List[Dict[str, str]],
+        top_k: int = 50,
+        top_p: float = 1.0,
+        temperature: float = 1.0,
+        max_tokens: Optional[int] = None,
+        stream: bool = False,
+    ):
+        if not (isinstance(top_k, int) and top_k >= 0):
+            raise ValueError("top_k must be a non-negative integer")
+        if not (0.0 <= top_p <= 1.0):
+            raise ValueError("top_p must be a float between 0.0 and 1.0")
+        if not (isinstance(temperature, (int, float)) and temperature > 0):
+            raise ValueError("temperature must be a positive number")
+
+        self.messages = messages
+        self.top_k = top_k
+        self.top_p = top_p
+        self.temperature = temperature
+        self.max_tokens = max_tokens
+        self.stream = stream
+
+
+class InferenceEngine:
+    """Unified inference engine backed by continuous-batching scheduler."""
+
+    def __init__(
+        self,
+        model: nn.Module,
+        tokenizer: AutoTokenizer,
+        max_batch_size: int = 1,
+        max_seq_len: Optional[int] = None,
+        max_prompt_len: int = 2048,
+        page_size: int = 128,
+    ):
+        self.model = model
+        self.tokenizer = tokenizer
+        self.scheduler = InferenceScheduler(
+            model=self.model,
+            tokenizer=self.tokenizer,
+            max_batch_size=max_batch_size,
+            max_seq_len=max_seq_len,
+            max_prompt_len=max_prompt_len,
+            page_size=page_size,
+        )
+
+        self.scheduler.start()
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.shutdown()
+        return False
+
+    def generate(
+        self,
+        prompt: Union[str, List[str]],
+        stream: bool = False,
+        max_tokens: Optional[int] = None,
+        temperature: float = 1.0,
+        top_p: float = 1.0,
+        top_k: int = 50,
+    ) -> Union[Generator, str, List[str]]:
+        is_batch = isinstance(prompt, list)
+        prompts = prompt if is_batch else [prompt]
+
+        if stream:
+            return self._generate_streaming(
+                prompts, is_batch, max_tokens, temperature, top_p, top_k
+            )
+        else:
+            return self._generate_non_streaming(
+                prompts, is_batch, max_tokens, temperature, top_p, top_k
+            )
+
+    def generate_async(
+        self,
+        prompt: str,
+        max_tokens: Optional[int] = None,
+        temperature: float = 1.0,
+        top_p: float = 1.0,
+        top_k: int = 50,
+    ) -> AsyncGenerator[str, None]:
+        sync_gen = self._generate_streaming(
+            [prompt], False, max_tokens, temperature, top_p, top_k
+        )
+
+        async def _agen():
+            loop = asyncio.get_event_loop()
+            while True:
+                token = await loop.run_in_executor(None, self._next_token, sync_gen)
+                if token is None:
+                    break
+                yield token
+
+        return _agen()
+
+    @staticmethod
+    def _next_token(gen: Generator) -> Optional[str]:
+        try:
+            return next(gen)
+        except StopIteration:
+            return None
+
+    def generate_with_request(
+        self, request: GenerationRequest
+    ) -> Union[Generator[str, None, None], str, List[str]]:
+        prompt = self.tokenizer.apply_chat_template(request.messages, tokenize=False)
+        return self.generate(
+            prompt=prompt,
+            stream=request.stream,
+            max_tokens=request.max_tokens,
+            temperature=request.temperature,
+            top_p=request.top_p,
+            top_k=request.top_k,
+        )
+
+    def _submit_tasks(
+        self,
+        prompts: List[str],
+        max_tokens: Optional[int],
+        temperature: float,
+        top_p: float,
+        top_k: int,
+    ) -> Tuple[GenerateResult, List[str]]:
+        n = len(prompts)
+        result = GenerateResult(count=n)
+        task_ids = []
+        for i, p in enumerate(prompts):
+            cb = self._make_callback(result, i)
+            task_id = self.scheduler.add_task(
+                prompt=p,
+                max_tokens=max_tokens,
+                temperature=temperature,
+                top_p=top_p,
+                top_k=top_k,
+                stream_callback=cb,
+            )
+            task_ids.append(task_id)
+        return result, task_ids
+
+    @staticmethod
+    def _make_callback(result: GenerateResult, idx: int):
+        def cb(token):
+            result.append(token, idx)
+
+        return cb
+
+    def _generate_streaming(
+        self,
+        prompts: List[str],
+        is_batch: bool,
+        max_tokens: Optional[int],
+        temperature: float,
+        top_p: float,
+        top_k: int,
+    ) -> Generator:
+        result, task_ids = self._submit_tasks(
+            prompts, max_tokens, temperature, top_p, top_k
+        )
+        n = len(prompts)
+        remaining = n
+        finished = [False] * n
+
+        def gen():
+            nonlocal remaining
+            try:
+                while remaining > 0:
+                    items = result.pop_all()
+                    for idx, token in items:
+                        if token is STOP:
+                            if not finished[idx]:
+                                finished[idx] = True
+                                remaining -= 1
+                        else:
+                            yield (idx, token) if is_batch else token
+                    if remaining > 0:
+                        result.wait(timeout=0.05)
+            finally:
+                for tid in task_ids:
+                    self.scheduler.remove_task(tid)
+
+        return gen()
+
+    def _generate_non_streaming(
+        self,
+        prompts: List[str],
+        is_batch: bool,
+        max_tokens: Optional[int],
+        temperature: float,
+        top_p: float,
+        top_k: int,
+    ) -> Union[str, List[str]]:
+        result, task_ids = self._submit_tasks(
+            prompts, max_tokens, temperature, top_p, top_k
+        )
+
+        try:
+            result.wait_completion()
+        except TimeoutError:
+            for tid in task_ids:
+                self.scheduler.remove_task(tid)
+            raise
+
+        for tid in task_ids:
+            self.scheduler.remove_task(tid)
+
+        res = result.get_results()
+        return res if is_batch else res[0]
+
+    def get_stats(self) -> Dict[str, Any]:
+        return self.scheduler.get_stats()
+
+    def shutdown(self):
+        self.scheduler.stop()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        gc.collect()

astrai/inference/sample.py

@@ -0,0 +1,190 @@
+"""Composable sampling strategies for logit transformation.
+
+Implements the Strategy pattern: each sampling technique
+(temperature, top-k, top-p) is a pluggable strategy that
+can be composed into a pipeline.
+
+All strategies accept both scalar and per-sample tensor
+parameters, so a single pipeline works for any batch size.
+"""
+
+from abc import ABC, abstractmethod
+from typing import List, Union
+
+import torch
+from torch import Tensor
+
+
+class BaseSamplingStrategy(ABC):
+    """Abstract base for a logit transformation strategy."""
+
+    @abstractmethod
+    def apply(self, logits: Tensor, filter_value: float = -float("inf")) -> Tensor:
+        """Applies the strategy to logits.
+
+        Args:
+            logits: Raw logits tensor (batch, vocab_size).
+            filter_value: Value assigned to filtered-out positions.
+
+        Returns:
+            Transformed logits tensor.
+        """
+
+
+class TemperatureStrategy(BaseSamplingStrategy):
+    """Divides logits by temperature to control randomness.
+
+    Args:
+        temperature: Scalar or ``[batch]`` tensor.
+    """
+
+    def __init__(self, temperature: Union[float, Tensor] = 1.0):
+        self.temperature = temperature
+
+    def apply(self, logits, filter_value=-float("inf")):
+        t = self.temperature
+        if isinstance(t, Tensor):
+            t = t.to(logits.device, non_blocking=True).view(-1, 1)
+            t = torch.clamp(t, min=1e-8)
+            if (t != 1.0).any():
+                logits = logits / t
+        elif t != 1.0:
+            logits = logits / max(t, 1e-8)
+        return logits
+
+
+class TopKStrategy(BaseSamplingStrategy):
+    """Keeps only the top-k logits, setting the rest to filter_value.
+
+    Args:
+        top_k: Scalar or ``[batch]`` tensor (0 disables).
+    """
+
+    def __init__(self, top_k: Union[int, Tensor] = 0):
+        self.top_k = top_k
+
+    def apply(self, logits, filter_value=-float("inf")):
+        tk = self.top_k
+        if isinstance(tk, Tensor):
+            tk = tk.to(logits.device, non_blocking=True).long().clamp(min=0)
+            max_k = int(tk.max().item())
+            if max_k <= 0:
+                return logits
+            max_k = min(max_k, logits.size(-1))
+            values, _ = torch.topk(logits, max_k, dim=-1)
+            per_row_k = tk.clamp(max=max_k)
+            thresholds = torch.full_like(logits[..., -1:], -float("inf"))
+            positive = per_row_k > 0
+            if positive.any():
+                row_idx = torch.arange(logits.size(0), device=logits.device)[positive]
+                thresholds[positive] = values[
+                    row_idx, per_row_k[positive] - 1
+                ].unsqueeze(-1)
+            logits[logits < thresholds] = filter_value
+            return logits
+        if tk > 0:
+            k = min(tk, logits.size(-1))
+            thresholds = torch.topk(logits, k, dim=-1)[0][..., -1:]
+            logits[logits < thresholds] = filter_value
+        return logits
+
+
+class TopPStrategy(BaseSamplingStrategy):
+    """Nucleus (top-p) filtering: keeps the smallest set of tokens whose
+    cumulative probability exceeds top_p.
+
+    Args:
+        top_p: Scalar or ``[batch]`` tensor (1.0 disables).
+    """
+
+    def __init__(self, top_p: Union[float, Tensor] = 1.0):
+        self.top_p = top_p
+
+    def _apply(self, logits, top_p, filter_value):
+        sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1)
+        cum_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
+        remove = cum_probs > top_p
+        remove[..., 1:] = remove[..., :-1].clone()
+        remove[..., 0] = False
+        mask = torch.zeros_like(logits, dtype=torch.bool)
+        mask.scatter_(1, sorted_indices, remove)
+        logits[mask] = filter_value
+        return logits
+
+    def apply(self, logits, filter_value=-float("inf")):
+        tp = self.top_p
+        if isinstance(tp, Tensor):
+            tp = tp.to(logits.device, non_blocking=True)
+            if (tp < 1.0).any():
+                logits = self._apply(logits, tp.view(-1, 1), filter_value)
+        elif tp < 1.0:
+            logits = self._apply(logits, tp, filter_value)
+        return logits
+
+
+class SamplingPipeline(BaseSamplingStrategy):
+    """Composes multiple sampling strategies into a single transformation.
+
+    Strategies are applied sequentially in the order they are provided,
+    matching the original temperature -> top-k -> top-p ordering.
+
+    Usage::
+
+        pipeline = SamplingPipeline([
+            TemperatureStrategy(0.8),
+            TopKStrategy(50),
+            TopPStrategy(0.95),
+        ])
+        logits = pipeline.apply(logits)
+        token = pipeline.sample(logits)       # softmax + multinomial
+    """
+
+    def __init__(self, strategies: List[BaseSamplingStrategy]):
+        self.strategies = strategies
+
+    def apply(self, logits, filter_value=-float("inf")):
+        for strategy in self.strategies:
+            logits = strategy.apply(logits, filter_value)
+        return logits
+
+    @torch.no_grad()
+    def sample(self, logits: Tensor, filter_value: float = -float("inf")) -> Tensor:
+        """Apply strategies then sample (softmax + multinomial).
+
+        Args:
+            logits: Raw logits ``[batch, vocab_size]``.
+
+        Returns:
+            Sampled token IDs ``[batch]``.
+        """
+        return torch.multinomial(
+            torch.softmax(self.apply(logits, filter_value), dim=-1),
+            num_samples=1,
+        ).squeeze(-1)
+
+
+@torch.inference_mode()
+def sample(
+    logits: Tensor,
+    temperature: Union[float, Tensor] = 1.0,
+    top_k: Union[int, Tensor] = 0,
+    top_p: Union[float, Tensor] = 1.0,
+    filter_value: float = -float("inf"),
+) -> Tensor:
+    """Apply sampling strategies then sample (softmax + multinomial).
+
+    Shortcut for ``SamplingPipeline(...).sample(logits)``.
+
+    Args:
+        logits: Raw logits ``[batch, vocab_size]``.
+
+    Returns:
+        Sampled token IDs ``[batch]``.
+    """
+    return SamplingPipeline(
+        [
+            TemperatureStrategy(temperature),
+            TopKStrategy(top_k),
+            TopPStrategy(top_p),
+        ]
+    ).sample(logits, filter_value)

astrai/model/__init__.py

@@ -0,0 +1,34 @@
+from astrai.model.automodel import AutoModel
+from astrai.model.components.attention import GQA
+from astrai.model.components.decoder_block import DecoderBlock
+from astrai.model.components.linear import Linear
+from astrai.model.components.lora import (
+    LoRAConfig,
+    inject_lora,
+    load_lora,
+    merge_lora,
+    save_lora,
+)
+from astrai.model.components.mlp import MLP
+from astrai.model.components.norm import RMSNorm
+from astrai.model.encoder import EmbeddingEncoder
+from astrai.model.transformer import AutoRegressiveLM
+
+__all__ = [
+    # Modules
+    "Linear",
+    "RMSNorm",
+    "MLP",
+    "GQA",
+    "DecoderBlock",
+    # Models
+    "AutoRegressiveLM",
+    "EmbeddingEncoder",
+    "AutoModel",
+    # LoRA
+    "LoRAConfig",
+    "inject_lora",
+    "merge_lora",
+    "save_lora",
+    "load_lora",
+]

astrai/model/automodel.py

@@ -0,0 +1,95 @@
+"""
+AutoModel base class for model loading and saving.
+"""
+
+from contextlib import contextmanager
+from pathlib import Path
+from typing import Self, Union
+
+import torch.nn as nn
+
+from astrai.config.model_config import BaseModelConfig, ConfigFactory
+from astrai.factory import BaseFactory
+from astrai.serialization import load_model_config, load_model_weights, save_model
+
+
+@contextmanager
+def _disable_random_init(enable: bool = True):
+    if not enable:
+        yield
+        return
+
+    names = (
+        "xavier_normal_",
+        "xavier_uniform_",
+        "kaiming_normal_",
+        "kaiming_uniform_",
+        "zeros_",
+        "ones_",
+        "constant_",
+        "normal_",
+        "uniform_",
+    )
+    orig = {n: getattr(nn.init, n) for n in names if hasattr(nn.init, n)}
+    for n in orig:
+        setattr(nn.init, n, lambda *a, **kw: None)
+    try:
+        yield
+    finally:
+        for n, fn in orig.items():
+            setattr(nn.init, n, fn)
+
+
+class AutoModel(BaseFactory["AutoModel"], nn.Module):
+    """
+    Autoregressive language model base class.
+    Provides model loading/saving, registration, and generation.
+    """
+
+    def __init__(self, config: BaseModelConfig):
+        super().__init__()
+        self.config = config
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        path: Union[str, Path],
+        disable_random_init: bool = True,
+        strict: bool = True,
+    ) -> nn.Module:
+
+        model_path = Path(path)
+
+        config_path = model_path / "config.json"
+        if not config_path.exists():
+            raise FileNotFoundError(f"Config file not found: {config_path}")
+
+        raw = load_model_config(str(model_path))
+        config = ConfigFactory.load(raw)
+        model_type = config.model_type or "autoregressive_lm"
+
+        actual_cls = AutoModel.get_component_class(model_type)
+
+        with _disable_random_init(enable=disable_random_init):
+            model = actual_cls(config)
+
+        weights_path = model_path / "model.safetensors"
+        if weights_path.exists():
+            state_dict = load_model_weights(str(model_path))
+            model.load_state_dict(state_dict, strict=strict)
+
+        return model
+
+    def save_pretrained(
+        self,
+        save_directory: Union[str, Path],
+    ):
+        save_model(
+            config=self.config.to_dict(),
+            state_dict=self.state_dict(),
+            save_directory=str(save_directory),
+        )
+
+    def to(self, *args, **kwargs) -> Self:
+        """Move model to device/dtype."""
+        return super().to(*args, **kwargs)

astrai/model/components/__init__.py

@@ -0,0 +1,25 @@
+from astrai.model.components.attention import GQA, MLA, repeat_kv
+from astrai.model.components.decoder_block import DecoderBlock
+from astrai.model.components.embedding import Embedding
+from astrai.model.components.linear import Linear
+from astrai.model.components.mlp import MLP
+from astrai.model.components.norm import RMSNorm
+from astrai.model.components.rope import (
+    RotaryEmbedding,
+    apply_rotary_emb,
+    get_rotary_emb,
+)
+
+__all__ = [
+    "Linear",
+    "RMSNorm",
+    "MLP",
+    "Embedding",
+    "GQA",
+    "MLA",
+    "DecoderBlock",
+    "RotaryEmbedding",
+    "apply_rotary_emb",
+    "get_rotary_emb",
+    "repeat_kv",
+]

astrai/model/components/attention.py

@@ -0,0 +1,212 @@
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+
+from astrai.factory import BaseFactory
+from astrai.inference.core.cache import KvcacheView
+from astrai.model.components.linear import Linear
+from astrai.model.components.norm import RMSNorm
+from astrai.model.components.rope import apply_rotary_emb
+
+
+def repeat_kv(x: Tensor, n_rep: int) -> Tensor:
+    bs, slen, n_heads, head_dim = x.shape
+    if n_rep == 1:
+        return x
+    return (
+        x[:, :, :, None, :]
+        .expand(bs, slen, n_heads, n_rep, head_dim)
+        .reshape(bs, slen, n_heads * n_rep, head_dim)
+    )
+
+
+class AttnFactory(BaseFactory[nn.Module]):
+    @classmethod
+    def create(cls, attn_type: str, **kwargs) -> nn.Module:
+        return super().create(attn_type, **kwargs)
+
+
+@AttnFactory.register("gqa")
+class GQA(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        n_heads: int,
+        n_kv_heads: int,
+        use_qk_norm: bool,
+        norm_eps: float,
+        use_gated_attention: bool,
+        layer_id: int,
+    ):
+        super().__init__()
+        assert dim % n_heads == 0
+        assert n_heads % n_kv_heads == 0
+
+        self.head_dim = dim // n_heads
+        self.layer_id = layer_id
+        self.dim = dim
+        self.n_heads = n_heads
+        self.n_kv_heads = n_kv_heads
+        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(dim, n_heads * self.head_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,
+        x: Tensor,
+        rotary_emb: Tensor,
+        attn_mask: Tensor = None,
+        paged_cache: Optional[KvcacheView] = None,
+    ) -> Tensor:
+        is_causal = attn_mask is None
+
+        q = self._split_heads(self.q_proj(x), self.n_heads)
+        k = self._split_heads(self.k_proj(x), self.n_kv_heads)
+        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 paged_cache is not None:
+            paged_cache.write(self.layer_id, k, v)
+            k, v = paged_cache.gather(self.layer_id)
+
+        k, v = repeat_kv(k, self.n_rep), repeat_kv(v, self.n_rep)
+
+        q, k, v = q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3)
+        sdqa_out = (
+            F.scaled_dot_product_attention(q, k, v, attn_mask, is_causal=is_causal)
+            .permute(0, 2, 1, 3)
+            .contiguous()
+            .flatten(2)
+        )
+
+        if self.use_gated_attention:
+            sdqa_out = sdqa_out * F.sigmoid(self.gate(x))
+
+        out = self.o_proj(sdqa_out)
+        return out
+
+
+@AttnFactory.register("mla")
+class MLA(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        n_heads: int,
+        n_kv_heads: int,
+        kv_lora_rank: int,
+        qk_nope_head_dim: int,
+        qk_rope_head_dim: int,
+        norm_eps: float,
+        use_qk_norm: bool,
+        use_gated_attention: bool,
+        layer_id: int,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.n_heads = n_heads
+        self.n_kv_heads = n_kv_heads
+        self.kv_lora_rank = kv_lora_rank
+        self.qk_nope_head_dim = qk_nope_head_dim
+        self.qk_rope_head_dim = qk_rope_head_dim
+        self.head_dim = qk_nope_head_dim + qk_rope_head_dim
+        self.layer_id = layer_id
+        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(dim, n_heads * self.head_dim, bias=False)
+
+        if self.use_qk_norm:
+            self.q_norm = RMSNorm(self.head_dim, norm_eps)
+            self.k_norm = RMSNorm(self.head_dim, norm_eps)
+        self.kv_a_proj = Linear(dim, kv_lora_rank, bias=False)
+        self.kv_norm = RMSNorm(kv_lora_rank, norm_eps)
+
+        self.kv_b_proj = Linear(
+            kv_lora_rank,
+            n_kv_heads * (2 * self.head_dim),
+        )
+
+        self.o_proj = Linear(dim, dim, bias=False)
+
+        if use_gated_attention:
+            self.gate = Linear(dim, dim, bias=False)
+
+    def forward(
+        self,
+        x: Tensor,
+        rotary_emb: Tensor,
+        attn_mask: Tensor = None,
+        paged_cache: Optional[KvcacheView] = None,
+    ) -> Tensor:
+        bsz, seq_len, _ = x.size()
+        is_causal = attn_mask is None
+
+        q = self.q_proj(x)
+        q = q.view(bsz, seq_len, self.n_heads, self.head_dim)
+
+        kv_compressed = self.kv_a_proj(x)
+        kv_compressed = self.kv_norm(kv_compressed)
+
+        kv = self.kv_b_proj(kv_compressed)
+        kv = kv.view(bsz, seq_len, self.n_kv_heads, -1)
+
+        k_nope, k_rope, v = torch.split(
+            kv, [self.qk_nope_head_dim, self.qk_rope_head_dim, self.head_dim], dim=-1
+        )
+
+        q_nope, q_rope = (
+            q[..., : self.qk_nope_head_dim],
+            q[..., self.qk_nope_head_dim :],
+        )
+        q_rope = apply_rotary_emb(q_rope, rotary_emb)
+        k_rope = apply_rotary_emb(k_rope, rotary_emb)
+
+        q = torch.cat([q_nope, q_rope], dim=-1)
+        k = torch.cat([k_nope, k_rope], dim=-1)
+
+        if self.use_qk_norm:
+            q = self.q_norm(q)
+            k = self.k_norm(k)
+
+        if paged_cache is not None:
+            paged_cache.write(self.layer_id, k, v)
+            k, v = paged_cache.gather(self.layer_id)
+
+        q = q.permute(0, 2, 1, 3)
+        k = k.permute(0, 2, 1, 3)
+        v = v.permute(0, 2, 1, 3)
+
+        attn_out = F.scaled_dot_product_attention(
+            q, k, v, attn_mask, is_causal=is_causal
+        )
+        attn_out = attn_out.permute(0, 2, 1, 3).contiguous().flatten(2)
+
+        if self.use_gated_attention:
+            attn_out = attn_out * F.sigmoid(self.gate(x))
+
+        out = self.o_proj(attn_out)
+        return out

astrai/model/components/decoder_block.py

@@ -0,0 +1,59 @@
+from typing import Optional
+
+import torch.nn as nn
+from torch import Tensor
+
+from astrai.inference.core.cache import KvcacheView
+from astrai.model.components.attention import AttnFactory
+from astrai.model.components.mlp import FFNFactory
+from astrai.model.components.norm import RMSNorm
+
+
+class DecoderBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        n_heads: int,
+        dim_ffn: int,
+        n_kv_heads: int,
+        norm_eps: float,
+        use_qk_norm: bool,
+        use_gated_attention: bool,
+        layer_id: int,
+        attn_type: str = "gqa",
+        ffn_type: str = "mlp",
+        **kwargs,
+    ):
+        super().__init__()
+        self.attention = AttnFactory.create(
+            attn_type,
+            dim=dim,
+            n_heads=n_heads,
+            n_kv_heads=n_kv_heads,
+            use_qk_norm=use_qk_norm,
+            norm_eps=norm_eps,
+            use_gated_attention=use_gated_attention,
+            layer_id=layer_id,
+            **kwargs,
+        )
+        self.input_norm = RMSNorm(dim, norm_eps)
+        self.post_attention_norm = RMSNorm(dim, norm_eps)
+        self.mlp = FFNFactory.create(ffn_type, dim, dim_ffn, **kwargs)
+
+    def forward(
+        self,
+        x: Tensor,
+        rotary_emb: Tensor,
+        attention_mask: Optional[Tensor] = None,
+        paged_cache: Optional[KvcacheView] = None,
+    ) -> Tensor:
+        attn_output = self.attention(
+            self.input_norm(x),
+            rotary_emb,
+            attention_mask,
+            paged_cache,
+        )
+        x = attn_output + x
+        x = self.mlp(self.post_attention_norm(x)) + x
+
+        return x

astrai/model/components/embedding.py

@@ -0,0 +1,16 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+
+
+class Embedding(nn.Module):
+    def __init__(self, vocab_size: int, embedding_dim: int):
+        super().__init__()
+        self.weight = nn.Parameter(torch.empty((vocab_size, embedding_dim)))
+
+    def reset_parameters(self):
+        nn.init.normal_(self.weight, mean=0.0, std=0.02)
+
+    def forward(self, x: Tensor) -> Tensor:
+        return F.embedding(x, self.weight)

astrai/model/components/linear.py

@@ -0,0 +1,21 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+
+
+class Linear(nn.Module):
+    def __init__(self, in_dim: int, out_dim: int, bias: bool = False):
+        super().__init__()
+        self.weight = nn.Parameter(torch.empty((out_dim, in_dim)))
+        self.bias = nn.Parameter(torch.zeros(out_dim)) if bias else None
+
+    def reset_parameters(self):
+        nn.init.kaiming_uniform_(self.weight, a=5**0.5)
+        if self.bias is not None:
+            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
+            bound = 1 / (fan_in**0.5)
+            nn.init.uniform_(self.bias, -bound, bound)
+
+    def forward(self, x: Tensor) -> Tensor:
+        return F.linear(x, self.weight, self.bias)

astrai/model/components/lora.py

@@ -0,0 +1,194 @@
+import logging
+from dataclasses import asdict, dataclass
+from pathlib import Path
+from typing import Optional, Set
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from astrai.model.components.linear import Linear
+from astrai.serialization import (
+    load_json,
+    load_safetensors,
+    save_json,
+    save_safetensors,
+)
+
+logger = logging.getLogger(__name__)
+
+TARGET_MODULES_ATTN = {"q_proj", "k_proj", "v_proj", "o_proj"}
+TARGET_MODULES_FFN = {"up", "gate", "down"}
+
+
+@dataclass
+class LoRAConfig:
+    r: int = 16
+    alpha: int = 32
+    target_modules: tuple = ("q_proj", "v_proj")
+
+
+class LoRALinear(nn.Module):
+    def __init__(self, base: Linear, r: int = 16, alpha: int = 32):
+        super().__init__()
+        self.register_parameter("weight", base.weight)
+        self.weight.requires_grad_(False)
+        self.bias = base.bias
+        if self.bias is not None:
+            self.bias.requires_grad_(False)
+
+        self.r = r
+        self.scaling = alpha / r
+        self.lora_A = nn.Parameter(torch.randn(r, self.weight.shape[1]) / r)
+        self.lora_B = nn.Parameter(torch.zeros(self.weight.shape[0], r))
+        self._merged = False
+
+    def forward(self, x):
+        out = F.linear(x, self.weight, self.bias)
+        if not self._merged:
+            out += (F.linear(x, self.lora_A) @ self.lora_B.T) * self.scaling
+        return out
+
+    def merge(self):
+        if self._merged:
+            return
+        self.weight.data += (self.lora_B @ self.lora_A) * self.scaling
+        self._merged = True
+        del self.lora_A
+        del self.lora_B
+
+
+def _collect_lora_info(model: nn.Module) -> dict:
+    names = {}
+    for n, m in model.named_modules():
+        if isinstance(m, Linear):
+            _, _, child = n.rpartition(".")
+            names.setdefault(child, []).append(n)
+    return names
+
+
+def _get_lora_count(model: nn.Module) -> int:
+    return sum(1 for m in model.modules() if isinstance(m, LoRALinear))
+
+
+def inject_lora(
+    model: nn.Module,
+    r: int = 16,
+    alpha: int = 32,
+    target_modules: Optional[Set[str]] = None,
+) -> LoRAConfig:
+    if target_modules is None:
+        target_modules = TARGET_MODULES_ATTN
+
+    available = _collect_lora_info(model)
+    injected = 0
+
+    for name, module in list(model.named_modules()):
+        if not isinstance(module, Linear):
+            continue
+        parent_name, _, child_name = name.rpartition(".")
+        if child_name not in target_modules:
+            continue
+        parent = model.get_submodule(parent_name) if parent_name else model
+        setattr(parent, child_name, LoRALinear(module, r=r, alpha=alpha))
+        injected += 1
+
+    if injected == 0:
+        logger.warning(
+            "No LoRA layers injected. Available Linear child names: %s. "
+            "target_modules: %s. Check model type and target_modules.",
+            sorted(available),
+            sorted(target_modules),
+        )
+    else:
+        logger.info("LoRA injected: %d layers (r=%d, alpha=%d)", injected, r, alpha)
+
+    return LoRAConfig(r=r, alpha=alpha, target_modules=tuple(target_modules))
+
+
+def merge_lora(model: nn.Module):
+    n = 0
+    for module in model.modules():
+        if isinstance(module, LoRALinear):
+            module.merge()
+            n += 1
+    if n == 0:
+        logger.warning("No LoRA layers to merge.")
+    else:
+        logger.info("Merged %d LoRA layers", n)
+
+
+def save_lora(model: nn.Module, save_dir: str, config: LoRAConfig):
+    lora_sd = {
+        k: v
+        for k, v in model.state_dict().items()
+        if k.endswith((".lora_A", ".lora_B"))
+    }
+    if not lora_sd:
+        raise RuntimeError(
+            "No LoRA parameters found in model. "
+            "The model may not have been injected or was already merged."
+        )
+
+    path = Path(save_dir)
+    path.mkdir(parents=True, exist_ok=True)
+    save_safetensors(lora_sd, path / "adapter_model.safetensors")
+    save_json(asdict(config), path / "adapter_config.json")
+    logger.info("LoRA adapter saved to %s (%d keys)", save_dir, len(lora_sd))
+
+
+def load_lora(model: nn.Module, load_dir: str) -> LoRAConfig:
+    path = Path(load_dir)
+    raw = load_json(path / "adapter_config.json")
+    config = LoRAConfig(
+        r=raw["r"], alpha=raw["alpha"], target_modules=tuple(raw["target_modules"])
+    )
+
+    existing = _get_lora_count(model)
+    if existing > 0:
+        logger.warning(
+            "Model already has %d LoRA layers. Skipping injection, "
+            "loading weights onto existing layers only.",
+            existing,
+        )
+    else:
+        inject_lora(
+            model,
+            r=config.r,
+            alpha=config.alpha,
+            target_modules=set(config.target_modules),
+        )
+
+    weights = load_safetensors(path / "adapter_model.safetensors")
+    try:
+        missing, unexpected = model.load_state_dict(weights, strict=False)
+    except RuntimeError as e:
+        msg = str(e)
+        if "size mismatch" in msg:
+            raise RuntimeError(
+                f"LoRA weight shapes do not match the model. "
+                f"The adapter config (r={config.r}) may not match the injected layers. "
+                f"Original error: {msg}"
+            ) from e
+        raise
+
+    injected = _get_lora_count(model)
+    if injected == 0:
+        raise RuntimeError(
+            "No LoRA layers found after loading. "
+            "Inject LoRA before calling load_lora, or check the adapter config."
+        )
+
+    if missing:
+        lora_missing = [k for k in missing if "lora" in k]
+        if lora_missing:
+            raise RuntimeError(
+                f"LoRA weight keys not found in model: {lora_missing}. "
+                f"The adapter config (r={config.r}) may not match the model."
+            )
+        logger.debug("LoRA load: %d missing base-weight keys (expected)", len(missing))
+    if unexpected:
+        logger.warning("LoRA load: %d unexpected keys", len(unexpected))
+
+    logger.info("LoRA adapter loaded from %s", load_dir)
+    return config

astrai/model/components/mlp.py

@@ -0,0 +1,93 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+
+from astrai.factory import BaseFactory
+from astrai.model.components.linear import Linear
+
+
+class FFNFactory(BaseFactory[nn.Module]):
+    @classmethod
+    def create(cls, ffn_type: str, dim: int, dim_ffn: int, **kwargs) -> nn.Module:
+        return super().create(ffn_type, dim, dim_ffn, **kwargs)
+
+
+@FFNFactory.register("mlp")
+class MLP(nn.Module):
+    def __init__(self, dim: int, dim_ffn: int):
+        super().__init__()
+        self.up = Linear(dim, dim_ffn)
+        self.gate = Linear(dim, dim_ffn)
+        self.down = Linear(dim_ffn, dim)
+
+    def forward(self, x: Tensor) -> Tensor:
+        gated = self.up(x) * F.silu(self.gate(x))
+        out = self.down(gated)
+        return out
+
+
+@FFNFactory.register("moe")
+class DeepSeekMoE(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        dim_ffn: int,
+        n_routed_experts: int,
+        n_shared_experts: int = 1,
+        n_activated_experts: int = 2,
+        topk_method: str = "greedy",
+    ):
+        super().__init__()
+        self.dim = dim
+        self.n_routed_experts = n_routed_experts
+        self.n_shared_experts = n_shared_experts
+        self.n_activated_experts = n_activated_experts
+        self.topk_method = topk_method
+
+        self.router = Linear(dim, n_routed_experts, bias=False)
+
+        self.shared_experts = nn.ModuleList(
+            [MLP(dim, dim_ffn) for _ in range(n_shared_experts)]
+        )
+        self.routed_experts = nn.ModuleList(
+            [MLP(dim, dim_ffn) for _ in range(n_routed_experts)]
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        bsz, seq_len, dim = x.shape
+        x_flat = x.view(-1, dim)
+
+        shared_out = self._shared_forward(x_flat)
+        routed_out = self._routed_forward(x_flat)
+
+        out = (shared_out + routed_out).view(bsz, seq_len, dim)
+        return out
+
+    def _shared_forward(self, x: Tensor) -> Tensor:
+        if self.n_shared_experts == 0:
+            return torch.zeros_like(x)
+        return sum(e(x) for e in self.shared_experts) / self.n_shared_experts
+
+    def _routed_forward(self, x: Tensor) -> Tensor:
+        N, D = x.shape
+        K = self.n_activated_experts
+
+        router_logits = self.router(x)
+        router_probs = torch.softmax(router_logits.float(), dim=-1).to(x.dtype)
+
+        topk_weights, topk_indices = torch.topk(router_probs, K, dim=-1)
+        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+
+        output = torch.zeros(N, D, device=x.device, dtype=x.dtype)
+        for expert_idx in range(self.n_routed_experts):
+            expert_mask = topk_indices == expert_idx
+            token_idx, k_idx = expert_mask.nonzero(as_tuple=True)
+            if token_idx.numel() == 0:
+                continue
+            expert_input = x[token_idx]
+            expert_output = self.routed_experts[expert_idx](expert_input)
+            weights = topk_weights[token_idx, k_idx].unsqueeze(-1)
+            output.index_add_(0, token_idx, expert_output * weights)
+
+        return output

astrai/model/components/norm.py

@@ -0,0 +1,15 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim, norm_eps):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(dim))
+        self.normalized_shape = (dim,)
+        self.norm_eps = norm_eps
+
+    def forward(self, x: Tensor) -> Tensor:
+        return F.rms_norm(x, self.normalized_shape, self.weight, self.norm_eps)

astrai/model/components/rope.py

@@ -0,0 +1,71 @@
+from typing import Dict, Optional
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+
+
+def get_rotary_emb(
+    dim: int,
+    max_len: int,
+    base: float = 10000,
+    device: Optional[torch.device] = None,
+) -> Tensor:
+    theta = base ** (-torch.arange(0, dim, 2, dtype=torch.float64, device=device) / dim)
+    t = torch.arange(0, max_len, dtype=torch.float64, device=device)
+    freqs = torch.outer(t, theta).float()
+    cos = torch.cos(freqs)
+    sin = torch.sin(freqs)
+    return torch.complex(cos, sin)
+
+
+def ntk_base(base: float, dim: int, factor: float) -> float:
+    return base * (factor ** (dim / (dim - 2)))
+
+
+def apply_rotary_emb(x: torch.Tensor, freqs_cis: Tensor) -> Tensor:
+    dtype = x.dtype
+    x_ = x.float().reshape(*x.shape[:-1], -1, 2)
+    x_complex = torch.view_as_complex(x_)
+    freqs_cis = freqs_cis.unsqueeze(2)
+    x_rotated = x_complex * freqs_cis
+    x_out = torch.view_as_real(x_rotated).flatten(-2)
+    return x_out.to(dtype)
+
+
+class RotaryEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        max_len: int,
+        base: float = 10000,
+        rope_scaling: Optional[Dict] = None,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.max_len = max_len
+        self.base = base
+        self.rope_scaling = rope_scaling
+
+        if rope_scaling is not None:
+            scaling_type = rope_scaling.get("type", "ntk")
+            factor = rope_scaling.get("factor", 1.0)
+            if scaling_type == "ntk":
+                self.base = ntk_base(base, dim, factor)
+
+        self._set_rotary_buffer(self.max_len)
+
+    def _set_rotary_buffer(self, max_len: int):
+        rotary_emb = get_rotary_emb(self.dim, max_len, self.base)
+        freqs_cis = torch.view_as_real(rotary_emb)
+        self.register_buffer("freqs_cis", freqs_cis, persistent=False)
+
+    def forward(self, x: Tensor, position_ids: Optional[Tensor] = None) -> Tensor:
+        if position_ids is None:
+            position_ids = (
+                torch.arange(x.size(1), device=x.device)
+                .unsqueeze(0)
+                .expand(x.size(0), -1)
+            )
+        position_freq_cis = self.freqs_cis[position_ids].float()
+        return torch.view_as_complex(position_freq_cis)

astrai/model/encoder.py

@@ -0,0 +1,99 @@
+from typing import Any, Mapping, Optional
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+
+from astrai.config.model_config import EncoderConfig
+from astrai.model.automodel import AutoModel
+from astrai.model.components.decoder_block import DecoderBlock
+from astrai.model.components.embedding import Embedding
+from astrai.model.components.norm import RMSNorm
+from astrai.model.components.rope import RotaryEmbedding
+from astrai.model.transformer import process_attention_mask
+
+
+@AutoModel.register("embedding")
+class EmbeddingEncoder(AutoModel):
+    def __init__(self, config: EncoderConfig):
+        super().__init__(config)
+        self.config = config
+        rope_dim = config.dim // config.n_heads
+        rope_base = config.rope_theta if config.rope_theta is not None else 10000
+        self.rotary_embedding = RotaryEmbedding(
+            rope_dim, config.max_len, rope_base, rope_scaling=config.rope_scaling
+        )
+        self.embed_tokens = Embedding(config.vocab_size, config.dim)
+
+        self.layers = nn.ModuleList(
+            [
+                DecoderBlock(
+                    config.dim,
+                    config.n_heads,
+                    config.dim_ffn,
+                    config.n_kv_heads,
+                    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.dim, config.norm_eps)
+
+        self.pooling_type = config.pooling_type or "mean"
+        self.normalize_embeddings = config.normalize_embeddings or False
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if hasattr(module, "reset_parameters"):
+            module.reset_parameters()
+
+    def load_state_dict(self, state_dict: Mapping[str, Any], strict=True, assign=False):
+        state_dict = dict(state_dict)
+        state_dict.pop("lm_head.weight", None)
+        return super().load_state_dict(state_dict, strict=strict, assign=assign)
+
+    def forward(
+        self,
+        input_ids: Tensor,
+        input_mask: Optional[Tensor] = None,
+        position_ids: Optional[Tensor] = None,
+    ) -> Tensor:
+        assert input_ids.ndim == 2
+        B, S = input_ids.shape
+
+        x = self.embed_tokens(input_ids)
+
+        rotary_emb = self.rotary_embedding(x, position_ids)
+        attn_mask = process_attention_mask(x, position_ids, input_mask, is_causal=False)
+
+        for layer in self.layers:
+            x = layer(x, rotary_emb, attn_mask, paged_cache=None)
+
+        hidden_states = self.norm(x)
+
+        if self.pooling_type == "cls":
+            pooled = hidden_states[:, 0]
+        elif self.pooling_type == "last":
+            if input_mask is not None:
+                lengths = input_mask.sum(dim=1) - 1
+                pooled = hidden_states[torch.arange(B, device=x.device), lengths]
+            else:
+                pooled = hidden_states[:, -1]
+        else:
+            if input_mask is not None:
+                mask = input_mask.unsqueeze(-1).to(dtype=hidden_states.dtype)
+                pooled = (hidden_states * mask).sum(dim=1) / mask.sum(dim=1).clamp(
+                    min=1.0
+                )
+            else:
+                pooled = hidden_states.mean(dim=1)
+
+        if self.normalize_embeddings:
+            pooled = torch.nn.functional.normalize(pooled, p=2, dim=-1)
+
+        return pooled

astrai/model/transformer.py

@@ -0,0 +1,152 @@
+from typing import Any, Dict, Mapping, Optional
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+
+from astrai.config.model_config import AutoRegressiveLMConfig
+from astrai.inference.core.cache import KvcacheView
+from astrai.model.automodel import AutoModel
+from astrai.model.components.decoder_block import DecoderBlock
+from astrai.model.components.embedding import Embedding
+from astrai.model.components.linear import Linear
+from astrai.model.components.norm import RMSNorm
+from astrai.model.components.rope import RotaryEmbedding
+
+
+def process_attention_mask(
+    input_tensor: Tensor,
+    position_ids: Optional[Tensor],
+    input_mask: Optional[Tensor] = None,
+    is_causal: bool = False,
+) -> Optional[Tensor]:
+    if position_ids is None:
+        return None
+    if input_mask is not None and input_mask.dim() > 2:
+        return input_mask
+
+    device = input_tensor.device
+    dtype = input_tensor.dtype
+    B, S = input_tensor.size()[:2]
+    T = position_ids.max().item() + 1
+
+    if input_mask is None:
+        if position_ids.min().item() == 0 and is_causal:
+            return None
+        pad = torch.ones(B, T, dtype=torch.bool, device=device)
+    else:
+        pad = input_mask[:, :T].to(device=device, dtype=torch.bool)
+
+    attend = pad.view(B, 1, T).expand(B, S, T).clone()
+    if is_causal:
+        attend &= position_ids.unsqueeze(-1) >= torch.arange(T, device=device)
+
+    return torch.full(
+        (B, 1, S, T), -torch.finfo(dtype).max / 2, dtype=dtype, device=device
+    ).masked_fill_(attend.unsqueeze(1), 0.0)
+
+
+@AutoModel.register("autoregressive_lm")
+class AutoRegressiveLM(AutoModel):
+    """Autoregressive language model with paged KV cache."""
+
+    def __init__(self, config: AutoRegressiveLMConfig):
+        super().__init__(config)
+        self.config = config
+        rope_dim = (
+            config.qk_rope_head_dim
+            if config.attn_type == "mla"
+            else config.dim // config.n_heads
+        )
+        rope_base = config.rope_theta if config.rope_theta is not None else 10000
+        self.rotary_embedding = RotaryEmbedding(
+            rope_dim, config.max_len, rope_base, rope_scaling=config.rope_scaling
+        )
+        self.embed_tokens = Embedding(config.vocab_size, config.dim)
+
+        self.layers = nn.ModuleList(
+            [
+                DecoderBlock(
+                    config.dim,
+                    config.n_heads,
+                    config.dim_ffn,
+                    config.n_kv_heads,
+                    config.norm_eps,
+                    config.use_qk_norm,
+                    config.use_gated_attention,
+                    layer_id,
+                    attn_type=config.attn_type,
+                    ffn_type=config.ffn_type,
+                    n_routed_experts=config.n_routed_experts,
+                    n_shared_experts=config.n_shared_experts,
+                    n_activated_experts=config.n_activated_experts,
+                    topk_method=config.topk_method,
+                    kv_lora_rank=config.kv_lora_rank,
+                    qk_nope_head_dim=config.qk_nope_head_dim,
+                    qk_rope_head_dim=config.qk_rope_head_dim,
+                )
+                for layer_id in range(config.n_layers)
+            ]
+        )
+
+        self.norm = RMSNorm(config.dim, config.norm_eps)
+        self.lm_head = Linear(config.dim, config.vocab_size)
+
+        if self.config.tie_weight is True:
+            self.lm_head.weight = self.embed_tokens.weight
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if hasattr(module, "reset_parameters"):
+            module.reset_parameters()
+
+    def load_state_dict(self, state_dict: Mapping[str, Any], strict=True, assign=False):
+        lm_head_key = "lm_head.weight"
+        embed_key = "embed_tokens.weight"
+
+        state_dict = dict(state_dict)
+
+        if self.config.tie_weight is True:
+            # same tensor for embed and lm_head
+            if embed_key in state_dict:
+                state_dict[lm_head_key] = state_dict[embed_key]
+        else:
+            if lm_head_key not in state_dict and embed_key in state_dict:
+                # clone to avoid sharing gradients
+                state_dict[lm_head_key] = torch.clone(state_dict[embed_key])
+
+        return super().load_state_dict(state_dict, strict, assign)
+
+    def state_dict(self, destination=None, prefix="", keep_vars=False):
+        state_dict = super().state_dict(
+            destination=destination, prefix=prefix, keep_vars=keep_vars
+        )
+
+        if self.config.tie_weight is True:
+            lm_head_key = prefix + "lm_head.weight"
+            if lm_head_key in state_dict:
+                del state_dict[lm_head_key]
+
+        return state_dict
+
+    def forward(
+        self,
+        input_ids: Tensor,
+        input_mask: Optional[Tensor] = None,
+        paged_cache: Optional[KvcacheView] = None,
+        position_ids: Optional[Tensor] = None,
+    ) -> Dict[str, Tensor]:
+        assert input_ids.ndim == 2
+
+        x = self.embed_tokens(input_ids)
+        rotary_emb = self.rotary_embedding(x, position_ids)
+        attn_mask = process_attention_mask(x, position_ids, input_mask, is_causal=True)
+
+        for layer in self.layers:
+            x = layer(x, rotary_emb, attn_mask, paged_cache)
+
+        hidden_states = self.norm(x)
+        logits = self.lm_head(hidden_states)
+
+        return {"logits": logits, "hidden_states": hidden_states}

astrai/parallel/__init__.py

@@ -0,0 +1,38 @@
+from astrai.parallel.executor import (
+    AccumOptimizer,
+    AccumScheduler,
+    BaseExecutor,
+    DDPExecutor,
+    ExecutorFactory,
+    FSDPExecutor,
+    GradientState,
+    NoneExecutor,
+)
+from astrai.parallel.module import ColumnParallelLinear, RowParallelLinear
+from astrai.parallel.setup import (
+    get_current_device,
+    get_rank,
+    get_world_size,
+    only_on_rank,
+    setup_parallel,
+    spawn_parallel_fn,
+)
+
+__all__ = [
+    "get_world_size",
+    "get_rank",
+    "get_current_device",
+    "only_on_rank",
+    "setup_parallel",
+    "spawn_parallel_fn",
+    "RowParallelLinear",
+    "ColumnParallelLinear",
+    "ExecutorFactory",
+    "BaseExecutor",
+    "GradientState",
+    "AccumOptimizer",
+    "AccumScheduler",
+    "NoneExecutor",
+    "DDPExecutor",
+    "FSDPExecutor",
+]

astrai/parallel/executor.py

@@ -0,0 +1,271 @@
+"""Unified training executor — parallel strategy + gradient accumulation."""
+
+import contextlib
+import logging
+from contextlib import contextmanager
+from typing import Optional, Tuple
+
+import torch
+import torch.nn as nn
+from torch.distributed.fsdp import FullStateDictConfig, StateDictType
+from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.optim import Optimizer
+from torch.optim.lr_scheduler import LRScheduler
+from torch.utils.data import DataLoader
+
+from astrai.factory import BaseFactory
+from astrai.parallel.setup import get_rank, get_world_size
+
+logger = logging.getLogger(__name__)
+
+
+class GradientState:
+    def __init__(self, grad_accum_steps: int = 1):
+        self.num_steps = max(grad_accum_steps, 1)
+        self._step: int = 0
+        self._sync_gradients: bool = True
+
+    @property
+    def sync_gradients(self) -> bool:
+        return self._sync_gradients
+
+    def _do_sync(self):
+        self._step += 1
+        self._sync_gradients = self._step % self.num_steps == 0
+
+
+class AccumOptimizer:
+    def __init__(self, optimizer: Optimizer, gradient_state: GradientState):
+        self.optimizer = optimizer
+        self.gradient_state = gradient_state
+
+    def step(self, closure=None):
+        if self.gradient_state.sync_gradients:
+            self.optimizer.step(closure)
+
+    def zero_grad(self):
+        if self.gradient_state.sync_gradients:
+            self.optimizer.zero_grad()
+
+    @property
+    def param_groups(self):
+        return self.optimizer.param_groups
+
+    def state_dict(self):
+        return self.optimizer.state_dict()
+
+    def load_state_dict(self, d):
+        self.optimizer.load_state_dict(d)
+
+
+class AccumScheduler:
+    def __init__(self, scheduler: LRScheduler, gradient_state: GradientState):
+        self.scheduler = scheduler
+        self.gradient_state = gradient_state
+
+    def step(self):
+        if self.gradient_state.sync_gradients:
+            self.scheduler.step()
+
+    def state_dict(self):
+        return self.scheduler.state_dict()
+
+    def load_state_dict(self, d):
+        self.scheduler.load_state_dict(d)
+
+    def get_last_lr(self):
+        return self.scheduler.get_last_lr()
+
+
+class BaseExecutor:
+    def __init__(self, grad_accum_steps: int = 1):
+        self.gradient_state = GradientState(grad_accum_steps)
+
+    def prepare(
+        self,
+        model: nn.Module,
+        optimizer: Optional[Optimizer] = None,
+        dataloader: Optional[DataLoader] = None,
+        scheduler: Optional[LRScheduler] = None,
+    ) -> Tuple[
+        nn.Module, Optional[Optimizer], Optional[DataLoader], Optional[LRScheduler]
+    ]:
+        model = self._prepare_model(model)
+        if optimizer is not None:
+            optimizer = AccumOptimizer(optimizer, self.gradient_state)
+        if scheduler is not None:
+            scheduler = AccumScheduler(scheduler, self.gradient_state)
+        return model, optimizer, dataloader, scheduler
+
+    def _prepare_model(self, model: nn.Module) -> nn.Module:
+        return model
+
+    def _no_sync(self, model: nn.Module):
+        return contextlib.nullcontext()
+
+    @contextmanager
+    def accumulate(self, model: nn.Module):
+        self.gradient_state._do_sync()
+        if not self.gradient_state.sync_gradients:
+            with self._no_sync(model):
+                yield
+        else:
+            yield
+
+    def backward(self, loss: torch.Tensor):
+        loss.backward()
+
+    def unwrap_model(self, model: nn.Module):
+        return model.state_dict()
+
+    @property
+    def use_distributed(self) -> bool:
+        return get_world_size() > 1
+
+    @property
+    def sync_gradients(self) -> bool:
+        return self.gradient_state.sync_gradients
+
+    @property
+    def grad_accum_steps(self) -> int:
+        return self.gradient_state.num_steps
+
+
+class ExecutorFactory(BaseFactory[BaseExecutor]):
+    pass
+
+
+@ExecutorFactory.register("none")
+class NoneExecutor(BaseExecutor):
+    pass
+
+
+@ExecutorFactory.register("ddp")
+class DDPExecutor(BaseExecutor):
+    def __init__(
+        self,
+        grad_accum_steps: int = 1,
+        dim: int = 0,
+        broadcast_buffers: bool = True,
+        init_sync: bool = True,
+        process_group=None,
+        bucket_cap_mb: int = 25,
+        find_unused_parameters: bool = False,
+        check_reduction: bool = False,
+        gradient_as_bucket_view: bool = False,
+        static_graph: bool = False,
+        delay_all_reduce_named_params=None,
+        param_to_hook_all_reduce=None,
+        mixed_precision=None,
+        device_mesh=None,
+    ):
+        super().__init__(grad_accum_steps=grad_accum_steps)
+        self._ddp_kwargs = dict(
+            dim=dim,
+            broadcast_buffers=broadcast_buffers,
+            init_sync=init_sync,
+            process_group=process_group,
+            bucket_cap_mb=bucket_cap_mb,
+            find_unused_parameters=find_unused_parameters,
+            check_reduction=check_reduction,
+            gradient_as_bucket_view=gradient_as_bucket_view,
+            static_graph=static_graph,
+            delay_all_reduce_named_params=delay_all_reduce_named_params,
+            param_to_hook_all_reduce=param_to_hook_all_reduce,
+            mixed_precision=mixed_precision,
+            device_mesh=device_mesh,
+        )
+
+    def _prepare_model(self, model: nn.Module) -> nn.Module:
+        if not self.use_distributed:
+            logger.warning("DDP backend selected but world_size=1, model not wrapped")
+            return model
+        local_rank = get_rank()
+        model = DDP(
+            model,
+            device_ids=[local_rank],
+            output_device=local_rank,
+            **self._ddp_kwargs,
+        )
+        logger.info("Model wrapped with DDP (world_size=%d)", get_world_size())
+        return model
+
+    def _no_sync(self, model: nn.Module):
+        if isinstance(model, DDP):
+            return model.no_sync()
+        return contextlib.nullcontext()
+
+    def unwrap_model(self, model: nn.Module):
+        if isinstance(model, DDP):
+            return model.module.state_dict()
+        return model.state_dict()
+
+
+@ExecutorFactory.register("fsdp")
+class FSDPExecutor(BaseExecutor):
+    def __init__(
+        self,
+        grad_accum_steps: int = 1,
+        process_group=None,
+        sharding_strategy=None,
+        cpu_offload=None,
+        auto_wrap_policy=None,
+        backward_prefetch=None,
+        mixed_precision=None,
+        ignored_modules=None,
+        param_init_fn=None,
+        sync_module_states: bool = False,
+        forward_prefetch: bool = False,
+        limit_all_gathers: bool = True,
+        ignored_states=None,
+        device_mesh=None,
+    ):
+        super().__init__(grad_accum_steps=grad_accum_steps)
+        self._fsdp_kwargs = {
+            k: v
+            for k, v in dict(
+                process_group=process_group,
+                sharding_strategy=sharding_strategy,
+                cpu_offload=cpu_offload,
+                auto_wrap_policy=auto_wrap_policy,
+                backward_prefetch=backward_prefetch,
+                mixed_precision=mixed_precision,
+                ignored_modules=ignored_modules,
+                param_init_fn=param_init_fn,
+                sync_module_states=sync_module_states,
+                forward_prefetch=forward_prefetch,
+                limit_all_gathers=limit_all_gathers,
+                use_orig_params=True,
+                ignored_states=ignored_states,
+                device_mesh=device_mesh,
+            ).items()
+            if v is not None
+        }
+        self._original_model: Optional[nn.Module] = None
+
+    def _prepare_model(self, model: nn.Module) -> nn.Module:
+        if not self.use_distributed:
+            logger.warning("FSDP backend selected but world_size=1, model not wrapped")
+            return model
+        self._original_model = model
+        device_id = torch.device("cuda", get_rank())
+        model = FSDP(model, device_id=device_id, **self._fsdp_kwargs)
+        logger.info("Model wrapped with FSDP (world_size=%d)", get_world_size())
+        return model
+
+    def _no_sync(self, model: nn.Module):
+        if isinstance(model, FSDP):
+            return model.no_sync()
+        return contextlib.nullcontext()
+
+    def unwrap_model(self, model: nn.Module):
+        if isinstance(model, FSDP) and self.use_distributed:
+            with FSDP.state_dict_type(
+                model,
+                StateDictType.FULL_STATE_DICT,
+                FullStateDictConfig(offload_to_cpu=True, rank0_only=False),
+            ):
+                return model.state_dict()
+
+        return model.state_dict()

astrai/parallel/module.py

@@ -0,0 +1,115 @@
+from typing import Dict
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+
+
+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)

astrai/parallel/setup.py

@@ -0,0 +1,166 @@
+import os
+from contextlib import contextmanager
+from functools import wraps
+from typing import Callable
+
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+
+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",
+):
+
+    if dist.is_available() and dist.is_initialized():
+        yield dist.group.WORLD
+        return
+
+    if world_size <= 1:
+        yield None
+        return
+
+    device_id = torch.device(device_type, 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):
+            ret_args = None
+            if get_rank() == rank:
+                ret_args = func(*args, **kwargs)
+
+            if sync and dist.is_available() and dist.is_initialized():
+                dist.barrier()
+
+            return ret_args
+
+        return wrapper
+
+    return decorator
+
+
+def wrapper_spawn_func(
+    rank: int,
+    world_size: int,
+    backend: str,
+    master_addr: str,
+    master_port: str,
+    device_type: str,
+    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,
+        ):
+            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",
+    start_method: str = "spawn",
+    **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_id = torch.device(device_type, 0)
+        os.environ["LOCAL_RANK"] = "0"
+        os.environ["WORLD_SIZE"] = "1"
+        os.environ["LOCAL_DEVICE"] = str(device_id)
+
+        func(**kwargs)
+        return
+
+    wrapper_spawn_func_args = (
+        world_size,
+        backend,
+        master_addr,
+        master_port,
+        device_type,
+        func,
+        kwargs,
+    )
+
+    mp.start_processes(
+        wrapper_spawn_func,
+        args=wrapper_spawn_func_args,
+        nprocs=world_size,
+        start_method=start_method,
+        join=True,
+    )

astrai/preprocessing/__init__.py

@@ -0,0 +1,14 @@
+from astrai.preprocessing.builder import (
+    BaseMaskBuilder,
+    MaskBuilderFactory,
+    SectionedMaskBuilder,
+)
+from astrai.preprocessing.pipeline import Pipeline, filter_by_length
+
+__all__ = [
+    "BaseMaskBuilder",
+    "MaskBuilderFactory",
+    "SectionedMaskBuilder",
+    "Pipeline",
+    "filter_by_length",
+]

astrai/preprocessing/builder.py

@@ -0,0 +1,159 @@
+"""Mask building strategies for preprocessing pipeline.
+
+The single :class:`SectionedMaskBuilder` handles all input formats
+via declarative ``input.sections`` config.
+"""
+
+from abc import ABC, abstractmethod
+from typing import Optional
+
+from astrai.factory import BaseFactory
+
+
+class BaseMaskBuilder(ABC):
+    """Convert a JSONL item into token ids and optional loss_mask."""
+
+    @abstractmethod
+    def build(self, item: dict, config, tokenizer) -> Optional[dict]:
+        """Build ``{ids, loss_mask?, domain}`` from a JSONL record.
+
+        Returns ``None`` to skip the item entirely.
+        """
+        ...
+
+
+class MaskBuilderFactory(BaseFactory["BaseMaskBuilder"]):
+    @classmethod
+    def _validate_component(cls, component_cls: type):
+        if not issubclass(component_cls, BaseMaskBuilder):
+            raise TypeError(
+                f"{component_cls.__name__} must inherit from BaseMaskBuilder"
+            )
+
+
+def _extract_domain(item: dict, domain_key: Optional[str]) -> str:
+    if not domain_key:
+        return "__default__"
+    val = item.get(domain_key, "__default__")
+    return val if isinstance(val, str) else "__default__"
+
+
+def _resolve_action(action: str, role: str, config) -> str:
+    """Resolve action to "train" or "mask".
+
+    - ``"train"`` / ``"mask"`` → literal
+    - ``"$role"`` → look up ``role`` in ``config.mask``, fall back to ``config.mask_default``
+    """
+    if action == "$role":
+        return config.mask.get(role, config.mask_default)
+    return action
+
+
+@MaskBuilderFactory.register("sectioned")
+class SectionedMaskBuilder(BaseMaskBuilder):
+    """Config-driven builder: iterates over ``input.sections`` in order.
+
+    Each section specifies a JSONL field + mask action.
+
+    Section spec::
+
+        {
+            "field":    "messages",   # JSONL key
+            "action":   "$role",      # "train" | "mask" | "$role"
+            "template": true,         # apply chat_template per message (optional)
+            "add_special_tokens": false  # override encode flag (optional)
+        }
+
+    Example configs::
+
+        # Chat
+        {"input": {"sections": [
+            {"field": "messages", "action": "$role", "template": true}
+        ]}}
+
+        # Instruction
+        {"input": {"sections": [
+            {"field": "prompt",   "action": "mask", "add_special_tokens": true},
+            {"field": "response", "action": "train"}
+        ]}}
+
+        # Text
+        {"input": {"sections": [
+            {"field": "text", "action": "train"}
+        ]}}
+    """
+
+    def build(self, item: dict, config, tokenizer) -> Optional[dict]:
+        sections = config.input.sections
+        if not sections:
+            return None
+
+        all_ids: list[int] = []
+        loss_mask: list[int] = []
+
+        has_template = any(s.get("template") for s in sections)
+        is_text_config = not has_template and all(
+            s["action"] == "train" for s in sections
+        )
+
+        if has_template and tokenizer.bos_token_id is not None:
+            all_ids.append(tokenizer.bos_token_id)
+            loss_mask.append(0)
+
+        first_section = True
+        for sec in sections:
+            field = sec["field"]
+            action = sec["action"]
+            use_template = sec.get("template", False)
+            add_special = sec.get(
+                "add_special_tokens", not use_template and first_section
+            )
+
+            if use_template:
+                messages = item.get(field)
+                if not isinstance(messages, list) or not messages:
+                    continue
+                for msg in messages:
+                    role = msg.get("role", "")
+                    act = _resolve_action(action, role, config)
+                    rendered = tokenizer.apply_chat_template(
+                        [msg], tokenize=False, add_generation_prompt=False
+                    )
+                    ids = tokenizer.encode(rendered, add_special_tokens=False)
+                    all_ids.extend(ids)
+                    val = 1 if act == "train" else 0
+                    loss_mask.extend([val] * len(ids))
+            else:
+                text = str(item.get(field, ""))
+                if not text.strip():
+                    continue
+                if is_text_config:
+                    pp = config.preprocessing
+                    if pp.min_chars > 0 and len(text) < pp.min_chars:
+                        continue
+                    if len(text) > pp.max_chars:
+                        continue
+                ids = tokenizer.encode(text, add_special_tokens=add_special)
+                all_ids.extend(ids)
+                val = 1 if action == "train" else 0
+                loss_mask.extend([val] * len(ids))
+
+            first_section = False
+
+        max_len = config.preprocessing.max_seq_len
+        all_ids = all_ids[:max_len]
+        loss_mask = loss_mask[: len(all_ids)]
+
+        if not all_ids:
+            return None
+
+        if has_template and len(all_ids) <= 1:
+            return None
+
+        result: dict = {
+            "sequence": all_ids,
+            "domain": _extract_domain(item, config.output.domain_key),
+        }
+        if not all(m == 1 for m in loss_mask):
+            result["loss_mask"] = loss_mask
+        return result

astrai/preprocessing/pipeline.py

@@ -0,0 +1,141 @@
+"""Config-driven JSONL preprocessing pipeline.
+
+Composes a :class:`BaseMaskBuilder` (selected by ``input.type``) with
+sharding and flush to ``.h5`` / ``.bin`` storage.
+"""
+
+import json
+import os
+from collections import defaultdict
+from itertools import chain
+from typing import Optional
+
+import torch
+import tqdm
+
+from astrai.config.preprocess_config import PipelineConfig
+from astrai.dataset.storage import save_bin, save_h5
+from astrai.preprocessing.builder import SectionedMaskBuilder
+from astrai.tokenize import AutoTokenizer
+
+_STR_TO_DTYPE: dict[str, torch.dtype] = {
+    "bool": torch.bool,
+    "uint8": torch.uint8,
+    "int8": torch.int8,
+    "int16": torch.int16,
+    "int32": torch.int32,
+    "int64": torch.int64,
+    "float16": torch.float16,
+    "float32": torch.float32,
+    "float64": torch.float64,
+}
+
+
+def filter_by_length(text: str, min_len: int = 50, max_len: int = 2_000_000) -> bool:
+    return min_len <= len(text) <= max_len
+
+
+class Pipeline:
+    """Tokenization pipeline driven by a declarative :class:`PipelineConfig`.
+
+    Usage::
+
+        config = PipelineConfig.from_json("sft_pipeline.json")
+        Pipeline(config, ["data.jsonl"], output_dir="out", tokenizer_path="params").run()
+    """
+
+    def __init__(
+        self,
+        config: PipelineConfig,
+        input_paths: list[str],
+        output_dir: str,
+        tokenizer_path: str,
+    ):
+        os.makedirs(output_dir, exist_ok=True)
+        self.config = config
+        self.paths = input_paths
+        self.output_dir = output_dir
+        self.tokenizer_path = tokenizer_path
+
+        self.mask_builder = SectionedMaskBuilder()
+
+    def transform(self, item: dict) -> Optional[dict]:
+        return self.mask_builder.build(item, self.config, self._tokenizer)
+
+    def run(self):
+        self._tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_path)
+        domains: dict = defaultdict(lambda: defaultdict(list))
+        total_tokens = 0
+        shard_idx: dict[str, int] = defaultdict(int)
+        count = 0
+
+        pp = self.config.preprocessing
+
+        for item in tqdm.tqdm(
+            self._iter_items(), desc="Tokenizing", unit="docs", mininterval=0.5
+        ):
+            if pp.max_items and count >= pp.max_items:
+                break
+
+            result = self.transform(item)
+            if result is None:
+                continue
+
+            ids = result.pop("sequence")
+            if not ids:
+                continue
+
+            domain = result.pop("domain", "__default__")
+            result["sequence"] = ids
+
+            bucket = domains[domain]
+            for key in list(bucket.keys()):
+                if key not in result:
+                    bucket[key].append([1] * len(ids))
+            for key, val in result.items():
+                bucket[key].append(val)
+
+            count += 1
+            total_tokens += len(ids)
+
+            if total_tokens >= self.config.output.max_tokens_per_shard:
+                self._flush(domains, shard_idx)
+                domains.clear()
+                total_tokens = 0
+
+        if total_tokens > 0:
+            self._flush(domains, shard_idx)
+
+        print(f"Done. {count} documents tokenized.")
+
+    def _iter_items(self):
+        for path in self.paths:
+            with open(path, "r", encoding="utf-8") as f:
+                for line in f:
+                    line = line.strip()
+                    if not line:
+                        continue
+                    yield json.loads(line)
+
+    def _flush(self, domains, shard_idx):
+        for domain, keys in domains.items():
+            idx = shard_idx[domain]
+            tensors = {}
+            for key, ids_list in keys.items():
+                dt = _STR_TO_DTYPE.get(
+                    self.config.output.dtype.get(key, "int32"), torch.int32
+                )
+                tensors[key] = [
+                    torch.tensor(list(chain.from_iterable(ids_list)), dtype=dt)
+                ]
+            chunk_dir = os.path.join(self.output_dir, domain)
+            fmt = self.config.output.storage_format
+            if fmt == "bin":
+                save_bin(os.path.join(chunk_dir, f"shard_{idx:04d}"), tensors)
+            else:
+                save_h5(chunk_dir, f"data_{idx:04d}", tensors)
+            shard_idx[domain] = idx + 1
+            tqdm.tqdm.write(
+                f"  saved {domain}/shard_{idx:04d}  "
+                f"({tensors['sequence'][0].numel():,} tokens)"
+            )

astrai/protocols.py

@@ -0,0 +1,21 @@
+"""Training component protocols — structural subtyping for optimizer/scheduler wrappers."""
+
+from typing import Any, Protocol, runtime_checkable
+
+
+@runtime_checkable
+class OptimizerProtocol(Protocol):
+    def step(self, closure=None): ...
+    def zero_grad(self): ...
+    @property
+    def param_groups(self) -> Any: ...
+    def state_dict(self) -> dict: ...
+    def load_state_dict(self, d: dict): ...
+
+
+@runtime_checkable
+class SchedulerProtocol(Protocol):
+    def step(self): ...
+    def state_dict(self) -> dict: ...
+    def load_state_dict(self, d: dict): ...
+    def get_last_lr(self): ...

astrai/serialization.py

@@ -0,0 +1,182 @@
+import io
+import json
+import time
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Dict, Union
+
+import safetensors.torch as st
+import torch
+import torch.distributed as dist
+
+from astrai.parallel.setup import get_rank
+
+_META_FILE = "meta.json"
+_CONFIG_FILE = "config.json"
+_WEIGHTS_FILE = "model.safetensors"
+
+
+def save_safetensors(state_dict: dict, path: Union[str, Path]):
+    st.save_file(state_dict, str(path))
+
+
+def load_safetensors(path: Union[str, Path], broadcast: bool = False) -> dict:
+    if not broadcast or not dist.is_initialized():
+        return st.load_file(str(path))
+
+    rank = get_rank()
+    if rank == 0:
+        state_dict = st.load_file(str(path))
+    else:
+        state_dict = {}
+    tmp = [state_dict]
+    dist.broadcast_object_list(tmp, src=0)
+    return tmp[0]
+
+
+def save_json(data: dict, path: Union[str, Path]):
+    with open(str(path), "w") as f:
+        json.dump(data, f, indent=2)
+
+
+def load_json(path: Union[str, Path], broadcast: bool = False) -> dict:
+    if not broadcast or not dist.is_initialized():
+        with open(str(path), "r") as f:
+            return json.load(f)
+
+    rank = get_rank()
+    if rank == 0:
+        with open(str(path), "r") as f:
+            data = json.load(f)
+    else:
+        data = {}
+    tmp = [data]
+    dist.broadcast_object_list(tmp, src=0)
+    return tmp[0]
+
+
+def save_torch(obj: Any, path: Union[str, Path]):
+    torch.save(obj, str(path))
+
+
+def load_torch(path: Union[str, Path], broadcast: bool = False) -> Any:
+    if not broadcast or not dist.is_initialized():
+        return torch.load(str(path), map_location="cpu", weights_only=False)
+
+    path = Path(path)
+    rank = get_rank()
+
+    if rank == 0:
+        with open(path, "rb") as f:
+            raw = f.read()
+        data_tensor = torch.frombuffer(bytearray(raw), dtype=torch.uint8)
+        num_bytes = torch.tensor([len(raw)], dtype=torch.long)
+    else:
+        num_bytes = torch.tensor([0], dtype=torch.long)
+
+    dist.broadcast(num_bytes, src=0)
+
+    if rank != 0:
+        data_tensor = torch.empty(num_bytes.item(), dtype=torch.uint8)
+
+    dist.broadcast(data_tensor, src=0)
+
+    buf = io.BytesIO(data_tensor.numpy().tobytes())
+    return torch.load(buf, map_location="cpu", weights_only=False)
+
+
+def save_model(config: dict, state_dict: dict, save_directory: str):
+    save_path = Path(save_directory)
+    save_path.mkdir(parents=True, exist_ok=True)
+    save_json(config, save_path / _CONFIG_FILE)
+    save_safetensors(state_dict, save_path / _WEIGHTS_FILE)
+
+
+def load_model_config(save_directory: str) -> dict:
+    return load_json(Path(save_directory) / _CONFIG_FILE)
+
+
+def load_model_weights(save_directory: str) -> dict:
+    return load_state_dict(Path(save_directory) / _WEIGHTS_FILE)
+
+
+def load_state_dict(path: Union[str, Path], broadcast: bool = False) -> dict:
+    path = Path(path)
+    if not broadcast or not dist.is_initialized():
+        return load_safetensors(path)
+
+    rank = get_rank()
+    if rank == 0:
+        state_dict = load_safetensors(path)
+        specs = [
+            (k, list(state_dict[k].shape), str(state_dict[k].dtype).split(".")[-1])
+            for k in sorted(state_dict)
+        ]
+    else:
+        state_dict = {}
+        specs = []
+
+    specs_list = [specs]
+    dist.broadcast_object_list(specs_list, src=0)
+    specs = specs_list[0]
+
+    for key, shape, dtype_name in specs:
+        dtype = getattr(torch, dtype_name)
+        if rank != 0:
+            tensor = torch.empty(shape, dtype=dtype, device="cpu")
+        else:
+            tensor = state_dict[key].contiguous().cpu()
+        dist.broadcast(tensor, src=0)
+        if rank != 0:
+            state_dict[key] = tensor
+    return state_dict
+
+
+@dataclass
+class Checkpoint:
+    state_dict: Dict[str, Any] = field(default_factory=dict)
+    epoch: int = 0
+    iteration: int = 0
+    extra: Dict[str, Any] = field(default_factory=dict)
+    meta: Dict[str, Any] = field(default_factory=dict)
+    config: Dict[str, Any] = field(default_factory=dict)
+
+    def save(self, save_dir: str):
+        save_path = Path(save_dir)
+        save_path.mkdir(parents=True, exist_ok=True)
+
+        if get_rank() != 0:
+            return
+
+        meta = {
+            "epoch": self.epoch,
+            "iteration": self.iteration,
+            "timestamp": time.strftime("%Y-%m-%dT%H:%M:%S"),
+            **self.meta,
+        }
+        save_json(meta, save_path / _META_FILE)
+        save_json(self.config, save_path / _CONFIG_FILE)
+        save_safetensors(self.state_dict, save_path / _WEIGHTS_FILE)
+        for key, value in self.extra.items():
+            save_torch(value, save_path / f"{key}.pt")
+
+    @classmethod
+    def load(cls, save_dir: str, broadcast: bool = False) -> "Checkpoint":
+        save_path = Path(save_dir)
+
+        meta = load_json(save_path / _META_FILE, broadcast)
+        config = load_json(save_path / _CONFIG_FILE, broadcast)
+        state_dict = load_state_dict(save_path / _WEIGHTS_FILE, broadcast=broadcast)
+
+        extra = {}
+        for f in sorted(save_path.iterdir()):
+            if f.suffix == ".pt":
+                extra[f.stem] = load_torch(f, broadcast=broadcast)
+
+        return cls(
+            state_dict=state_dict,
+            epoch=meta.get("epoch", 0),
+            iteration=meta.get("iteration", 0),
+            extra=extra,
+            config=config,
+        )

astrai/tokenize/__init__.py

@@ -0,0 +1,8 @@
+from astrai.tokenize.chat_template import ChatTemplate, MessageType
+from astrai.tokenize.tokenizer import AutoTokenizer
+
+__all__ = [
+    "AutoTokenizer",
+    "ChatTemplate",
+    "MessageType",
+]

astrai/tokenize/chat_template.py

@@ -0,0 +1,74 @@
+from typing import Any, Dict, List, Optional
+
+from jinja2 import Template
+
+type MessageType = Dict[str, Any]
+
+
+class ChatTemplate:
+    """A chat template with Jinja2 rendering support.
+
+    Attributes:
+        name: Unique identifier for the template.
+        template_str: Jinja2 template string.
+        description: Optional description.
+        default_variables: Optional dictionary of default variable values.
+        special_tokens: Optional dictionary mapping token names to their string values.
+    """
+
+    def __init__(
+        self,
+        name: str = "",
+        template_str: str = "",
+        description: str = "",
+        default_variables: Optional[Dict[str, Any]] = None,
+        special_tokens: Optional[Dict[str, str]] = None,
+    ):
+        self.name = name
+        self.template_str = template_str
+        self.description = description
+        self.default_variables = default_variables or {}
+        self.special_tokens = special_tokens or {}
+        self._compiled: Template = Template(template_str)
+
+    @classmethod
+    def from_string(
+        cls,
+        template_str: str,
+        description: str = "",
+        default_variables: Optional[Dict[str, Any]] = None,
+        special_tokens: Optional[Dict[str, str]] = None,
+    ) -> "ChatTemplate":
+        """Create a ChatTemplate instance directly from a template string."""
+        return cls(
+            name="",
+            template_str=template_str,
+            description=description,
+            default_variables=default_variables,
+            special_tokens=special_tokens,
+        )
+
+    def render(
+        self,
+        messages: List[MessageType],
+        system_prompt: Optional[str] = None,
+        **extra_variables: Any,
+    ) -> str:
+        """Render the template with given messages and variables.
+
+        Args:
+            messages: List of message dicts with 'role' and 'content'.
+            system_prompt: Optional system prompt string.
+            **extra_variables: Additional variables to pass to the template.
+                These override default_variables and special_tokens.
+
+        Returns:
+            Rendered prompt string.
+        """
+        # Merge default variables, special tokens, and extra variables
+        variables = {**self.default_variables, **self.special_tokens, **extra_variables}
+        variables["messages"] = messages
+        if system_prompt is not None:
+            variables["system_prompt"] = system_prompt
+
+        return self._compiled.render(**variables)

astrai/tokenize/tokenizer.py

@@ -0,0 +1,264 @@
+"""
+Tokenizer module with implementation and auto-loading support.
+"""
+
+import json
+from pathlib import Path
+from typing import Dict, List, Optional, Union
+
+from tokenizers import Tokenizer
+
+from astrai.tokenize.chat_template import ChatTemplate
+
+
+class AutoTokenizer:
+    """Base tokenizer class with automatic loading support"""
+
+    TOKENIZER_CLASSES = {}  # Registry for auto-loading
+
+    def __init__(
+        self,
+        path: Optional[Union[str, Path]] = None,
+        special_token_map: Optional[Dict[str, str]] = None,
+        chat_template: Optional[str] = None,
+    ):
+        self._tokenizer: Tokenizer = None
+        self._chat_template: Optional[ChatTemplate] = None
+        self._special_token_map: Optional[Dict] = special_token_map or {}
+
+        if chat_template:
+            self.set_chat_template(chat_template)
+
+        if path:
+            self.load(path)
+
+    def load(self, path: Union[str, Path]):
+        """Load tokenizer from directory."""
+        path = Path(path)
+        tokenizer_file = path / "tokenizer.json"
+        config_file = path / "tokenizer_config.json"
+        self._tokenizer = Tokenizer.from_file(str(tokenizer_file))
+
+        if config_file.exists():
+            with open(config_file, "r", encoding="utf-8") as f:
+                config = json.load(f)
+
+            if "special_tokens" in config:
+                self._special_token_map.update(config["special_tokens"])
+
+            # Load chat template from config
+            if "chat_template" in config:
+                self.set_chat_template(config["chat_template"])
+
+    @classmethod
+    def from_pretrained(cls, path: Union[str, Path]) -> "AutoTokenizer":
+        """Load tokenizer from pretrained directory.
+
+        Raises:
+            FileNotFoundError: If tokenizer.json is missing.
+            RuntimeError: If tokenizer failed to initialize.
+        """
+        path = Path(path)
+        tokenizer_file = path / "tokenizer.json"
+        if not tokenizer_file.exists():
+            raise FileNotFoundError(
+                f"Tokenizer file not found: {tokenizer_file}. "
+                "A valid tokenizer.json is required."
+            )
+        instance = cls(path)
+        if instance._tokenizer is None:
+            raise RuntimeError(
+                f"Failed to load tokenizer from {path}. "
+                "The tokenizer.json may be corrupted or incompatible."
+            )
+        return instance
+
+    def save_pretrained(self, save_path: str):
+        """
+        Save tokenizer to pretrained directory.
+
+        Args:
+            save_path: Path to save the tokenizer
+        """
+
+        if self._tokenizer is None:
+            raise RuntimeError(
+                "Tokenizer not initialized. Load or create a tokenizer first."
+            )
+
+        save_path = Path(save_path)
+        save_path.mkdir(parents=True, exist_ok=True)
+
+        # Save tokenizer
+        self._tokenizer.save(str(save_path / "tokenizer.json"))
+
+        # Save tokenizer config
+        config = {}
+        if self._special_token_map is not None:
+            config["special_tokens"] = self._special_token_map
+        if self._chat_template is not None:
+            config["chat_template"] = self._chat_template.template_str
+
+        with open(save_path / "tokenizer_config.json", "w", encoding="utf-8") as f:
+            json.dump(config, f, ensure_ascii=False, indent=2)
+
+    @classmethod
+    def register_tokenizer(cls, name: str, tokenizer_class: type):
+        """
+        Register a new tokenizer class.
+
+        Args:
+            name: Name to register the tokenizer class under
+            tokenizer_class: The tokenizer class to register
+        """
+        cls.TOKENIZER_CLASSES[name] = tokenizer_class
+
+    def encode(
+        self,
+        tokens: Union[str, List[str]],
+        out_ids: bool = True,
+        is_pretokenized: bool = False,
+        add_special_tokens: bool = True,
+    ) -> List:
+        """Encode text to tokens or token IDs."""
+        if self._tokenizer is None:
+            raise RuntimeError(
+                "Tokenizer not initialized. Load or create a tokenizer first."
+            )
+
+        if isinstance(tokens, str):
+            encoded = self._tokenizer.encode(
+                tokens,
+                is_pretokenized=is_pretokenized,
+                add_special_tokens=add_special_tokens,
+            )
+            return encoded.ids if out_ids else encoded.tokens
+        else:
+            encoded_list = self._tokenizer.encode_batch(
+                tokens,
+                is_pretokenized=is_pretokenized,
+                add_special_tokens=add_special_tokens,
+            )
+            return [
+                encoded.ids if out_ids else encoded.tokens for encoded in encoded_list
+            ]
+
+    def decode(self, tokens: List[int], skip_special_tokens: bool = True) -> str:
+        """Decode token IDs to text."""
+        if self._tokenizer is None:
+            raise RuntimeError(
+                "Tokenizer not initialized. Load or create a tokenizer first."
+            )
+
+        return self._tokenizer.decode(tokens, skip_special_tokens=skip_special_tokens)
+
+    def __len__(self) -> int:
+        if self._tokenizer is None:
+            return 0
+        return self._tokenizer.get_vocab_size()
+
+    def __getattr__(self, key: str):
+        """
+        Dynamically intercept special token attribute access.
+        Supports three forms:
+          - tokenizer.bos_token   → returns string
+          - tokenizer.bos_token_id → returns corresponding integer ID
+          - tokenizer.stop_ids → returns list of corresponding integer IDs for all special tokens
+        """
+        # Handle stop_ids - return IDs for all special tokens
+        if key == "stop_ids":
+            stop_ids = []
+
+            if self._tokenizer is None:
+                return stop_ids
+
+            for val in self._special_token_map.values():
+                token_id = self._tokenizer.token_to_id(val)
+                if token_id is not None:
+                    stop_ids.append(token_id)
+
+            return stop_ids
+
+        # Handle _id suffix (e.g., bos_token_id -> bos_token)
+        if key.endswith("_id"):
+            base_attr = key[:-3]  # Remove "_id"
+            token_str = self._special_token_map.get(base_attr)
+            if token_str is None:
+                return None
+            if self._tokenizer is None:
+                raise RuntimeError("Tokenizer not loaded, cannot convert token to id.")
+            return self._tokenizer.token_to_id(token_str)
+
+        # Handle regular string attributes
+        if key in self._special_token_map:
+            return self._special_token_map.get(key)
+
+        # Other attributes trigger default AttributeError
+        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{key}'")
+
+    @property
+    def vocab_size(self) -> int:
+        return len(self)
+
+    def set_chat_template(self, template: Union[str, ChatTemplate]):
+        """
+        Set the chat template for the tokenizer.
+
+        Args:
+            template: Either a template name (str) registered in the global registry,
+                      or a ChatTemplate instance, or a Jinja2 template string.
+
+        Raises:
+            KeyError: If template name is not registered.
+        """
+        if isinstance(template, str):
+            self._chat_template = ChatTemplate.from_string(template)
+        elif isinstance(template, ChatTemplate):
+            self._chat_template = template
+        else:
+            raise ValueError("Invalid template type, must be str or ChatTemplate.")
+
+    def apply_chat_template(
+        self,
+        messages: List[Dict[str, str]],
+        system_prompt: Optional[str] = None,
+        tokenize: bool = True,
+        add_generation_prompt: bool = True,
+        **kwargs,
+    ) -> Union[str, List[int]]:
+        """
+        Apply the chat template to messages and optionally tokenize the result.
+
+        Args:
+            messages: List of message dicts with 'role' and 'content'.
+            system_prompt: Optional system prompt string (auto-converted to first message).
+            tokenize: Whether to return token IDs (True) or raw string (False).
+            add_generation_prompt: Whether to add the generation prompt (default: True).
+            **kwargs: Additional variables to pass to the template.
+
+        Returns:
+            Either the rendered string or list of token IDs.
+
+        Raises:
+            RuntimeError: If chat template is not set.
+        """
+        if self._chat_template is None:
+            raise RuntimeError(
+                "Chat template not set. Use set_chat_template() to set a template first."
+            )
+
+        # Auto-convert system_prompt to first message if provided
+        if system_prompt:
+            messages = [{"role": "system", "content": system_prompt}] + list(messages)
+
+        # Render the template
+        rendered = self._chat_template.render(
+            messages=messages,
+            add_generation_prompt=add_generation_prompt,
+            **kwargs,
+        )
+
+        if tokenize:
+            return self.encode(rendered)
+
+        return rendered

astrai/trainer/__init__.py

@@ -0,0 +1,24 @@
+from astrai.trainer.optim import Muon
+from astrai.trainer.schedule import BaseScheduler, SchedulerFactory
+from astrai.trainer.strategy import BaseStrategy, StrategyFactory
+from astrai.trainer.train_callback import (
+    CallbackFactory,
+    TrainCallback,
+)
+from astrai.trainer.trainer import Trainer
+
+__all__ = [
+    # Main trainer
+    "Trainer",
+    # Optimizer
+    "Muon",
+    # Strategy factory
+    "StrategyFactory",
+    "BaseStrategy",
+    # Scheduler factory
+    "SchedulerFactory",
+    "BaseScheduler",
+    # Callback factory
+    "TrainCallback",
+    "CallbackFactory",
+]

astrai/trainer/metric_util.py

@@ -0,0 +1,75 @@
+from typing import Any, Callable, Dict
+
+import torch
+import torch.nn as nn
+
+
+def _grad_stat(
+    model: nn.Module, fn: Callable[[torch.Tensor], Any], default: Any
+) -> dict:
+    results = {}
+    for name, param in model.named_parameters():
+        results[name] = default
+        if param.grad is not None:
+            results[name] = fn(param.grad.data)
+    return results
+
+
+def grad_norm(model: nn.Module, norm_type: int = 2) -> Dict[str, float]:
+    return _grad_stat(model, lambda g: g.norm(norm_type).item(), 0.0)
+
+
+def grad_std(model: nn.Module) -> Dict[str, float]:
+    return _grad_stat(model, lambda g: g.std().item(), 0.0)
+
+
+def grad_max(model: nn.Module) -> Dict[str, float]:
+    return _grad_stat(model, lambda g: g.max().item(), -float("inf"))
+
+
+def grad_min(model: nn.Module) -> Dict[str, float]:
+    return _grad_stat(model, lambda g: g.min().item(), float("inf"))
+
+
+def grad_mean(model: nn.Module) -> Dict[str, float]:
+    return _grad_stat(model, lambda g: g.mean().item(), 0.0)
+
+
+def grad_nan_num(model: nn.Module) -> Dict[str, int]:
+    return _grad_stat(model, lambda g: g.isnan().sum().item(), 0)
+
+
+def ctx_get_loss(ctx):
+    return ctx.loss
+
+
+def ctx_get_lr(ctx):
+    return ctx.optimizer.param_groups[-1]["lr"]
+
+
+def ctx_get_val_loss(ctx):
+    return ctx.val_loss
+
+
+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)

astrai/trainer/optim.py

@@ -0,0 +1,143 @@
+import torch
+from torch.optim import Optimizer
+
+
+def _zeropower_via_newtonschulz(G: torch.Tensor, steps: int = 5):
+    assert G.ndim == 2
+    X = G
+    scale = max(1, G.size(0) / G.size(1)) ** 0.5
+    X = X / (X.norm() + 1e-7) * scale
+    if steps == 0:
+        return X
+    a, b, c = (3.4445, -4.7750, 2.0315)
+    for _ in range(steps):
+        A = X @ X.T
+        B = A @ X
+        X = a * X + b * B + c * (A @ B)
+    return X
+
+
+class Muon(Optimizer):
+    def __init__(
+        self,
+        params,
+        lr: float = 2e-3,
+        momentum: float = 0.95,
+        weight_decay: float = 0.0,
+        nesterov: bool = True,
+        ns_steps: int = 5,
+        adamw_lr: float = None,
+        adamw_betas: tuple = (0.9, 0.95),
+        adamw_eps: float = 1e-8,
+        adamw_wd: float = 0.0,
+    ):
+        defaults = dict(
+            lr=lr,
+            momentum=momentum,
+            weight_decay=weight_decay,
+            nesterov=nesterov,
+            ns_steps=ns_steps,
+            adamw_lr=adamw_lr if adamw_lr is not None else lr * 0.1,
+            adamw_betas=adamw_betas,
+            adamw_eps=adamw_eps,
+            adamw_wd=adamw_wd,
+        )
+        super().__init__(params, defaults)
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            params_2d, params_1d = [], []
+            grads_2d, grads_1d = [], []
+
+            for p in group["params"]:
+                if p.grad is None:
+                    continue
+                if p.grad.is_sparse:
+                    raise RuntimeError("Muon does not support sparse gradients")
+                if p.ndim >= 2:
+                    params_2d.append(p)
+                    grads_2d.append(p.grad)
+                else:
+                    params_1d.append(p)
+                    grads_1d.append(p.grad)
+
+            if params_2d:
+                self._muon_update_foreach(params_2d, grads_2d, group)
+            if params_1d:
+                self._adamw_update_foreach(params_1d, grads_1d, group)
+
+        return loss
+
+    def _muon_update_foreach(self, params_2d, grads_2d, group):
+        lr = group["lr"]
+        momentum = group["momentum"]
+        wd = group["weight_decay"]
+        nesterov = group["nesterov"]
+        ns_steps = group["ns_steps"]
+
+        if wd != 0:
+            torch._foreach_mul_(params_2d, 1 - lr * wd)
+
+        if nesterov:
+            grads_2d = torch._foreach_add(grads_2d, params_2d, alpha=wd)
+
+        bufs = []
+        for p, grad in zip(params_2d, grads_2d):
+            state = self.state[p]
+            if "momentum_buffer" not in state:
+                state["momentum_buffer"] = torch.zeros_like(grad)
+            bufs.append(state["momentum_buffer"])
+
+        torch._foreach_lerp_(bufs, grads_2d, 1 - momentum)
+
+        for p, buf in zip(params_2d, bufs):
+            update = _zeropower_via_newtonschulz(buf, steps=ns_steps)
+            scale = max(1, p.size(0) / p.size(1)) ** 0.5
+            p.add_(update, alpha=-lr * scale)
+
+    def _adamw_update_foreach(self, params_1d, grads_1d, group):
+        lr = group["adamw_lr"]
+        betas = group["adamw_betas"]
+        eps = group["adamw_eps"]
+        wd = group["adamw_wd"]
+
+        steps: list[int] = []
+        exp_avgs, exp_avg_sqs = [], []
+        has_state = []
+        for p in params_1d:
+            state = self.state[p]
+            if not state:
+                state["step"] = 0
+                state["exp_avg"] = torch.zeros_like(p)
+                state["exp_avg_sq"] = torch.zeros_like(p)
+                has_state.append(False)
+            else:
+                has_state.append(True)
+            state["step"] += 1
+            steps.append(state["step"])
+            exp_avgs.append(state["exp_avg"])
+            exp_avg_sqs.append(state["exp_avg_sq"])
+
+        beta1, beta2 = betas
+
+        torch._foreach_lerp_(exp_avgs, grads_1d, 1 - beta1)
+        grads_sq = torch._foreach_mul(grads_1d, grads_1d)
+        torch._foreach_lerp_(exp_avg_sqs, grads_sq, 1 - beta2)
+
+        bias_correction1 = [1 - beta1**s for s in steps]
+        bias_correction2 = [1 - beta2**s for s in steps]
+
+        if wd != 0:
+            torch._foreach_mul_(params_1d, 1 - lr * wd)
+
+        exp_avg_corrected = torch._foreach_div(exp_avgs, bias_correction1)
+        denom = torch._foreach_div(exp_avg_sqs, bias_correction2)
+        denom = torch._foreach_sqrt(denom)
+        torch._foreach_add_(denom, eps)
+        torch._foreach_addcdiv_(params_1d, exp_avg_corrected, denom, value=-lr)

astrai/trainer/schedule.py

@@ -0,0 +1,194 @@
+"""Learning rate scheduler implementations with factory pattern."""
+
+import math
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Type
+
+from torch.optim.lr_scheduler import LRScheduler
+
+from astrai.factory import BaseFactory
+
+
+class BaseScheduler(LRScheduler, ABC):
+    """Base scheduler class for all other schedulers."""
+
+    def __init__(self, optimizer, last_epoch: int = -1):
+        super().__init__(optimizer, last_epoch)
+
+    @abstractmethod
+    def get_lr(self) -> List[float]:
+        """Calculate the current learning rate."""
+        raise NotImplementedError
+
+    def state_dict(self) -> Dict[str, Any]:
+        return super().state_dict()
+
+    def load_state_dict(self, state_dict: Dict[str, Any]):
+        super().load_state_dict(state_dict)
+
+
+class SchedulerFactory(BaseFactory["BaseScheduler"]):
+    """Factory class for creating learning rate schedulers.
+
+    Supports decorator-based registration for extensible scheduler types.
+    Also supports creation from ScheduleConfig objects.
+
+    Example usage:
+        @SchedulerFactory.register("custom")
+        class CustomScheduler(BaseScheduler):
+            ...
+
+        scheduler = SchedulerFactory.create("custom", optimizer, **kwargs)
+    """
+
+    @classmethod
+    def _validate_component(cls, scheduler_cls: Type[BaseScheduler]):
+        """Validate that the scheduler class inherits from BaseScheduler."""
+        if not issubclass(scheduler_cls, BaseScheduler):
+            raise TypeError(f"{scheduler_cls.__name__} must inherit from BaseScheduler")
+
+    @classmethod
+    def create(
+        cls, optimizer, schedule_type: str = "none", **kwargs
+    ) -> "BaseScheduler":
+        """Create a scheduler instance by type name.
+
+        Args:
+            optimizer: PyTorch optimizer
+            schedule_type: Type of scheduler ("cosine", "sgdr")
+            **kwargs: Arguments passed to the scheduler constructor
+
+        Returns:
+            Scheduler instance
+        """
+        return super().create(schedule_type, optimizer, **kwargs)
+
+    @classmethod
+    def available_types(cls) -> list:
+        """Return list of registered scheduler type names."""
+        return cls.list_registered()
+
+
+# ----------- Scheduler implementations -----------
+
+
+@SchedulerFactory.register("cosine")
+class CosineScheduler(BaseScheduler):
+    """Cosine decay scheduler with warmup, implemented as PyTorch LRScheduler."""
+
+    def __init__(
+        self,
+        optimizer,
+        warmup_steps: int,
+        lr_decay_steps: int,
+        min_rate: float = 0.05,
+        last_epoch: int = -1,
+    ):
+        self.warmup_steps = warmup_steps
+        self.lr_decay_steps = lr_decay_steps
+        self.min_rate = min_rate
+        self.total_steps = warmup_steps + lr_decay_steps
+        super().__init__(optimizer, last_epoch)
+
+    def get_lr(self) -> List[float]:
+        # warmup
+        if self.last_epoch < self.warmup_steps:
+            warmup_factor = max(self.min_rate, self.last_epoch / self.warmup_steps)
+            return [base_lr * warmup_factor for base_lr in self.base_lrs]
+
+        # cosine decay
+        decay_progress = (self.last_epoch - self.warmup_steps) / self.lr_decay_steps
+        decay_progress = min(decay_progress, 1.0)
+        cosine_decay = 0.5 * (1.0 + math.cos(math.pi * decay_progress))
+        decay_factor = max(self.min_rate, cosine_decay)
+        return [base_lr * decay_factor for base_lr in self.base_lrs]
+
+    def state_dict(self):
+        state = super().state_dict()
+        state.update(
+            {
+                "warmup_steps": self.warmup_steps,
+                "lr_decay_steps": self.lr_decay_steps,
+                "min_rate": self.min_rate,
+                "total_steps": self.total_steps,
+            }
+        )
+        return state
+
+    def load_state_dict(self, state_dict):
+        self.warmup_steps = state_dict.pop("warmup_steps")
+        self.lr_decay_steps = state_dict.pop("lr_decay_steps")
+        self.min_rate = state_dict.pop("min_rate")
+        self.total_steps = state_dict.pop("total_steps")
+        super().load_state_dict(state_dict)
+
+
+@SchedulerFactory.register("sgdr")
+class SGDRScheduler(BaseScheduler):
+    """SGDR (Stochastic Gradient Descent with Warm Restarts) scheduler."""
+
+    def __init__(
+        self,
+        optimizer,
+        warmup_steps: int,
+        cycle_length: int,
+        min_rate: float = 0.05,
+        t_mult: int = 2,
+        last_epoch: int = -1,
+    ):
+        self.warmup_steps = warmup_steps
+        self.cycle_length = cycle_length
+        self.min_rate = min_rate
+        self.t_mult = t_mult
+
+        super().__init__(optimizer, last_epoch)
+
+    def get_lr(self):
+        # warmup
+        if self.last_epoch < self.warmup_steps:
+            warmup_factor = max(self.min_rate, self.last_epoch / self.warmup_steps)
+            return [base_lr * warmup_factor for base_lr in self.base_lrs]
+
+        # SGDR
+        steps_since_warmup = self.last_epoch - self.warmup_steps
+
+        # 1. Calculate current cycle and position within cycle
+        current_cycle_length = self.cycle_length
+        total_cycles_length = 0
+        cycle_num = 0
+
+        while total_cycles_length + current_cycle_length <= steps_since_warmup:
+            total_cycles_length += current_cycle_length
+            current_cycle_length *= self.t_mult
+            cycle_num += 1
+
+        steps_in_cycle = steps_since_warmup - total_cycles_length
+
+        # 2. Cosine annealing within the current cycle
+        cosine_factor = 0.5 * (
+            1 + math.cos(math.pi * steps_in_cycle / current_cycle_length)
+        )
+        learning_rate_factor = self.min_rate + (1 - self.min_rate) * cosine_factor
+
+        return [base_lr * learning_rate_factor for base_lr in self.base_lrs]
+
+    def state_dict(self):
+        """Returns the state of the scheduler as a dict."""
+        state = super().state_dict()
+        state.update(
+            {
+                "warmup_steps": self.warmup_steps,
+                "cycle_length": self.cycle_length,
+                "min_rate": self.min_rate,
+                "t_mult": self.t_mult,
+            }
+        )
+        return state
+
+    def load_state_dict(self, state_dict):
+        """Loads the scheduler's state."""
+        self.warmup_steps = state_dict.pop("warmup_steps")
+        self.cycle_length = state_dict.pop("cycle_length")
+        self.min_rate = state_dict.pop("min_rate")
+        self.t_mult = state_dict.pop("t_mult")
+        super().load_state_dict(state_dict)

astrai/trainer/strategy.py

@@ -0,0 +1,334 @@
+"""Training strategy implementations with factory pattern."""
+
+from abc import ABC, abstractmethod
+from typing import Any, Callable, Dict, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+
+from astrai.factory import BaseFactory
+
+
+def create_ref_model(model_fn, state_dict: dict) -> nn.Module:
+    """Create a frozen reference model from model_fn + full state dict."""
+    ref_model = model_fn()
+    ref_model.load_state_dict(state_dict)
+    ref_model.requires_grad_(False)
+    ref_model.eval()
+    return ref_model
+
+
+def move_to_device(batch: Dict[str, Tensor], device: str) -> Any:
+    """Move batch tensors to specified device with non-blocking transfer."""
+    return {key: value.to(device, non_blocking=True) for key, value in batch.items()}
+
+
+def get_logprobs(
+    model: Union[nn.Module, Callable[..., Dict[str, Tensor]]],
+    input_ids: Tensor,
+    mask: Tensor,
+    reduction: str,
+):
+    """Compute token-wise log probabilities from model outputs.
+
+    Args:
+        model: The language model
+        input_ids: Input token IDs of shape [batch_size, seq_len]
+        mask: Attention mask of shape [batch_size, seq_len]
+        reduction: How to reduce over sequence dimension ("mean", "sum", "none")
+
+    Returns:
+        Log probabilities with reduction applied over sequence dimension
+    """
+    allowed_reductions = ["mean", "sum", "none"]
+    if reduction not in allowed_reductions:
+        raise ValueError(
+            f"reduction must be one of {allowed_reductions}, got '{reduction}'"
+        )
+
+    shifted_input_ids = input_ids[:, 1:]
+    shifted_mask = mask[:, 1:]
+
+    logits = model(input_ids[:, :-1], mask[:, :-1])["logits"]
+    log_probs = torch.log_softmax(logits.float(), dim=-1)
+
+    token_logprobs = torch.gather(
+        log_probs, dim=-1, index=shifted_input_ids.unsqueeze(-1)
+    ).squeeze(-1)
+
+    if reduction == "mean":
+        return (token_logprobs * shifted_mask).sum(dim=-1) / shifted_mask.sum(
+            dim=-1
+        ).clamp(min=1.0)
+    elif reduction == "sum":
+        return (token_logprobs * shifted_mask).sum(dim=-1)
+    else:
+        return token_logprobs * shifted_mask
+
+
+class BaseStrategy(ABC):
+    """Abstract base class for training strategies."""
+
+    def __init__(
+        self, model: Union[Callable[..., Dict[str, Tensor]]], device: str, **kwargs
+    ):
+        self.model = model
+        self.device = device
+        self.executor = kwargs.pop("executor", None)
+        self.model_fn = kwargs.pop("model_fn", None)
+        self.extra_kwargs = kwargs
+
+    @abstractmethod
+    def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
+        """Compute loss for the given batch.
+
+        Args:
+            batch: Dictionary containing batch tensors
+
+        Returns:
+            Computed loss tensor
+        """
+        raise NotImplementedError
+
+    def __call__(self, batch: Dict[str, Tensor]) -> Tensor:
+        """Allow calling strategy directly as a callable."""
+        return self.compute_loss(batch)
+
+
+class StrategyFactory(BaseFactory["BaseStrategy"]):
+    """Factory class for creating training strategy instances.
+
+    Supports decorator-based registration for extensible strategy types.
+    All default strategies (seq, sft, dpo, grpo) are automatically registered.
+
+    Example usage:
+        @StrategyFactory.register("custom")
+        class CustomStrategy(BaseStrategy):
+            ...
+
+        strategy = StrategyFactory.create("custom", model, device)
+    """
+
+    @classmethod
+    def _validate_component(cls, strategy_cls: type):
+        """Validate that the strategy class inherits from BaseStrategy."""
+        if not issubclass(strategy_cls, BaseStrategy):
+            raise TypeError(f"{strategy_cls.__name__} must inherit from BaseStrategy")
+
+    @classmethod
+    def create(cls, train_type: str, model, device: str, **kwargs) -> "BaseStrategy":
+        """Create a strategy instance based on training type.
+
+        Args:
+            train_type: Type of training ("seq", "sft", "dpo", "grpo")
+            model: Model instance for the strategy
+            device: Device to run the strategy on
+            **kwargs: Additional arguments passed to strategy constructor
+
+        Returns:
+            Strategy instance
+        """
+        return super().create(train_type, model, device, **kwargs)
+
+    @classmethod
+    def available_strategies(cls) -> list:
+        """Return list of registered strategy names."""
+        return cls.list_registered()
+
+
+# ============== Strategy Classes ==============
+# All strategies are registered at class definition time using the decorator
+
+
+@StrategyFactory.register("seq")
+class SEQStrategy(BaseStrategy):
+    """Standard next-token prediction training strategy.
+
+    Computes cross-entropy loss for next token prediction.
+    """
+
+    def __init__(self, model, device, label_smoothing: float = 0.0, **kwargs):
+        super().__init__(model, device, **kwargs)
+        self.label_smoothing = label_smoothing
+
+    def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
+        batch = move_to_device(batch, self.device)
+        input_ids, target_ids = batch["input_ids"], batch["target_ids"]
+        logits = self.model(input_ids=input_ids)["logits"]
+
+        loss = F.cross_entropy(
+            input=logits.flatten(0, 1).float(),
+            target=target_ids.flatten(),
+            label_smoothing=self.label_smoothing,
+        )
+
+        return loss
+
+
+@StrategyFactory.register("sft")
+class SFTStrategy(BaseStrategy):
+    """Supervised Fine-tuning strategy with loss masking.
+
+    Applies cross-entropy loss only to tokens where loss_mask is True.
+    """
+
+    def __init__(self, model, device, label_smoothing: float = 0.0, **kwargs):
+        super().__init__(model, device, **kwargs)
+        self.label_smoothing = label_smoothing
+
+    def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
+        batch = move_to_device(batch, self.device)
+        input_ids, target_ids, loss_mask = (
+            batch["input_ids"],
+            batch["target_ids"],
+            batch["loss_mask"],
+        )
+
+        ignore_index = -100
+        logits = self.model(input_ids=input_ids)["logits"]
+        target_ids = target_ids.masked_fill(loss_mask == 0, ignore_index)
+
+        loss = F.cross_entropy(
+            input=logits.flatten(0, 1).float(),
+            target=target_ids.flatten(),
+            ignore_index=ignore_index,
+            label_smoothing=self.label_smoothing,
+        )
+
+        return loss
+
+
+@StrategyFactory.register("dpo")
+class DPOStrategy(BaseStrategy):
+    """Direct Preference Optimization strategy.
+
+    Implements the DPO loss from the paper "Direct Preference Optimization".
+    Uses a reference model to compute KL divergence penalty.
+    """
+
+    def __init__(
+        self,
+        model: nn.Module,
+        device: str,
+        beta: float = 0.1,
+        reduction: str = "mean",
+        **kwargs,
+    ):
+        super().__init__(model, device, **kwargs)
+        self.ref_model = create_ref_model(
+            self.model_fn, self.executor.unwrap_model(model)
+        ).to(device=self.device)
+        self.beta = beta
+        self.reduction = reduction
+
+    def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
+        batch = move_to_device(batch, self.device)
+        chosen_ids, rejected_ids = batch["chosen"], batch["rejected"]
+        chosen_mask, rejected_mask = batch["chosen_mask"], batch["rejected_mask"]
+
+        concat_ids = torch.cat([chosen_ids, rejected_ids], dim=0)
+        concat_mask = torch.cat([chosen_mask, rejected_mask], dim=0)
+
+        log_pi = get_logprobs(self.model, concat_ids, concat_mask, self.reduction)
+
+        with torch.no_grad():
+            log_ref = get_logprobs(
+                self.ref_model, concat_ids, concat_mask, self.reduction
+            )
+
+        log_pi_chosen = log_pi[: chosen_ids.shape[0]]
+        log_pi_rejected = log_pi[chosen_ids.shape[0] :]
+        log_ref_chosen = log_ref[: chosen_ids.shape[0]]
+        log_ref_rejected = log_ref[chosen_ids.shape[0] :]
+
+        pi_log_ratio = log_pi_chosen - log_pi_rejected
+        ref_log_ratio = log_ref_chosen - log_ref_rejected
+
+        ratio_diff = pi_log_ratio - ref_log_ratio
+        dpo_loss = -F.logsigmoid(self.beta * ratio_diff).mean()
+
+        return dpo_loss
+
+
+@StrategyFactory.register("grpo")
+class GRPOStrategy(BaseStrategy):
+    """Group Relative Policy Optimization strategy.
+
+    On-policy GRPO following DeepSeek-R1: the policy model is updated while
+    a frozen ref_model stores the old-policy log-probs.  ratio = exp(logπ_θ - logπ_ref),
+    clipped PPO objective.  Call ``sync_ref_model()`` after each data-generation round.
+    """
+
+    def __init__(
+        self,
+        model: nn.Module,
+        device: str,
+        clip_eps: float = 0.2,
+        kl_coef: float = 0.01,
+        group_size: int = 4,
+        reduction: str = "mean",
+        sync_interval: int = 200,
+        **kwargs,
+    ):
+        super().__init__(model, device, **kwargs)
+        self.ref_model = create_ref_model(
+            self.model_fn, self.executor.unwrap_model(model)
+        ).to(device=self.device)
+        self.clip_eps = clip_eps
+        self.kl_coef = kl_coef
+        self.group_size = group_size
+        self.reduction = reduction
+        self.sync_interval = sync_interval
+        self._step = 0
+
+    def sync_ref_model(self):
+        """Copy current model weights to ref model."""
+        self.ref_model.load_state_dict(self.executor.unwrap_model(self.model))
+
+    def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
+        self._step += 1
+        if self._step % self.sync_interval == 0:
+            self.sync_ref_model()
+
+        batch = move_to_device(batch, self.device)
+        prompts = batch["prompts"]
+        responses = batch["responses"]
+        masks = batch["masks"]
+        rewards = batch["rewards"]
+
+        batch_size, group_size, response_len = responses.shape
+        responses_flat = responses.view(-1, response_len)
+        masks_flat = masks.view(-1, response_len)
+        prompt_expanded = prompts.unsqueeze(1).repeat(1, group_size, 1).flatten(0, 1)
+
+        full_sequences = torch.cat([prompt_expanded, responses_flat], dim=-1)
+        full_masks = torch.cat([torch.ones_like(prompt_expanded), masks_flat], dim=-1)
+
+        log_probs_policy = get_logprobs(
+            self.model, full_sequences, full_masks, self.reduction
+        )
+        log_probs_policy = log_probs_policy.view(batch_size, group_size)
+
+        with torch.no_grad():
+            log_probs_ref = get_logprobs(
+                self.ref_model, full_sequences, full_masks, self.reduction
+            )
+            log_probs_ref = log_probs_ref.view(batch_size, group_size)
+
+        eps = torch.finfo(log_probs_policy.dtype).eps
+        mean = rewards.mean(dim=-1, keepdim=True)
+        std = rewards.std(dim=-1, keepdim=True)
+        advantages = (rewards - mean) / (std + eps)
+
+        ratio = torch.exp(log_probs_policy - log_probs_ref)
+
+        surr1 = ratio * advantages
+        surr2 = torch.clamp(ratio, 1 - self.clip_eps, 1 + self.clip_eps) * advantages
+
+        policy_loss = -torch.min(surr1, surr2).mean()
+        kl_penalty = self.kl_coef * (log_probs_policy - log_probs_ref).square().mean()
+        total_loss = policy_loss + kl_penalty
+
+        return total_loss

astrai/trainer/train_callback.py

@@ -0,0 +1,333 @@
+import json
+import logging
+import os
+import sys
+import time
+from pathlib import Path
+from typing import IO, Callable, List, Optional, Protocol, runtime_checkable
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from torch.nn.utils import clip_grad_norm_
+from torch.utils.checkpoint import checkpoint as torch_checkpoint
+from tqdm import tqdm
+
+from astrai.factory import BaseFactory
+from astrai.parallel import only_on_rank
+from astrai.parallel.setup import get_current_device, get_rank
+from astrai.serialization import Checkpoint
+from astrai.trainer.metric_util import (
+    ctx_get_grad_max,
+    ctx_get_grad_mean,
+    ctx_get_grad_min,
+    ctx_get_grad_nan_num,
+    ctx_get_grad_norm,
+    ctx_get_grad_std,
+    ctx_get_loss,
+    ctx_get_lr,
+    ctx_get_val_loss,
+)
+from astrai.trainer.train_context import TrainContext
+
+logger = logging.getLogger(__name__)
+
+
+@runtime_checkable
+class TrainCallback(Protocol):
+    """
+    Callback interface for trainer.
+    """
+
+    def on_train_begin(self, context: TrainContext):
+        """Called at the beginning of training."""
+
+    def on_train_end(self, context: TrainContext):
+        """Called at the end of training."""
+
+    def on_epoch_begin(self, context: TrainContext):
+        """Called at the beginning of each epoch."""
+
+    def on_epoch_end(self, context: TrainContext):
+        """Called at the end of each epoch."""
+
+    def on_batch_begin(self, context: TrainContext):
+        """Called at the beginning of each batch."""
+
+    def on_batch_end(self, context: TrainContext):
+        """Called at the end of each batch."""
+
+    def on_optimizer_step(self, context: TrainContext):
+        """Called on every optimizer step (sync step only)."""
+
+    def on_error(self, context: TrainContext):
+        """Called when an error occurs during training."""
+
+
+class CallbackFactory(BaseFactory[TrainCallback]):
+    """Factory for registering and creating training callbacks.
+
+    Example:
+        @CallbackFactory.register("my_callback")
+        class MyCallback(TrainCallback):
+            ...
+
+        callback = CallbackFactory.create("my_callback", **kwargs)
+    """
+
+
+@CallbackFactory.register("gradient_clipping")
+class GradientClippingCallback(TrainCallback):
+    """
+    Gradient clipping callback for trainer.
+    """
+
+    def __init__(self, max_grad_norm: float):
+        self.max_grad_norm = max_grad_norm
+
+    def on_optimizer_step(self, context: TrainContext):
+        clip_grad_norm_(context.model.parameters(), self.max_grad_norm)
+
+
+@CallbackFactory.register("gradient_checkpointing")
+class GradientCheckpointingCallback(TrainCallback):
+    """
+    Activation checkpointing callback — trades compute for memory
+    by recomputing specified module activations during the backward pass.
+
+    Args:
+        modules: Module types to apply checkpointing to.
+    """
+
+    def __init__(self, modules: Optional[List[type]] = None):
+        self.modules = tuple(modules) if modules else ()
+
+    def _enable(self, module: nn.Module):
+        if self.modules and isinstance(module, self.modules):
+            fn = module.forward
+            module._original_forward = fn
+            module.forward = lambda *a, **kw: torch_checkpoint(
+                fn, *a, use_reentrant=False, **kw
+            )
+
+    @staticmethod
+    def _disable(module: nn.Module):
+        if hasattr(module, "_original_forward"):
+            module.forward = module._original_forward
+            del module._original_forward
+
+    def on_train_begin(self, context: TrainContext):
+        context.model.apply(self._enable)
+        logger.info("Gradient checkpointing enabled")
+
+    def on_train_end(self, context: TrainContext):
+        context.model.apply(self._disable)
+
+
+@CallbackFactory.register("checkpoint")
+class CheckpointCallback(TrainCallback):
+    """
+    Checkpoint callback for trainer.
+    """
+
+    extra_keys = ("optimizer", "scheduler")
+
+    def __init__(
+        self,
+        save_dir: str,
+        interval: int,
+        weight_only: bool = False,
+        save_extra_fn: Optional[Callable[["TrainContext"], dict]] = None,
+    ):
+        self.save_dir = save_dir
+        self.interval = interval
+        self.weight_only = weight_only
+        self.save_extra_fn = save_extra_fn or CheckpointCallback.save_extra
+        self.last_ckpt_iter = 0
+
+    def _save_checkpoint(self, context: TrainContext):
+        state_dict = context.executor.unwrap_model(context.model)
+        self.last_ckpt_iter = context.iteration
+
+        if get_rank() == 0:
+            save_path = os.path.join(
+                self.save_dir, f"epoch_{context.epoch}_iter_{context.iteration}"
+            )
+            extra = self.save_extra_fn(context)
+            context.checkpoint = Checkpoint(
+                state_dict=state_dict,
+                epoch=context.epoch,
+                iteration=context.iteration,
+                extra=extra,
+                config=context.model_config,
+            )
+            context.checkpoint.save(save_path)
+
+    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)
+
+    @staticmethod
+    def save_extra(context: TrainContext) -> dict:
+        extra = {}
+        for name in CheckpointCallback.extra_keys:
+            obj = getattr(context, name, None)
+            if obj:
+                extra[name] = obj.state_dict()
+        return extra
+
+
+@CallbackFactory.register("progress_bar")
+class ProgressBarCallback(TrainCallback):
+    """
+    Progress bar callback for trainer.
+    """
+
+    def __init__(
+        self, num_epoch: int, log_interval: int = 100, file: Optional[IO[str]] = None
+    ):
+        self.num_epoch = num_epoch
+        self.log_interval = log_interval
+        self.file = file
+        self.progress_bar: tqdm = None
+
+    @only_on_rank(0)
+    def on_epoch_begin(self, context: TrainContext):
+        self.progress_bar = tqdm(
+            context.dataloader,
+            desc=f"Epoch {context.epoch + 1}/{self.num_epoch}",
+            dynamic_ncols=True,
+            file=self.file or sys.stdout,
+        )
+
+    @only_on_rank(0)
+    def on_batch_end(self, context: TrainContext):
+        postfix = {
+            "loss": f"{context.loss:.4f}",
+            "lr": f"{context.optimizer.param_groups[-1]['lr']:.2e}",
+        }
+        if context.val_loss > 0:
+            postfix["val_loss"] = f"{context.val_loss:.4f}"
+        self.progress_bar.set_postfix(postfix)
+        self.progress_bar.update(1)
+
+    @only_on_rank(0)
+    def on_epoch_end(self, context: TrainContext):
+        _ = context
+        if self.progress_bar:
+            self.progress_bar.close()
+
+
+@CallbackFactory.register("metric_logger")
+class MetricLoggerCallback(TrainCallback):
+    def __init__(
+        self,
+        log_dir: str,
+        save_interval: int,
+        log_interval: int = 10,
+        metrics: List[str] = None,
+    ):
+        self.last_log_iter = 0
+        self.save_interval = save_interval
+        self.log_interval = log_interval
+        self.metrics = metrics or ["loss", "lr"]
+
+        self.log_dir = Path(log_dir) if log_dir else Path.cwd() / "logs"
+        self.log_dir.mkdir(parents=True, exist_ok=True)
+
+        self.log_cache = []
+
+        self._metric_funcs = {
+            "loss": ctx_get_loss,
+            "lr": ctx_get_lr,
+            "val_loss": ctx_get_val_loss,
+            "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-%dT%H:%M:%S"),
+            "epoch": context.epoch,
+            "iter": context.iteration,
+            **{m: self._metric_funcs[m](context) for m in self.metrics},
+        }
+
+    @only_on_rank(0)
+    def _add_log(self, log_data):
+        self.log_cache.append(log_data)
+
+    @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 context.iteration % self.log_interval == 0:
+            log_data = self._get_log_data(context)
+            self._add_log(log_data)
+
+        if context.iteration - self.last_log_iter >= self.save_interval:
+            self._save_log(context.epoch, context.iteration)
+            self.last_log_iter = context.iteration
+
+    def on_train_end(self, context):
+        if context.iteration != self.last_log_iter:
+            self._save_log(context.epoch, context.iteration)
+
+    def on_error(self, context):
+        self._save_log(context.epoch, context.iteration)
+
+
+@CallbackFactory.register("validation")
+class ValidationCallback(TrainCallback):
+    def _run_validation(self, context: TrainContext):
+        context.model.eval()
+
+        total_loss = 0.0
+        num_batches = 0
+
+        with torch.no_grad():
+            for batch in context.val_dataloader:
+                loss = context.strategy(batch)
+                total_loss += loss.item()
+                num_batches += 1
+
+        avg_loss = total_loss / max(num_batches, 1)
+
+        if context.world_size > 1 and dist.is_initialized():
+            loss_tensor = torch.tensor([avg_loss], device=get_current_device())
+            dist.all_reduce(loss_tensor, op=dist.ReduceOp.AVG)
+            avg_loss = loss_tensor.item()
+
+        context.val_loss = avg_loss
+        context.model.train()
+
+        step_count = context.iteration // context.config.grad_accum_steps
+        logger.info(
+            f"Epoch {context.epoch + 1}, Step {step_count}, Val Loss: {avg_loss:.4f}"
+        )
+
+    def on_optimizer_step(self, context: TrainContext):
+        if context.val_dataloader is None:
+            return
+        cfg = context.config
+        if cfg.val_step <= 0:
+            return
+        step_count = context.iteration // cfg.grad_accum_steps
+        if step_count % cfg.val_step == 0:
+            self._run_validation(context)

astrai/trainer/train_context.py

@@ -0,0 +1,170 @@
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Optional, Self
+
+import torch.nn as nn
+from torch.utils.data import DataLoader
+
+from astrai.config.train_config import TrainConfig
+from astrai.dataset import ResumableDistributedSampler
+from astrai.model.components.lora import inject_lora
+from astrai.parallel.executor import BaseExecutor, ExecutorFactory
+from astrai.parallel.setup import get_current_device, get_rank, get_world_size
+from astrai.protocols import OptimizerProtocol, SchedulerProtocol
+from astrai.serialization import Checkpoint, load_json, load_model_weights
+from astrai.trainer.strategy import BaseStrategy, StrategyFactory
+
+
+@dataclass
+class TrainContext:
+    model: nn.Module = field(default=None)
+    strategy: BaseStrategy = field(default=None)
+    dataloader: DataLoader = field(default=None)
+    optimizer: OptimizerProtocol = field(default=None)
+    scheduler: SchedulerProtocol = field(default=None)
+    checkpoint: Checkpoint = field(default=None)
+    config: TrainConfig = field(default=None)
+    model_config: dict = field(default_factory=dict)
+    executor: BaseExecutor = field(default=None)
+
+    epoch: int = field(default=0)
+    iteration: int = field(default=0)
+    loss: float = field(default=0.0)
+    val_dataloader: DataLoader = field(default=None)
+    val_loss: float = field(default=0.0)
+
+    world_size: int = field(default=1)
+    rank: int = field(default=0)
+    kwargs: dict = field(default_factory=dict)
+
+
+class TrainContextBuilder:
+    def __init__(
+        self,
+        config: TrainConfig,
+    ):
+        self.config = config
+        self._resume_dir: Optional[str] = None
+
+    def with_resume_dir(self, resume_dir: Optional[str]) -> Self:
+        self._resume_dir = resume_dir
+        return self
+
+    def build(self) -> TrainContext:
+        cfg = self.config
+        device = get_current_device()
+
+        executor = ExecutorFactory.create(
+            cfg.parallel_mode,
+            grad_accum_steps=cfg.grad_accum_steps,
+            **cfg.executor_kwargs,
+        )
+
+        model = cfg.model_fn()
+        model = model.to(device=device)
+
+        model_config = {}
+        if self._resume_dir:
+            config_path = Path(self._resume_dir) / "config.json"
+            if config_path.exists():
+                model_config = load_json(config_path)
+
+        if not model_config and hasattr(model, "config"):
+            model_config = model.config.to_dict()
+
+        context = TrainContext(
+            model=model,
+            world_size=get_world_size(),
+            rank=get_rank(),
+            config=cfg,
+            model_config=model_config,
+            executor=executor,
+        )
+
+        if self._resume_dir is not None:
+            resume_path = Path(self._resume_dir)
+            if (resume_path / "meta.json").exists():
+                checkpoint = Checkpoint.load(self._resume_dir)
+                state_dict = checkpoint.state_dict
+                if checkpoint.config:
+                    context.model_config = checkpoint.config
+            else:
+                checkpoint = None
+                state_dict = load_model_weights(self._resume_dir)
+            model.load_state_dict(state_dict, strict=False)
+            if checkpoint is not None:
+                context.epoch = cfg.start_epoch
+                context.iteration = cfg.start_batch
+            context.checkpoint = checkpoint
+
+        if cfg.lora is not None:
+            inject_lora(
+                model,
+                r=cfg.lora.r,
+                alpha=cfg.lora.alpha,
+                target_modules=set(cfg.lora.target_modules),
+            )
+
+        context.optimizer = cfg.optimizer_fn(model)
+        context.scheduler = cfg.scheduler_fn(context.optimizer)
+
+        sampler_offset = context.iteration * cfg.batch_per_device
+        sampler = ResumableDistributedSampler(
+            data_source=cfg.dataset,
+            start_epoch=context.epoch,
+            start_iter=sampler_offset,
+            seed=cfg.random_seed,
+        )
+        context.dataloader = DataLoader(
+            cfg.dataset,
+            batch_size=cfg.batch_per_device,
+            sampler=sampler,
+            num_workers=cfg.num_workers,
+            pin_memory=cfg.pin_memory,
+            prefetch_factor=cfg.prefetch_factor,
+        )
+
+        if cfg.val_dataset is not None:
+            val_sampler = ResumableDistributedSampler(
+                data_source=cfg.val_dataset,
+                start_epoch=0,
+                start_iter=0,
+                seed=cfg.random_seed,
+                shuffle=False,
+            )
+            context.val_dataloader = DataLoader(
+                cfg.val_dataset,
+                batch_size=cfg.batch_per_device,
+                sampler=val_sampler,
+                num_workers=cfg.num_workers,
+                pin_memory=cfg.pin_memory,
+                prefetch_factor=cfg.prefetch_factor,
+            )
+
+        context.model, context.optimizer, context.dataloader, context.scheduler = (
+            executor.prepare(
+                model,
+                context.optimizer,
+                context.dataloader,
+                context.scheduler,
+            )
+        )
+
+        if context.checkpoint and context.checkpoint.extra:
+            extra = context.checkpoint.extra
+            for name in ("optimizer", "scheduler"):
+                if name in extra:
+                    obj = getattr(context, name, None)
+                    if obj is not None:
+                        obj.load_state_dict(extra[name])
+
+        context.strategy = StrategyFactory.create(
+            model=context.model,
+            train_type=cfg.strategy,
+            device=device,
+            executor=executor,
+            model_fn=cfg.model_fn,
+            **cfg.extra_kwargs,
+        )
+
+        return context

astrai/trainer/trainer.py

@@ -0,0 +1,109 @@
+import logging
+from typing import List, Optional
+
+from astrai.config import TrainConfig
+from astrai.parallel.setup import spawn_parallel_fn
+from astrai.trainer.train_callback import (
+    CallbackFactory,
+    TrainCallback,
+)
+from astrai.trainer.train_context import TrainContext, TrainContextBuilder
+
+logger = logging.getLogger(__name__)
+
+
+class Trainer:
+    def __init__(
+        self, train_config: TrainConfig, callbacks: Optional[List[TrainCallback]] = None
+    ):
+        self.train_config = train_config
+        default_callbacks = self._get_default_callbacks()
+        self.callbacks = (
+            default_callbacks + callbacks if callbacks else default_callbacks
+        )
+
+    def _get_default_callbacks(self) -> List[TrainCallback]:
+        cfg = self.train_config
+        callbacks = [
+            CallbackFactory.create(
+                "gradient_checkpointing",
+                modules=cfg.gradient_checkpointing_modules,
+            ),
+            CallbackFactory.create(
+                "checkpoint",
+                cfg.ckpt_dir,
+                cfg.ckpt_interval,
+            ),
+            CallbackFactory.create(
+                "metric_logger",
+                log_dir=cfg.log_dir,
+                save_interval=cfg.ckpt_interval,
+                log_interval=cfg.log_interval,
+                metrics=cfg.metrics,
+            ),
+            CallbackFactory.create("progress_bar", cfg.n_epoch),
+            CallbackFactory.create("gradient_clipping", cfg.max_grad_norm),
+            CallbackFactory.create("validation"),
+        ]
+        return callbacks
+
+    def _call_callbacks(self, method_name: str, context: TrainContext):
+        for callback in self.callbacks:
+            method = getattr(callback, method_name, None)
+            if method:
+                method(context)
+
+    def _trainer_loop(self, resume_dir: Optional[str] = None):
+        context = (
+            TrainContextBuilder(self.train_config).with_resume_dir(resume_dir).build()
+        )
+        executor = context.executor
+        self._call_callbacks("on_train_begin", context)
+
+        try:
+            context.model.train()
+
+            for epoch in range(context.epoch, context.config.n_epoch):
+                context.epoch = epoch
+                self._call_callbacks("on_epoch_begin", context)
+
+                for batch in context.dataloader:
+                    self._call_callbacks("on_batch_begin", context)
+
+                    with executor.accumulate(context.model):
+                        loss = context.strategy(batch)
+                        context.loss = loss.item()
+                        stand_loss = loss / executor.grad_accum_steps
+                        executor.backward(stand_loss)
+                        context.iteration += 1
+                        self._call_callbacks("on_batch_end", context)
+
+                        if executor.sync_gradients:
+                            self._call_callbacks("on_optimizer_step", context)
+                            context.optimizer.step()
+                            context.optimizer.zero_grad()
+
+                            if context.scheduler:
+                                context.scheduler.step()
+
+                self._call_callbacks("on_epoch_end", context)
+
+        except Exception as e:
+            logger.error("Training failed: %s", str(e), exc_info=True)
+            self._call_callbacks("on_error", context)
+            raise
+        finally:
+            self._call_callbacks("on_train_end", context)
+
+    def train(self, resume_dir: Optional[str] = None):
+        cfg = self.train_config
+        spawn_parallel_fn(
+            self._trainer_loop,
+            backend=cfg.backend,
+            world_size=cfg.nprocs,
+            master_addr=cfg.master_addr,
+            master_port=cfg.master_port,
+            device_type=cfg.device_type,
+            start_method=cfg.start_method,
+            resume_dir=resume_dir,
+        )

benchmark.py

@@ -1,215 +0,0 @@
-import torch
-from typing import Dict, Any
-from dataclasses import dataclass
-from khaosz.core.transformer import TransformerConfig, Transformer
-
-
-@dataclass
-class BenchmarkResult:
-    total_tokens: int
-    total_time: float
-    tokens_per_second: float
-    metadata: Dict[str, Any]
-
-
-class GenerationBenchmark:
-    def __init__(
-        self,
-        config: TransformerConfig,
-        device: str = "cuda",
-        dtype: torch.dtype = torch.float16
-    ):
-        self.config = config
-        self.device = device
-        self.dtype = dtype
-        self.model = Transformer(config).to(device=device, dtype=dtype)
-        self.model.eval()
-    
-    def _initialize_kv_cache(self, batch_size: int, max_len: int) -> list:
-        """初始化KV缓存"""
-        kv_cache = []
-        head_dim = self.config.n_dim // self.config.n_head
-        for _ in range(self.config.n_layer):
-            k_cache = torch.zeros(
-                (batch_size, max_len, self.config.n_kvhead, head_dim),
-                device=self.device, dtype=self.dtype
-            )
-            v_cache = torch.zeros(
-                (batch_size, max_len, self.config.n_kvhead, head_dim),
-                device=self.device, dtype=self.dtype
-            )
-            kv_cache.append((k_cache, v_cache))
-        return kv_cache
-    
-    def _prepare_inputs(self, batch_size: int, prompt_length: int, total_length: int):
-        prompt_ids = torch.randint(
-            low=0,
-            high=self.config.vocab_size,
-            size=(batch_size, prompt_length),
-            device=self.device,
-            dtype=torch.long
-        )
-        
-        gen_ids = torch.randint(
-            low=0,
-            high=self.config.vocab_size,
-            size=(batch_size, total_length - prompt_length),
-            device=self.device,
-            dtype=torch.long
-        )
-        
-        return prompt_ids, gen_ids
-    
-    @torch.inference_mode()
-    def run_prefill_benchmark(
-        self,
-        batch_size: int = 1,
-        prompt_length: int = 512,
-        num_trials: int = 10,
-    ) -> BenchmarkResult:
-        
-        for _ in range(3):
-            prompt_ids, _ = self._prepare_inputs(batch_size, prompt_length, prompt_length)
-            _ = self.model(prompt_ids)
-        
-        torch.cuda.synchronize()
-        
-        total_time = 0.0
-        total_tokens = batch_size * prompt_length * num_trials
-        
-        for trial in range(num_trials):
-            prompt_ids, _ = self._prepare_inputs(batch_size, prompt_length, prompt_length)
-            start_event = torch.cuda.Event(enable_timing=True)
-            end_event = torch.cuda.Event(enable_timing=True)
-            
-            start_event.record()
-            _ = self.model(prompt_ids)
-            end_event.record()
-            torch.cuda.synchronize()
-            
-            trial_time = start_event.elapsed_time(end_event) / 1000
-            total_time += trial_time
-            
-            print(f"Trial {trial + 1}/{num_trials}: {prompt_length} tokens in {trial_time:.3f}s "
-                  f"({prompt_length / trial_time:.1f} tokens/s)")
-        
-        return BenchmarkResult(
-            total_tokens=total_tokens,
-            total_time=total_time,
-            tokens_per_second=total_tokens / total_time,
-            metadata={
-                "benchmark_type": "prefill",
-                "batch_size": batch_size,
-                "prompt_length": prompt_length,
-                "dtype": self.dtype,
-                "device": self.device,
-            }
-        )
-    
-    @torch.inference_mode()
-    def run_decoding_benchmark(
-        self,
-        batch_size: int = 1,
-        prompt_length: int = 512,
-        gen_length: int = 128,
-        num_trials: int = 5,
-    ) -> BenchmarkResult:
-        
-        total_time = 0.0
-        total_tokens = batch_size * gen_length * num_trials
-        
-        for trial in range(num_trials):
-            
-            prompt_ids, gen_ids = self._prepare_inputs(batch_size, prompt_length, prompt_length + gen_length)
-            kv_cache = self._initialize_kv_cache(batch_size, self.config.m_len)
-            _ = self.model(prompt_ids, persistent_key_values=kv_cache, start_pos=0)
-            
-            torch.cuda.synchronize()
-
-            start_event = torch.cuda.Event(enable_timing=True)
-            end_event = torch.cuda.Event(enable_timing=True)
-            
-            start_event.record()
-            
-            current_pos = prompt_length
-            for i in range(gen_length):
-                input_token = gen_ids[:, i:i+1]
-                _ = self.model(input_token, persistent_key_values=kv_cache, start_pos=current_pos)
-                current_pos += 1
-            
-            end_event.record()
-            torch.cuda.synchronize()
-            
-            trial_time = start_event.elapsed_time(end_event) / 1000
-            total_time += trial_time
-            
-            print(f"Trial {trial + 1}/{num_trials}: {gen_length} tokens in {trial_time:.3f}s "
-                  f"({gen_length / trial_time:.1f} tokens/s)")
-
-        
-        return BenchmarkResult(
-            total_tokens=total_tokens,
-            total_time=total_time,
-            tokens_per_second=total_tokens / total_time,
-            metadata={
-                "benchmark_type": "generation",
-                "batch_size": batch_size,
-                "prompt_length": prompt_length,
-                "gen_length": gen_length,
-                "dtype": self.dtype,
-                "device": self.device,
-            }
-        )
-
-
-def print_benchmark_result(result: BenchmarkResult):
-    """打印基准测试结果"""
-    benchmark_type = result.metadata["benchmark_type"]
-    
-    print(f"\n{' ' + benchmark_type.upper().replace('_', ' ') + ' Benchmark ':-^80}")
-    print(f"Total Tokens Processed: {result.total_tokens:,}")
-    print(f"Time Consumed: {result.total_time:.3f}s")
-    print(f"Throughput: {result.tokens_per_second:,.1f} tokens/s")
-    
-    if benchmark_type == "prefill":
-        print(f"Batch Size: {result.metadata['batch_size']} | Prompt Length: {result.metadata['prompt_length']}")
-    elif benchmark_type == "generation":
-        print(f"Batch Size: {result.metadata['batch_size']} | Gen Length: {result.metadata['gen_length']}")
-    
-    print(f"Device: {result.metadata['device']} | Dtype: {result.metadata['dtype']}")
-    print("-" * 80)
-
-
-if __name__ == "__main__":
-    config = TransformerConfig(
-        vocab_size=10000,
-        n_dim=1536,
-        n_head=24,
-        n_kvhead=4,
-        d_ffn=6912,
-        m_len=2048,
-        n_layer=24,
-        norm_eps=1e-5,
-    )
-    
-    benchmark = GenerationBenchmark(config)
-    
-    print("=" * 80)
-    print("Running Transformer Generation Benchmark")
-    print("=" * 80)
-    
-    prefill_result = benchmark.run_prefill_benchmark(
-        batch_size=4,
-        prompt_length=512,
-        num_trials=5
-    )
-    print_benchmark_result(prefill_result)
-    
-    gen_result = benchmark.run_decoding_benchmark(
-        batch_size=4,
-        prompt_length=512,
-        gen_length=128,
-        num_trials=5
-    )
-    print_benchmark_result(gen_result)
-    

docker-compose.yml

@@ -0,0 +1,44 @@
+services:
+  server:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    user: "${UID:-1000}:${GID:-1000}"
+    ports:
+      - "8000:8000"
+    volumes:
+      - ./params:/app/params:ro
+    command: python -m scripts.tools.server --port 8000 --device cuda
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 60s
+    restart: unless-stopped
+
+  server-cpu:
+    profiles: [cpu]
+    build:
+      context: .
+      dockerfile: Dockerfile
+    user: "${UID:-1000}:${GID:-1000}"
+    ports:
+      - "8000:8000"
+    volumes:
+      - ./params:/app/params:ro
+    command: python -m scripts.tools.server --port 8000 --device cpu
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 120s
+    restart: unless-stopped

generate.py

@@ -1,101 +0,0 @@
-import os
-import torch
-import json
-import torch
-import argparse
-
-from khaosz import Khaosz
-from typing import List
-from tqdm import tqdm
-
-
-PROJECT_ROOT = os.path.dirname(os.path.abspath(__file__))
-
-def batch_generate(
-    model: Khaosz,
-    queries: List[str],
-    temperature: float, 
-    top_k: int, 
-    top_p: float,
-    batch_size: int,
-) -> List:
-    assert batch_size > 0
-    sorted_queries = sorted(queries, key=lambda x: len(x), reverse=True)
-    original_indices = {query: idx for idx, query in enumerate(queries)}
-    
-    responses = [None] * len(queries) 
-    total_batches = (len(sorted_queries) + batch_size - 1) // batch_size 
-    
-    for i in tqdm(range(0, total_batches * batch_size, batch_size), desc="Generating responses"):
-        batch_queries = sorted_queries[i: min(i + batch_size, len(queries))]
-        if not isinstance(batch_queries, list):
-            batch_queries = [batch_queries]
-        
-        batch_responses = model.batch_generate(
-            queries=batch_queries,
-            temperature=temperature,
-            top_k=top_k,
-            top_p=top_p
-        )
-        
-        for batch_query, batch_response in zip(batch_queries, batch_responses):
-            print(f"Q: {batch_query[:50]} \nR: {batch_response[:50]})")
-        
-        for query, response in zip(batch_queries, batch_responses):
-            original_idx = original_indices[query] 
-            responses[original_idx] = response  
-
-    return responses
-
-
-def processor(
-    model: Khaosz,
-    input_json_file: str,
-    output_json_file: str,
-    batch_size: int,
-    temperature: float,
-    top_p: float,
-    top_k: int,
-    question_key: str="question",
-):
-    with open(input_json_file, "r", encoding='utf-8') as f:
-        input_dict = [json.loads(line) for line in f]
-        queries = [item[question_key] for item in input_dict]
-    
-    output_dict = batch_generate(
-        model=model,
-        queries=queries,
-        temperature=temperature,
-        top_k=top_k,
-        top_p=top_p,
-        batch_size=batch_size
-    )
-    
-    with open(output_json_file, "w", encoding='utf-8') as f:
-        json.dump(output_dict, f, indent=4, ensure_ascii=False)
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Run generate with a Khaosz model.")
-    
-    parser.add_argument("--model_dir", type=str, required=True, help="Path to the model directory.")
-    parser.add_argument("--input_json_file", type=str, required=True, help="Path to the input JSONL file.")
-    parser.add_argument("--output_json_file", type=str, required=True, help="Path to the output JSONL file.")
-    parser.add_argument("--question_key", type=str, default="question", help="Key for the question in the input JSON.")
-    parser.add_argument("--temperature", type=float, default=0.60, help="Temperature for generating responses.")
-    parser.add_argument("--top_p", type=float, default=0.95, help="Top-p value for generating responses.")
-    parser.add_argument("--top_k", type=int, default=30, help="Top-k value for generating responses.")
-    parser.add_argument("--batch_size", type=int, default=1, help="Batch size for generating responses.")    
-
-    args = parser.parse_args()
-    model = Khaosz(args.model_dir).to(device='cuda', dtype=torch.bfloat16)
-    
-    processor(
-        model,
-        input_json_file=args.input_json_file,
-        output_json_file=args.output_json_file,
-        question_key=args.question_key,
-        batch_size=args.batch_size,
-        temperature=args.temperature,
-        top_k=args.top_k,
-        top_p=args.top_p
-    )

khaosz/__init__.py

@@ -1,56 +0,0 @@
-__version__ = "1.2.2"
-__author__ = "ViperEkura"
-
-from khaosz.model import Khaosz
-from khaosz.core.transformer import Transformer, TransformerConfig
-from khaosz.utils.retriever import Retriever
-from khaosz.utils.splitter import (
-    SemanticTextSplitter, 
-    PriorityTextSplitter
-)
-from khaosz.core.tokenizer import BpeTokenizer
-from khaosz.core.parameter import ParameterLoader
-from khaosz.core.generator import (
-    TextGenerator,
-    ChatGenerator, 
-    StreamGenerator, 
-    BatchGenerator, 
-    RetrievalGenerator, 
-    EmbeddingEncoder
-)
-from khaosz.trainer import (
-    Trainer,
-    DatasetLoader,
-    TrainConfig,
-    StrategyFactory,
-    SchedulerFactory
-)
-
-__all__ = [
-    # model
-    "Khaosz",
-    
-    # module
-    "Transformer",
-    "TransformerConfig",
-    "BpeTokenizer",
-    "ParameterLoader",
-    "TextGenerator",
-    "ChatGenerator",
-    "StreamGenerator",
-    "BatchGenerator",
-    "RetrievalGenerator",
-    "EmbeddingEncoder",
-    
-    # trainer
-    "Trainer",
-    "DatasetLoader",
-    "TrainConfig",
-    "StrategyFactory",
-    "SchedulerFactory",
-    
-    # utils
-    "Retriever",
-    "SemanticTextSplitter",
-    "PriorityTextSplitter",
-]

khaosz/core/__init__.py

@@ -1,27 +0,0 @@
-from khaosz.core.tokenizer import BpeTokenizer
-from khaosz.core.transformer import Transformer, TransformerConfig
-from khaosz.core.parameter import ParameterLoader, ModelParameter, Checkpoint
-from khaosz.core.generator import (
-    TextGenerator,
-    ChatGenerator, 
-    StreamGenerator, 
-    BatchGenerator, 
-    RetrievalGenerator, 
-    EmbeddingEncoder
-)
-
-
-__all__ = [
-    "Transformer",
-    "TransformerConfig",
-    "BpeTokenizer",
-    "ParameterLoader",
-    "ModelParameter",
-    "Checkpoint",
-    "TextGenerator",
-    "ChatGenerator",
-    "StreamGenerator",
-    "BatchGenerator",
-    "RetrievalGenerator",
-    "EmbeddingEncoder"
-]

khaosz/core/generator.py

@@ -1,568 +0,0 @@
-import torch
-from torch import Tensor 
-from typing import List, Tuple, Union, Optional, Generator, Self
-from khaosz.core.parameter import ModelParameter
-
-
-def build_prompt(query: str, history: Optional[List[Tuple[str, str]]] = None) -> str:
-    """ 
-    Build prompt for query and history
-    
-    Args:
-        query(str): query string
-        history(Optional[List[Tuple[str, str]]]): history list of query and response
-        
-    Returns:
-        str: prompt string
-        
-    """
-    prompt_parts = []
-    
-    if history is None:
-        history = []
-    
-    for his_query, his_response in history:
-        prompt_parts.append(f"<|user|> {his_query} <|system|> <bos>{his_response}<eos>")
-        
-    if query is not None:
-        prompt_parts.append(f"<|user|> {query} <|system|> <bos>")
-    
-    return "\n".join(prompt_parts)
-
-def pad_sequence(ids_list: List[List[int]], max_ids_len: int, pad_id: int) -> List[List[int]]:
-    """ 
-    Pad a list of sequences to a fixed length.
-    
-    Args:
-        ids_list (List[List[int]]): A list of sequences.
-        max_ids_len (int): The maximum length of sequences.
-        pad_id (int): The id to pad sequences.
-        
-    Returns:
-        List[List[int]]: A list of padded sequences.
-        
-    """
-    new_ids_list = []
-    for ids in ids_list:
-        pad_len = max_ids_len - len(ids)
-        padded_seq = [pad_id] * pad_len + ids
-        new_ids_list.append(padded_seq)
-    
-    return new_ids_list
-
-def apply_sampling_strategies(
-    logits: Tensor,
-    temperature: float,
-    top_k: int,
-    top_p: float,
-    filter_value: float = -float("inf")
-) -> Tensor:
-    """ 
-    Apply sampling strategies to the logits tensor.
-    
-    Args:
-        logits (Tensor): The logits tensor.
-        temperature (float): The temperature parameter.
-        top_k (int): The top-k parameter.
-        top_p (float): The top-p parameter.
-        filter_value (float, optional): The filter value. Defaults to -float("inf").
-        
-    Returns:
-        Tensor: The sampled logits tensor.
-        
-    """
-    
-    if temperature != 1.0:
-        logits = logits / temperature
-    
-    if top_k > 0:
-        top_k = min(top_k, logits.size(-1))
-        indices_to_remove = logits < torch.topk(logits, top_k, dim=-1)[0][..., -1, None]
-        logits[indices_to_remove] = filter_value
-    
-    if top_p < 1.0:
-        sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1)
-        cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
-        
-        sorted_indices_to_remove = cumulative_probs > top_p
-        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
-        sorted_indices_to_remove[..., 0] = 0
-        
-        indices_to_remove = torch.zeros_like(logits, dtype=torch.bool)
-        indices_to_remove.scatter_(
-            dim=1,
-            index=sorted_indices,
-            src=sorted_indices_to_remove
-        )
-        
-        logits[indices_to_remove] = filter_value
-    
-    return logits
-
-
-class KVCacheManager:
-    def __init__(
-        self, 
-        num_layers: int, 
-        batch_size: int,
-        max_len: int, 
-        num_heads: int, 
-        head_dim: int, 
-        device: torch.device = "cuda", 
-        dtype: torch.dtype = torch.bfloat16
-    ):
-        self.num_layers = num_layers
-        self.batch_size = batch_size
-        self.max_len = max_len
-        self.num_heads = num_heads
-        self.head_dim = head_dim
-        self.device = device
-        self.dtype = dtype
-        
-        self._kv_cache: List[Tuple[Tensor, Tensor]] = None
-        self._seq_mask: Tensor = None
-        self._initialize()
-
-    def _initialize(self):
-        self._kv_cache = []
-        for _ in range(self.num_layers):
-            k_cache = torch.zeros(
-                (self.batch_size, self.max_len, self.num_heads, self.head_dim),
-                device=self.device, dtype=self.dtype
-            )
-            v_cache = torch.zeros(
-                (self.batch_size, self.max_len, self.num_heads, self.head_dim),
-                device=self.device, dtype=self.dtype
-            )
-            self._kv_cache.append((k_cache, v_cache))
-        
-        self._seq_mask = torch.ones(
-            (self.batch_size, self.max_len),
-            device=self.device, dtype=torch.bool
-        )
-
-    def update(self, active_mask: Tensor):
-        for i in range(self.num_layers):
-            k_cache, v_cache = self._kv_cache[i]
-            new_k_cache, new_v_cache = k_cache[active_mask], v_cache[active_mask]
-            self._kv_cache[i] = (new_k_cache, new_v_cache)
-        
-        self._seq_mask = self._seq_mask[active_mask]
-
-    def reset(self, full_reset=False):
-        if full_reset:
-            self._kv_cache = None
-            self._seq_mask = None
-        else:
-            self._initialize()
-    
-    def set_seq_mask(self, input_ids: Tensor, pad_id: int):
-        batch_size, seq_len = input_ids.shape
-        bool_mask = (input_ids != pad_id)
-        self._seq_mask[: batch_size, : seq_len] = bool_mask
-
-    def get_kvcache(self) -> List[Tuple[Tensor, Tensor]]:
-        return self._kv_cache
-    
-    def get_seq_mask(self) -> Tensor:        
-        return self._seq_mask
-    
-
-class GeneratorCore:
-    def __init__(self, parameter: ModelParameter):
-        self.model = parameter.model
-        self.tokenizer = parameter.tokenizer
-        self.config = parameter.config
-
-    def compute_logits(
-        self,
-        input_ids: Tensor,
-        attn_mask: Optional[Tensor] = None,
-        kv_caches: Optional[List[Tuple[Tensor, Tensor]]] = None,
-        start_pos: int = 0
-    ) -> Tuple[Tensor, int]:
-        with torch.inference_mode():
-            outputs = self.model(input_ids, attn_mask, kv_caches, start_pos)
-            logits = outputs["logits"][:, -1, :]
-            cache_increase = input_ids.size(-1)   
-        
-        return logits, cache_increase
-
-    def to(self, *args, **kargs) -> Self:
-        self.model.to(*args, **kargs)
-        return self
-
-
-class EmbeddingEncoderCore:
-    def __init__(self, parameter: ModelParameter):
-        self.model = parameter.model
-        self.tokenizer = parameter.tokenizer
-        self.config = parameter.config
-    
-    def encode(self, sentence: Union[str, List[str]]) -> Union[Tensor, List[Tensor]]:
-        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
-        
-        all_fragments = []
-        fragment_origin_idx = []
-        
-        for i, seq in enumerate(batch_ids):
-            if len(seq) > max_model_len:
-                fragments = [seq[j:j+max_model_len] for j in range(0, len(seq), max_model_len)]
-                all_fragments.extend(fragments)
-                fragment_origin_idx.extend([i] * len(fragments))
-            else:
-                all_fragments.append(seq)
-                fragment_origin_idx.append(i)
-        
-        #if empty fragments
-        if not all_fragments or not ids:
-            return [] if with_batch else torch.tensor([])
-        
-        device = next(self.model.parameters()).device
-        max_len = min(max(len(seq) for seq in all_fragments), max_model_len)
-        
-        padded_ids = []
-        masks = []
-        for seq in all_fragments:
-            pad_len = max_len - len(seq)
-            padded_seq = seq + [self.tokenizer.pad_id] * pad_len
-            mask = [token_id != self.tokenizer.pad_id for token_id in padded_seq]
-            padded_ids.append(padded_seq)
-            masks.append(mask)
-        
-        input_tensor = torch.tensor(padded_ids, device=device, dtype=torch.long)
-        seq_mask = torch.tensor(masks, device=device, dtype=torch.bool)
-        
-        with torch.inference_mode():
-            outputs = self.model(input_tensor, seq_mask)["hidden_states"]
-            # [num_fragments, seq_len, hidden_size]
-            fragment_embs = torch.mul(outputs, seq_mask.unsqueeze(-1))  
-        
-        sentence_embs: List[Tensor] = []
-        for i in range(len(batch_ids)):
-            indices = [idx for idx, orig_idx in enumerate(fragment_origin_idx) if orig_idx == i]
-            if indices is not None:
-                sum_frags = torch.sum(fragment_embs[indices, :, :], dim=1)      # [frags, hidden_size]
-                length = torch.sum(seq_mask[indices, :], dim=1).unsqueeze(1)    # [frags, 1]
-                emb = torch.sum(sum_frags / length, dim=0)                      # [frags, hidden_size]
-                sentence_embs.append(emb.flatten())
-        
-        if with_batch:
-            return [emb.flatten() for emb in sentence_embs]
-        else:
-            return sentence_embs[0].flatten()
-
-    def to(self, *args, **kargs) -> Self:
-        self.model.to(*args, **kargs)
-        return self
-
-
-class TextGenerator(GeneratorCore):
-    def __init__(self, parameter: ModelParameter):
-        super().__init__(parameter)
-    
-    def generate(
-            self, 
-            query: str, 
-            temperature: float,
-            top_k: int,
-            top_p: float,
-        ) -> str:
-        assert temperature >= 0.0
-        assert top_k >= 0
-        assert top_p >= 0.0 and top_p <= 1.0
-        
-        device = next(self.model.parameters()).device
-        cache_manager = KVCacheManager(
-            num_layers=self.config.n_layer, 
-            batch_size=1, 
-            max_len=self.config.m_len, 
-            num_heads=self.config.n_kvhead,
-            head_dim=self.config.n_dim // self.config.n_head,
-            device=device,
-        )
-        
-        ids = self.tokenizer.encode(query)
-        input_ids = torch.tensor([ids], device=device, dtype=torch.long)
-        
-        start_cache_pos = len(ids)
-        cur_cache_pos = 0
-        self.model.eval()
-        
-        while len(ids) < self.config.m_len:
-            kv_caches = cache_manager.get_kvcache()
-            logits, cache_increase = self.compute_logits(
-                input_ids,
-                kv_caches=kv_caches, 
-                start_pos=cur_cache_pos
-            )
-            logits = apply_sampling_strategies(logits, temperature, top_k, top_p)
-            probs = torch.softmax(logits, dim=-1)
-            next_token_id = torch.multinomial(probs, num_samples=1)
-            
-            input_ids = next_token_id
-            ids.append(next_token_id.item())
-            cur_cache_pos += cache_increase
-            
-            if next_token_id.item() in self.tokenizer.stop_ids:
-                break
-        
-        response = self.tokenizer.decode(ids[start_cache_pos:])
-        
-        return response
-
-
-
-class ChatGenerator(GeneratorCore):
-    def __init__(self, parameter: ModelParameter):
-        super().__init__(parameter)
-        
-    def generate(
-            self, 
-            query: str, 
-            history: List[Tuple[str, str]],
-            temperature: float,
-            top_k: int,
-            top_p: float,
-        ) -> str:
-        
-        assert temperature >= 0.0
-        assert top_k >= 0
-        assert top_p >= 0.0 and top_p <= 1.0
-
-        if history is None:
-            history = []
-        
-        device = next(self.model.parameters()).device
-        cache_manager = KVCacheManager(
-            num_layers=self.config.n_layer, 
-            batch_size=1, 
-            max_len=self.config.m_len, 
-            num_heads=self.config.n_kvhead,
-            head_dim=self.config.n_dim // self.config.n_head,
-            device=device,
-        )
-        ids = self.tokenizer.encode(build_prompt(query, history))
-        input_ids = torch.tensor([ids], device=device, dtype=torch.long)
-        cpy_history = history.copy()
-        
-        start_cache_pos = len(ids)
-        cur_cache_pos = 0
-        self.model.eval()
-        
-        
-        while len(ids) < self.config.m_len:
-            kv_caches = cache_manager.get_kvcache()
-            logits, cache_increase = self.compute_logits(
-                input_ids,
-                kv_caches=kv_caches, 
-                start_pos=cur_cache_pos
-            )
-            logits = apply_sampling_strategies(logits, temperature, top_k, top_p)
-            probs = torch.softmax(logits, dim=-1)
-            next_token_id = torch.multinomial(probs, num_samples=1)
-            
-            input_ids = next_token_id
-            ids.append(next_token_id.item())
-            cur_cache_pos += cache_increase
-            
-            if next_token_id.item() in self.tokenizer.stop_ids:
-                break
-        
-        response = self.tokenizer.decode(ids[start_cache_pos:])
-        cpy_history.append((query, response))
-        
-        return response, cpy_history
-    
-
-class StreamGenerator(GeneratorCore):
-    def __init__(self, parameter: ModelParameter):
-        super().__init__(parameter)
-    
-    def generate(
-            self, 
-            query: str, 
-            history: List[Tuple[str, str]],
-            temperature: float,
-            top_k: int,
-            top_p: float,
-        ) -> Generator[Tuple[str, List[Tuple[str, str]]], None, None]:
-        
-        assert temperature >= 0.0
-        assert top_k >= 0
-        assert top_p >= 0.0 and top_p <= 1.0
-
-        if history is None:
-            history = []
-        
-        device = next(self.model.parameters()).device
-        cache_manager = KVCacheManager(
-            num_layers=self.config.n_layer, 
-            batch_size=1, 
-            max_len=self.config.m_len, 
-            num_heads=self.config.n_kvhead,
-            head_dim=self.config.n_dim // self.config.n_head,
-            device=device,
-        )
-        ids = self.tokenizer.encode(build_prompt(query, history))
-        input_ids = torch.tensor([ids], device=device, dtype=torch.long)
-        cpy_history = history.copy()
-        
-        start_cache_pos = len(ids)
-        cur_cache_pos = 0
-        self.model.eval()
-        
-        
-        while len(ids) < self.config.m_len:
-            kv_caches = cache_manager.get_kvcache()
-            logits, cache_increase = self.compute_logits(
-                input_ids,
-                kv_caches=kv_caches, 
-                start_pos=cur_cache_pos
-            )
-            logits = apply_sampling_strategies(logits, temperature, top_k, top_p)
-            probs = torch.softmax(logits, dim=-1)
-            next_token_id = torch.multinomial(probs, num_samples=1)
-            
-            input_ids = next_token_id
-            ids.append(next_token_id.item())
-            cur_cache_pos += cache_increase
-            
-            response = self.tokenizer.decode(ids[start_cache_pos:])
-            yield response, cpy_history + [(query, response)]
-            
-            if next_token_id.item() in self.tokenizer.stop_ids:
-                yield response + "\n", cpy_history + [(query, response)]
-                break
-    
-
-class BatchGenerator(GeneratorCore):
-    def __init__(self, parameter: ModelParameter):
-        super().__init__(parameter)
-    
-    def generate(
-        self, 
-        queries: List[str],
-        histories: List[List[Tuple[str, str]]],
-        temperature: float, 
-        top_k: int, 
-        top_p: float 
-    ) -> List[str]:
-        
-        assert temperature >= 0.0
-        assert top_k >= 0
-        assert top_p >= 0.0 and top_p <= 1.0
-        
-        batch_size = len(queries)
-        if histories is None:
-            histories = [[] for _ in range(batch_size)]
-
-        prompts = [build_prompt(query, history) for query, history in zip(queries, histories)]
-        ids_list = [self.tokenizer.encode(prompt) for prompt in prompts]
-        max_ids_len = max(len(ids) for ids in ids_list)
-        ids_list = pad_sequence(ids_list, max_ids_len, self.tokenizer.pad_id)
-        
-        device = next(self.model.parameters()).device
-        cache_manager = KVCacheManager(
-            num_layers=self.config.n_layer, 
-            batch_size=batch_size, 
-            max_len=self.config.m_len, 
-            num_heads=self.config.n_kvhead,
-            head_dim=self.config.n_dim // self.config.n_head,
-            device=device,
-        )
-        
-        input_tensor = torch.tensor(ids_list, device=device, dtype=torch.long)
-        cache_manager.set_seq_mask(input_tensor, self.tokenizer.pad_id)
-        activate_task_mask = [True] * batch_size
-        
-        start_cache_pos = max_ids_len
-        cur_cache_pos = 0
-        
-        while max_ids_len < self.config.m_len and sum(activate_task_mask) != 0:
-            kv_caches = cache_manager.get_kvcache()
-            attn_mask =cache_manager.get_seq_mask()
-            
-            logits, cache_increase = self.compute_logits(
-                input_tensor,
-                attn_mask=attn_mask,
-                kv_caches=kv_caches, 
-                start_pos=cur_cache_pos
-            )
-            
-            cur_cache_pos += cache_increase
-            logits = apply_sampling_strategies(logits, temperature, top_k, top_p)
-            probs = torch.softmax(logits, dim=-1)
-            next_token_id = torch.multinomial(probs, num_samples=1)
-            
-            active_mask = []
-            c_ids = 0
-            
-            for i in range(batch_size):
-                if activate_task_mask[i]:
-                    token = next_token_id[c_ids, :].item()
-                    ids_list[i].append(token)
-                    c_ids += 1
-                    
-                    is_active = not token in self.tokenizer.stop_ids
-                    activate_task_mask[i] = is_active 
-                    active_mask.append(is_active)
-            
-            active_mask = torch.tensor(active_mask, device=device, dtype=torch.bool)
-            cache_manager.update(active_mask)
-            input_tensor = next_token_id[active_mask, :]
-
-            max_ids_len += 1
-        
-        
-        responses = [str()] * batch_size
-        for i in range(batch_size):
-            responses[i] = self.tokenizer.decode(ids_list[i][start_cache_pos:])
-            histories[i].append((queries[i], responses[i]))
-        
-        return responses
-        
-
-
-class RetrievalGenerator(GeneratorCore):
-    def __init__(self, retriever_parameter: ModelParameter):
-        super().__init__(retriever_parameter)
-    
-    def generate(
-        self,
-        retrieved: List[str],
-        query: str, 
-        history: List[Tuple[str, str]],
-        temperature: float,
-        top_k: int,
-        top_p: float,
-    ) -> str:
-        assert temperature >= 0.0
-        assert top_k >= 0
-        assert top_p >= 0.0 and top_p <= 1.0
-        
-        if history is None:
-            history = []
-            
-        retrieved = "\n".join([f"{idx + 1}. {key}" for idx, key in enumerate(retrieved)]) if retrieved else ""
-        retrieved_query = f"{retrieved}<eos>\n\n根据以上内容回答: {query}" if retrieved else query
-        parameter = ModelParameter(self.model, self.tokenizer, self.config)
-        
-        return ChatGenerator(parameter).generate(
-            retrieved_query, 
-            history,
-            temperature=temperature,
-            top_k=top_k,
-            top_p=top_p,
-        )
-
-class EmbeddingEncoder(EmbeddingEncoderCore):
-    def __init__(self, parameter: ModelParameter):
-        super().__init__(parameter)
-        
-    def encode(self, sentence: Union[str, List[str]]) -> Union[Tensor, List[Tensor]]:
-        return super().encode(sentence)
-        

khaosz/core/parameter.py

@@ -1,237 +0,0 @@
-import pickle as pkl
-import matplotlib.pyplot as plt
-import safetensors.torch as st
-import torch.nn as nn
-import torch.optim as optim
-
-from dataclasses import dataclass, field
-from typing import Any, Dict, List, Optional, Self, Union
-from pathlib import Path
-
-from khaosz.core.tokenizer import BpeTokenizer
-from khaosz.core.transformer import TransformerConfig, Transformer
-
-
-class BaseModelIO:
-    """Base class for model I/O operations."""
-    
-    def __init__(
-        self, 
-        model: Optional[nn.Module] = None, 
-        tokenizer: Optional[BpeTokenizer] = None,
-        config: Optional[TransformerConfig] = None
-    ):
-        self.model = model
-        self.tokenizer = tokenizer or BpeTokenizer()
-        self.config = config or TransformerConfig()
-    
-    def _get_file_paths(self, directory: Union[str, Path]) -> dict[str, Path]:
-        """Get standardized file paths for model components."""
-        dir_path = Path(directory)
-        return {
-            "model": dir_path / "model.safetensors",
-            "config": dir_path / "config.json", 
-            "tokenizer": dir_path / "tokenizer.json"
-        }
-    
-    def save_components(self, save_dir: Union[str, Path]):
-        """Save core model components."""
-        paths = self._get_file_paths(save_dir)
-        paths["model"].parent.mkdir(parents=True, exist_ok=True)
-        
-        if self.model is not None:
-            st.save_file(self.model.state_dict(), str(paths["model"]))
-        self.config.save(str(paths["config"]))
-        self.tokenizer.save(str(paths["tokenizer"]))
-    
-    def load_components(self, load_dir: Union[str, Path]) -> Self:
-        """Load core model components."""
-        paths = self._get_file_paths(load_dir)
-        
-        self.config.load(str(paths["config"]))
-        self.tokenizer.load(str(paths["tokenizer"]))
-        
-        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:
-        """Move model to device."""
-        if self.model is not None:
-            self.model.to(*args, **kwargs)
-        return self
-
-
-@dataclass
-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: TransformerConfig = field(
-        default_factory=TransformerConfig,
-        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:
-        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: TransformerConfig = field(
-        default_factory=TransformerConfig,
-        metadata={"help": "Transformer model configuration."}
-    )
-    optim_state: Dict[str, Any] = field(
-        default=None,
-        metadata={"help": "Optimizer state."}
-    )
-    sampler_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."}
-    )
-    
-    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",
-            "optim_state": paths["model"].parent / "optim_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["optim_state"]), "wb") as f:
-            pkl.dump(self.optim_state, f)
-            
-        # Save sampler state
-        with open(str(paths["sampler_state"]), "wb") as f:
-            pkl.dump(self.sampler_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["optim_state"].exists():
-            with open(str(paths["optim_state"]), "rb") as f:
-                self.optim_state = pkl.load(f)
-        
-        # Load sampler state
-        if paths["sampler_state"].exists():
-            with open(str(paths["sampler_state"]), "rb") as f:
-                self.sampler_state = pkl.load(f)
-        
-        return self
-    
-    def _plot_loss(self, save_path: str): 
-        """Plot and save loss curve."""
-        if not self.loss_list:
-            return
-        
-        current_iter = len(self.loss_list)
-            
-        plt.figure(figsize=(10, 6))
-        plt.plot(self.loss_list)
-        plt.title(f"Training Loss - Iteration {current_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: TransformerConfig,
-        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/core/tokenizer.py

@@ -1,119 +0,0 @@
-from tokenizers import Tokenizer, Encoding
-from tokenizers import decoders, processors, normalizers, pre_tokenizers
-from tokenizers.models import BPE
-from tokenizers.trainers import BpeTrainer
-from typing import List, Union
-
-
-class BpeTokenizer:
-    def __init__(self, path=None):
-        self._control_tokens = ["<bos>", "<eos>", "<pad>"]
-        self._special_tokens = ["<|user|>", "<|system|>"]
-        model = BPE()
-        tokenizer = Tokenizer(model)
-        tokenizer.normalizer = normalizers.Sequence([
-            normalizers.NFC()
-        ])
-        tokenizer.pre_tokenizer = pre_tokenizers.Sequence([
-            pre_tokenizers.Punctuation(behavior="isolated"),
-            pre_tokenizers.Metaspace(prepend_scheme="never"),
-            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=False)
-        ])
-        tokenizer.decoder = decoders.Sequence([
-            decoders.ByteLevel(),
-            decoders.Metaspace(prepend_scheme="never")
-        ])
-        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:
-        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))]
-        detail_vocab_size = vocab_size - (len(reserved_tokens) + len(self._special_tokens))
-        
-        alphabet = pre_tokenizers.ByteLevel.alphabet()
-        min_size = len(alphabet) + len(self._control_tokens)
-        assert detail_vocab_size > min_size
-        
-        trainer = BpeTrainer(
-            vocab_size=detail_vocab_size,
-            min_frequency=min_freq,
-            limit_alphabet=detail_vocab_size // 4,
-            max_token_length=18,
-            special_tokens=self._control_tokens,
-            show_progress=True,
-            initial_alphabet=alphabet,
-        )
-        
-        return trainer, detail_vocab_size, reserved_tokens
-
-    def train(self, files, vocab_size, min_freq, reserved_token_size=100):
-        trainer, _, reserved_tokens = self._prepare_trainer(
-            vocab_size=vocab_size,
-            min_freq=min_freq,
-            reserved_token_size=reserved_token_size
-        )
-        self._tokenizer.train(files=files, trainer=trainer)
-        self._tokenizer.add_special_tokens(self._special_tokens + reserved_tokens)
-            
-    def train_from_iterator(self, iterator, vocab_size, min_freq, reserved_token_size=100):
-        trainer, _, reserved_tokens = self._prepare_trainer(
-            vocab_size=vocab_size,
-            min_freq=min_freq,
-            reserved_token_size=reserved_token_size
-        )
-        self._tokenizer.train_from_iterator(iterator=iterator, trainer=trainer)
-        self._tokenizer.add_special_tokens(self._special_tokens + reserved_tokens)
-            
-    def save(self, path):
-        self._tokenizer.save(path)
-        
-    def load(self, path):
-        self._tokenizer = Tokenizer.from_file(path)
-
-    def encode(self, tokens: Union[str, List[str]], out_ids: bool=True, add_special_tokens: bool=False) -> List:
-        if isinstance(tokens, str):
-            encoded: Encoding = self._tokenizer.encode(tokens, add_special_tokens=add_special_tokens)
-            return encoded.ids if out_ids else encoded.tokens
-        elif isinstance(tokens, list):
-            encoded_list: List[Encoding] = self._tokenizer.encode_batch(tokens, add_special_tokens=add_special_tokens)
-            return [encoded.ids if out_ids else encoded.tokens for encoded in encoded_list]
-
-    def decode(self, tokens: List[int], skip_special_tokens: bool=True) -> str:
-        return self._tokenizer.decode(tokens, skip_special_tokens=skip_special_tokens)
-    
-    def __len__(self) -> int:
-        return self._tokenizer.get_vocab_size()
-    
-    @property
-    def stop_ids(self) -> List[int]:
-        stop_ids = []
-        for token in self._control_tokens:
-            stop_ids.append(self._tokenizer.token_to_id(token))
-        return stop_ids
-    
-    @property
-    def bos_id(self) -> int:
-        return self._tokenizer.token_to_id("<bos>")
-    
-    @property
-    def eos_id(self) -> int:
-        return self._tokenizer.token_to_id("<eos>")
-    
-    @property
-    def pad_id(self) -> int:
-        return self._tokenizer.token_to_id("<pad>")
-    
-    @property
-    def user_id(self) -> int:
-        return self._tokenizer.token_to_id("<|user|>")
-    
-    @property
-    def system_id(self) -> int:
-        return self._tokenizer.token_to_id("<|system|>")

khaosz/core/transformer.py

@@ -1,346 +0,0 @@
-import json
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from torch import Tensor
-from torch.nn import init
-from dataclasses import asdict, dataclass
-from typing import List, Optional, Self, Tuple
-
-
-def repeat_kv(x: Tensor, n_rep: int) -> Tensor:
-    """ 
-    Repeat k times along the dimension for attention heads.
-    Args:
-        x (Tensor): The input tensor.
-        n_rep (int): The number of repetitions.
-    Returns:
-        Tensor: The repeated tensor.
-    """
-    
-    bs, slen, n_heads, head_dim = x.shape
-    if n_rep == 1:
-        return x
-    return (
-        x[:, :, :, None, :]
-        .expand(bs, slen, n_heads, n_rep, head_dim)
-        .reshape(bs, slen, n_heads * n_rep, head_dim)
-    )
-
-def get_rotary_emb(
-        dim: int, 
-        max_len: int, 
-        base: float = 10000, 
-        device: torch.device = "cuda",
-    ) -> torch.Tensor:
-    """ 
-    Get the rotary embedding for the given dimension and maximum length.
-    Args:
-        dim (int): The dimension of the input.
-        max_len (int): The maximum length of the input.
-        base (float, optional): The base for the frequency. Defaults to 10000.
-        device (torch.device, optional): The device to use. Defaults to "cuda".
-    Returns:
-        Tensor: The rotary embedding tensor.
-    """
-
-    theta = base ** (-torch.arange(0, dim, 2, device=device).float() / dim)
-    t = torch.arange(0, max_len, device=device).float()
-    freqs = torch.outer(t, theta)
-    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
-    
-    return freqs_cis
-
-def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor:
-    """
-    Apply rotary embedding to the input tensor.
-    Args:
-        x (Tensor): The input tensor.
-        freqs_cis (Tensor): The rotary embedding tensor.
-    Returns:
-        Tensor: The output tensor.
-    """
-    
-    dtype = x.dtype
-    seq_len = x.size(1)
-
-    x_complex = torch.view_as_complex(x.view(*x.shape[:-1], -1, 2).float())
-    freqs_cis = freqs_cis.reshape(1, seq_len, 1, -1)
-    x_out = torch.view_as_real(x_complex * freqs_cis).flatten(3)
-    
-    return x_out.to(dtype)
-
-def process_attention_mask(
-        seq_mask: Tensor, 
-        start_pos: int = 0,
-        seq_len: int = 0,
-        is_causal: bool = False,
-        device: torch.device = "cuda", 
-        dtype: torch.dtype = torch.float32
-    ) -> Tensor:
-    """
-    Create attention mask for GQA
-    Args:
-        seq_mask (Tensor): A tensor indicating whether each position is valid or not.
-        start_pos (int): The starting position of the sequence.
-        seq_len (int): The length of the sequence.
-        is_causal (bool): Whether the attention is causal or not.
-        device (torch.device): The device to use.
-    Returns:
-        Tensor: The attention mask tensor.
-    """
-    
-    if seq_mask is None:
-        if start_pos != 0:
-            # for single prompt chat
-            seq_mask = torch.ones((1, seq_len), dtype=torch.bool, device=device)
-        else:
-            return None
-    
-    if seq_mask.dim() > 2:
-        # shape (bsz, seq_len) or (bsz,n_heads, seq_len, seq_len + start_pos)
-        # if ndim > 2, it's 4D tensor
-        return seq_mask
-    
-    batch_size = seq_mask.size(0)
-    seq_mask = seq_mask[:, :start_pos + seq_len].to(device=device, dtype=torch.bool)
-    # (bsz, start_pos + seq_len)
-    expanded_mask = seq_mask.unsqueeze(1).expand(batch_size, seq_len, start_pos + seq_len)
-    # (bsz, seq_len, start_pos + seq_len)
-    
-    if is_causal:
-        causal_mask = torch.tril(
-            torch.ones((seq_len, start_pos + seq_len), dtype=torch.bool, device=device), 
-            diagonal=start_pos
-        )
-        causal_mask = causal_mask.unsqueeze(0).expand(batch_size, seq_len, start_pos + seq_len)
-        expanded_mask = expanded_mask & causal_mask
-    
-    attention_mask = torch.zeros_like(expanded_mask, dtype=dtype, device=device)
-    attention_mask = attention_mask.masked_fill_(~expanded_mask, -torch.finfo(dtype).max / 2).unsqueeze(1)
-    # (bsz, 1, seq_len, seq_len + start_pos)
-    
-    return attention_mask
-
-
-@dataclass
-class TransformerConfig:
-    # basic config
-    vocab_size: Optional[int] = None
-    n_dim: Optional[int] = None
-    n_head: Optional[int] = None
-    n_layer: Optional[int] = None
-    m_len: Optional[int] = None
-    norm_eps: Optional[float] = None
-    d_ffn: Optional[int] = None
-    
-    # GQA
-    n_kvhead: Optional[int] = None
-    
-    
-    def load(self, config_path: str) -> Self:
-        with open(config_path, 'r') as f:
-            config: dict = 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:
-        config_dict = asdict(self)
-        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)
-            
-
-class Linear(nn.Module):
-    def __init__(self, in_dim: int, out_dim: int, bias: bool=False):
-        super().__init__()
-        self.weight = nn.Parameter(torch.empty((out_dim, in_dim)))
-        self.bias = nn.Parameter(torch.zeros(out_dim)) if bias else None
-        init.normal_(self.weight, mean=0, std=0.006)
-        
-    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):
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(n_dim))
-        self.norm_eps = norm_eps
-
-    def forward(self, x: Tensor) -> Tensor:
-        dtype = x.dtype
-        x = x.float()
-        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):
-        super().__init__()
-        self.up = Linear(n_dim, d_ffn)
-        self.gate = Linear(n_dim, d_ffn)
-        self.down = Linear(d_ffn, n_dim)
-    
-    def forward(self, x: Tensor) -> Tensor:
-        gated = self.up(x) * F.silu(self.gate(x))
-        out = self.down(gated)
-        return out
-
-
-class GQA(nn.Module):
-    def __init__(
-        self, 
-        n_dim: int, 
-        n_head: int, 
-        n_kvhead: int, 
-    ):
-        super().__init__()
-        assert n_dim % n_head == 0
-        assert n_head % n_kvhead == 0
-        
-        self.head_dim = n_dim // n_head
-        self.n_dim = n_dim
-        self.n_heads = n_head
-        self.n_kvheads = n_kvhead
-        self.n_rep = n_head // n_kvhead
-        
-        self.q_proj = Linear(n_dim, n_head * self.head_dim)
-        self.k_proj = Linear(n_dim, n_kvhead * self.head_dim)
-        self.v_proj = Linear(n_dim, n_kvhead * self.head_dim)
-        self.o_proj = Linear(n_dim, n_dim)
-    
-    def forward(
-        self,
-        x: Tensor, 
-        freqs_cis: Tensor, 
-        mask: Tensor = None,
-        kv_cache: Optional[Tuple[Tensor, Tensor]] = None,
-        start_pos: int = 0
-    ) -> Tensor:
-        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)
-        v = self._split_heads(self.v_proj(x), self.n_kvheads)
-        q, k = apply_rotary_emb(q, freqs_cis), apply_rotary_emb(k, freqs_cis)
-        
-        if kv_cache is not None:
-            k_cache, v_cache = kv_cache
-
-            # copy to cache
-            k_cache[:bsz, start_pos:start_pos + seq_len] = k
-            v_cache[:bsz, start_pos:start_pos + seq_len] = v
-            
-            # get cache
-            k = k_cache[:bsz, :start_pos + seq_len]
-            v = v_cache[:bsz, :start_pos + seq_len]
-        
-        k, v = repeat_kv(k, self.n_rep), repeat_kv(v, self.n_rep)
-        
-        # (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)
-        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))
-
-        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):
-        super().__init__()
-        self.attention = GQA(n_dim, n_head, n_kvhead)
-        self.norm_attn = RMSNorm(n_dim, norm_eps)
-        self.ffn = MLP(n_dim, d_ffn)
-        self.norm_ffn = RMSNorm(n_dim, norm_eps)
-
-    def forward(
-        self,
-        x: Tensor,
-        freqs_cis: Tensor,
-        attention_mask: Optional[Tensor] = None,
-        kv_cache: Optional[Tuple[Tensor, Tensor]] = None,
-        start_pos: int = 0
-    ) -> Tensor:
-        # attention
-        attn_output = self.attention(
-            self.norm_attn(x), 
-            freqs_cis, 
-            attention_mask, 
-            kv_cache, 
-            start_pos
-        )
-        x = attn_output + x
-        
-        # feed forward
-        x = self.ffn(self.norm_ffn(x)) + x
-        
-        return x
-
-
-class Transformer(nn.Module):
-    def __init__(self, config: TransformerConfig):
-        super().__init__()
-        self.embedding = nn.Parameter(torch.empty(config.vocab_size, config.n_dim))
-        self.layers = nn.ModuleList([
-            DecoderBlock(
-                config.n_dim, 
-                config.n_head, 
-                config.d_ffn, 
-                config.n_kvhead, 
-                config.norm_eps
-            )
-            for _ in range(config.n_layer)
-        ])
-        self.norm = RMSNorm(config.n_dim, config.norm_eps)
-        self.freq_cis = get_rotary_emb(config.n_dim // config.n_head, config.m_len)
-        init.normal_(self.embedding, mean=0, std=0.02)
-    
-    def forward(
-        self, 
-        input_ids: Tensor, 
-        input_mask: Optional[Tensor]=None,
-        persistent_key_values: Optional[List[Tuple[Tensor, Tensor]]]=None,
-        start_pos: int = 0
-    ) -> Tensor:
-        assert input_ids.ndim == 2
-        seq_len = input_ids.size(-1)
-        x = F.embedding(input_ids, self.embedding)
-        
-        self.freq_cis = self.freq_cis.to(x.device)
-        freqs_cis = self.freq_cis[start_pos:start_pos+seq_len]
-        has_kvcache = persistent_key_values is not None
-        
-        attn_mask = process_attention_mask(
-            input_mask, 
-            start_pos=start_pos,
-            seq_len=seq_len,
-            is_causal=has_kvcache,
-            device=x.device,
-            dtype=x.dtype
-        )
-        
-        for i, layer in enumerate(self.layers):
-            kv_cache = persistent_key_values[i] if persistent_key_values else None
-            x = layer(x, freqs_cis, attn_mask, kv_cache, start_pos)
-        
-        hidden_states = self.norm(x)        
-        logits = F.linear(hidden_states,  self.embedding)
-        
-        return {
-            "logits": logits,
-            "hidden_states": hidden_states
-        }
-        

khaosz/model.py

@@ -1,112 +0,0 @@
-from torch import Tensor
-from typing import List, Tuple, Generator, Union
-
-from khaosz.core.generator import (
-    TextGenerator,
-    ChatGenerator, 
-    StreamGenerator, 
-    BatchGenerator, 
-    RetrievalGenerator, 
-    EmbeddingEncoder
-)
-from khaosz.core.parameter import ParameterLoader
-
-
-class Khaosz:
-    def __init__(self, model_dir: str):
-        self.parameter = ParameterLoader.load(model_dir)
-    
-    def to(self, *args, **kwargs):
-        self.parameter.to(*args, **kwargs)
-        return self
-
-    def generate(
-            self, 
-            query: str, 
-            history: List[Tuple[str, str]]=None,
-            temperature: float=0.8,
-            top_k: int=50,
-            top_p: float=0.95,
-        ) -> str:
-            generator = ChatGenerator(self.parameter)
-            return generator.generate(
-                query, 
-                history=history,
-                temperature=temperature, 
-                top_k=top_k, 
-                top_p=top_p,
-            )
-    
-    def batch_generate(
-            self, 
-            queries: List[str],
-            histories: List[Tuple[str, str]]=None,
-            temperature: float=0.8,
-            top_k: int=50,
-            top_p: float=0.95,
-        ) -> List[str]:
-            generator = BatchGenerator(self.parameter)
-            return generator.generate(
-                queries, 
-                histories=histories,
-                temperature=temperature, 
-                top_k=top_k, 
-                top_p=top_p,
-            )
-        
-    
-    def stream_generate(
-            self, 
-            query: str, 
-            history: List[Tuple[str, str]]=None,
-            temperature: float=0.8,
-            top_k: int=50,
-            top_p: float=0.95,
-        ) -> Generator[Tuple[str, List[Tuple[str, str]]], None, None]:
-            stream_generator = StreamGenerator(self.parameter)
-            return stream_generator.generate(
-                    query, 
-                    history=history,
-                    temperature=temperature, 
-                    top_k=top_k, 
-                    top_p=top_p,
-                )
-    
-    def retrieve_generate(
-            self,
-            retrieved,
-            query: str, 
-            history: List[Tuple[str, str]] = None,
-            temperature: float=0.8,
-            top_k: int=50,
-            top_p: float=0.95,
-        ) -> str:
-            generator = RetrievalGenerator(self.parameter)
-            return generator.generate(
-                retrieved,
-                query,
-                history=history,
-                temperature=temperature, 
-                top_k=top_k, 
-                top_p=top_p,
-            )
-    
-    def text_generate(            
-            self, 
-            query: str, 
-            temperature: float=0.8,
-            top_k: int=50,
-            top_p: float=0.95,
-        ) -> str:
-        generator = TextGenerator(self.parameter)
-        
-        return generator.generate(
-                query,
-                temperature=temperature, 
-                top_k=top_k, 
-                top_p=top_p,
-            )
-    
-    def encode(self, sentence: Union[str, List[str]]) -> Union[Tensor, List[Tensor]]:
-        encoder = EmbeddingEncoder(self.parameter)
-        return encoder.encode(sentence)

khaosz/trainer/__init__.py

@@ -1,34 +0,0 @@
-from khaosz.trainer.data_util import DatasetLoader
-from khaosz.trainer.trainer import Trainer
-from khaosz.trainer.strategy import (
-    TrainConfig, 
-    CosineScheduleConfig, 
-    SgdrScheduleConfig,
-    StrategyFactory,
-    SchedulerFactory
-)
-from khaosz.trainer.trainer_callback import (
-    TrainerCallback,
-    ProgressBarCallback,
-    CheckpointCallback,
-    TrainerCallback,
-    SchedulerCallback
-)
-
-__all__ = [
-    # strategy
-    "DatasetLoader",
-    "Trainer",
-    "TrainConfig",
-    "CosineScheduleConfig",
-    "SgdrScheduleConfig",
-    "StrategyFactory",
-    "SchedulerFactory",
-    
-    # callback
-    "TrainerCallback",
-    "ProgressBarCallback",
-    "CheckpointCallback",
-    "TrainerCallback",
-    "SchedulerCallback",
-]

khaosz/trainer/data_util.py

@@ -1,326 +0,0 @@
-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 typing import Callable, List, Dict, Literal, 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
-
-def build_attention_mask(input_ids: Tensor, user_token_id: int, multi_turn: bool) -> Tensor:
-    seq_len = input_ids.size(0)
-    turn_id = input_ids.eq(user_token_id).cumsum(dim=-1)
-    
-    iq = turn_id.view(seq_len, 1)
-    ik = turn_id.view(1, seq_len)
-    
-    # fix the causual attention mask(iq >= ik condition)
-    seq_mask = (iq >= ik) if multi_turn else (iq == ik)
-    attention_mask = torch.tril(seq_mask)
-    
-    # fix the shape  (bsz, 1, seq_len, seq_len) unsqueeze for broadcast
-    return attention_mask.unsqueeze(0)
-
-def build_loss_mask(input_ids: Tensor, bos_token_id: int, eos_token_id: int) -> Tensor:
-    token_markers = torch.zeros_like(input_ids, dtype=torch.int8)
-    
-    is_bos_token = input_ids.eq(bos_token_id)
-    is_eos_token = input_ids.eq(eos_token_id)
-    
-    # fix the eos_token_id bug(change target_ids to input_ids)
-    token_markers[is_bos_token] = 1
-    token_markers[is_eos_token] = -1 
-    
-    cumulative_markers = torch.cumsum(token_markers, dim=-1)
-    min_cumulative = cumulative_markers.min(dim=-1, keepdim=True).values
-    loss_mask = cumulative_markers - min_cumulative
-    
-    return loss_mask.to(dtype=torch.bool)
-
-
-class BaseSegmentFetcher:
-    def __init__(self, segments: List[Tensor]):
-        self.segments = segments
-        self.cum_lengths = []
-        total = 0
-        for seg in segments:
-            total += len(seg)
-            self.cum_lengths.append(total)
-        self.total_length = total if segments else 0
-
-    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):
-            raise ValueError("begin_idx or end_idx out of bounds")
-        if begin_idx >= end_idx:
-            return torch.tensor([], dtype=torch.long)
-        
-        seg_start_idx = bisect.bisect_right(self.cum_lengths, begin_idx - 1)
-        seg_end_idx = bisect.bisect_left(self.cum_lengths, end_idx - 1)
-
-        result_segments = []
-
-        for i in range(seg_start_idx, seg_end_idx + 1):
-            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])
-
-        return torch.cat(result_segments, dim=0)
-    
-
-class MutiSegmentFetcher:
-    def __init__(self, muti_segments: MutiSeg):
-        self.muti_keys = list(muti_segments.keys())
-        self.muti_fetchers = {
-            key: BaseSegmentFetcher(segments)
-            for key, segments in muti_segments.items()
-        }
-        
-    def key_fetch(self, begin_idx: int, end_idx: int, keys: Union[str, List[str]]) -> Union[Tensor, Seg]:
-        fetch_dict = {} 
-        keys = [keys] if isinstance(keys, str) else keys
-        
-        for key in keys:
-            fetcher = self.muti_fetchers[key]
-            fetch_tensor = fetcher.fetch_data(begin_idx, end_idx)
-            fetch_dict[key] = fetch_tensor
-
-        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]:
-        return self.key_fetch(begin_idx, end_idx, self.muti_keys)
-
-
-class BaseDataset(Dataset, ABC):
-    def __init__(self, chunk_size: int, device: str):
-        super().__init__()
-        self.segments: MutiSeg = {}
-        self.chunk_size = chunk_size
-        self.total_samples = 0
-        self.device = device
-
-    def save(self, save_path: str):
-        keys = list(self.segments.keys())
-        if not keys:
-            return
-        
-        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)
-        
-    @abstractmethod
-    def __getitem__(self, index: int) -> Dict[str, Tensor]:
-        raise NotImplementedError
-        
-    def __len__(self) -> int:
-        assert self.total_samples // self.chunk_size > 0
-        return self.total_samples // self.chunk_size
-    
-
-class SeqDataset(BaseDataset):
-    def __init__(
-        self, 
-        chunk_size, 
-        device='cuda'
-    ):
-        super().__init__(chunk_size, device)
-        self.fetcher = MutiSegmentFetcher(self.segments)
-
-    def _fetch_data(self, begin_idx: int, end_idx: int) -> Tensor:
-        return self.fetcher.key_fetch(begin_idx, end_idx, "sequence")
-    
-    def __getitem__(self, index):
-        begin_idx = index * self.chunk_size 
-        end_idx = min(begin_idx + self.chunk_size, self.total_samples - 1)
-        
-        x = self._fetch_data(begin_idx, end_idx).to(device=self.device, dtype=torch.long)
-        y = self._fetch_data(begin_idx + 1, end_idx + 1).to(device=self.device, dtype=torch.long)
-        
-        return {"input_ids": x, "target_ids": y}
-    
-    
-    
-class SftDataset(BaseDataset):
-    def __init__(
-        self, 
-        chunk_size, 
-        bos_token_id, 
-        eos_token_id, 
-        user_token_id, 
-        multi_turn=False, 
-        device='cuda'
-    ):
-        super().__init__(chunk_size, device)
-        self.fetcher = MutiSegmentFetcher(self.segments)
-        self.bos_token_id = bos_token_id
-        self.eos_token_id = eos_token_id
-        self.user_token_id = user_token_id
-        self.multi_turn = multi_turn
-    
-    def _fetch_data(self, begin_idx: int, end_idx: int, key: str) -> Tensor:
-        return self.fetcher.key_fetch(begin_idx, end_idx, key)
-    
-    def __getitem__(self, index):
-        begin_idx = index * self.chunk_size 
-        end_idx = min(begin_idx + self.chunk_size, self.total_samples - 1)
-        
-        x = self._fetch_data(begin_idx, end_idx, "sequence").to(device=self.device, dtype=torch.long)
-        y = self._fetch_data(begin_idx + 1, end_idx + 1, "sequence").to(device=self.device, dtype=torch.long)
-        
-        # fix the eos_token_id bug(change target_ids to input_ids)
-        loss_mask = build_loss_mask(x, self.bos_token_id, self.eos_token_id)
-        attn_mask = build_attention_mask(x, self.user_token_id, self.multi_turn)
-        
-        return {"input_ids": x, "target_ids": y, "loss_mask": loss_mask, "attn_mask": attn_mask}
-
-
-class DpoDataset(BaseDataset):
-    def __init__(self, chunk_size: int, device="cuda"):
-        super().__init__(chunk_size, device)
-        self.fetcher = MutiSegmentFetcher(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)
-
-    def __getitem__(self, index: int):
-        start_idx = index * self.chunk_size
-        end_idx = min(start_idx + self.chunk_size, self.total_samples - 1)
-        
-        chosen = self._fetch_data(start_idx, end_idx, "chosen").to(device=self.device, dtype=torch.long)
-        rejected = self._fetch_data(start_idx, end_idx, "rejected").to(device=self.device, dtype=torch.long)
-        chosen_mask = self._fetch_data(start_idx, end_idx, "chosen_mask").to(device=self.device, dtype=torch.bool)
-        rejected_mask = self._fetch_data(start_idx, end_idx, "rejected_mask").to(device=self.device, dtype=torch.bool)
-
-        return {"chosen": chosen, "rejected": rejected, "chosen_mask": chosen_mask, "rejected_mask": rejected_mask}
-
-
-class PpoDataset(BaseDataset):
-    def __init__(self, chunk_size: int, device="cuda"):
-        super().__init__(chunk_size, device)
-        self.fetcher = MutiSegmentFetcher(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)
-    
-    def __getitem__(self, index: int) -> Dict[str, Tensor]:
-
-        begin_idx = index * self.chunk_size
-        end_idx = min(begin_idx + self.chunk_size, self.total_samples - 1)
-        
-
-        input_ids =  self._fetch_data(begin_idx, end_idx, "input_ids").to(self.device),
-        actions = self._fetch_data(begin_idx, end_idx, "actions").to(self.device),
-        logprobs = self._fetch_data(begin_idx, end_idx, "logprobs").to(self.device),
-        rewards =  self._fetch_data(begin_idx, end_idx, "rewards").to(self.device)
-        
-        return {"input_ids": input_ids, "actions": actions, "logprobs": logprobs, "rewards": rewards}
-    
-
-class DatasetLoader:
-    @staticmethod       
-    def load(
-        train_type: Literal["seq", "sft", "dpo"],
-        load_path: Union[str, List[str]],
-        max_len: int, 
-        device: str,
-        **kwargs
-        ) -> BaseDataset:
-        
-        dataset_router: Dict[str, Callable[[int, torch.device], BaseDataset]] = {
-            "seq": lambda m_len, device: SeqDataset(m_len, device=device),
-            "sft": lambda m_len, device: SftDataset(
-                m_len, 
-                device=device,
-                bos_token_id=kwargs.get("bos_token_id"),
-                eos_token_id=kwargs.get("eos_token_id"),
-                user_token_id=kwargs.get("user_token_id"),
-                multi_turn=kwargs.get("multi_turn")
-            ),
-            "dpo": lambda m_len, device: DpoDataset(m_len, device=device),
-        }
-        dataset = dataset_router[train_type](max_len, device)
-        dataset.load(load_path)
-        
-        return dataset
-    
-
-class RandomSampler(Sampler[int]):
-    def __init__(self, data_source, generator=None, seed=42):
-        self.data_source = data_source
-        self.seed = seed
-        self.epoch = 0
-        self.current_iter = 0
-        self._indices = None
-        
-        if generator is None:
-            self.generator = torch.Generator()
-            self.generator.manual_seed(seed)
-        else:
-            self.generator = generator
-    
-    def _generate_indices(self):
-        n = len(self.data_source)
-        self._indices = torch.randperm(n, generator=self.generator).tolist()
-    
-    def __iter__(self):
-        n = len(self.data_source)
-        
-        if self._indices is None:
-            self._generate_indices()
-        
-        start = self.current_iter % n
-        for i in range(start, n):
-            yield self._indices[i]
-            self.current_iter += 1
-        
-        self.epoch += 1
-        self._indices = None
-    
-    def __len__(self):
-        return len(self.data_source)
-    
-    def state_dict(self):
-        return {
-            'epoch': self.epoch,
-            'current_iter': self.current_iter,
-            'seed': self.seed,
-            'generator_state': self.generator.get_state() if self.generator else None,
-            'indices': self._indices
-        }
-    
-    def load_state_dict(self, state_dict):
-        self.epoch = state_dict['epoch']
-        self.current_iter = state_dict['current_iter']
-        self.seed = state_dict['seed']
-        
-        if self.generator and state_dict['generator_state'] is not None:
-            self.generator.set_state(state_dict['generator_state'])
-        
-        self._indices = state_dict['indices']

khaosz/trainer/strategy.py

@@ -1,396 +0,0 @@
-import copy
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from torch import Tensor
-from torch.optim import Optimizer
-from torch.utils.data import Dataset
-from typing import Any, Literal, Tuple, Callable, Dict
-from abc import ABC, abstractmethod
-from dataclasses import asdict, dataclass, field
-
-
-def get_logprobs(model:nn.Module, 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)
-    
-    shifted_log_probs = log_probs[:, :-1, :] 
-    shifted_input_ids = input_ids[:, 1:]
-    shifted_response_mask = mask[:, 1:]
-    
-    token_logprobs = torch.gather(
-        shifted_log_probs, 
-        dim=-1, 
-        index=shifted_input_ids.unsqueeze(-1)
-    ).squeeze(-1)
-    
-    prompt_mask = input_mask[:, 1:]
-    valid_mask = (prompt_mask & shifted_response_mask).float()
-    
-    return (token_logprobs * valid_mask).sum(dim=-1)
-
-
-class BaseStrategy(ABC):
-    def __init__(self, model: nn.Module):
-        self.model = model
-    
-    @abstractmethod
-    def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
-        raise NotImplementedError
-    
-    def __call__(self, batch: Tuple[Tensor, ...]) -> Tensor:
-        return self.compute_loss(batch)
-
-
-class SeqStrategy(BaseStrategy):
-    def __init__(self, model):
-        super().__init__(model)
-    
-    def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
-        input_ids, target_ids = batch["input_ids"], batch["target_ids"]
-        B, L = input_ids.size()
-        logits: Tensor = self.model(input_ids=input_ids)["logits"]
-        
-        loss = F.cross_entropy(
-            input=logits.view(B * L, -1),
-            target=target_ids.flatten()
-        )
-        return loss
-    
-
-class SftStrategy(BaseStrategy):
-    def __init__(self, model: nn.Module):
-        super().__init__(model)
-    
-    def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
-        input_ids, target_ids = batch["input_ids"], batch["target_ids"]
-        loss_mask, attn_mask = batch["loss_mask"], batch["attn_mask"]
-        
-        ignore_index = -100
-        B, L = input_ids.size()
-        
-        logits: Tensor = self.model(
-            input_ids=input_ids, 
-            input_mask=attn_mask
-        )["logits"]
-        
-        target_ids = target_ids.masked_fill(loss_mask == 0, ignore_index)
-        
-        loss = F.cross_entropy(
-            input=logits.view(B * L, -1),
-            target=target_ids.flatten(),
-            ignore_index=ignore_index
-        )
-        
-        return loss
-
-
-class DpoStrategy(BaseStrategy):
-    def __init__(self, model, pad_token_id, beta):
-        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.beta = beta
-        
-    def compute_loss(self, batch: Tuple[Tensor, ...]) -> Tensor:
-        good_ids, bad_ids = batch["chosen"], batch["rejected"]
-        good_mask, bad_mask = batch["chosen_mask"], batch["rejected_mask"]
-        
-        log_pi_good = get_logprobs(self.model, good_ids, good_mask, self.pad_token_id)
-        log_pi_bad = get_logprobs(self.model, bad_ids, bad_mask, self.pad_token_id)
-        
-        with torch.no_grad():
-            log_ref_good = get_logprobs(self.ref_model, good_ids, good_mask, self.pad_token_id)
-            log_ref_bad = get_logprobs(self.ref_model, bad_ids, bad_mask, self.pad_token_id)
-        
-        pi_log_ratio = log_pi_good - log_pi_bad
-        ref_log_ratio = log_ref_good - log_ref_bad
-
-        ratio_diff = pi_log_ratio - ref_log_ratio
-        
-        dpo_loss = -F.logsigmoid(self.beta * ratio_diff).mean()
-        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, **kwargs):
-        train_strategy: Dict[str, Callable[[], BaseStrategy]] = {
-            "seq": lambda: SeqStrategy(model),
-            "sft": lambda: SftStrategy(model),
-            "dpo": lambda: DpoStrategy(
-                model, 
-                kwargs.get("pad_token_id"), 
-                kwargs.get("dpo_beta")
-            )
-        }
-        strategy = train_strategy[train_type]()
-        return strategy
-    
-
-@dataclass
-class TrainConfig:
-    
-    strategy: BaseStrategy = field(
-        default=None,
-        metadata={"help": "Training strategy."}
-    )
-    dataset: Dataset = field(
-        default=None,
-        metadata={"help": "Dataset for training."}
-    )
-    optimizer: Optimizer = field(
-        default=None,
-        metadata={"help": "Optimizer for training."}
-    )
-    checkpoint_dir: str = field(
-        default="./checkpoint",
-        metadata={"help": "Checkpoint directory."}
-    )
-    n_epoch: int = field(
-        default=1,
-        metadata={"help": "Number of epochs for training."}
-    )
-    batch_size: int = field(
-        default=4,
-        metadata={"help": "Batch size 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."}
-    )
-    max_grad_norm: float = field(
-        default=1.0,
-        metadata={"help": "Maximum gradient norm."}
-    )
-    random_seed: int = field(
-        default=3407,
-        metadata={"help": "Random seed."}
-    )
-
-    def get_kwargs(self)-> Dict[str, Any]:
-        config_dict = asdict(self)
-        return {k: v for k, v in config_dict.items() if v is not None}
-    
-
-@dataclass
-class ScheduleConfig(ABC):
-    schedule_type: str = field(
-        default="cosine",
-        metadata={
-            "help": "Type of learning rate schedule.", 
-            "choices": ["cosine", "sgdr"]
-        }
-    )
-    warmup_steps: int = field(
-        default=1000,
-        metadata={"help": "Number of warmup steps."}
-    )
-    min_rate: float = field(
-        default=0.05,
-        metadata={"help": "Minimum learning rate multiplier."}
-    )
-    
-    @abstractmethod
-    def get_kwargs(self) -> Dict[str, Any]:
-        raise NotImplementedError
-    
-    def validate(self) -> None:
-        """Validate configuration parameters."""
-        if self.warmup_steps < 0:
-            raise ValueError(f"warmup_steps must be non-negative, got {self.warmup_steps}")
-        if not 0 <= self.min_rate <= 1:
-            raise ValueError(f"min_rate must be between 0 and 1, got {self.min_rate}")
-
-
-@dataclass
-class CosineScheduleConfig(ScheduleConfig):
-    total_steps: int = field(
-        default=None,
-        metadata={"help": "Total training steps for cosine schedule."}
-    )
-    schedule_type: Literal["cosine"] = "cosine"
-    
-    def get_kwargs(self) -> Dict[str, Any]:
-        if self.total_steps is None:
-            raise ValueError("total_steps must be specified for cosine schedule")
-            
-        return {
-            "schedule_type": self.schedule_type,
-            "warmup_steps": self.warmup_steps,
-            "lr_decay_steps": self.total_steps - self.warmup_steps,
-            "min_rate": self.min_rate
-        }
-    
-    def validate(self) -> None:
-        super().validate()
-        if self.total_steps is not None and self.total_steps <= self.warmup_steps:
-            raise ValueError(f"total_steps ({self.total_steps}) must be greater than warmup_steps ({self.warmup_steps})")
-
-
-@dataclass
-class SgdrScheduleConfig(ScheduleConfig):
-    cycle_length: int = field(
-        default=1000,
-        metadata={"help": "Length of the first cycle in steps."}
-    )
-    t_mult: int = field( 
-        default=2,
-        metadata={"help": "Multiplier for cycle length growth."}
-    )
-    schedule_type: Literal["sgdr"] = "sgdr"
-
-    def get_kwargs(self) -> Dict[str, Any]:
-        return {
-            "schedule_type": self.schedule_type,
-            "warmup_steps": self.warmup_steps,
-            "cycle_length": self.cycle_length,
-            "min_rate": self.min_rate,
-            "t_mult": self.t_mult
-        }
-    
-    def validate(self) -> None:
-        super().validate()
-        if self.cycle_length <= 0:
-            raise ValueError(f"cycle_length must be positive, got {self.cycle_length}")
-        if self.t_mult < 1:
-            raise ValueError(f"t_mult must be >= 1, got {self.t_mult}")
-
-
-class SchedulerFactory:
-    """Factory for creating learning rate schedule functions."""
-    
-    @staticmethod
-    def get_sgdr_schedule(
-        warmup_steps: int, 
-        cycle_length: int, 
-        min_rate: float = 0.05, 
-        t_mult: int = 2
-    ) -> Callable[[int], float]:
-        """
-        Create SGDR (Stochastic Gradient Descent with Warm Restarts) schedule.
-        
-        Args:
-            warmup_steps: Number of warmup steps
-            cycle_length: Length of the first cycle
-            min_rate: Minimum learning rate multiplier
-            t_mult: Cycle length multiplier
-            
-        Returns:
-            Schedule function that takes current step and returns LR multiplier
-        """
-        
-        def sgdr_schedule(current_step: int) -> float:
-            # Warmup phase
-            if current_step < warmup_steps:
-                return max(min_rate, current_step / warmup_steps)
-            
-            # SGDR phase
-            steps_since_warmup = current_step - warmup_steps
-            
-            # Find current cycle and position within cycle
-            cycle_start = 0
-            current_cycle_length = cycle_length
-            cycle_index = 0
-            
-            while steps_since_warmup >= cycle_start + current_cycle_length:
-                cycle_start += current_cycle_length
-                current_cycle_length *= t_mult
-                cycle_index += 1
-            
-            position_in_cycle = steps_since_warmup - cycle_start
-            progress = position_in_cycle / current_cycle_length
-            
-            # Cosine annealing within cycle
-            return max(min_rate, 0.5 * (1 + math.cos(math.pi * progress)))
-        
-        return sgdr_schedule
-
-    @staticmethod
-    def get_cosine_schedule(
-        warmup_steps: int, 
-        lr_decay_steps: int, 
-        min_rate: float = 0.05
-    ) -> Callable[[int], float]:
-        """
-        Create cosine decay schedule with warmup.
-        
-        Args:
-            warmup_steps: Number of warmup steps
-            lr_decay_steps: Number of steps for cosine decay after warmup
-            min_rate: Minimum learning rate multiplier
-            
-        Returns:
-            Schedule function that takes current step and returns LR multiplier
-        """
-        
-        def cosine_schedule(current_step: int) -> float:
-            if current_step < warmup_steps:
-                # Linear warmup
-                return max(min_rate, current_step / warmup_steps)
-            else:
-                # Cosine decay
-                decay_progress = (current_step - warmup_steps) / lr_decay_steps
-                decay_progress = min(decay_progress, 1.0)  # Clamp at 1.0
-                return max(min_rate, 0.5 * (1.0 + math.cos(math.pi * decay_progress)))
-        
-        return cosine_schedule
-
-    @staticmethod
-    def load_schedule_fn(scedule_config: ScheduleConfig) -> Callable[[int], float]:
-        kwargs = scedule_config.get_kwargs()
-        schedule_type = kwargs.pop("schedule_type")
-        
-        if schedule_type == "cosine":
-            return SchedulerFactory.get_cosine_schedule(**kwargs)
-        elif schedule_type == "sgdr":
-            return SchedulerFactory.get_sgdr_schedule(**kwargs)
-        else:
-            raise ValueError(f"Unsupported schedule type: {schedule_type}")
-        

khaosz/trainer/trainer.py

@@ -1,140 +0,0 @@
-import logging
-from typing import Optional, List, cast
-from torch.utils.data import DataLoader
-
-from khaosz.core import ModelParameter, Checkpoint
-from khaosz.trainer.data_util import RandomSampler
-from khaosz.trainer.strategy import TrainConfig, ScheduleConfig
-from khaosz.trainer.trainer_callback import (
-    TrainerCallback, 
-    ProgressBarCallback, 
-    CheckpointCallback, 
-    GradientClippingCallback,
-    SchedulerCallback
-)
-
-logger = logging.getLogger(__name__)
-
-class Trainer:
-    def __init__(
-        self,
-        parameter: ModelParameter,
-        train_config: TrainConfig,
-        schedule_config: ScheduleConfig,
-        callbacks: Optional[List[TrainerCallback]] = None
-    ):
-        self.parameter = parameter
-        self.train_config = train_config
-        self.schedule_config = schedule_config
-        self.callbacks = callbacks or self._get_default_callbacks()
-        
-    def _get_default_callbacks(self) -> List[TrainerCallback]:
-        return [
-            ProgressBarCallback(),
-            CheckpointCallback(self.train_config.checkpoint_interval),
-            GradientClippingCallback(),
-            SchedulerCallback(self.schedule_config),
-        ]
-
-    def _set_train_kwargs(self, kwargs: dict):
-        seed = self.train_config.random_seed
-        sampler = RandomSampler(data_source=self.train_config.dataset, seed=seed)
-        optim = self.train_config.optimizer
-        checkpoint = cast(Checkpoint, kwargs.get('checkpoint', None))
-        
-        if checkpoint is None:
-            checkpoint = Checkpoint(
-                model=self.parameter.model,
-                tokenizer=self.parameter.tokenizer,
-                config=self.parameter.config,
-                sampler_state=None,
-                optim_state=None,
-                loss_list=[]
-            )
-        
-        sampler_state = checkpoint.sampler_state
-        optim_state = checkpoint.optim_state
-        
-        if sampler_state: 
-            sampler.load_state_dict(sampler_state)
-        
-        if optim_state: 
-            optim.load_state_dict(optim_state)
-            
-        checkpoint.optim_state = optim.state_dict()
-        checkpoint.sampler_state = sampler.state_dict()
-
-        dataloader = DataLoader(
-            self.train_config.dataset, 
-            batch_size=self.train_config.batch_size, 
-            sampler=sampler
-        )
-        
-        kwargs["dataloader"] = dataloader
-        kwargs["optimizer"] = self.train_config.optimizer
-        kwargs["epoch"] = sampler.epoch
-        kwargs["current_iter"] = sampler.current_iter
-        kwargs["sampler"] = sampler
-        kwargs["checkpoint"] = checkpoint
-        
-    def _call_callbacks(self, method_name: str, **kwargs):
-        for callback in self.callbacks:
-            method = getattr(callback, method_name, None)
-            if method:
-                method(self, **kwargs)
-
-    def train(
-        self,
-        checkpoint: Optional[Checkpoint] = None
-    ) -> Checkpoint:
-        
-        # train        
-        train_kwargs = {
-            'checkpoint': checkpoint,
-            'dataloader': None,
-            'optimizer': None,
-            'sampler': None,
-            'epoch':  0,
-            'current_iter': 0, 
-            'loss': 0.0,
-        }
-        
-        self._set_train_kwargs(train_kwargs)
-        self._call_callbacks('on_train_begin', **train_kwargs)
-        
-        dataloader = train_kwargs['dataloader']
-        checkpoint = train_kwargs['checkpoint']
-        start_epoch = train_kwargs['epoch']
-        
-        try:
-            self.parameter.model.train()
-            for epoch in range(start_epoch, self.train_config.n_epoch):
-                # epoch
-                train_kwargs["epoch"] = epoch
-                self._call_callbacks('on_epoch_begin', **train_kwargs)
-                for batch in dataloader:
-                    
-                    if train_kwargs["current_iter"] % self.train_config.accumulation_steps == 0:
-                        # step
-                        self._call_callbacks('on_step_begin', **train_kwargs)
-                        self.train_config.optimizer.step()
-                        self.train_config.optimizer.zero_grad()
-                        self._call_callbacks('on_step_end', **train_kwargs)
-                        
-                    # batch
-                    self._call_callbacks('on_batch_begin', **train_kwargs)
-                    loss = self.train_config.strategy(batch)
-                    train_kwargs["loss"] = loss.item()
-                    train_kwargs["current_iter"] += 1
-                    loss.backward()
-                    
-                    self._call_callbacks('on_batch_end', **train_kwargs)
-
-                self._call_callbacks('on_epoch_end', **train_kwargs)
-        
-        except Exception as e:
-            logger.error(f"Training failed: {str(e)}", exc_info=True)
-            raise
-        finally:
-            self._call_callbacks('on_train_end', **train_kwargs)
-            return checkpoint

khaosz/trainer/trainer_callback.py

@@ -1,169 +0,0 @@
-import os
-import torch.optim as optim
-
-from tqdm import tqdm
-from torch.nn.utils import clip_grad_norm_
-from torch.optim.lr_scheduler import LambdaLR
-from typing import Optional, cast, TYPE_CHECKING
-from khaosz.core.parameter import Checkpoint
-from khaosz.trainer.data_util import RandomSampler
-from khaosz.trainer.strategy import ScheduleConfig, SchedulerFactory
-
-if TYPE_CHECKING:
-    from khaosz.trainer.trainer import Trainer
-
-
-class TrainerCallback:
-    """ 
-    Callback interface for trainer.
-    and we use '_' to ignore unused parameters.
-    """
-
-    def on_train_begin(self, trainer: 'Trainer', **kwargs):
-        """ Called at the beginning of training. """
-        _ = trainer, kwargs
-    
-    def on_train_end(self, trainer: 'Trainer', **kwargs):
-        """ Called at the end of training. """
-        _ = trainer, kwargs
-
-    def on_epoch_begin(self, trainer: 'Trainer', **kwargs):
-        """ Called at the beginning of each epoch. """
-        _ = trainer, kwargs
-
-    def on_epoch_end(self, trainer: 'Trainer', **kwargs):
-        """  Called at the end of each epoch. """
-        _ = trainer, kwargs
-
-    def on_batch_begin(self, trainer: 'Trainer', **kwargs):
-        """ Called at the beginning of each batch. """
-        _ = trainer, kwargs
-
-    def on_batch_end(self, trainer: 'Trainer', **kwargs):
-        """ Called at the end of each batch. """
-        _ = trainer, kwargs
-
-    def on_step_begin(self, trainer: 'Trainer', **kwargs):
-        """ Called at the beginning of each step. """
-        _ = trainer, kwargs
-
-    def on_step_end(self, trainer: 'Trainer', **kwargs):
-        """ Called at the end of each step."""
-        _ = trainer, kwargs
-
-
-class ProgressBarCallback(TrainerCallback):
-    """ 
-    Progress bar callback for trainer.
-    """
-    def __init__(self):
-        self.progress_bar: tqdm = None
-    
-    def on_epoch_begin(self, trainer: 'Trainer', **kwargs):
-        epoch = kwargs.get('epoch')
-        dataloader = kwargs.get('dataloader')
-        self.progress_bar = tqdm(
-            dataloader, 
-            desc=f"Epoch {epoch+1}/{trainer.train_config.n_epoch}", 
-            dynamic_ncols=True
-        )
-    
-    def on_batch_end(self, trainer: 'Trainer', **kwargs):
-        _ = trainer
-        loss = kwargs.get('loss')
-        optimizer = cast(optim.Optimizer, kwargs.get('optimizer'))
-        self.progress_bar.set_postfix({
-            "loss": f"{loss:.4f}",
-            "lr": f"{optimizer.param_groups[-1]['lr']:.2e}"
-        })
-        self.progress_bar.update(1)
-    
-    def on_epoch_end(self, trainer: 'Trainer', **kwargs):
-        _ = trainer, kwargs
-        if self.progress_bar:
-            self.progress_bar.close()
-
-
-class CheckpointCallback(TrainerCallback):
-    """ 
-    Checkpoint callback for trainer.
-    """
-    def __init__(self, checkpoint_interval: int):
-        self.checkpoint_interval = checkpoint_interval
-        self.last_ckpt_iter = 0
-    
-    @staticmethod
-    def _save_checkpoint(trainer: 'Trainer', **kwargs):
-        current_iter = kwargs.get('current_iter')
-        random_sampler = cast(RandomSampler, kwargs.get('sampler'))
-        optimizer = cast(optim.Optimizer, kwargs.get('optimizer'))
-        checkpoint = cast(Checkpoint, kwargs.get('checkpoint'))
-        
-        save_path = os.path.join(trainer.train_config.checkpoint_dir, f"iter_{current_iter}")
-        checkpoint.sampler_state = random_sampler.state_dict()
-        checkpoint.optim_state = optimizer.state_dict()
-        
-        checkpoint.save(save_path)
-    
-    def on_batch_end(self, trainer: 'Trainer', **kwargs):
-        current_iter = kwargs.get('current_iter')
-        checkpoint = cast(Checkpoint, kwargs.get('checkpoint'))
-        loss = kwargs.get('loss')
-        checkpoint.loss_list.append(loss)
-        
-        if current_iter - self.last_ckpt_iter >= self.checkpoint_interval:
-            CheckpointCallback._save_checkpoint(trainer, **kwargs)
-            self.last_ckpt_iter = current_iter
-    
-    def on_train_end(self, trainer: 'Trainer', **kwargs):
-        current_iter = kwargs.get('current_iter')
-        if current_iter != self.last_ckpt_iter:
-            CheckpointCallback._save_checkpoint(trainer, **kwargs)
-            self.last_ckpt_iter = current_iter
-
-
-class GradientClippingCallback(TrainerCallback):
-    """ 
-    Gradient clipping callback for trainer.
-    """
-    def on_step_begin(self, trainer: 'Trainer', **kwargs):
-        _ = kwargs
-        clip_grad_norm_(
-            trainer.parameter.model.parameters(),
-            trainer.train_config.max_grad_norm
-        )
-
-
-class SchedulerCallback(TrainerCallback):
-    """
-    Scheduler callback for trainer.
-    """
-    def __init__(self, schedule_config: ScheduleConfig):
-        self.schedule_config = schedule_config
-        self.scheduler: Optional[LambdaLR] = None
-        self.current_iter = 0
-    
-    def on_train_begin(self, trainer: 'Trainer', **kwargs):
-        self.current_iter = kwargs.get('current_iter')
-
-        for group in trainer.train_config.optimizer.param_groups:
-            if "initial_lr" not in group:
-                group["initial_lr"] = group["lr"] 
-
-        self.schedule_config.validate()
-        lambda_scheduler_fn = SchedulerFactory.load_schedule_fn(
-            self.schedule_config
-        )
-        
-        self.scheduler = LambdaLR(
-            trainer.train_config.optimizer,
-            lambda_scheduler_fn,
-            last_epoch=self.current_iter - 1
-        )
-    
-    def on_batch_end(self, trainer: 'Trainer', **kwargs):
-        _ =  trainer, kwargs
-        
-        if self.scheduler:
-            self.scheduler.step()
-            self.current_iter += 1

khaosz/utils/retriever.py

@@ -1,88 +0,0 @@
-import torch
-import sqlite3
-import numpy as np
-from torch import Tensor
-from typing import Dict, List, Tuple
-
-
-class Retriever:
-    def __init__(self, db_path=None):
-        self.data: Dict[str, Tensor] = {}
-        self.embedding_cache: Tensor = None
-        self.is_caculated: bool = False
-        
-        if db_path is not None:
-            self.load(db_path)
-                
-    def retrieve(self, query: Tensor, top_k: int) -> List[Tuple[str, float]]:
-        if not self.data:
-            return []
-        
-        query = query.flatten().unsqueeze(1)                            # [dim, 1]
-        norm_embeddings = self._embeddings.to(
-            device=query.device,
-            dtype=query.dtype
-        )                                                               # [n_vectors, dim]
-        sim_scores = torch.matmul(norm_embeddings, query).squeeze()     # [n_vectors]
-        
-        top_k = min(top_k, len(self.data))
-        indices = sim_scores.topk(top_k).indices
-        keys = list(self.data.keys())
-        
-        return [(keys[i], sim_scores[i].item()) for i in indices]
-    
-    def add_vector(self, key: str, vector_data: Tensor):
-        self.is_caculated = False
-        self.data[key] = vector_data.flatten().float().cpu()
-        
-    def delete_vector(self, key: str):
-        self.is_caculated = False
-        self.data.pop(key, None)
-    
-    def save(self, db_path):
-        conn = sqlite3.connect(db_path)
-        cursor = conn.cursor()
-        self._init_db(cursor)
-        cursor.execute('DELETE FROM vectors')
-        
-        for item, vec in self.data.items():
-            vec_bytes = vec.numpy().tobytes()
-            cursor.execute('INSERT OR REPLACE INTO vectors (key, vector) VALUES (?, ?)', 
-                           (item, vec_bytes))
-        
-        conn.commit()
-        conn.close()
-
-    def load(self, db_path):
-        conn = sqlite3.connect(db_path)
-        cursor = conn.cursor()
-        self._init_db(cursor)
-        cursor.execute('SELECT key, vector FROM vectors')
-        rows = cursor.fetchall()
-        self.data = {}
-        
-        for row in rows:
-            key, vec_bytes = row
-            vec_numpy = np.frombuffer(vec_bytes, dtype=np.float32).copy()
-            vec = torch.from_numpy(vec_numpy)
-            self.data[key] = vec
-        
-        conn.close()
-        
-    def _init_db(self,cursor: sqlite3.Cursor):
-        # Create table if not exists (in case loading from a new database)
-        cursor.execute('''
-            CREATE TABLE IF NOT EXISTS vectors (
-                id INTEGER PRIMARY KEY AUTOINCREMENT,
-                key TEXT UNIQUE NOT NULL,
-                vector BLOB NOT NULL
-            )''')
-    
-    @property
-    def _embeddings(self) -> Tensor:
-        if not self.is_caculated:
-            embeddings = torch.stack(list(self.data.values())) 
-            norm_embeddings = embeddings / torch.norm(embeddings, dim=-1, keepdim=True)
-            self.embedding_cache = norm_embeddings
-        
-        return self.embedding_cache