.github/ISSUE_TEMPLATE/bug_report.md

@@ -2,7 +2,7 @@
 name: Bug report
 about: Create a report to help us improve
 title: "[BUG]"
-labels: bug
+labels: enhancement
 assignees: ''
 
 ---

.github/PULL_REQUEST_TEMPLATE.md

@@ -16,9 +16,9 @@ Please delete options that are not relevant.
 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`)
+- [ ] My code follows the style guidelines of this project (run `ruff format .` and `ruff check --fix .`)
 - [ ] I have performed a self-review of my own code
-- [ ] Code is self-documenting (no unnecessary comments)
+- [ ] I have commented my code, particularly in hard-to-understand areas
 - [ ] 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

.gitignore

@@ -15,7 +15,6 @@
 !/.gitattributes
 !/.dockerignore
 !/Dockerfile
-!/docker-compose.yml
 !/assets/**
 !/CONTRIBUTING.md
 !/LICENSE

CONTRIBUTING.md

@@ -1,100 +1,68 @@
 # Contributing to AstrAI
 
-Thank you for your interest in contributing! This document provides step-by-step guidelines.
+Thank you for your interest in contributing to AstrAI! This document provides guidelines and steps for contributing.
 
-## Quick Start
+## How to Contribute
 
+### Reporting Issues
+If you encounter a bug or have a feature request, please open an issue on GitHub. Include as much detail as possible:
+- A clear description of the problem or request.
+- Steps to reproduce (for bugs).
+- Your environment (Python version, OS, etc.).
+
+### Submitting Changes
+1. **Fork** the repository.
+2. **Clone** your fork:
    ```bash
    git clone https://github.com/your-username/AstrAI.git
    cd AstrAI
-pip install -e ".[dev]"     # install with dev dependencies (pytest, ruff)
    ```
+3. **Create a feature branch**:
+   ```bash
+   git checkout -b feature/your-feature-name
+   ```
+4. **Make your changes**. Follow the code style guidelines below.
+5. **Commit your changes** with a descriptive commit message:
+   ```bash
+   git commit -m "Add: brief description of the change"
+   ```
+6. **Push** to your fork:
+   ```bash
+   git push origin feature/your-feature-name
+   ```
+7. **Open a Pull Request** (PR) against the `main` branch of the upstream repository.
 
-## Before You Commit
+## Code Style
 
-Run the following checks **in order** — CI will reject if any fail.
+AstrAI uses [Ruff](https://docs.astral.sh/ruff/) for code formatting and linting. Please ensure your code is formatted before submitting.
-
-### 1. Format
 
+- Run Ruff to format and lint:
   ```bash
   ruff format .
+  ruff check --fix .
   ```
+- The project uses **double quotes** for strings and **4‑space indentation** (as configured in `pyproject.toml`).
 
-> **Note**: `ruff format` may rename parameters (e.g. `mask` → `attn_mask`).  
+## Testing
-> Always review the diff after formatting.
 
-### 2. Import sorting
+If you add or modify functionality, please include appropriate tests.
 
+- Run the test suite with:
   ```bash
-ruff check . --select I
+  pytest
   ```
-
+- Ensure all tests pass before submitting your PR.
-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.
+All submissions will be reviewed. We may request changes or discuss alternatives. Please be responsive to feedback.
-- 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).
+By contributing, you agree that your contributions will be licensed under the same [GPL-3.0 License](LICENSE) that covers the project.
 
 ---
 
-Questions? Ask in [GitHub Discussions](https://github.com/ViperEkura/AstrAI/discussions) or open an issue.
+If you have any questions, feel free to ask in the [GitHub Discussions](https://github.com/ViperEkura/AstrAI/discussions) or open an issue.
+
+Happy contributing!

Dockerfile

@@ -1,7 +1,7 @@
 # AstrAI Dockerfile - Multi-stage Build (Optimized)
 
 # Build stage - use base image with minimal build tools
-FROM ubuntu:24.04 AS builder
+FROM nvidia/cuda:12.6.0-base-ubuntu24.04 AS builder
 
 WORKDIR /app
 
@@ -18,7 +18,7 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-ins
 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 source code and install dependencies
 COPY astrai/ ./astrai/
 COPY pyproject.toml .
 RUN pip install --no-cache-dir --upgrade pip \
@@ -26,14 +26,13 @@ RUN pip install --no-cache-dir --upgrade pip \
     --extra-index-url https://download.pytorch.org/whl/cu126
 
 # Production stage
-FROM ubuntu:24.04 AS production
+FROM nvidia/cuda:12.6.0-base-ubuntu24.04 AS production
 
 WORKDIR /app
 
-# Install Python 3.12 runtime and healthcheck dependency
+# Install Python 3.12 runtime
 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

README.md

@@ -27,6 +27,9 @@
 
 ## 📖 Table of Contents
 
+<details open>
+<summary><b>English</b></summary>
+
 - [Features](#features)
 - [Quick Start](#quick-start)
 - [Documentation](#documentation)
@@ -34,6 +37,8 @@
 - [Community](#community)
 - [License](#license)
 
+</details>
+
 ---
 
 <a id="english"></a>
@@ -46,8 +51,7 @@
 - 💡 **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.
+- 🤗 **HuggingFace Integration**: Compatible with HuggingFace models and datasets.
-- 🔌 **Dual API Compatibility**: Supports both OpenAI and Anthropic chat completion APIs out of the box.
 
 ### Quick Start
 
@@ -65,52 +69,19 @@ For development dependencies:
 pip install -e ".[dev]"
 ```
 
-#### Download Pre-trained Model
-
-Download pre-trained model weights (1B bilingual checkpoint) to `params/`:
-
-```bash
-python scripts/demo/download.py
-```
-
-Or download manually from [HuggingFace](https://huggingface.co/ViperEk/KHAOSZ) into `params/`.
-
 #### Train a Model
 
 ```bash
-export CUDA_VISIBLE_DEVICES=0,1,2,3
+python scripts/tools/train.py \
-
-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 \
+  --param_path=/path/to/param_path
-    --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 reference at [Parameter Guide](assets/docs/params.md).
-
 #### Generate Text
 
 ```bash
-python scripts/tools/generate.py \
+python scripts/tools/generate.py --param_path=/path/to/param_path
-    --param_path /path/to/model \
-    --input_json_file /path/to/input.jsonl \
-    --output_json_file /path/to/output.jsonl
 ```
 
 #### Docker
@@ -133,19 +104,13 @@ docker run --gpus all -p 8000:8000 astrai:latest \
 
 # 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
 ```
 
 > **Note**: `--gpus all` is required for CUDA support. Without it, `torch.cuda.is_available()` will return `False`.
 
 #### Start HTTP Server
 
-Start the inference server with OpenAI and Anthropic-compatible HTTP API:
+Start the inference server with OpenAI-compatible HTTP API:
 
 ```bash
 python -m scripts.tools.server --port 8000 --device cuda
@@ -154,7 +119,7 @@ python -m scripts.tools.server --port 8000 --device cuda
 Make requests:
 
 ```bash
-# OpenAI-compatible
+# Chat API (OpenAI compatible)
 curl -X POST http://localhost:8000/v1/chat/completions \
   -H "Content-Type: application/json" \
   -d '{
@@ -162,7 +127,7 @@ curl -X POST http://localhost:8000/v1/chat/completions \
     "max_tokens": 512
   }'
 
-# OpenAI-compatible streaming
+# Streaming response
 curl -X POST http://localhost:8000/v1/chat/completions \
   -H "Content-Type: application/json" \
   -d '{
@@ -171,27 +136,6 @@ curl -X POST http://localhost:8000/v1/chat/completions \
     "max_tokens": 500
   }'
 
-# 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
-  }'
-
-# Anthropic-compatible streaming with stop sequences
-curl -X POST http://localhost:8000/v1/messages \
-  -H "Content-Type: application/json" \
-  -d '{
-    "model": "astrai",
-    "messages": [{"role": "user", "content": "Write a story"}],
-    "max_tokens": 500,
-    "stream": true,
-    "stop_sequences": ["The end"]
-  }'
-
 # Health check
 curl http://localhost:8000/health
 ```
@@ -214,18 +158,16 @@ python scripts/demo/generate_batch.py
 python scripts/demo/generate_ar.py
 ```
 
-Watch a video walkthrough on [bilibili](https://www.bilibili.com/video/BV1fuLB6yEj6).
+Watch a video walkthrough on [bilibili](https://www.bilibili.com/video/BV1z5RPYHEkd).
 
 ### Documentation
 
 | Document | Description |
 |----------|-------------|
 | [Parameter Guide](./assets/docs/params.md) | Training & inference parameters |
-| [Architecture](./assets/docs/architecture.md) | System architecture, class diagram & design patterns |
+| [Design Document](./assets/docs/design.md) | Framework architecture & module design |
-| [Training](./assets/docs/training.md) | Training loop, strategies & formulas |
+| [Data Flow](./assets/docs/dataflow.md) | Data processing pipeline details |
-| [Inference](./assets/docs/inference.md) | KVCache, continuous batching, sampling & HTTP API |
+| [Model Introduction](./assets/docs/introduction.md) | Model architecture & technical details |
-| [Data Flow](./assets/docs/dataflow.md) | Data pipeline, storage backends & dataset architecture |
-| [Preprocessing](./assets/docs/preprocessing.md) | Declarative JSON-driven data preprocessing |
 
 ### Contributing
 

assets/docs/README-zh-CN.md

@@ -52,8 +52,7 @@
 - 💡 **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。
+- 🤗 **HuggingFace 集成**: 兼容 HuggingFace 模型与数据集。
-- 🔌 **双 API 兼容**: 同时支持 OpenAI 和 Anthropic 聊天补全 API，开箱即用。
 
 ### 快速开始
 
@@ -71,52 +70,19 @@ pip install -e .
 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
+python scripts/tools/train.py \
-
-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 \
+  --param_path=/path/to/param_path
-    --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 \
+python scripts/tools/generate.py --param_path=/path/to/param_path
-    --param_path /path/to/model \
-    --input_json_file /path/to/input.jsonl \
-    --output_json_file /path/to/output.jsonl
 ```
 
 #### Docker
@@ -139,19 +105,13 @@ docker run --gpus all -p 8000:8000 astrai:latest \
 
 # 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：
+启动推理服务器，支持 OpenAI 兼容的 HTTP API：
 
 ```bash
 python -m scripts.tools.server --port 8000 --device cuda
@@ -160,7 +120,7 @@ python -m scripts.tools.server --port 8000 --device cuda
 发起请求：
 
 ```bash
-# OpenAI 兼容
+# Chat API（OpenAI 兼容）
 curl -X POST http://localhost:8000/v1/chat/completions \
   -H "Content-Type: application/json" \
   -d '{
@@ -168,7 +128,7 @@ curl -X POST http://localhost:8000/v1/chat/completions \
     "max_tokens": 512
   }'
 
-# OpenAI 兼容流式
+# 流式响应
 curl -X POST http://localhost:8000/v1/chat/completions \
   -H "Content-Type: application/json" \
   -d '{
@@ -177,27 +137,6 @@ curl -X POST http://localhost:8000/v1/chat/completions \
     "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
 ```
@@ -220,18 +159,16 @@ python scripts/demo/generate_batch.py
 python scripts/demo/generate_ar.py
 ```
 
-观看 [bilibili](https://www.bilibili.com/video/BV1fuLB6yEj6) 上的视频演示。
+观看 [bilibili](https://www.bilibili.com/video/BV1z5RPYHEkd) 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 |
+| [设计文档](./design.md) | 系统架构与模块设计 |
-| [训练文档](./training.md) | 训练循环、策略与公式 |
+| [数据流程](./dataflow.md) | 数据处理管道详解 |
-| [推理文档](./inference.md) | KVCache、连续批处理、采样与 HTTP API |
+| [模型介绍](./introduction.md) | 模型架构与技术细节 |
-| [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
 

assets/docs/dataflow.md

@@ -1,64 +1,269 @@
-# Data Flow
+# AstrAI Data Flow Documentation
 
-This document describes the data pipeline: from raw text to model input tensors.
+This document describes the data flow of the AstrAI project (a training and inference framework for autoregressive Transformer language models). It covers the complete flow from raw data to model training and inference.
 
 ## Overview
 
-```
+AstrAI adopts a modular design with the following main components:
-Raw Text → AutoTokenizer → Token IDs → .h5/.bin → Store.load() → Store.fetch() → Dataset → Sampler → DataLoader → Training/Inference
+- **Dataset Module** (`astrai/dataset/`): Dataset, sampler, serialization tools
+- **Model Module** (`astrai/model/`): AutoModel, Transformer model and its submodules
+- **Training Module** (`astrai/trainer/`): Trainer, training context, strategies, schedulers
+- **Inference Module** (`astrai/inference/`): Inference engine with continuous batching, streaming generation
+- **Config Module** (`astrai/config/`): Model, training, scheduler, and other configurations
+- **Factory Module** (`astrai/factory/`): Registry, BaseFactory for component registration
+- **Parallel Module** (`astrai/parallel/`): Distributed training support
+- **Serialization Module** (`astrai/serialization/`): HDF5 data loading, checkpoint management
+
+The data flow can generally be divided into two main lines: **Training Data Flow** and **Inference Data Flow**.
+
+## Data Flow Diagram
+
+```mermaid
+flowchart LR
+    subgraph A[Data Preparation]
+        direction TB
+        A1[Raw Text] --> A2[AutoTokenizer]
+        A2 --> A3[Serialize to .h5 files]
+        A3 --> A4[BaseDataset]
+        A4 --> A5[ResumableDistributedSampler]
+        A5 --> A6[PyTorch DataLoader]
+    end
+
+    subgraph B[Training]
+        direction TB
+        B1[Batch Data] --> B2[TrainContextBuilder]
+        B2 --> B3[TrainContext]
+        B3 --> B4[BaseStrategy]
+        B4 --> B5[Transformer]
+        B5 --> B6[Compute Loss]
+        B6 --> B7[Backward]
+        B7 --> B8[Optimizer]
+        B8 --> B9[LRScheduler]
+        B9 --> B10[CheckpointCallback]
+    end
+
+    subgraph C[Inference]
+        direction TB
+        C1[Checkpoint] --> C2[AutoModel]
+        C2 --> C3[Transformer + Tokenizer]
+        C3 --> C4[GenerationRequest + apply_chat_template]
+        C4 --> C5[InferenceEngine]
+        C5 --> C6[InferenceScheduler]
+        C6 --> C7[apply_sampling_strategies]
+        C7 --> C8[Transformer Forward]
+        C8 --> C9[KV Cache + Prefix Cache]
+        C9 --> C10{End Condition?}
+        C10 -->|No| C8
+        C10 -->|Yes| C11[Output Text]
+    end
+
+    A --> B
+    B --> C
 ```
 
-## Data Preparation
+## Detailed Module Descriptions
 
-Raw text is tokenized via `AutoTokenizer.encode()` and saved as HDF5 (`.h5`) or binary (`.bin` + `meta.json`) files with keyed tensor groups.
+### 1. Dataset Module
 
-Storage format is auto-detected by `detect_format()`; backends are dispatched via registry:
+#### 1.1 Serialization (`serialization.py`)
+- **`save_h5`**: Saves multiple tensors by groups as HDF5 files (`.h5`), each key corresponds to a list of tensors
+- **`load_h5`**: Loads `.h5` files, returns `Dict[str, List[Tensor]]`, supports shared memory (`share_memory=True`)
+- **`Checkpoint` class**: Encapsulates model state dict, training epoch, iteration count; supports safetensors format for saving and loading
 
-```
+#### 1.2 Dataset (`dataset.py`)
-StoreFactory.create("h5")  → H5Store
+- **`BaseDataset`**: Abstract base class, defines common logic for window sampling, stride, etc.
-StoreFactory.create("bin") → MmapStore
+- **`BaseSegmentFetcher`** and **`MultiSegmentFetcher`**: Efficiently fetch data from specified index ranges in multiple segments
-```
+- **`DatasetFactory`**: Factory pattern, supports dynamic registration of dataset types (`seq`, `sft`, `dpo`, `grpo`)
+- After dataset loading, multiple data keys (such as `"sequence"`, `"mask"`) are managed through `MultiSegmentFetcher`
 
-H5 backend supports shared memory via `.share_memory_()`. Bin (mmap) uses OS page-cache sharing natively.
+#### 1.3 Sampler (`sampler.py`)
+- **`ResumableDistributedSampler`**: Resumable sampler supporting distributed training
+- Records current epoch and iteration position, enabling training resume from breakpoints
+- Supports shuffle and drop_last options
 
-## Data Keys by Training Type
+### 2. Model Module
 
-| Type | Storage Keys |
+#### 2.1 Transformer / AutoModel (`transformer.py`, `automodel.py`)
-|------|-------------|
+- **`AutoModel`**: Base class for autoregressive language models with `from_pretrained()` and `save_pretrained()` methods
-| `seq` | `sequence` (→ input_ids, target_ids via offset-by-1) |
+- **`Transformer`**: Core autoregressive decoder architecture (registered via `@AutoModel.register('transformer')`)
-| `sft` | `sequence`, `loss_mask` |
+- Contains embedding layer, multi-layer `DecoderBlock`, RMSNorm, and linear output head
-| `dpo` | `chosen`, `rejected`, `chosen_mask`, `rejected_mask` |
+- Supports weight tying (`tie_weight=True`) to reduce parameter count
-| `grpo` | `prompts`, `responses`, `masks`, `rewards` |
+- Uses Rotary Position Embedding (RoPE) to inject position information
+- Supports loading from safetensors format with automatic model type detection from `config.json`
 
-## Dataset Architecture
+#### 2.2 Submodules (`module.py`)
+- **`RotaryEmbedding`**: Generates RoPE cos/sin cache
+- **`DecoderBlock`**: Contains multi-head attention (supports GQA and MLA), feedforward network (FFN), residual connections
+- **`GQA`**: Grouped Query Attention implementation
+- **`MLA`**: Multi-Latent Attention implementation (like Qwen2-VL)
+- **`MLP`**: Feed-forward network with SiLU activation and gated mechanism
+- **`RMSNorm`**: Layer normalization variant
+- **`Linear`**, **`Embedding`**: Custom linear layer and embedding layer, supporting parallelism wrappers
 
-```
+### 3. Training Module
-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`).
+#### 3.1 Training Context (`train_context.py`)
+- **`TrainContext`**: Data class encapsulating all components needed for training (model, optimizer, data loader, strategy, etc.)
+- **`TrainContextBuilder`**: Builder pattern, progressively assembles training context, supports resume from checkpoint
 
-`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()`.
+#### 3.2 Trainer (`trainer.py`)
+- **`Trainer`**: Main training loop, manages callbacks (progress bar, checkpoint, metric logging, gradient clipping, scheduler)
+- Supports distributed training (launches multi-process via `spawn_parallel_fn`)
+- Training steps include:
+  1. `on_train_begin` → 2. `on_epoch_begin` → 3. `on_batch_begin` → 4. Forward/loss calculation → 5. `on_batch_end` → 6. Gradient accumulation → 7. `on_step_begin` → 8. Optimizer update → 9. `on_step_end` → 10. `on_epoch_end`
 
-## Sampler
+#### 3.3 Strategy (`strategy.py`)
+- **`BaseStrategy`**: Defines training strategy interface
+- **`SEQStrategy`**: Standard next-token prediction training
+- **`SFTStrategy`**: Supervised Fine-tuning with loss masking
+- **`DPOStrategy`**: Direct Preference Optimization
+- **`GRPOStrategy`**: Group Relative Policy Optimization
+- Strategy receives batch data, executes model forward pass, loss calculation, returns loss tensor
+- Created dynamically by `StrategyFactory` according to configuration
 
-`ResumableDistributedSampler` supports checkpoint-aware distributed sampling:
+#### 3.4 Scheduler (`schedule.py`)
+- **`BaseScheduler`**: Abstract base class defining learning rate scheduling interface
+- **`CosineScheduler`**: Cosine decay scheduler with warmup
+- **`SGDRScheduler`**: Stochastic Gradient Descent with Warm Restarts
+- **`SchedulerFactory`**: Factory pattern, supports registration of various schedulers
+- Scheduler is automatically created according to configuration and bound to optimizer
 
-- Tracks `start_epoch` / `start_iter` for resume
+#### 3.5 Callbacks (`train_callback.py`)
-- Shuffle via `torch.Generator(seed + epoch)`
+- **`TrainCallback`**: Protocol interface for trainer callbacks
-- Per-replica index slicing for DDP
+- **`CheckpointCallback`**: Saves model checkpoints at configurable intervals
+- **`ProgressBarCallback`**: Displays training progress
+- **`MetricLoggerCallback`**: Logs training metrics to JSON files
+- **`GradientClippingCallback`**: Clips gradient norms
+- **`SchedulerCallback`**: Steps learning rate scheduler
 
-## DataLoader
+### 4. Factory Module
 
-Standard PyTorch `DataLoader` with configurable `batch_size`, `num_workers`, `pin_memory`, `prefetch_factor`. Sampler produces indices; dataloader fetches tensor batches via `__getitem__`.
+#### 4.1 Registry and BaseFactory (`factory.py`)
+- **`Registry`**: Flexible registry for component classes with category and priority support
+- **`BaseFactory`**: Generic factory class for component registration and creation
+- Supports decorator-based registration pattern for extensible components
+- Provides methods for registration, retrieval, and listing with filtering
 
-> Document Update Time: 2026-05-30
+### 5. Parallel Module
+
+#### 5.1 Setup (`setup.py`)
+- **`spawn_parallel_fn`**: Spawns multiple processes for distributed training using PyTorch multiprocessing
+- **`setup_parallel`**: Context manager for initializing distributed process group (NCCL/CCL backend)
+- **`only_on_rank`**: Decorator to execute functions only on specific ranks
+- **`get_rank`**: Returns current process rank in distributed group
+- **`get_world_size`**: Returns total number of processes in distributed group
+- **`get_current_device`**: Returns current device from environment
+
+#### 5.2 Parallel Layers (`module.py`)
+- **`ParallelModel`**: Base class for parallel models with process group
+- **`ColumnParallelLinear`**: Column-parallel linear layer with input splitting and output gathering
+- **`RowParallelLinear`**: Row-parallel linear layer with output reduction
+
+### 6. Inference Module
+
+#### 6.1 Inference Engine (`engine.py`)
+- **`InferenceEngine`**: Unified inference interface, supports streaming and non-streaming generation
+- **`InferenceScheduler`**: Continuous batching scheduler with dynamic batch composition
+- **`GenerationRequest`**: Encapsulates generation parameters (top_k, top_p, temperature, max_len, messages, etc.)
+- **`messages` format**: List of message dictionaries with `role` (system/user/assistant) and `content`
+- **`apply_chat_template`** (from `tokenizer.py`): Converts messages into prompt string using ChatML format
+- Provides streaming (`stream=True`) and non-streaming (`stream=False`) generation interfaces
+- Supports continuous batching with `max_batch_size` and `max_seq_len` parameters
+- Uses separate model and tokenizer initialization for flexibility
+
+#### 6.2 Scheduler (`scheduler.py`)
+- **`Task`**: Individual generation task with state management (PENDING, RUNNING, FINISHED, ABORTED)
+- **`TaskStatus`**: Task state enumeration
+- **`apply_sampling_strategies`**: Applies temperature, top-k, top-p sampling to logits
+- **`PrefixCacheManager`**: Radix tree-based prefix cache with LRU eviction for efficient KV cache reuse
+- **`RadixNode`**: Tree node structure for prefix caching
+- Continuous batching: new requests can join at any time, completed requests are released immediately
+
+#### 6.3 Server (`server.py`)
+- FastAPI-based HTTP inference server
+- OpenAI-compatible `/v1/chat/completions` endpoint
+- Health check and statistics endpoints
+- Supports both streaming and non-streaming responses
+
+### 7. Tokenizer Module
+
+#### 7.1 Tokenizer (`tokenizer.py`)
+- Implemented based on HuggingFace tokenizers library (Byte-Level BPE)
+- **`AutoTokenizer`**: Auto-loading tokenizer class
+- Supports special tokens: `<｜begin▁of▁sentence｜>`, `<｜end▁of▁sentence｜>`, `<｜▁pad▁｜>`, `<｜im▁start｜>`, `<｜im▁end｜>`
+- Provides `encode`/`decode` methods for mutual conversion between text and token IDs
+- Uses `AutoTokenizer` for loading pre-trained tokenizers
+
+#### 7.2 Chat Template (`chat_template.py`)
+- **`ChatTemplate`**: Jinja2-based chat template with rendering support
+- Handles multi-role message formatting (system, user, assistant)
+- Supports dynamic prompts and generation prompts
+
+## Training Data Flow - Detailed Steps
+
+1. **Data Preparation**
+   - Raw text is converted to token ID sequences through AutoTokenizer
+   - Token ID sequences (possibly with masks, labels, etc.) are saved by groups as `.h5` files
+   - Files can contain multiple segments, each segment corresponds to a tensor
+
+2. **Dataset Loading**
+   - `BaseDataset`'s `load` method calls `load_h5`, obtaining `segments` dictionary
+   - Create `MultiSegmentFetcher` to manage data for multiple keys
+   - Calculate total sample count, and determine start/end indices for each sample based on window size and stride
+
+3. **Sampling and Batch Loading**
+   - `ResumableDistributedSampler` generates index sequence based on current epoch and iteration position
+   - PyTorch `DataLoader` uses sampler to get indices, calls dataset's `__getitem__` to get actual data
+   - Batch data shape is `[batch_size, window_size]` (or varies according to specific dataset type)
+
+4. **Strategy Forward and Loss Calculation**
+   - Batch data is passed to strategy (such as `SEQStrategy`)
+   - Strategy internally calls `Transformer` model, obtaining logits
+   - Calculate cross-entropy loss (or DPO loss, etc.) according to task type
+   - Return loss tensor
+
+5. **Backpropagation and Optimization**
+   - Loss is normalized by dividing by accumulation steps, then `loss.backward()` is executed
+   - After accumulating `accumulation_steps` batches, optimizer `step()` and `zero_grad()` are executed
+   - Learning rate scheduler updates learning rate after each step
+
+6. **Checkpoint Saving**
+   - `CheckpointCallback` saves checkpoints at set intervals
+   - Checkpoints contain model state dict, current epoch, iteration, and other metadata
+   - Saved in safetensors format, ensuring safety and efficiency
+
+## Inference Data Flow - Detailed Steps
+
+1. **Model Loading**
+   - Load `Transformer` model from checkpoint via `AutoModel.from_pretrained()`
+   - Set model to evaluation mode (`model.eval()`), enable inference mode (`torch.inference_mode`)
+
+2. **Prompt Construction and Encoding**
+   - User messages (list of dict with role and content) are converted to ChatML format string through `apply_chat_template` method in tokenizer
+   - Tokenizer encodes prompt string to token ID sequence `input_ids`
+   - For batch generation, use `pad_sequence` for padding
+
+3. **Autoregressive Generation Loop**
+   - Initialize KV cache (optional) and prefix cache
+   - Loop until generating `max_len` tokens or encountering stop token:
+     - Input current `input_ids` (or cached new token) to model, obtain `logits`
+     - Apply `apply_sampling_strategies` (temperature, top-k, top-p) to `logits`
+     - Sample next token ID from the processed distribution
+     - Append new token to `input_ids`, while updating KV cache
+     - For streaming generation, yield each token to caller immediately
+
+4. **Decoding and Output**
+   - Decode generated token ID sequence to text through tokenizer
+   - Remove special tokens, return plain text response
+
+## Checkpoint and Serialization
+
+- **Training Checkpoint**: Saves model parameters, optimizer state, scheduler state, current epoch and iteration
+- **Model Parameters**: Supports safetensors format, automatically handles special logic like weight tying during loading
+- **Dataset Serialization**: HDF5 format supports efficient random access and shared memory, suitable for large-scale pre-training data
+
+## Summary
+
+The data flow design of AstrAI reflects the characteristics of modularity, extensibility, and resumability. The training data flow supports large-scale distributed training through chunk loading, resumable sampling, gradient accumulation, and other mechanisms; the inference data flow achieves efficient text generation using KV cache, prefix caching, and sampling strategies. Clear interfaces between modules facilitate customization and extension.
+
+> Document Update Time: 2026-04-09

assets/docs/design.md

@@ -0,0 +1,694 @@
+## 1. Why I Created This Project
+
+There are many large language models on the market today, such as GPT, LLaMA, and others, with tens of billions or even hundreds of billions of parameters. But honestly, these models have extremely high hardware requirements, making them inaccessible for ordinary developers. I thought: **Can we create a model that is both useful and can run on ordinary computers?** This is also what most people currently hope for - a locally deployable AI project that achieves complete privatization while maintaining some level of intelligence.
+
+Thus, the AstrAI project was born - 1B parameters, Chinese-English bilingual, supporting dialogue, text generation, and the training code is open source!
+
+## 2. System Architecture
+
+```mermaid
+classDiagram
+    namespace config {
+        class ModelConfig {
+            +int vocab_size
+            +int dim
+            +int n_layers
+            +float norm_eps
+            +int dim_ffn
+            +bool tie_weight
+            +int max_len
+            +float rope_theta
+            +int n_heads
+            +int n_kv_heads
+            +bool use_qk_norm
+            +bool use_gated_attention
+            +load(config_path) ModelConfig
+            +save(config_path)
+        }
+
+        class TrainConfig {
+            +nn.Module model
+            +str strategy
+            +Dataset dataset
+            +Callable optimizer_fn
+            +Callable scheduler_fn
+            +int n_epoch
+            +int batch_size
+            +int accumulation_steps
+            +float max_grad_norm
+            +int start_epoch
+            +int start_batch
+            +str ckpt_dir
+            +int ckpt_interval
+            +int random_seed
+            +int num_workers
+            +int prefetch_factor
+            +bool pin_memory
+            +int nprocs
+            +str backend
+            +str master_addr
+            +str master_port
+            +Callable parallel_wrapper
+            +Callable state_dict_fn
+            +List[int] device_ids
+            +str device_type
+            +dict extra_kwargs
+            +validate()
+        }
+
+    }
+
+    namespace dataset {
+        class BaseDataset {
+            +int window_size
+            +int stride
+            +MultiSegmentFetcher fetcher
+            +load(load_path)
+            +__getitem__(index)
+            +__len__()
+        }
+
+        class SEQDataset {
+            +__getitem__(index) Dict
+        }
+
+        class SFTDataset {
+            +__getitem__(index) Dict
+        }
+
+        class DPODataset {
+            +__getitem__(index) Dict
+        }
+
+        class GRPODataset {
+            +__getitem__(index) Dict
+        }
+
+        class BaseSegmentFetcher {
+            +List~Tensor~ segments
+            +List~int~ cum_lengths
+            +int total_length
+            +fetch_data(begin_idx, end_idx) Tensor
+        }
+
+        class MultiSegmentFetcher {
+            +Dict multi_fetchers
+            +List multi_keys
+            +key_fetch(begin_idx, end_idx, keys) Dict
+            +fetch_data(begin_idx, end_idx) Dict
+        }
+
+        class ResumableDistributedSampler {
+            +int start_epoch
+            +int start_iter
+        }
+
+        class DatasetFactory {
+            +Registry _registry
+            +register(name) decorator
+            +create(train_type, window_size, stride) BaseDataset
+            +load(train_type, load_path, window_size, stride) BaseDataset
+        }
+
+        class Checkpoint {
+            +dict state_dict
+            +int epoch
+            +int iteration
+            +save(save_dir)
+            +load(save_dir) Checkpoint
+        }
+    }
+
+    namespace model {
+        class AutoModel {
+            +ModelConfig config
+            +Dict _registry
+            +register(model_type) decorator
+            +get_model_class(model_type) Type
+            +from_pretrained(path, disable_random_init) nn.Module
+            +save_pretrained(save_directory)
+            +to(*args, **kwargs) Self
+        }
+
+        class Transformer {
+            +ModelConfig config
+            +RotaryEmbedding rotary_embedding
+            +Embedding embed_tokens
+            +ModuleList layers
+            +RMSNorm norm
+            +Linear lm_head
+            +forward(input_ids, input_mask, persistent_key_values, start_pos) Dict
+            +load_state_dict(state_dict)
+            +state_dict()
+        }
+
+        class DecoderBlock {
+            +GQA attention
+            +RMSNorm input_norm
+            +MLP mlp
+            +RMSNorm post_attention_norm
+            +forward(x, rotary_emb, attention_mask, kv_cache, start_pos) Tensor
+        }
+
+        class GQA {
+            +int n_heads
+            +int n_kv_heads
+            +int head_dim
+            +Linear q_proj, k_proj, v_proj, o_proj
+            +RMSNorm q_norm, k_norm
+            +forward(x, rotary_emb, mask, kv_cache, start_pos) Tensor
+        }
+
+        class MLA {
+            +int n_heads
+            +int n_kv_heads
+            +int head_dim
+            +Linear q_a_proj, q_b_proj, q_c_proj
+            +Linear kv_a_proj, kv_b_proj, kv_c_proj
+            +Linear o_proj
+            +RMSNorm q_norm, k_norm
+            +forward(x, rotary_emb, mask, kv_cache, start_pos) Tensor
+        }
+
+        class MLP {
+            +Linear up, gate, down
+            +forward(x) Tensor
+        }
+
+        class RMSNorm {
+            +Parameter weight
+            +float norm_eps
+            +forward(x) Tensor
+        }
+
+        class Linear {
+            +Parameter weight
+            +Parameter bias
+            +forward(x) Tensor
+        }
+
+        class RotaryEmbedding {
+            +int dim
+            +int max_len
+            +float base
+            +forward(x, start_pos) Tuple~Tensor, Tensor~
+        }
+
+        class Embedding {
+            +Parameter weight
+            +forward(x) Tensor
+        }
+    }
+
+    namespace tokenize {
+        class AutoTokenizer {
+            +List~str~ stop_ids
+            +int bos_id
+            +int eos_id
+            +int pad_id
+            +vocab_size int
+            +encode(tokens, out_ids, add_special_tokens) List~int~
+            +decode(tokens, skip_special_tokens) str
+            +apply_chat_template(messages, tokenize) Union~str, List[int]~
+            +set_chat_template(template)
+            +load(path)
+            +from_pretrained(path) AutoTokenizer
+            +save_pretrained(save_path)
+        }
+
+        class ChatTemplate {
+            +String template_str
+            +render(messages, add_generation_prompt) str
+            +from_string(template) ChatTemplate
+        }
+    }
+
+    namespace factory {
+        class Registry {
+            +Dict _entries
+            +register(name, component_cls, category, priority)
+            +get(name) Type
+            +list_names() List~str~
+        }
+
+        class BaseFactory {
+            +Registry _registry
+            +register(name, category, priority) decorator
+            +create(name, *args, **kwargs) T
+            +list_registered() list
+        }
+    }
+
+    namespace trainer {
+        class Trainer {
+            +TrainConfig train_config
+            +List~TrainCallback~ callbacks
+            +train(checkpoint)
+            +_build_context(checkpoint) TrainContext
+            +_get_default_callbacks() List~TrainCallback~
+        }
+
+        class TrainContext {
+            +nn.Module model
+            +BaseStrategy strategy
+            +DataLoader dataloader
+            +Optimizer optimizer
+            +LRScheduler scheduler
+            +Checkpoint checkpoint
+            +int epoch
+            +int iteration
+            +float loss
+            +int world_size
+            +int rank
+        }
+
+        class TrainContextBuilder {
+            +TrainConfig config
+            +with_checkpoint(checkpoint) TrainContextBuilder
+            +with_dataloader() TrainContextBuilder
+            +with_strategy() TrainContextBuilder
+            +build() TrainContext
+        }
+
+        class BaseStrategy {
+            +nn.Module model
+            +str device
+            +compute_loss(batch) Tensor
+        }
+
+        class StrategyFactory {
+            +Registry _registry
+            +register(name) decorator
+            +create(model, train_type, device, **kwargs) BaseStrategy
+        }
+
+        class SEQStrategy {
+            +float label_smoothing
+            +compute_loss(batch) Tensor
+        }
+
+        class SFTStrategy {
+            +float label_smoothing
+            +compute_loss(batch) Tensor
+        }
+
+        class DPOStrategy {
+            +nn.Module ref_model
+            +float beta
+            +str reduction
+            +compute_loss(batch) Tensor
+        }
+
+        class GRPOStrategy {
+            +nn.Module ref_model
+            +float clip_eps
+            +float kl_coef
+            +int group_size
+            +compute_loss(batch) Tensor
+        }
+
+        class BaseScheduler {
+            +get_lr() List~float~
+            +step()
+        }
+
+        class SchedulerFactory {
+            +Registry _registry
+            +register(name) decorator
+            +create(optimizer, schedule_type, **kwargs) BaseScheduler
+        }
+
+        class CosineScheduler {
+            +int warmup_steps
+            +int lr_decay_steps
+            +float min_rate
+        }
+
+        class SGDRScheduler {
+            +int warmup_steps
+            +int cycle_length
+            +float min_rate
+            +int t_mult
+        }
+
+        class TrainCallback {
+            +on_train_begin(context)
+            +on_train_end(context)
+            +on_epoch_begin(context)
+            +on_epoch_end(context)
+            +on_step_begin(context)
+            +on_step_end(context)
+            +on_batch_begin(context)
+            +on_batch_end(context)
+            +on_error(context)
+        }
+
+        class GradientClippingCallback {
+            +float max_grad_norm
+            +on_step_begin(context)
+        }
+
+        class SchedulerCallback {
+            +on_train_begin(context)
+            +on_batch_end(context)
+        }
+
+        class CheckpointCallback {
+            +str save_dir
+            +int interval
+            +_save_checkpoint(context)
+            +on_batch_end(context)
+            +on_train_end(context)
+            +on_error(context)
+        }
+
+        class ProgressBarCallback {
+            +int num_epoch
+            +on_epoch_begin(context)
+            +on_batch_end(context)
+            +on_epoch_end(context)
+        }
+
+        class MetricLoggerCallback {
+            +str log_dir
+            +int save_interval
+            +on_batch_end(context)
+            +on_train_end(context)
+        }
+
+        class CallbackFactory {
+            +Registry _registry
+            +register(name) decorator
+            +create(name, **kwargs) TrainCallback
+        }
+    }
+
+    namespace inference {
+        class InferenceEngine {
+            +nn.Module model
+            +AutoTokenizer tokenizer
+            +InferenceScheduler scheduler
+            +int max_batch_size
+            +Optional int max_seq_len
+            +int max_prefix_len
+            +int cache_capacity
+            +Tensor kv_cache
+            +Tensor seq_mask
+            +generate(prompt, stream, max_tokens, temperature, top_p, top_k) Union[Generator, str, List[str]]
+            +generate_with_request(request) Union[Generator, str, List[str]]
+            +get_stats() Dict
+            +shutdown()
+        }
+
+        class InferenceScheduler {
+            +nn.Module model
+            +AutoTokenizer tokenizer
+            +ModelConfig config
+            +Tuple kv_cache
+            +Tensor seq_mask
+            +PrefixCacheManager prefix_cache
+            +List waiting_queue
+            +List active_tasks
+            +add_task(prompt, max_tokens, temperature, top_p, top_k, stream_callback) str
+            +remove_task(task_id)
+            +start()
+            +stop()
+            +get_stats() Dict
+        }
+
+        class PrefixCacheManager {
+            +RadixNode root
+            +int max_capacity
+            +List lru
+            +insert(token_ids, slot)
+            +find_longest_prefix(token_ids) Tuple[int, int]
+            +release(token_ids)
+        }
+
+        class RadixNode {
+            +Dict children
+            +int hash
+            +int slot
+            +int ref_count
+            +float last_access
+            +List token_sequence
+        }
+
+        class Task {
+            +str task_id
+            +List prompt_ids
+            +int max_tokens
+            +float temperature
+            +float top_p
+            +int top_k
+            +TaskStatus status
+            +List output_ids
+            +int input_tokens
+            +int output_tokens
+            +int slot
+            +Callable stream_callback
+            +is_finished(stop_ids) bool
+        }
+
+        class TaskStatus {
+            +str PENDING
+            +str RUNNING
+            +str FINISHED
+            +str ABORTED
+        }
+
+        class Server {
+            +start()
+            +predict(request)
+        }
+
+        class GenerationRequest {
+            +int top_k
+            +float top_p
+            +float temperature
+            +int max_len
+            +List~Dict~ messages
+            +stream bool
+        }
+
+        class _Result {
+            +List~str~ tokens
+            +List~str~ results
+            +List~bool~ done_flags
+            +append(token, idx)
+            +get_results() List~str~
+        }
+
+        class ChatMessage {
+            +str role
+            +str content
+        }
+
+        class ChatCompletionRequest {
+            +List~ChatMessage~ messages
+            +float temperature
+            +float top_p
+            +int top_k
+            +int max_tokens
+            +bool stream
+            +Optional~str~ system_prompt
+        }
+
+        class CompletionResponse {
+            +str id
+            +str object
+            +int created
+            +str model
+            +List~Dict~ choices
+        }
+    }
+
+    namespace parallel {
+        class ParallelSetup {
+            +spawn_parallel_fn(fn, nprocs)
+            +setup_parallel(rank, world_size, backend, master_addr, master_port, device_type, device_ids)
+        }
+
+        class ParallelModel {
+            +dist.ProcessGroup process_group
+            +int rank
+            +int world_size
+        }
+
+        class ColumnParallelLinear {
+            +forward(x) Tensor
+        }
+
+        class RowParallelLinear {
+            +forward(x) Tensor
+        }
+    }
+
+    %% Relationships
+    TrainConfig --> ModelConfig : uses
+    TrainConfig --> BaseDataset : uses
+    TrainConfig --> StrategyFactory : selects
+    StrategyFactory ..> BaseStrategy : creates
+    BaseStrategy <|-- SEQStrategy
+    BaseStrategy <|-- SFTStrategy
+    BaseStrategy <|-- DPOStrategy
+    BaseStrategy <|-- GRPOStrategy
+    DPOStrategy --> Transformer : uses
+    GRPOStrategy --> Transformer : uses
+    Trainer --> TrainConfig : configures
+    Trainer --> TrainContextBuilder : builds
+    Trainer --> TrainCallback : manages
+    TrainContextBuilder --> TrainContext : creates
+    TrainContext --> Checkpoint : manages
+    TrainContext --> BaseStrategy : uses
+    TrainContext --> BaseScheduler : uses
+    AutoModel --> ModelConfig : contains
+    SchedulerFactory ..> BaseScheduler : creates
+    BaseScheduler <|-- CosineScheduler
+    BaseScheduler <|-- SGDRScheduler
+    CallbackFactory ..> TrainCallback : creates
+    TrainCallback <|-- GradientClippingCallback
+    TrainCallback <|-- SchedulerCallback
+    TrainCallback <|-- CheckpointCallback
+    TrainCallback <|-- ProgressBarCallback
+    TrainCallback <|-- MetricLoggerCallback
+    InferenceEngine --> InferenceScheduler : uses
+    InferenceScheduler --> Task : manages
+    InferenceScheduler --> TaskStatus : uses
+    InferenceScheduler --> Transformer : uses
+    InferenceEngine --> Transformer : uses
+    InferenceEngine --> GenerationRequest : uses
+    Server --> InferenceEngine : uses
+    Server --> ChatMessage : uses
+    Server --> ChatCompletionRequest : uses
+    Server --> CompletionResponse : uses
+    ParallelSetup --> Trainer : enables
+    BaseDataset <|-- SEQDataset
+    BaseDataset <|-- SFTDataset
+    BaseDataset <|-- DPODataset
+    BaseDataset <|-- GRPODataset
+    DatasetFactory ..> BaseDataset : creates
+    BaseSegmentFetcher --> MultiSegmentFetcher : used by
+    MultiSegmentFetcher --> BaseDataset : used by
+    AutoModel <|-- Transformer
+    AutoModel --> ModelConfig : contains
+    Transformer --> DecoderBlock : uses
+    Transformer --> RotaryEmbedding : uses
+    Transformer --> Embedding : uses
+    DecoderBlock --> GQA : uses
+    DecoderBlock --> MLA : uses
+    DecoderBlock --> MLP : uses
+    DecoderBlock --> RMSNorm : uses
+    TrainContextBuilder --> ResumableDistributedSampler : creates
+    ResumableDistributedSampler --> BaseDataset : samples
+    ParallelModel <|-- RowParallelLinear
+    ParallelModel <|-- ColumnParallelLinear
+    AutoTokenizer --> ChatTemplate : uses
+    InferenceScheduler --> PrefixCacheManager : uses
+    InferenceScheduler --> RadixNode : uses
+    Checkpoint ..> Checkpoint : saves/loads
+    TrainConfig --> DatasetFactory : selects
+    TrainConfig --> SchedulerFactory : selects
+    TrainConfig --> CallbackFactory : selects
+    AutoModel ..> AutoTokenizer : loads with
+    BaseFactory <|-- DatasetFactory
+    BaseFactory <|-- StrategyFactory
+    BaseFactory <|-- SchedulerFactory
+    BaseFactory <|-- CallbackFactory
+```
+
+### Module Overview
+
+| Module | Components | Description |
+|--------|------------|-------------|
+| **astrai.config** | ModelConfig, TrainConfig | Configuration management |
+| **astrai.dataset** | BaseDataset, SEQDataset, SFTDataset, DPODataset, GRPODataset, BaseSegmentFetcher, MultiSegmentFetcher, ResumableDistributedSampler, DatasetFactory, Checkpoint | Dataset loading and management |
+| **astrai.model** | AutoModel, Transformer, DecoderBlock, GQA, MLA, MLP, RMSNorm, Linear, RotaryEmbedding, Embedding | Neural network model |
+| **astrai.tokenize** | AutoTokenizer, ChatTemplate | Tokenizer and chat template |
+| **astrai.trainer** | Trainer, TrainContext, TrainContextBuilder, BaseStrategy, StrategyFactory, BaseScheduler, SchedulerFactory, TrainCallback, CallbackFactory | Training workflow management |
+| **astrai.inference** | InferenceEngine, InferenceScheduler, Task, TaskStatus, Server, GenerationRequest, PrefixCacheManager, ChatMessage, ChatCompletionRequest, CompletionResponse | Inference service with continuous batching |
+| **astrai.parallel** | ParallelSetup, ColumnParallelLinear, RowParallelLinear | Distributed parallel |
+| **astrai.factory** | Registry, BaseFactory | Generic component registration |
+
+### Design Patterns
+
+| Pattern | Classes | Purpose |
+|---------|---------|---------|
+| **Strategy** | `BaseStrategy`, `SEQStrategy`, `SFTStrategy`, `DPOStrategy`, `GRPOStrategy`, `StrategyFactory` | Flexible training strategy switching, supports SEQ/SFT/DPO/GRPO |
+| **Builder** | `TrainContextBuilder` | Chain-building training context, step-by-step initialization of components |
+| **Factory** | `StrategyFactory`, `SchedulerFactory`, `DatasetFactory`, `CallbackFactory`, `BaseFactory` | Decorator registration mechanism, dynamically create training strategies, schedulers, datasets, and callbacks |
+| **Observer** | `TrainCallback`, `CallbackFactory` | Callback mechanism for training process monitoring (checkpoint, early stopping, metrics) |
+| **Singleton** | `TrainContext` | Training process global state management |
+| **Registry** | `BaseFactory`, `Registry` | Generic component registration with category and priority support |
+| **Producer-Consumer** | `InferenceScheduler`, `Task`, `waiting_queue`, `active_tasks` | Continuous batching with dynamic task queue management |
+| **Event-Driven** | `threading.Event`, `_task_event` | Non-blocking wait mechanism for task scheduling using Python's `threading` module |
+| **AutoModel Registry** | `AutoModel`, `Transformer` | Model type registration and dynamic loading via decorator pattern |
+| **Generator Pattern** | `_Result`, `GenerationRequest` | Event-based result notification for streaming/non-streaming generation |
+
+### Core Relationships
+
+1. **Configuration → Training**: `TrainConfig` contains `ModelConfig`, holds model, dataset, optimizer and other references
+2. **Training Flow**: `Trainer` → `TrainContextBuilder` → `TrainContext`, uses `BaseStrategy` to compute loss
+3. **Strategy Selection**: `StrategyFactory` creates corresponding strategy instance based on `train_type`
+4. **Inference Flow**: `Server` → `InferenceEngine` → `InferenceScheduler` → `Transformer`, supports continuous batching with streaming/non-streaming
+5. **Distributed Support**: `ParallelSetup` provides multi-process training capability for `Trainer`
+6. **Dataset Loading**: `DatasetFactory` creates datasets (SEQDataset, SFTDataset, DPODataset, GRPODataset), supports HDF5 loading via `BaseSegmentFetcher` and `MultiSegmentFetcher`
+7. **Checkpoint Management**: `Checkpoint` handles model state serialization/deserialization with safetensors
+8. **Scheduler Support**: `SchedulerFactory` creates learning rate schedulers (CosineScheduler, SGDRScheduler)
+9. **AutoModel Loading**: `AutoModel.from_pretrained()` dynamically loads model based on `config.json` model_type, uses `Registry` pattern for model type registration
+
+## 3. Training Process
+
+The common training process for large language models (LLM) typically includes three stages: **Pre-training (SEQ)**, **Supervised Fine-Tuning (SFT)**, and **Reinforcement Learning from Human Feedback (DPO/GRPO)**. This system is designed to support seamless end-to-end flow, achieving efficient switching and state management of different training stages through modular strategies.
+
+### Core Formulas
+
+**Pre-training (SEQ):**
+
+$$
+L_{\text{PT}} = - \sum_{t=1}^{T} \log P(x_t \mid x_{\lt t}; \theta)
+$$
+
+**SFT:**
+
+$$
+L_{\text{SFT}} = - \sum_{t=P+1}^{P+L} \log P(s_t \mid s_{\lt t}; \theta)
+$$
+
+**DPO:**
+
+$$
+L_{\text{DPO}} = -\mathbb{E}_{(x, y_w, y_l) \sim D} \left[ \log \sigma\left( \beta \log \frac{\pi_\theta(y_w \mid x)}{\pi_{\text{ref}}(y_w \mid x)} - \beta \log \frac{\pi_\theta(y_l \mid x)}{\pi_{\text{ref}}(y_l \mid x)} \right) \right]
+$$
+
+**GRPO:**
+
+GRPO (Group Relative Policy Optimization) computes advantages from multiple responses to the same prompt, then optimizes using a PPO-style clipped objective:
+
+$$
+\text{Advantage}_i = \frac{r_i - \mu}{\sigma + \epsilon}
+$$
+
+Where $r_i$ is the reward for the $i$-th response, $\mu$ and $\sigma$ are the mean and standard deviation of group rewards.
+
+$$
+L_{\text{GRPO}} = -\mathbb{E} \left[ \min\left( \frac{\pi_\theta(a|s)}{\pi_{\text{ref}}(a|s)} \cdot A, \text{clip}\left(\frac{\pi_\theta(a|s)}{\pi_{\text{ref}}(a|s)}, 1-\epsilon, 1+\epsilon\right) \cdot A \right) \right] + \lambda \cdot D_{KL}
+$$
+
+In this implementation, an off-policy approach is used ($\pi_\theta = \pi_{\text{ref}}$), and the policy loss simplifies to:
+
+$$
+L_{\text{policy}} = -\mathbb{E}[A]
+$$
+
+The KL divergence term uses mean squared error approximation:
+
+$$
+L_{KL} = \lambda \cdot \mathbb{E} \left[ (\log \pi_\theta - \log \pi_{\text{ref}})^2 \right]
+$$
+
+The final loss is the sum of both: $L = L_{\text{policy}} + L_{KL}$
+
+Through the above three-stage progressive training, the model completes its evolution from a general language foundation to a specialized, highly-aligned dialogue intelligence.
+
+> Document Update Time: 2026-04-09

assets/docs/inference.md

@@ -1,152 +0,0 @@
-# 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

@@ -0,0 +1,299 @@
+## Model Introduction
+
+### 1. Model Architecture
+
+This model uses the Transformer architecture with GQA mechanism (q_head=24, kv_head=4), which saves KV cache memory compared to traditional MHA. The model is built by stacking 32 layers of Transformer blocks, with 1.0 billion parameters. Transformer is an autoregressive model that calculates the relationship between all previous tokens to obtain the probability distribution of the next token.
+
+The model now uses the **AutoModel** base class for flexible loading and saving:
+
+```python
+from astrai.model import AutoModel
+
+# Load model from checkpoint
+model = AutoModel.from_pretrained("path/to/model")
+
+# Save model to new directory
+model.save_pretrained("path/to/save")
+```
+
+The Transformer model is registered via `@AutoModel.register('transformer')` decorator, allowing easy extension for new model types.
+
+```mermaid
+flowchart TB
+    subgraph Layers["Transformer Layers"]
+        direction TB
+        A[Input Embedding] --> B[Transformer Block\nLayer 1]
+        B --> C[Transformer Block\nLayer ...]
+        C --> D[Transformer Block\nLayer 32]
+        D --> E[RMSNorm]
+        E --> F[Linear]
+        F --> G[SoftMax]
+    end
+
+    subgraph TransformerBlock["Transformer Block"]
+        direction TB
+        H[x] --> I[RMSNorm]
+        I --> J[Linear → Q/K/V]
+        J --> K[Q]
+        J --> L[K]
+        J --> M[V]
+        K --> N[RoPE]
+        L --> O[RoPE]
+        N --> P["Q @ K^T / sqrt(d)"]
+        O --> P
+        P --> Q[Masked SoftMax]
+        Q --> R[S @ V]
+        M --> R
+        R --> S[Linear]
+        S --> T[+]
+        H --> T
+        T --> U[RMSNorm]
+        U --> V[Linear]
+        V --> W[SiLU]
+        V --> X[×]
+        W --> X
+        X --> Y[Linear]
+        Y --> Z[+]
+        T --> Z
+        Z --> AA[x']
+    end
+
+    classDef main fill:#e6f3ff,stroke:#0066cc;
+    classDef block fill:#fff2e6,stroke:#cc6600;
+    class Layers main;
+    class TransformerBlock block;
+```
+
+What is an autoregressive model? After splitting a sentence into tokens, the model predicts the probability distribution of the next token. This means the model calculates the probability of the next possible token and its corresponding probability based on the given context (the sequence of tokens that have already appeared).
+
+#### 1. Autoregression
+
+In autoregressive modeling, when a sentence is tokenized into a sequence of tokens, the model learns to predict what comes next. Given a sequence of tokens as input, the model calculates a probability distribution over all possible next tokens. This distribution tells us how likely each potential next token is, given the current context.
+
+For instance, if the input sequence contains tokens representing a question, the model might predict that certain response tokens have higher probabilities than others. The sampling process then selects one token from this distribution—controlled by parameters like top_k, top_p, and temperature—to serve as the next token in the sequence.
+
+Once a token is selected, it is appended to the input sequence, and the model repeats this process. The updated sequence is then fed back into the model to predict the next token. This iterative process continues until either a special end-of-sequence token is generated, or the maximum sequence length is reached. These control tokens are essential because without them, the model would continue generating tokens indefinitely, eventually exhausting available memory.
+
+#### 2. Causal Mask
+
+Transformers use attention mechanism. The input shape is generally [bsz, seq_len], and the output is [bsz, seq_len, n_dim]. To predict the next token, the model's input and output must be offset by one position. The target predicted by the model must be offset by one position, and during training we also use the offset-by-one method:
+
+```
+sequence : [[1, 2, 3, 4, 5, 6]]
+input_ids: [[1, 2, 3, 4, 5]]
+target_ids: [[2, 3, 4, 5, 6]]
+```
+
+The attention score calculation formula is:
+
+$$ s_{ij} = softmax(\frac{q_i^Tk_j}{\sqrt{d_k}}) $$
+$$ s_{ij} := s_{ij} + mask_{ij} $$
+
+Here, the attention score represents the degree to which the model attends to the similarity between two tokens.
+
+For decoder-only structure models, to prevent the model from "stealing" information from future positions, a mask needs to be added during attention calculation. We need to apply a mask before attention score calculation. This mask is typically a lower triangular matrix, and for a sequence of length n, its shape is [n, n]. Below is an example of how to create such a causal mask matrix for a sequence of length 5:
+
+```
+[[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]]
+```
+
+In this matrix, 0 represents positions that can be attended to, while -inf represents positions that should be masked (i.e., should not be attended to). Because this matrix ensures that after the softmax, the parts of the attention scores where $j > i$ change from `inf` to 0, meaning the model cannot see future information.
+
+#### 3. Rotary Position Embedding
+
+Rotary Position Embedding (RoPE) is a position encoding method designed to solve the problem of lacking direct modeling of sequence position information in Transformer models. Unlike traditional position encodings (such as sine and cosine function position encodings), RoPE embeds position information directly into the Query (Q) and Key (K) vectors, allowing the model to more naturally handle relative position relationships in sequences.
+
+$$ q_i = R_i W_q x_i $$
+$$ k_j = R_j W_k x_j $$
+$$ q_i^T k_j = (R_i W_q x_i)^T( R_j W_k x_j) = x_i^T W_q^T R_{i-j} W_k x_j $$
+
+The $R_{i-j}$ controls the attenuation of attention for different tokens at different relative distances. When the absolute value of $i - j$ is larger, the degree of attenuation is stronger. This approach allows the model to learn relative position relationships, enabling the model to scale and adapt to longer sequences.
+
+## KV Cache Implementation
+
+According to the attention calculation formula:
+
+$$
+\begin{align*}
+o_i &= \sum_j s_{ij} v_{j} \newline
+s_{ij} &= \text{softmax}\left( \frac{q_{i} k_{j}}{\sqrt{d_k}} \right)
+\end{align*}
+$$
+
+Since the model is an autoregressive model, we only need to calculate for the last part of the sequence, meaning the index $i$ is fixed as the last element of the sequence, and we compute $o_{n}$:
+
+$$
+\begin{align*}
+o_n &= \sum_j s_{j}v_{j} \newline
+s_j &= \text{softmax}\left(\frac{q_n k_{j}}{\sqrt{d_k}} \right)
+\end{align*}
+$$
+
+If we expand the expression:
+
+$$
+o_n = \sum_j \text{softmax}\left(\frac{q_n k_{j}}{\sqrt{d_k}}\right)v_{j}
+$$
+
+In the above expression, only k and v have length indices, while $q$ does not. Therefore, during the calculation process, the input of $q$ is fixed as the last token from the previous input, while $k$ and $v$ need to be cached for parts of different lengths. Also, when caching, note that position encoding calculation should be performed before KV cache computation, otherwise there will be position encoding calculation errors.
+
+### 4. AutoModel Loading
+
+The project now uses the **AutoModel** base class for flexible model loading and saving:
+
+```python
+from astrai.model import AutoModel
+
+# Load model from checkpoint
+model = AutoModel.from_pretrained("path/to/model")
+
+# Save model to new directory
+model.save_pretrained("path/to/save")
+```
+
+The Transformer model is registered via `@AutoModel.register('transformer')` decorator, allowing easy extension for new model types. The `from_pretrained` method automatically loads the `config.json` to determine the model type and uses safetensors format for weights.
+
+### 5. Continuous Batching Inference
+
+The inference engine supports **continuous batching** for efficient batch processing:
+
+```python
+from astrai.inference import InferenceEngine, GenerationRequest
+
+# Create inference engine with continuous batching
+engine = InferenceEngine(
+    model=model,
+    tokenizer=tokenizer,
+    max_batch_size=8,
+    max_seq_len=4096,
+)
+
+# Use GenerationRequest with messages format
+request = GenerationRequest(
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Hello"},
+    ],
+    temperature=0.8,
+    top_p=0.95,
+    top_k=50,
+    max_len=1024,
+    stream=True,
+)
+
+# Generate with streaming
+for token in engine.generate_with_request(request):
+    print(token, end="", flush=True)
+```
+
+The continuous batching feature allows dynamic batch composition where new requests can join at any time and completed requests are released immediately.
+
+## HTTP API Usage
+
+The inference server provides HTTP endpoints for remote inference. Start the server first:
+
+```bash
+python -m scripts.tools.server --port 8000
+```
+
+### OpenAI-Compatible Endpoint
+
+The server provides an OpenAI-compatible chat completion endpoint at `/v1/chat/completions`:
+
+```bash
+curl -X POST http://localhost:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "messages": [
+      {"role": "system", "content": "You are a helpful assistant."},
+      {"role": "user", "content": "Hello, how are you?"}
+    ],
+    "temperature": 0.8,
+    "max_tokens": 2048,
+    "stream": false
+  }'
+```
+
+**Request Parameters:**
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `messages` | List[dict] | Required | Chat messages with role and content |
+| `temperature` | float | 0.8 | Sampling temperature (0.0-2.0) |
+| `top_p` | float | 0.95 | Nucleus sampling threshold |
+| `top_k` | int | 50 | Top-k sampling parameter |
+| `max_tokens` | int | 2048 | Maximum tokens to generate |
+| `stream` | bool | false | Enable streaming response |
+| `system_prompt` | str | None | System prompt override |
+
+**Response (non-streaming):**
+```json
+{
+  "id": "chatcmpl-1234567890",
+  "object": "chat.completion",
+  "created": 1234567890,
+  "model": "astrai",
+  "choices": [
+    {
+      "index": 0,
+      "message": {"role": "assistant", "content": "Hello! I'm doing well..."},
+      "finish_reason": "stop"
+    }
+  ]
+}
+```
+
+### Streaming Response
+
+Enable streaming for real-time token-by-token output:
+
+```bash
+curl -X POST http://localhost:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "messages": [{"role": "user", "content": "Write a story"}],
+    "stream": true,
+    "max_tokens": 500
+  }'
+```
+
+The server uses Server-Sent Events (SSE) with content type `text/event-stream`.
+
+### Simple Generation Endpoint
+
+For basic text generation without chat format:
+
+```bash
+curl -X POST "http://localhost:8000/generate?query=Hello&max_len=1000" \
+  -H "Content-Type: application/json"
+```
+
+Or with conversation history:
+
+```bash
+curl -X POST "http://localhost:8000/generate" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "query": "What is AI?",
+    "history": [["Hello", "Hi there!"], ["How are you?", "I'm doing well"]],
+    "temperature": 0.8,
+    "max_len": 2048
+  }'
+```
+
+### Health Check
+
+Monitor server and model status:
+
+```bash
+curl http://localhost:8000/health
+# {"status": "ok", "model_loaded": true, "engine_ready": true}
+
+curl http://localhost:8000/stats
+# {"requests_total": 10, "tokens_generated": 5000, ...}
+```
+
+> Document Update Time: 2026-04-09

assets/docs/params.md

@@ -4,97 +4,138 @@
 
 ### Basic Parameters
 
-| Parameter | Description | Default |
+| Parameter | Description | Default Value |
-|-----------|-------------|---------|
+|-----------|-------------|---------------|
-| `--train_type` | Training type (`seq`, `sft`, `dpo`, `grpo`) | required |
+| `--train_type` | Training type (seq, sft, dpo, grpo) | required |
+| `--model_type` | Model type for AutoModel loading (e.g., transformer) | transformer |
 | `--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 |
+| `--batch_size` | Batch size | 4 |
-| `--grad_accum_steps` | Gradient accumulation steps between optimizer steps | 1 |
+| `--accumulation_steps` | Gradient accumulation steps | 1 |
 
 ### Learning Rate Scheduling
 
-| Parameter | Description | Default |
+| Parameter | Description | Default Value |
-|-----------|-------------|---------|
+|-----------|-------------|---------------|
-| `--warmup_ratio` | Fraction of total steps used for LR warmup | 0.05 |
+| `--warmup_steps` | Warmup steps | 1000 |
-| `--max_lr` | Maximum learning rate (cosine decay after warmup) | 3e-4 |
+| `--max_lr` | Maximum learning rate (warmup + cosine decay) | 3e-4 |
-| `--max_grad_norm` | Maximum gradient norm for clipping | 1.0 |
+| `--max_grad_norm` | Maximum gradient norm | 1.0 |
 
-### Optimizer (AdamW)
+### Checkpoint
 
-| Parameter | Description | Default |
+| Parameter | Description | Default Value |
-|-----------|-------------|---------|
+|-----------|-------------|---------------|
+| `--ckpt_interval` | Checkpoint save interval (iterations) | 5000 |
+| `--ckpt_dir` | Checkpoint save directory | checkpoint |
+| `--resume_dir` | Resume training from specified path | - |
+
+### Optimizer Parameters
+
+| Parameter | Description | Default Value |
+|-----------|-------------|---------------|
 | `--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 |
+| Parameter | Description | Default Value |
-|-----------|-------------|---------|
+|-----------|-------------|---------------|
-| `--window_size` | Max input sequence length | model config `max_len` |
+| `--random_seed` | Random seed | 3407 |
-| `--stride` | Stride for sliding window over sequences | None |
+| `--num_workers` | DataLoader workers | 0 |
-| `--random_seed` | Random seed for reproducibility | 3407 |
+| `--prefetch_factor` | Prefetch factor for dataloader | None |
-| `--num_workers` | DataLoader worker processes | 4 |
+| `--pin_memory` | Enable pin_memory | False |
-| `--no_pin_memory` | Disable pin_memory (enabled by default) | (flag) |
+| `--no_pin_memory` | Disable pin_memory | - |
-
-### 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 |
+| Parameter | Description | Default Value |
-|-----------|-------------|---------|
+|-----------|-------------|---------------|
-| `--nprocs` | Number of GPUs / processes | 1 |
+| `--nprocs` | Number of GPUs | 1 |
-| `--parallel_mode` | Parallel strategy (`none`, `ddp`, or `fsdp`) | none |
+| `--device_type` | Device type (cuda/cpu) | cuda |
-| `--device_type` | Device type | cuda |
-| `--start_method` | Multiprocessing start method (`spawn`, `fork`, `forkserver`) | spawn |
 
-### Strategy-specific
+### Other Parameters
 
-| Parameter | Description | Default | Used by |
+| Parameter | Description | Default Value |
-|-----------|-------------|---------|---------|
+|-----------|-------------|---------------|
-| `--dpo_beta` | DPO beta value | 0.1 | `dpo` |
+| `--window_size` | Maximum input sequence length | model config max_len |
-| `--label_smoothing` | Label smoothing for cross-entropy loss | 0.05 | `seq`, `sft` |
+| `--stride` | Input sequence stride | - |
-| `--group_size` | GRPO group size | 4 | `grpo` |
+| `--dpo_beta` | DPO beta value | 0.1 |
-| `--grpo_clip_eps` | GRPO clipping epsilon | 0.2 | `grpo` |
+| `--grpo_clip_eps` | GRPO clip epsilon | 0.2 |
-| `--grpo_kl_coef` | GRPO KL penalty coefficient | 0.01 | `grpo` |
+| `--grpo_kl_coef` | GRPO KL coefficient | 0.01 |
-| `--grpo_sync_interval` | GRPO ref_model sync interval (steps) | 200 | `grpo` |
+| `--grpo_group_size` | GRPO group size | 4 |
-
+| `--label_smoothing` | Label smoothing parameter | 0.1 |
-### Usage Example
+| `--start_epoch` | Starting epoch | 0 |
-
+| `--start_batch` | Starting batch | 0 |
-```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
+## Generation Parameters
+
+### GenerationRequest Parameters
+
+| Parameter | Description | Default Value |
+|-----------|-------------|---------------|
+| `messages` | List of message dictionaries (role, content) | required |
+| `temperature` | Sampling temperature (higher = more random) | 1.0 |
+| `top_p` | Nucleus sampling threshold | 1.0 |
+| `top_k` | Top-k sampling count | 50 |
+| `max_len` | Maximum generation length | 1024 |
+| `stream` | Whether to stream output | False |
+
+### Usage Example
+
+```python
+import torch
+from astrai.model import AutoModel
+from astrai.tokenize import Tokenizer
+from astrai.inference import InferenceEngine, GenerationRequest
+
+# Load model using AutoModel
+model = AutoModel.from_pretrained("your_model_dir")
+
+# Load tokenizer
+tokenizer = Tokenizer("your_model_dir")
+
+# Create engine with separate model and tokenizer
+engine = InferenceEngine(
+    model=model,
+    tokenizer=tokenizer,
+)
+
+# Build request with messages format
+request = GenerationRequest(
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Hello"},
+    ],
+    temperature=0.8,
+    top_p=0.95,
+    top_k=50,
+    max_len=1024,
+)
+
+# Generate (streaming)
+for token in engine.generate_with_request(request):
+    print(token, end="", flush=True)
+
+# Or use simple generate interface
+result = engine.generate(
+    prompt="Hello",
+    stream=False,
+    max_tokens=1024,
+    temperature=0.8,
+    top_p=0.95,
+    top_k=50,
+)
+```
+
+### Generation Modes
+
+| Mode | Description |
+|------|-------------|
+| `stream=True` | Streaming output, yields token by token |
+| `stream=False` | Non-streaming output, returns complete result |
+
+> Document Update Time: 2026-04-09

assets/docs/preprocessing.md

@@ -1,346 +0,0 @@
-# Preprocessing Pipeline
-
-Declarative JSON-driven data preprocessing. One `SectionedMaskBuilder` handles all formats via `input.sections` (single-output) or `input.sources` (multi-output).
-
-## Philosophy
-
-| Component | Responsibility |
-|-----------|---------------|
-| `tokenizer_config.json` (`chat_template`) | Formatting -- how roles become tokens |
-| `pipeline.json` (`mask`) | Masking -- which roles participate in training |
-
-A single config file captures the entire pipeline, reusable and version-controllable.
-
-## Config Structure
-
-```json
-{
-  "input":         {},   // sections (single) or sources (multi)
-  "mask":          {},   // role → "train" | "mask"
-  "mask_default":  "mask",
-  "preprocessing": {},
-  "output":        {}
-}
-```
-
-### Section Fields
-
-| Field | Type | Default | Description |
-|-------|------|---------|-------------|
-| `field` | str | -- | JSONL key to read |
-| `action` | str | -- | `"train"` / `"mask"` / `"$role"` |
-| `template` | bool | `false` | Apply `chat_template` per message |
-| `add_special_tokens` | bool | `true` for first non-template section | Add special tokens during encode |
-
-### Source Fields (multi-output mode)
-
-| Field | Type | Default | Description |
-|-------|------|---------|-------------|
-| `sections` | list[dict] | -- | Same as single-output section list |
-| `list_field` | bool | `false` | JSONL field holds a list; tokenise each element |
-| `mask_key` | str | `"{key}_mask"` | Explicit output key for loss mask |
-
----
-
-## Quick Start
-
-### SFT Chat
-
-Input JSONL:
-
-```json
-{"messages": [{"role": "system", "content": "You are helpful."}, {"role": "user", "content": "Hi"}, {"role": "assistant", "content": "Hello!"}]}
-```
-
-Config:
-
-```json
-{
-  "input": {
-    "sections": [
-      {"field": "messages", "action": "$role", "template": true}
-    ]
-  },
-  "mask": {
-    "system": "mask",
-    "user": "mask",
-    "assistant": "train"
-  },
-  "mask_default": "mask",
-  "preprocessing": {
-    "max_seq_len": 2048
-  },
-  "output": {
-    "storage_format": "bin",
-    "dtype": {"loss_mask": "bool"}
-  }
-}
-```
-
-Output keys: `sequence` (int32), `loss_mask` (bool)
-
-### SFT Instruction
-
-Input JSONL:
-
-```json
-{"prompt": "Translate to French: Hello", "response": "Bonjour"}
-```
-
-Config:
-
-```json
-{
-  "input": {
-    "sections": [
-      {"field": "prompt",   "action": "mask", "add_special_tokens": true},
-      {"field": "response", "action": "train"}
-    ]
-  },
-  "mask_default": "mask",
-  "preprocessing": {
-    "max_seq_len": 2048
-  }
-}
-```
-
-Output keys: `sequence`, `loss_mask`
-
-### Pretrain
-
-Input JSONL:
-
-```json
-{"text": "Artificial Intelligence is a field of computer science..."}
-```
-
-Config:
-
-```json
-{
-  "input": {
-    "sections": [
-      {"field": "text", "action": "train"}
-    ]
-  },
-  "preprocessing": {
-    "max_seq_len": 8192,
-    "min_chars": 100
-  }
-}
-```
-
-Output keys: `sequence` (no `loss_mask` — all tokens trained)
-
-### DPO
-
-Input JSONL:
-
-```json
-{"chosen": [{"role": "user", "content": "What is 2+2?"}, {"role": "assistant", "content": "4"}], "rejected": [{"role": "user", "content": "What is 2+2?"}, {"role": "assistant", "content": "5"}]}
-```
-
-Config:
-
-```json
-{
-  "input": {
-    "sources": {
-      "chosen": {
-        "sections": [
-          {"field": "chosen", "action": "$role", "template": true}
-        ]
-      },
-      "rejected": {
-        "sections": [
-          {"field": "rejected", "action": "$role", "template": true}
-        ]
-      }
-    }
-  },
-  "mask": {
-    "user": "mask",
-    "assistant": "train"
-  },
-  "mask_default": "mask"
-}
-```
-
-Output keys: `chosen`, `chosen_mask`, `rejected`, `rejected_mask`
-
-### GRPO
-
-Input JSONL:
-
-```json
-{"prompt": [{"role": "user", "content": "What is 2+2?"}], "responses": ["4", "Five", "Four"], "rewards": [1.0, 0.3, 0.8]}
-```
-
-Config:
-
-```json
-{
-  "input": {
-    "sources": {
-      "prompts": {
-        "sections": [
-          {"field": "prompt", "action": "mask", "template": true}
-        ]
-      },
-      "responses": {
-        "sections": [
-          {"field": "responses", "action": "train"}
-        ],
-        "list_field": true,
-        "mask_key": "masks"
-      },
-      "rewards": {
-        "sections": [
-          {"field": "rewards", "action": "value"}
-        ]
-      }
-    }
-  },
-  "mask": {
-    "user": "mask",
-    "assistant": "train"
-  },
-  "mask_default": "mask"
-}
-```
-
-Output keys: `prompts`, `responses`, `masks`, `rewards` (float32)
-
-- `action: "value"` — extract raw values from JSONL without tokenisation
-- `list_field: true` — tokenise each list element independently, then concatenate
-- `mask_key: "masks"` — rename the auto-generated mask key (default: `responses_mask`)
-
----
-
-## Configuration Reference
-
-### `input`
-
-| Field | Type | Default | Description |
-|-------|------|---------|-------------|
-| `sections` | list[dict] or null | `null` | Section specs for single-output mode |
-| `sources` | dict[str, dict] or null | `null` | Source specs for multi-output mode (DPO/GRPO) |
-
-When `sources` is set, `sections` is ignored.
-
-### `mask`
-
-| Field | Type | Default | Description |
-|-------|------|---------|-------------|
-| `mask` | dict | `{}` | `{role: "train" \| "mask"}` |
-| `mask_default` | str | `"mask"` | Default action for unlisted roles |
-
-### `preprocessing`
-
-| Field | Type | Default | Description |
-|-------|------|---------|-------------|
-| `max_seq_len` | int | `2048` | Truncate sequences to this length |
-| `min_chars` | int | `50` | Skip text-mode items shorter than this |
-| `max_chars` | int | `2000000` | Skip text-mode items longer than this |
-| `max_items` | int or null | `null` | Stop after N documents |
-
-### `output`
-
-| Field | Type | Default | Description |
-|-------|------|---------|-------------|
-| `domain_key` | str or null | `null` | JSONL key for domain grouping |
-| `storage_format` | str | `"bin"` | `"bin"` (mmap) or `"h5"` |
-| `max_tokens_per_shard` | int | `100000000` | Flush threshold in cumulative tokens |
-| `dtype` | dict[str, str] | `{}` | Per-key tensor dtype override (e.g. `{"loss_mask": "bool"}`) |
-
----
-
-## Mask Algorithm
-
-### Template mode (`template: true`)
-
-For each message in the field's array:
-
-1. Prepend BOS token (masked)
-2. Render through `chat_template` for that single message
-3. Encode rendered text
-4. Apply mask rule for the message's role
-
-### Non-template mode
-
-Encode the field value as text. Mask value is 1 (train) or 0 (mask) per the section's `action`.
-
-### Text config detection
-
-When no section uses `template` and all sections have `action: "train"`, the builder skips mask generation entirely — all tokens are trained.
-
----
-
-## Output Layout
-
-### Single-Shard (`bin`)
-
-```
-output/
-  __default__/
-    meta.json
-    sequence.bin
-    loss_mask.bin
-  wiki/
-    meta.json
-    sequence.bin
-    loss_mask.bin
-```
-
-### Multi-Shard (`bin`)
-
-When `max_tokens_per_shard` is exceeded:
-
-```
-output/
-  __default__/
-    shard_0000/
-      meta.json
-      sequence.bin
-      loss_mask.bin
-    shard_0001/
-      meta.json
-      sequence.bin
-      loss_mask.bin
-```
-
-`MmapStore` discovers all shards under the domain directory via `rglob("meta.json")`.
-
----
-
-## CLI
-
-```bash
-# SFT
-python scripts/tools/preprocess.py data/sft/*.jsonl -o output/sft/ -c configs/sft_chat.json
-
-# DPO
-python scripts/tools/preprocess.py data/dpo/*.jsonl -o output/dpo/ -c configs/dpo.json --tokenizer_path params
-
-# GRPO
-python scripts/tools/preprocess.py data/grpo/*.jsonl -o output/grpo/ -c configs/grpo.json
-```
-
----
-
-## Python API
-
-```python
-from astrai.preprocessing.pipeline import Pipeline
-from astrai.config.preprocess_config import PipelineConfig
-
-config = PipelineConfig.from_json("sft.json")
-Pipeline(
-    config,
-    ["data_part1.jsonl", "data_part2.jsonl"],
-    output_dir="output/",
-    tokenizer_path="params",
-).run()
-```
-
-> Document Update Time: 2026-06-03

assets/docs/training.md

@@ -1,201 +0,0 @@
-# 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

@@ -1,9 +1,8 @@
-__version__ = "1.3.7"
+__version__ = "1.3.3"
 __author__ = "ViperEkura"
 
 from astrai.config import (
-    AutoRegressiveLMConfig,
+    ModelConfig,
-    EncoderConfig,
     TrainConfig,
 )
 from astrai.dataset import DatasetFactory
@@ -12,14 +11,13 @@ from astrai.inference import (
     GenerationRequest,
     InferenceEngine,
 )
-from astrai.model import AutoModel, AutoRegressiveLM
+from astrai.model import AutoModel, Transformer
 from astrai.tokenize import AutoTokenizer
 from astrai.trainer import CallbackFactory, SchedulerFactory, StrategyFactory, Trainer
 
 __all__ = [
-    "AutoRegressiveLM",
+    "Transformer",
-    "AutoRegressiveLMConfig",
+    "ModelConfig",
-    "EncoderConfig",
     "TrainConfig",
     "DatasetFactory",
     "AutoTokenizer",

astrai/config/__init__.py

@@ -1,25 +1,8 @@
-from astrai.config.model_config import (
+from astrai.config.model_config import ModelConfig
-    AutoRegressiveLMConfig,
-    BaseModelConfig,
-    ConfigFactory,
-    EncoderConfig,
-)
-from astrai.config.preprocess_config import (
-    InputConfig,
-    OutputConfig,
-    PipelineConfig,
-    ProcessingConfig,
-)
 from astrai.config.train_config import TrainConfig
 
 __all__ = [
-    "BaseModelConfig",
+    # Model configuration
-    "AutoRegressiveLMConfig",
+    "ModelConfig",
-    "EncoderConfig",
-    "ConfigFactory",
     "TrainConfig",
-    "InputConfig",
-    "OutputConfig",
-    "PipelineConfig",
-    "ProcessingConfig",
 ]

astrai/config/base.py

@@ -1,98 +0,0 @@
-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

@@ -1,92 +1,42 @@
 import json
-from dataclasses import dataclass
+from dataclasses import asdict, dataclass
-from typing import Any, Dict, Optional, Self
+from typing import 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):
+class ModelConfig:
-    """Base config with ``model_type`` dispatch and file I/O."""
+    # basic config
-
     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
 
+    # RoPE
     max_len: Optional[int] = None
     rope_theta: Optional[float] = None
-    rope_scaling: Optional[dict] = None
 
-    attn_type: str = "gqa"
+    # 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
+    def load(self, config_path: str) -> Self:
-    qk_nope_head_dim: Optional[int] = None
+        config = {}
-    qk_rope_head_dim: Optional[int] = None
+        with open(config_path, "r") as f:
+            config.update(json.load(f))
 
-    ffn_type: str = "mlp"
+        for key, value in config.items():
-    n_routed_experts: Optional[int] = None
+            if hasattr(self, key):
-    n_shared_experts: Optional[int] = None
+                setattr(self, key, value)
-    n_activated_experts: Optional[int] = None
-    topk_method: Optional[str] = None
 
+        return self
 
-@dataclass
+    def save(self, config_path: str):
-@ConfigFactory.register("embedding")
+        config_dict = {k: v for k, v in asdict(self).items() if v is not None}
-class EncoderConfig(BaseModelConfig):
+        with open(config_path, "w") as f:
-    """Configuration for embedding encoder model."""
+            json.dump(config_dict, f, indent=4)
-
-    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

@@ -1,109 +0,0 @@
-"""Pipeline configuration for JSONL preprocessing.
-
-Supports single-sequence (SFT/pretrain) and multi-output (DPO/GRPO)
-modes, both driven declaratively through ``input.sections`` or
-``input.sources``.
-"""
-
-from dataclasses import dataclass, field
-from typing import Dict, List, Optional
-
-from astrai.config.base import BaseConfig
-
-
-@dataclass
-class InputConfig(BaseConfig):
-    """Declarative input mapping.
-
-    Single-output mode (backward-compatible)::
-
-        {"input": {"sections": [{"field": "messages", ...}]}}
-
-    Multi-output mode (DPO / GRPO)::
-
-        {"input": {"sources": {
-            "chosen": {"sections": [{"field": "chosen", ...}]},
-            "rejected": {"sections": [{"field": "rejected", ...}]},
-        }}}
-    """
-
-    sections: Optional[List[Dict]] = None
-    sources: Optional[Dict[str, Dict]] = None
-
-
-@dataclass
-class ProcessingConfig(BaseConfig):
-    """Processing configuration.
-
-    Parameters
-    ----------
-    max_seq_len : int
-        Maximum sequence length (default: 2048).
-    min_chars : int
-        Minimum number of characters to keep (default: 50).
-    max_chars : int
-        Maximum number of characters to keep (default: 2_000_000).
-    max_items : Optional[int]
-        Maximum number of items to process (default: None, unlimited).
-    packing_strategy : str
-        How to pack sequences into a contiguous stream.
-
-        - ``"simple"``: sequential concatenation (default, backward compatible).
-        - ``"bfd"``: best-fit decreasing bin packing, minimises wasted tokens.
-        - ``"bfd_split"``: BFD with over-length sequences split into chunks.
-    max_packed_len : int
-        Maximum length of a packed bin. Sequences longer than this are
-        truncated or split depending on ``packing_strategy`` (default: 8192).
-    truncation_mode : str
-        How to truncate sequences longer than ``max_packed_len``.
-
-        - ``"keep_start"``: keep the first ``max_packed_len`` tokens (default).
-        - ``"keep_end"``: keep the last ``max_packed_len`` tokens.
-    """
-
-    max_seq_len: int = 2048
-    min_chars: int = 50
-    max_chars: int = 2_000_000
-    max_items: Optional[int] = None
-    packing_strategy: str = "simple"
-    max_packed_len: int = 8192
-    truncation_mode: str = "keep_start"
-
-
-@dataclass
-class OutputConfig(BaseConfig):
-    """Output configuration.
-
-    Parameters
-    ----------
-    domain_key : Optional[str]
-        Domain key for the output store (default: None).
-    storage_format : str
-        Storage format, one of ``"bin"``, ``"jsonl"`` (default: ``"bin"``).
-    max_tokens_per_shard : int
-        Maximum tokens per shard before splitting (default: 100_000_000).
-    dtype : Dict[str, str]
-        Per-key dtype overrides, e.g. ``{"input_ids": "int32"}`` (default: {}).
-    position_ids_mode : Optional[str]
-        How to compute position_ids in packed sequences.
-
-        - ``None`` / ``"none"``: do not generate (backward compatible).
-        - ``"doc_reset"``: reset to 0 at each document boundary.
-        - ``"continuous"``: sequential 0, 1, 2, ... (pretrain, single doc).
-    """
-
-    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)
-    position_ids_mode: Optional[str] = None
-
-
-@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

@@ -1,4 +1,4 @@
-from dataclasses import dataclass, field, fields
+from dataclasses import dataclass, field
 from typing import Callable, List, Optional
 
 import torch.nn as nn
@@ -6,44 +6,27 @@ 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):
+class TrainConfig:
     # basic setting
-    model_fn: Callable[[], nn.Module] = field(
+    model: nn.Module = field(default=None, metadata={"help": "Model for training."})
-        default=None, metadata=required(help="Model factory for training.")
+    strategy: str = field(default=None, metadata={"help": "Training strategy."})
-    )
+    dataset: Dataset = field(default=None, metadata={"help": "Dataset 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.")
+        default=None, metadata={"help": "Optimizer factory for training."}
     )
     scheduler_fn: Callable[[Optimizer], LRScheduler] = field(
-        default=None, metadata=required(help="Scheduler factory for training.")
+        default=None, metadata={"help": "Scheduler factory for training."}
     )
     n_epoch: int = field(default=1, metadata={"help": "Number of epochs for training."})
-    batch_per_device: int = field(
+    batch_size: int = field(default=4, metadata={"help": "Batch size for training."})
-        default=4, metadata={"help": "Batch size per device."}
+    accumulation_steps: int = field(
-    )
-    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."})
@@ -57,25 +40,6 @@ class TrainConfig(BaseConfig):
         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(
@@ -102,37 +66,20 @@ class TrainConfig(BaseConfig):
     master_port: str = field(
         default="29500", metadata={"help": "Master port for distributed training."}
     )
-    parallel_mode: str = field(
+    parallel_wrapper: Optional[Callable] = field(
-        default="none",
+        default=None, metadata={"help": "Parallel function for training."}
-        metadata={"help": "Parallel strategy: none, ddp, fsdp."},
     )
-    start_method: str = field(
+    state_dict_fn: Optional[Callable] = field(
-        default="spawn",
+        default=None, metadata={"help": "Parallel function for state  dict saving."}
-        metadata={"help": "Multiprocessing start method (spawn/fork/forkserver)."},
     )
 
     # others
+    device_ids: Optional[List[int]] = field(
+        default=None, metadata={"help": "Device ids for distributed training."}
+    )
     device_type: str = field(
         default="cuda", metadata={"help": "Device type for distributed training."}
     )
-    val_dataset: Optional[Dataset] = field(
-        default=None, metadata={"help": "Dataset for validation."}
-    )
-    val_split: Optional[float] = field(
-        default=None,
-        metadata={
-            "help": "Ratio to split from training dataset for validation (e.g. 0.05). Ignored if val_dataset is set."
-        },
-    )
-    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."}
     )
@@ -141,6 +88,14 @@ class TrainConfig(BaseConfig):
         self.validate()
 
     def validate(self):
-        for fld in fields(self):
+        required_fields = [
-            if fld.metadata.get("required") and getattr(self, fld.name) is None:
+            "model",
-                raise ValueError(f"TrainConfig.{fld.name} is required but got None.")
+            "strategy",
+            "dataset",
+            "optimizer_fn",
+            "scheduler_fn",
+        ]
+
+        for field_name in required_fields:
+            if getattr(self, field_name) is None:
+                raise ValueError(f"{field_name} is required.")

astrai/dataset/__init__.py

@@ -1,31 +1,19 @@
 from astrai.dataset.dataset import (
     BaseDataset,
+    BaseSegmentFetcher,
     DatasetFactory,
+    MultiSegmentFetcher,
 )
 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__ = [
+    # Base classes
     "BaseDataset",
+    # Factory
     "DatasetFactory",
-    "Store",
+    # Fetchers
-    "StoreFactory",
+    "BaseSegmentFetcher",
-    "H5Store",
+    "MultiSegmentFetcher",
-    "MmapStore",
+    # Sampler
-    "detect_format",
-    "save_h5",
-    "load_h5",
-    "save_bin",
-    "load_bin",
     "ResumableDistributedSampler",
 ]

astrai/dataset/dataset.py

@@ -1,86 +1,140 @@
 """Dataset implementations with factory pattern for training."""
 
+import bisect
 from abc import ABC, abstractmethod
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Union
 
 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
+from astrai.serialization import load_h5
+
+
+class BaseSegmentFetcher:
+    """Fetches data segments across multiple tensor segments.
+
+    Maintains cumulative lengths for efficient range queries across
+    multiple discontinuous segments.
+    """
+
+    def __init__(self, segments: List[Tensor]):
+        self.segments = segments
+        self.cum_lengths = []
+
+        total = 0
+        for seg in segments:
+            total += torch.numel(seg)
+            self.cum_lengths.append(total)
+
+        self.total_length = total
+
+    def __len__(self) -> int:
+        return self.total_length
+
+    def fetch_data(self, begin_idx: int, end_idx: int) -> Tensor:
+        """Fetch data in the range [begin_idx, end_idx).
+
+        Args:
+            begin_idx: Starting index (inclusive)
+            end_idx: Ending index (exclusive)
+
+        Returns:
+            Concatenated tensor of data in the specified range
+        """
+        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)
+
+        # Find segment boundaries for the range
+        seg_start_idx = bisect.bisect_right(self.cum_lengths, begin_idx)
+        seg_end_idx = bisect.bisect_left(self.cum_lengths, end_idx)
+
+        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]))
+            data = self.segments[i][start:end]
+            result_segments.append(data)
+
+        return torch.cat(result_segments, dim=0)
+
+
+class MultiSegmentFetcher:
+    """Manages multiple segment fetchers for different data keys.
+
+    Each key corresponds to a different type of data (e.g., "sequence", "mask").
+    """
+
+    def __init__(self, multi_segments: Dict):
+        self.multi_keys = list(multi_segments.keys())
+        self.multi_fetchers = {
+            key: BaseSegmentFetcher(segments)
+            for key, segments in multi_segments.items()
+        }
+
+    def __len__(self) -> int:
+        """Returns the minimum length across all fetchers."""
+        len_list = [len(seg) for seg in self.multi_fetchers.values()]
+        return min(len_list)
+
+    def key_fetch(
+        self, begin_idx: int, end_idx: int, keys: Union[str, List[str]]
+    ) -> Dict:
+        """Fetch data for specific keys.
+
+        Args:
+            begin_idx: Starting index
+            end_idx: Ending index
+            keys: Single key or list of keys to fetch
+
+        Returns:
+            Dictionary of tensors if multiple keys, single tensor if one key
+        """
+        fetch_dict = {}
+        keys = [keys] if isinstance(keys, str) else keys
+
+        for key in keys:
+            fetcher = self.multi_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) -> Dict:
+        """Fetch all keys."""
+        return self.key_fetch(begin_idx, end_idx, self.multi_keys)
 
 
 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.segments = {}
         self.window_size = window_size
         self.stride = stride
-        self.storage: Optional[Store] = None
+        self.total_samples = None
+        self.fetcher: Optional[MultiSegmentFetcher] = None
 
-    @property
+    def load(self, load_path: str):
-    def required_keys(self) -> List[str]:
+        """Load dataset from HDF5 file.
-        """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
+            load_path: Path to the HDF5 data 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:
+        self.segments = load_h5(load_path)
-            storage_type = detect_format(load_path)
+        self.fetcher = MultiSegmentFetcher(self.segments)
-        self.storage = StoreFactory.create(storage_type)
+        self.total_samples = len(self.fetcher)
-        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.
@@ -91,16 +145,10 @@ class BaseDataset(Dataset, ABC):
         Returns:
             Tuple of (begin_idx, end_idx)
         """
-        if self.storage is None:
+        assert self.total_samples > self.window_size
-            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)
+        begin_idx = min(index * self.stride, self.total_samples - 1 - self.window_size)
-        end_idx = min(begin_idx + self.window_size, total - 1)
+        end_idx = min(begin_idx + self.window_size, self.total_samples - 1)
 
         return begin_idx, end_idx
 
@@ -113,12 +161,10 @@ class BaseDataset(Dataset, ABC):
         raise NotImplementedError
 
     def __len__(self) -> int:
-        if self.storage is None:
+        assert self.total_samples is not None
+        if self.total_samples <= self.window_size:
             return 0
-        total = len(self.storage)
+        return (self.total_samples - 1 - self.window_size) // self.stride + 1
-        if total <= self.window_size:
-            return 0
-        return (total - 1 - self.window_size) // self.stride + 1
 
 
 class DatasetFactory(BaseFactory["BaseDataset"]):
@@ -137,7 +183,7 @@ class DatasetFactory(BaseFactory["BaseDataset"]):
     """
 
     @classmethod
-    def _validate_component(cls, dataset_cls: type):
+    def _validate_component(cls, dataset_cls: type) -> None:
         """Validate that the dataset class inherits from BaseDataset."""
         if not issubclass(dataset_cls, BaseDataset):
             raise TypeError(f"{dataset_cls.__name__} must inherit from BaseDataset")
@@ -163,7 +209,6 @@ class DatasetFactory(BaseFactory["BaseDataset"]):
         load_path: str,
         window_size: int,
         stride: Optional[int] = None,
-        storage_type: Optional[str] = None,
     ) -> "BaseDataset":
         """Create and load a dataset in one step.
 
@@ -172,7 +217,6 @@ class DatasetFactory(BaseFactory["BaseDataset"]):
             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
@@ -181,7 +225,7 @@ class DatasetFactory(BaseFactory["BaseDataset"]):
             stride = window_size
 
         dataset = cls.create(train_type, window_size, stride)
-        dataset.load(load_path, storage_type=storage_type)
+        dataset.load(load_path)
 
         return dataset
 
@@ -191,6 +235,10 @@ class DatasetFactory(BaseFactory["BaseDataset"]):
         return cls.list_registered()
 
 
+# ============== Dataset Classes ==============
+# All dataset classes are registered at class definition time using the decorator
+
+
 @DatasetFactory.register("seq")
 class SEQDataset(BaseDataset):
     """Dataset for sequential next-token prediction training."""
@@ -198,12 +246,8 @@ class SEQDataset(BaseDataset):
     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")
+        return self.fetcher.key_fetch(begin_idx, end_idx, "sequence")
 
     def __getitem__(self, index):
         begin_idx, end_idx = self.get_index(index)
@@ -221,27 +265,21 @@ class SFTDataset(BaseDataset):
     def __init__(self, window_size: int, stride: int):
         super().__init__(window_size, stride)
 
-    @property
-    def required_keys(self) -> List[str]:
-        return ["sequence", "loss_mask", "position_ids"]
-
     def _fetch_data(self, begin_idx: int, end_idx: int, key: str) -> Tensor:
-        return self.storage.fetch(begin_idx, end_idx, key)
+        return self.fetcher.key_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")
+        x = self._fetch_data(begin_idx, end_idx, "sequence").to(dtype=torch.long)
-        y = self._fetch_data(begin_idx + 1, end_idx + 1, "sequence")
+        y = self._fetch_data(begin_idx + 1, end_idx + 1, "sequence").to(
-        position_ids = self._fetch_data(begin_idx, end_idx, "position_ids")
+            dtype=torch.long
-        loss_mask = self._fetch_data(begin_idx + 1, end_idx + 1, "loss_mask")
+        )
+        loss_mask = self._fetch_data(begin_idx + 1, end_idx + 1, "loss_mask").to(
+            dtype=torch.bool
+        )
 
-        return {
+        return {"input_ids": x, "target_ids": y, "loss_mask": loss_mask}
-            "input_ids": x.to(dtype=torch.long),
-            "target_ids": y.to(dtype=torch.long),
-            "position_ids": position_ids.to(dtype=torch.long),
-            "loss_mask": loss_mask.to(dtype=torch.bool),
-        }
 
 
 @DatasetFactory.register("dpo")
@@ -251,12 +289,8 @@ class DPODataset(BaseDataset):
     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)
+        return self.fetcher.key_fetch(begin_idx, end_idx, key)
 
     def __getitem__(self, index: int):
         begin_idx, end_idx = self.get_index(index)
@@ -285,21 +319,15 @@ class GRPODataset(BaseDataset):
     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)
+        return self.fetcher.key_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)
+        prompts = self._fetch_data(begin_idx, end_idx, "prompts")
-        responses = self._fetch_data(begin_idx, end_idx, "responses").to(
+        responses = self._fetch_data(begin_idx, end_idx, "responses")
-            dtype=torch.long
+        masks = self._fetch_data(begin_idx, end_idx, "masks")
-        )
-        masks = self._fetch_data(begin_idx, end_idx, "masks").to(dtype=torch.bool)
         rewards = self._fetch_data(begin_idx, end_idx, "rewards")
 
         return {

astrai/dataset/sampler.py

@@ -43,7 +43,6 @@ class ResumableDistributedSampler(Sampler[int]):
         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
 
@@ -75,10 +74,5 @@ class ResumableDistributedSampler(Sampler[int]):
         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
+        return self.num_samples_per_replica

astrai/dataset/storage.py

@@ -1,271 +0,0 @@
-"""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 glob
-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 = [
-        Path(p)
-        for pattern in ("*.h5", "*.hdf5")
-        for p in glob.glob(str(root / "**" / pattern), recursive=True)
-    ]
-    if h5_files:
-        return "h5"
-    bin_files = [Path(p) for p in glob.glob(str(root / "**" / "*.bin"), recursive=True)]
-    if bin_files:
-        has_meta = (root / "meta.json").exists() or len(
-            [Path(p) for p in glob.glob(str(root / "**" / "meta.json"), recursive=True)]
-        ) > 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 = [
-            Path(p) for p in glob.glob(str(root / "**" / "meta.json"), recursive=True)
-        ]
-        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

@@ -1,6 +1,5 @@
 """Base factory class for extensible component registration."""
 
-import inspect
 from abc import ABC
 from typing import Callable, Dict, Generic, List, Optional, Tuple, Type, TypeVar
 
@@ -23,7 +22,7 @@ class Registry:
         component_cls: Type,
         category: Optional[str] = None,
         priority: int = 0,
-    ):
+    ) -> None:
         """Register a component class with optional category and priority."""
         if name in self._entries:
             raise ValueError(f"Component '{name}' is already registered")
@@ -123,10 +122,6 @@ class BaseFactory(ABC, Generic[T]):
     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
@@ -144,21 +139,10 @@ class BaseFactory(ABC, Generic[T]):
                 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]):
+    def _validate_component(cls, component_cls: Type[T]) -> None:
         """Validate that the component class is valid for this factory.
 
         Override this method in subclasses to add custom validation.
@@ -171,26 +155,6 @@ class BaseFactory(ABC, Generic[T]):
         """
         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.

astrai/inference/__init__.py

@@ -1,85 +1,25 @@
-"""Inference module for continuous batching.
+"""Inference module for continuous batching."""
 
-Layers:
+from astrai.inference.engine import (
-  - core/:        Core inference loop (cache, executor, scheduler, task)
+    GenerationRequest,
-  - api/:         HTTP orchestration (ProtocolHandler, server)
+    InferenceEngine,
-  - 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,
-    get_app,
-    run_server,
 )
-from astrai.inference.api.anthropic import AnthropicResponseBuilder
+from astrai.inference.scheduler import (
-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,
+    apply_sampling_strategies,
-    page_hash,
-)
-from astrai.inference.engine import GenerationRequest, InferenceEngine
-from astrai.inference.sample import (
-    BaseSamplingStrategy,
-    SamplingPipeline,
-    TemperatureStrategy,
-    TopKStrategy,
-    TopPStrategy,
-    sample,
 )
 
 __all__ = [
+    # Engine
     "InferenceEngine",
-    "GenerationRequest",
+    # Scheduler
     "InferenceScheduler",
-    "Executor",
-    "STOP",
     "Task",
-    "TaskManager",
     "TaskStatus",
-    "Allocator",
+    # Request
-    "KVCache",
+    "GenerationRequest",
-    "KvcacheView",
+    # Sampling
-    "PagePool",
+    "apply_sampling_strategies",
-    "PrefixCache",
-    "Storage",
-    "TaskTable",
-    "page_hash",
-    "sample",
-    "BaseSamplingStrategy",
-    "TemperatureStrategy",
-    "TopKStrategy",
-    "TopPStrategy",
-    "SamplingPipeline",
-    "ProtocolHandler",
-    "StopChecker",
-    "GenContext",
-    "OpenAIResponseBuilder",
-    "AnthropicResponseBuilder",
-    "ChatMessage",
-    "ChatCompletionRequest",
-    "AnthropicMessage",
-    "MessagesRequest",
-    "get_app",
-    "run_server",
 ]

astrai/inference/api/__init__.py

@@ -1,27 +0,0 @@
-"""Inference API: protocol handler, stop checker, and FastAPI server.
-
-``app`` is no longer a module-level global. Use :func:`get_app` to access the
-lazy singleton FastAPI instance.
-"""
-
-from astrai.inference.api.protocol import GenContext, ProtocolHandler, StopChecker
-from astrai.inference.api.server import (
-    AnthropicMessage,
-    ChatCompletionRequest,
-    ChatMessage,
-    MessagesRequest,
-    get_app,
-    run_server,
-)
-
-__all__ = [
-    "ProtocolHandler",
-    "StopChecker",
-    "GenContext",
-    "AnthropicMessage",
-    "ChatCompletionRequest",
-    "ChatMessage",
-    "MessagesRequest",
-    "get_app",
-    "run_server",
-]

astrai/inference/api/anthropic.py

@@ -1,141 +0,0 @@
-"""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

@@ -1,140 +0,0 @@
-"""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

@@ -1,182 +0,0 @@
-"""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

@@ -1,187 +0,0 @@
-"""
-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.
-
-``app`` is lazily constructed — importing this module does NOT create a FastAPI instance.
-Use :func:`get_app` to access the singleton.
-"""
-
-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 APIRouter, 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__)
-
-_app_instance: Optional[FastAPI] = None
-
-
-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")
-
-
-router = APIRouter()
-
-
-def _create_engine(
-    param_path: Path,
-    device: str = "cuda",
-    dtype: torch.dtype = torch.bfloat16,
-    max_batch_size: int = 16,
-) -> InferenceEngine:
-    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_app() -> FastAPI:
-    """Return the singleton FastAPI instance (lazily created on first call)."""
-    global _app_instance
-    if _app_instance is None:
-        _app_instance = FastAPI(
-            title="AstrAI Inference Server",
-            version="0.2.0",
-            lifespan=lifespan,
-        )
-        _app_instance.include_router(router)
-        _app_instance.state.server_config = {}
-        _app_instance.state.engine = None
-    return _app_instance
-
-
-def _get_engine() -> InferenceEngine:
-    engine = get_app().state.engine
-    if engine is None:
-        raise HTTPException(status_code=503, detail="Engine not initialized")
-    return engine
-
-
-@router.get("/health")
-async def health():
-    app = get_app()
-    return {
-        "status": "ok",
-        "model_loaded": app.state.engine is not None,
-    }
-
-
-@router.get("/stats")
-async def get_stats():
-    return _get_engine().get_stats()
-
-
-@router.post("/v1/chat/completions")
-async def chat_completion(request: ChatCompletionRequest):
-    engine = _get_engine()
-    handler = ProtocolHandler(request, engine, OpenAIResponseBuilder())
-    return await handler.handle()
-
-
-@router.post("/v1/messages")
-async def create_message(request: MessagesRequest):
-    engine = _get_engine()
-    handler = ProtocolHandler(request, engine, AnthropicResponseBuilder())
-    return await handler.handle()
-
-
-def run_server(
-    param_path: Path,
-    host: str = "0.0.0.0",
-    port: int = 8000,
-    reload: bool = False,
-    device: str = "cuda",
-    dtype: torch.dtype = torch.bfloat16,
-    max_batch_size: int = 16,
-):
-    app = get_app()
-    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

@@ -1,32 +0,0 @@
-"""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

@@ -1,368 +0,0 @@
-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

@@ -1,94 +0,0 @@
-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

@@ -1,212 +0,0 @@
-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

@@ -1,209 +0,0 @@
-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

@@ -1,66 +1,17 @@
-"""Unified inference engine for continuous batching."""
+"""Unified inference engine."""
 
-import asyncio
 import gc
+import logging
 import threading
-from typing import Any, AsyncGenerator, Dict, Generator, List, Optional, Tuple, Union
+from typing import Any, Dict, Generator, List, Optional, Union
 
 import torch
 import torch.nn as nn
 
-from astrai.inference.core.scheduler import InferenceScheduler
+from astrai.inference.scheduler import InferenceScheduler
-from astrai.inference.core.task import STOP
 from astrai.tokenize import AutoTokenizer
 
-
+logger = logging.getLogger(__name__)
-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:
@@ -72,26 +23,73 @@ class GenerationRequest:
         top_k: int = 50,
         top_p: float = 1.0,
         temperature: float = 1.0,
-        max_tokens: Optional[int] = None,
+        max_len: int = 1024,
         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.max_len = max_len
         self.stream = stream
 
+        self._validate()
+
+    def _validate(self):
+        """Validate request parameters."""
+        if not (isinstance(self.top_k, int) and self.top_k >= 0):
+            raise ValueError("top_k must be a non-negative integer")
+        if not (0.0 <= self.top_p <= 1.0):
+            raise ValueError("top_p must be a float between 0.0 and 1.0")
+        if not (isinstance(self.temperature, (int, float)) and self.temperature >= 0):
+            raise ValueError("temperature must be a non-negative number")
+
+
+class _Result:
+    """Unified result holder for streaming/non-streaming modes."""
+
+    def __init__(self, count: int = 1, stream: bool = False):
+        self._stream = stream
+        self._lock = threading.Lock()
+        self._event = threading.Event()
+        self.tokens: List[str] = []
+        self.results: List[str] = [""] * count if count > 1 else [""]
+        self.done_flags: List[bool] = [False] * count
+        self._completed_count = 0
+
+    def append(self, token: str, idx: int = 0):
+        with self._lock:
+            if self._stream:
+                self.tokens.append(token)
+            else:
+                if token == "[DONE]":
+                    if not self.done_flags[idx]:
+                        self.done_flags[idx] = True
+                        self._completed_count += 1
+                        if self._completed_count == len(self.results):
+                            self._event.set()
+                else:
+                    self.results[idx] += token
+        self._event.set()
+
+    def pop_all(self) -> List[str]:
+        with self._lock:
+            tokens = self.tokens.copy()
+            self.tokens.clear()
+            if not tokens:
+                self._event.clear()
+            return tokens
+
+    def wait(self, timeout: float = None) -> bool:
+        return self._event.wait(timeout=timeout)
+
+    def get_results(self) -> List[str]:
+        with self._lock:
+            return self.results.copy()
+
 
 class InferenceEngine:
-    """Unified inference engine backed by continuous-batching scheduler."""
+    """Unified inference engine for continuous batching."""
 
     def __init__(
         self,
@@ -99,26 +97,55 @@ class InferenceEngine:
         tokenizer: AutoTokenizer,
         max_batch_size: int = 1,
         max_seq_len: Optional[int] = None,
-        max_prompt_len: int = 2048,
+        max_prefix_len: int = 512,
-        page_size: int = 128,
+        cache_capacity: int = 1000,
     ):
+        """
+        Initialize inference engine with separate model and tokenizer.
+
+        Args:
+            model: The language model for inference (nn.Module, e.g., Transformer)
+            tokenizer: The tokenizer for encoding/decoding text
+            config: Model configuration
+            max_batch_size: Maximum batch size for continuous batching
+            max_seq_len: Maximum sequence length (defaults to config.max_len)
+            max_prefix_len: Maximum prefix length for cache (default: 512)
+            cache_capacity: Maximum number of cached prefixes (default: 1000)
+        """
         self.model = model
         self.tokenizer = tokenizer
+
+        # Get device and dtype from model parameters
+        try:
+            first_param = next(model.parameters())
+            device = first_param.device
+            dtype = first_param.dtype
+        except StopIteration:
+            # Model has no parameters, use default device/dtype
+            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+            dtype = torch.float32
+
         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,
+            max_prefix_len=max_prefix_len,
-            page_size=page_size,
+            cache_capacity=cache_capacity,
+            device=device,
+            dtype=dtype,
         )
 
+        self.kv_cache = self.scheduler.kv_cache
+        self.seq_mask = self.scheduler.seq_mask
+
         self.scheduler.start()
 
     def __enter__(self):
         return self
 
     def __exit__(self, exc_type, exc_val, exc_tb):
+        """Handle exceptions on exit."""
         self.shutdown()
         return False
 
@@ -126,162 +153,139 @@ class InferenceEngine:
         self,
         prompt: Union[str, List[str]],
         stream: bool = False,
-        max_tokens: Optional[int] = None,
+        max_tokens: int = 1024,
         temperature: float = 1.0,
         top_p: float = 1.0,
         top_k: int = 50,
-    ) -> Union[Generator, str, List[str]]:
+        abort_on_exception: bool = True,
+    ) -> Union[Generator[str, None, None], str, List[str]]:
+        """Unified generation interface.
+
+        Args:
+            abort_on_exception: If True, abort the generation when consumer
+                stops iterating (GeneratorExit/StopIteration). Default: True.
+        """
         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
+                prompts,
+                is_batch,
+                max_tokens,
+                temperature,
+                top_p,
+                top_k,
+                abort_on_exception,
             )
         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]]:
+        """Generate with GenerationRequest object."""
+        # Use tokenizer's chat template with messages
         prompt = self.tokenizer.apply_chat_template(request.messages, tokenize=False)
+
         return self.generate(
             prompt=prompt,
             stream=request.stream,
-            max_tokens=request.max_tokens,
+            max_tokens=request.max_len,
             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],
+        max_tokens: int,
         temperature: float,
         top_p: float,
         top_k: int,
-    ) -> Generator:
+        abort_on_exception: bool = True,
-        result, task_ids = self._submit_tasks(
+    ) -> Union[Generator[str, None, None], List[Generator[str, None, None]]]:
-            prompts, max_tokens, temperature, top_p, top_k
+        """Generate with streaming output.
+
+        Args:
+            abort_on_exception: If True, abort the task when generator is
+                stopped early by consumer (GeneratorExit/StopIteration).
+        """
+        if is_batch:
+            raise NotImplementedError("Batch streaming is not implemented yet")
+
+        result = _Result(stream=True)
+
+        task_id = self.scheduler.add_task(
+            prompt=prompts[0],
+            max_tokens=max_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+            stream_callback=result.append,
         )
-        n = len(prompts)
-        remaining = n
-        finished = [False] * n
 
         def gen():
-            nonlocal remaining
             try:
-                while remaining > 0:
+                while True:
-                    items = result.pop_all()
+                    tokens = result.pop_all()
-                    for idx, token in items:
+                    for token in tokens:
-                        if token is STOP:
+                        if token == "[DONE]":
-                            if not finished[idx]:
+                            return
-                                finished[idx] = True
+                        yield token
-                                remaining -= 1
-                        else:
-                            yield (idx, token) if is_batch else token
-                    if remaining > 0:
                     result.wait(timeout=0.05)
-            finally:
+            except Exception:
-                for tid in task_ids:
+                # Consumer stopped iterating - abort the task
-                    self.scheduler.remove_task(tid)
+                if abort_on_exception:
+                    self.scheduler.remove_task(task_id)
+                raise
 
+        gen.task_id = task_id
         return gen()
 
     def _generate_non_streaming(
         self,
         prompts: List[str],
         is_batch: bool,
-        max_tokens: Optional[int],
+        max_tokens: int,
         temperature: float,
         top_p: float,
         top_k: int,
     ) -> Union[str, List[str]]:
-        result, task_ids = self._submit_tasks(
+        """Generate without streaming."""
-            prompts, max_tokens, temperature, top_p, top_k
+        result = _Result(count=len(prompts))
+
+        for i, p in enumerate(prompts):
+            # Create closure to capture current index value using factory function
+            def make_callback(idx):
+                def callback(token):
+                    result.append(idx, token)
+
+                return callback
+
+            self.scheduler.add_task(
+                prompt=p,
+                max_tokens=max_tokens,
+                temperature=temperature,
+                top_p=top_p,
+                top_k=top_k,
+                stream_callback=make_callback(i),
             )
 
-        try:
+        result.wait()
-            result.wait_completion()
+        results = result.get_results()
-        except TimeoutError:
+        return results if is_batch else results[0]
-            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]:
+        """Get engine statistics."""
         return self.scheduler.get_stats()
 
-    def shutdown(self):
+    def shutdown(self) -> None:
+        """Shutdown the engine and release all resources."""
         self.scheduler.stop()
         if torch.cuda.is_available():
             torch.cuda.empty_cache()

astrai/inference/sample.py

@@ -1,190 +0,0 @@
-"""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/inference/scheduler.py

@@ -0,0 +1,637 @@
+"""Inference scheduler for continuous batching."""
+
+import threading
+import time
+import uuid
+from typing import Any, Callable, Dict, List, Optional, Tuple
+
+import torch
+from torch import Tensor
+
+from astrai.model.automodel import AutoModel
+from astrai.tokenize import AutoTokenizer
+
+
+class RadixNode:
+    """Radix tree node for prefix cache."""
+
+    def __init__(self):
+        self.children: Dict[int, "RadixNode"] = {}  # token_id -> child node
+        self.hash: Optional[int] = None  # 64-bit hash of the prefix
+        self.slot: int = -1  # KV Cache slot, valid only for leaf nodes
+        self.ref_count: int = 0  # number of tasks referencing this prefix
+        self.last_access: float = 0.0  # timestamp for LRU
+        self.token_sequence: list = []  # full token sequence from root to this node
+
+
+class PrefixCacheManager:
+    """Prefix cache manager using Radix tree with LRU eviction."""
+
+    def __init__(self, max_capacity: int = 1000, base: int = 131, mod: int = 10**9 + 7):
+        self.root = RadixNode()
+        self.base = base
+        self.mod = mod
+        self.max_capacity = max_capacity
+        self.lru: List[Tuple[float, RadixNode]] = []  # (timestamp, node) for LRU
+
+    def insert(self, token_ids: Tuple[int, ...], slot: int) -> None:
+        """Insert a prefix, increase ref_count if already exists, otherwise create new node."""
+        node = self.root
+        path = []
+        h = 0
+        for i, token_id in enumerate(token_ids):
+            if token_id not in node.children:
+                node.children[token_id] = RadixNode()
+            node = node.children[token_id]
+            h = (h * self.base + token_id) % self.mod
+            node.hash = h
+            path.append(token_id)
+            node.token_sequence = list(
+                path
+            )  # store full sequence for exact verification
+
+        # Leaf node: set slot and increase ref_count
+        if node.slot == -1:
+            node.slot = slot
+        node.ref_count += 1
+        node.last_access = time.time()
+        self._update_lru(node)
+        self._evict_if_needed()
+
+    def find_longest_prefix(self, token_ids: List[int]) -> Optional[Tuple[int, int]]:
+        """Find longest matching prefix, return (prefix_len, slot).
+
+        During traversal, compute hash per token and compare with node hash.
+        If hash matches, perform full token sequence verification to avoid
+        hash collision errors.
+        """
+        node = self.root
+        best_len = 0
+        best_slot = -1
+        h = 0
+
+        for i, token_id in enumerate(token_ids):
+            if token_id not in node.children:
+                break
+            node = node.children[token_id]
+            h = (h * self.base + token_id) % self.mod
+            if node.hash == h:  # hash matches
+                # Exact verification: compare full token sequence
+                if node.token_sequence == token_ids[: i + 1]:
+                    best_len = i + 1
+                    best_slot = node.slot
+                    node.last_access = time.time()
+                    self._update_lru(node)
+
+        if best_len > 0:
+            return (best_len, best_slot)
+        return None
+
+    def release(self, token_ids: Tuple[int, ...]) -> None:
+        """Release reference to a prefix, decrease ref_count. If zero, mark as evictable."""
+        node = self.root
+        for token_id in token_ids:
+            if token_id not in node.children:
+                return
+            node = node.children[token_id]
+        if node.ref_count > 0:
+            node.ref_count -= 1
+            if node.ref_count == 0:
+                node.slot = -1  # slot can be reused
+
+    def _update_lru(self, node: RadixNode) -> None:
+        """Update LRU list, move node to most recently used position."""
+        self.lru = [(ts, n) for (ts, n) in self.lru if n is not node]
+        self.lru.append((node.last_access, node))
+
+    def _evict_if_needed(self) -> None:
+        """If cache entries exceed capacity, evict least recently used leaf nodes (ref_count must be 0)."""
+        if len(self.lru) <= self.max_capacity:
+            return
+        # Sort by timestamp
+        self.lru.sort(key=lambda x: x[0])
+        for ts, node in self.lru:
+            if node.ref_count == 0:
+                # Remove leaf node from tree (need to recursively delete empty branches)
+                self._remove_node(node)
+                self.lru.remove((ts, node))
+                if len(self.lru) <= self.max_capacity:
+                    break
+
+    def _remove_node(
+        self,
+        node: RadixNode,
+        parent: Optional[RadixNode] = None,
+        child_key: Optional[int] = None,
+    ) -> None:
+        """Remove node from tree, including empty parent nodes."""
+        # First, recursively remove all children
+        for child_key, child_node in list(node.children.items()):
+            self._remove_node(child_node, node, child_key)
+
+        # Clear the node's leaf properties
+        node.slot = -1
+        node.hash = None
+        node.token_sequence = []
+        node.children.clear()
+
+        # If this node has no children and has a parent, remove the reference from parent
+        if parent is not None and child_key is not None and len(node.children) == 0:
+            if child_key in parent.children:
+                del parent.children[child_key]
+
+
+class TaskStatus:
+    """Task state for continuous batching."""
+
+    PENDING = "pending"
+    RUNNING = "running"
+    FINISHED = "finished"
+    ABORTED = "aborted"
+
+
+class Task:
+    """Individual task for continuous batching."""
+
+    def __init__(
+        self,
+        task_id: str,
+        prompt_ids: List[int],
+        max_tokens: int = 1024,
+        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.slot: int = -1
+        self.prefix_len: int = 0  # prefix cache matched length
+        self.arrival_time = time.time()
+        self.finish_time: Optional[float] = None
+
+        self.stream_callback = stream_callback
+
+    def is_finished(self, stop_ids: List[int]) -> bool:
+        """Check if task is finished."""
+        return (
+            bool(self.output_ids and self.output_ids[-1] in stop_ids)
+            or self.output_tokens >= self.max_tokens
+        )
+
+
+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."""
+    # Clone logits to avoid inplace updates on inference tensor
+    logits = logits.clone()
+
+    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 InferenceScheduler:
+    """Inference scheduler with continuous batching support."""
+
+    def __init__(
+        self,
+        model: AutoModel,
+        tokenizer: AutoTokenizer,
+        max_batch_size: int = 16,
+        max_seq_len: Optional[int] = None,
+        max_prefix_len: int = 512,
+        cache_capacity: int = 1000,
+        device: str = "cuda",
+        dtype: torch.dtype = torch.bfloat16,
+    ):
+        config = model.config
+
+        self.model = model
+        self.tokenizer = tokenizer
+        self.max_batch_size = max_batch_size
+        self.max_seq_len = max_seq_len or config.max_len
+        self.max_prefix_len = max_prefix_len
+        self.device = device or next(model.parameters()).device
+        self.dtype = dtype or next(model.parameters()).dtype
+
+        # Initialize prefix cache
+        self.prefix_cache = PrefixCacheManager(max_capacity=cache_capacity)
+
+        num_kv_heads = config.n_kv_heads
+        head_dim = config.dim // config.n_heads
+        n_layers = config.n_layers
+
+        k_cache = torch.empty(
+            (
+                max_batch_size,
+                self.max_seq_len,
+                n_layers,
+                num_kv_heads,
+                head_dim,
+            ),
+            device=self.device,
+            dtype=self.dtype,
+        )
+        v_cache = torch.empty(
+            (
+                max_batch_size,
+                self.max_seq_len,
+                n_layers,
+                num_kv_heads,
+                head_dim,
+            ),
+            device=self.device,
+            dtype=self.dtype,
+        )
+        self.kv_cache = (k_cache, v_cache)
+        self.seq_mask = torch.ones(
+            (max_batch_size, self.max_seq_len), device=self.device, dtype=torch.bool
+        )
+
+        self.waiting_queue: List[Task] = []
+        self.active_tasks: List[Task] = []
+
+        self._running = False
+        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: int = 1024,
+        temperature: float = 1.0,
+        top_p: float = 1.0,
+        top_k: int = 50,
+        stream_callback: Optional[Callable[[str], None]] = None,
+    ) -> str:
+        """Add a new task to the waiting queue."""
+        task_id = f"task_{int(time.time())}_{uuid.uuid4().hex[:8]}"
+        prompt_ids = self.tokenizer.encode(prompt)
+
+        # Truncate if exceeds max_prefix_len
+        if len(prompt_ids) > self.max_prefix_len:
+            prompt_ids = prompt_ids[: self.max_prefix_len]
+
+        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,
+        )
+
+        # Find longest matching prefix from cache
+        match = self.prefix_cache.find_longest_prefix(prompt_ids)
+        if match:
+            prefix_len, slot = match
+            task.prefix_len = prefix_len
+            task.slot = slot
+        else:
+            task.prefix_len = 0
+            task.slot = -1
+
+        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) -> None:
+        """Remove a task from the scheduler."""
+        with self._lock:
+            self.waiting_queue = [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]
+
+    def _remove_finished_tasks(self) -> None:
+        """Remove finished tasks from active batch."""
+        finished = []
+        for task in self.active_tasks:
+            if task.is_finished(self.tokenizer.stop_ids):
+                task.status = TaskStatus.FINISHED
+                task.finish_time = time.time()
+                finished.append(task)
+                self._total_tokens += task.output_tokens
+
+        for task in finished:
+            slot = task.slot
+            if slot >= 0 and slot < len(self.active_tasks):
+                self.seq_mask[slot, :] = False
+
+            # Release prefix cache reference
+            if task.prefix_len > 0:
+                self.prefix_cache.release(tuple(task.prompt_ids[: task.prefix_len]))
+
+            task.slot = -1
+
+        self.active_tasks = [
+            t for t in self.active_tasks if t.status != TaskStatus.FINISHED
+        ]
+
+    def _refill_active_batch(self) -> None:
+        """Refill active batch with waiting tasks."""
+        available_slots = self.max_batch_size - len(self.active_tasks)
+        if available_slots <= 0:
+            return
+
+        with self._lock:
+            to_add = [
+                self.waiting_queue.pop(0)
+                for _ in range(min(available_slots, len(self.waiting_queue)))
+            ]
+            for task in to_add:
+                task.slot = self._allocate_slot()
+                task.status = TaskStatus.RUNNING
+                self.active_tasks.append(task)
+
+    def _allocate_slot(self) -> int:
+        """Allocate an available slot for a task."""
+        for i in range(self.max_batch_size):
+            if not any(t.slot == i for t in self.active_tasks):
+                return i
+        return -1
+
+    def _execute_prefill(self, tasks: List[Task]) -> None:
+        """Execute Prefill phase with incremental prefill support."""
+        if not tasks:
+            return
+
+        # Group tasks by prefix cache status
+        fully_cached, partial, full = [], [], []
+        for task in tasks:
+            total_len, prefix_len = len(task.prompt_ids), task.prefix_len
+            if prefix_len == total_len:
+                fully_cached.append(task)
+            elif prefix_len > 0:
+                partial.append(task)
+            else:
+                full.append(task)
+
+        # Handle fully cached tasks
+        for t in fully_cached:
+            t.input_tokens, t.output_tokens = len(t.prompt_ids), 0
+            if t.slot >= 0:
+                self.seq_mask[t.slot, : t.input_tokens] = True
+
+        if full:
+            self._execute_full_prefill(full)
+        if partial:
+            self._execute_partial_prefill(partial)
+
+    def _execute_full_prefill(self, tasks: List[Task]) -> None:
+        """Execute full prefill for tasks without prefix cache."""
+        if not tasks:
+            return
+
+        tasks = sorted(tasks, key=lambda t: t.slot)
+
+        prompt_lens = [len(task.prompt_ids) for task in tasks]
+        max_len = max(prompt_lens)
+
+        input_ids = torch.zeros(
+            len(tasks), max_len, dtype=torch.long, device=self.device
+        )
+        for i, task in enumerate(tasks):
+            if len(task.prompt_ids) > 0:
+                input_ids[i, : len(task.prompt_ids)] = torch.tensor(
+                    task.prompt_ids, device=self.device
+                )
+
+        if self.tokenizer.pad_id is not None:
+            input_mask = torch.ne(input_ids, self.tokenizer.pad_id)
+        else:
+            input_mask = torch.ones(
+                input_ids.shape, dtype=torch.bool, device=self.device
+            )
+
+        with torch.inference_mode():
+            self.model(
+                input_ids,
+                input_mask=input_mask,
+                start_pos=0,
+                persistent_key_values=self.kv_cache,
+            )
+
+        for i, task in enumerate(tasks):
+            task.input_tokens = prompt_lens[i]
+            task.output_tokens = 0
+            # Insert new prefix into cache
+            self.prefix_cache.insert(tuple(task.prompt_ids), task.slot)
+
+        for task in tasks:
+            if task.slot >= 0:
+                self.seq_mask[task.slot, : task.input_tokens] = True
+
+    def _execute_partial_prefill(self, tasks: List[Task]) -> None:
+        """Execute incremental prefill for tasks with partial prefix cache match."""
+        for task in tasks:
+            total_len = len(task.prompt_ids)
+            prefix_len = task.prefix_len
+
+            if prefix_len >= total_len:
+                task.input_tokens = total_len
+                task.output_tokens = 0
+                continue
+
+            # Get new tokens that need prefill
+            new_ids = task.prompt_ids[prefix_len:]
+            new_len = len(new_ids)
+
+            if new_len == 0:
+                task.input_tokens = total_len
+                task.output_tokens = 0
+                continue
+
+            # Build input for incremental prefill
+            input_ids = torch.tensor([new_ids], dtype=torch.long, device=self.device)
+
+            # Input mask should cover from position 0 to prefix_len + new_len
+            # The prefix part uses cached KV, new part needs computation
+            input_mask = torch.ones(
+                (1, prefix_len + new_len), dtype=torch.bool, device=self.device
+            )
+
+            with torch.inference_mode():
+                self.model(
+                    input_ids,
+                    input_mask=input_mask,
+                    start_pos=prefix_len,
+                    persistent_key_values=self.kv_cache,
+                )
+
+            task.input_tokens = total_len
+            task.output_tokens = 0
+
+            # Insert full prefix into cache (ref_count already increased in add_task)
+            self.prefix_cache.insert(tuple(task.prompt_ids), task.slot)
+
+            if task.slot >= 0:
+                self.seq_mask[task.slot, : task.input_tokens] = True
+
+    def _execute_decode(self, tasks: List[Task], start_pos: int) -> None:
+        """Execute Decode phase."""
+        if not tasks:
+            return
+
+        tasks = sorted(tasks, key=lambda t: t.slot)
+
+        input_ids = torch.zeros(len(tasks), dtype=torch.long, device=self.device)
+        for i, task in enumerate(tasks):
+            if task.output_ids:
+                input_ids[i] = task.output_ids[-1]
+            else:
+                input_ids[i] = task.prompt_ids[-1]
+
+        input_tensor = input_ids.unsqueeze(1)
+        active_mask = torch.ones((len(tasks), 1), dtype=torch.bool, device=self.device)
+
+        with torch.inference_mode():
+            outputs = self.model(
+                input_tensor,
+                input_mask=active_mask,
+                persistent_key_values=self.kv_cache,
+                start_pos=start_pos,
+            )
+            logits = outputs["logits"][:, -1, :]
+
+        next_token_ids = []
+        for i, task in enumerate(tasks):
+            logit = logits[i : i + 1]
+            logit = apply_sampling_strategies(
+                logit,
+                task.temperature,
+                task.top_k,
+                task.top_p,
+            )
+            probs = torch.softmax(logit, dim=-1)
+            next_token = torch.multinomial(probs, num_samples=1)
+            next_token_ids.append(next_token.item())
+
+        for task, next_token in zip(tasks, next_token_ids):
+            task.output_ids.append(next_token)
+            task.output_tokens += 1
+
+            pos = task.input_tokens + task.output_tokens
+            if task.slot >= 0 and pos < self.max_seq_len:
+                self.seq_mask[task.slot, pos] = True
+
+            if task.stream_callback:
+                token_str = self.tokenizer.decode([next_token])
+                task.stream_callback(token_str)
+
+        for task in tasks:
+            if task.output_tokens >= task.max_tokens or (
+                task.output_ids and task.output_ids[-1] in self.tokenizer.stop_ids
+            ):
+                if task.stream_callback:
+                    task.stream_callback("[DONE]")
+
+    def _run_generation_loop(self) -> None:
+        """Main generation loop."""
+        while self._running:
+            self._remove_finished_tasks()
+            self._refill_active_batch()
+
+            if not self.active_tasks:
+                self._task_event.wait(timeout=0.01)
+                self._task_event.clear()
+                continue
+
+            new_tasks = [t for t in self.active_tasks if t.output_tokens == 0]
+            decode_tasks = [t for t in self.active_tasks if t.output_tokens > 0]
+
+            if decode_tasks:
+                start_pos = max(t.input_tokens + t.output_tokens for t in decode_tasks)
+            else:
+                start_pos = 0
+
+            if new_tasks:
+                self._execute_prefill(new_tasks)
+                decode_tasks = new_tasks
+                start_pos = max(t.input_tokens for t in decode_tasks)
+
+            if decode_tasks:
+                self._execute_decode(decode_tasks, start_pos)
+
+            if not self.active_tasks and not self.waiting_queue:
+                self._task_event.wait(timeout=0.05)
+                self._task_event.clear()
+
+    def start(self) -> None:
+        """Start the generation loop."""
+        if not self._running:
+            self._running = True
+            self._loop_thread = threading.Thread(target=self._run_generation_loop)
+            self._loop_thread.daemon = True
+            self._loop_thread.start()
+
+    def stop(self) -> None:
+        """Stop the generation loop."""
+        self._running = False
+        if hasattr(self, "_loop_thread"):
+            self._loop_thread.join(timeout=1.0)
+
+        # Clear KV cache to free GPU memory
+        if self.kv_cache is not None:
+            k_cache, v_cache = self.kv_cache
+            if k_cache is not None:
+                k_cache.detach()
+            if v_cache is not None:
+                v_cache.detach()
+
+        # Clear seq mask
+        self.seq_mask.detach()
+
+        # Clear task lists
+        self.waiting_queue.clear()
+        self.active_tasks.clear()
+
+    def get_stats(self) -> Dict[str, Any]:
+        """Get scheduler statistics."""
+        return {
+            "total_tasks": self._total_tasks,
+            "total_tokens": self._total_tokens,
+            "active_tasks": len(self.active_tasks),
+            "waiting_queue": len(self.waiting_queue),
+        }

astrai/inference/server.py

@@ -0,0 +1,321 @@
+"""
+Inference Server with Continuous Batching Support
+
+FastAPI server for inference with continuous batching.
+Provides OpenAI-compatible chat completion endpoints.
+"""
+
+import json
+import logging
+from contextlib import asynccontextmanager
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+import torch
+import uvicorn
+from fastapi import FastAPI, HTTPException
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel, Field
+
+from astrai.inference.engine import InferenceEngine
+from astrai.model import AutoModel
+from astrai.tokenize import AutoTokenizer
+
+logger = logging.getLogger(__name__)
+
+# Global model parameter and engine (loaded once)
+_engine: Optional[InferenceEngine] = None
+_model_param: Optional[Any] = None
+_project_root = Path(__file__).parent.parent.parent
+
+# Server configuration (set before running server)
+_server_config: Dict[str, Any] = {
+    "device": "cuda",
+    "dtype": torch.bfloat16,
+    "param_path": None,
+    "max_batch_size": 16,
+}
+
+
+def configure_server(
+    device: str = "cuda",
+    dtype: torch.dtype = torch.bfloat16,
+    param_path: Optional[Path] = None,
+    max_batch_size: int = 16,
+):
+    """Configure server settings before starting.
+
+    Args:
+        device: Device to load model on (e.g., "cuda", "cpu", "cuda:0")
+        dtype: Data type for model weights (e.g., torch.bfloat16, torch.float16)
+        param_path: Path to model parameters directory
+        max_batch_size: Maximum batch size for continuous batching
+    """
+    _server_config["device"] = device
+    _server_config["dtype"] = dtype
+    _server_config["param_path"] = param_path
+    _server_config["max_batch_size"] = max_batch_size
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """Lifespan context manager for startup and shutdown events."""
+    global _model_param, _engine
+    # Startup: Load model with configured settings
+    try:
+        load_model(
+            param_path=_server_config["param_path"],
+            device=_server_config["device"],
+            dtype=_server_config["dtype"],
+            max_batch_size=_server_config["max_batch_size"],
+        )
+    except Exception as e:
+        logger.error(f"Failed to load model: {e}")
+        raise
+    yield
+    # Shutdown: Cleanup engine
+    if _engine:
+        _engine.shutdown()
+        logger.info("Inference engine shutdown complete")
+
+
+app = FastAPI(title="AstrAI Inference Server", version="0.2.0", lifespan=lifespan)
+
+
+def load_model(
+    param_path: Optional[Path] = None,
+    device: str = "cuda",
+    dtype: torch.dtype = torch.bfloat16,
+    max_batch_size: int = 16,
+):
+    """Load model parameters and initialize inference engine."""
+    global _model_param, _engine
+    if param_path is None:
+        param_path = _project_root / "params"
+    if not param_path.exists():
+        raise FileNotFoundError(f"Parameter directory not found: {param_path}")
+
+    # Load tokenizer separately
+    tokenizer = AutoTokenizer.from_pretrained(param_path)
+    _model_param = AutoModel.from_pretrained(param_path)
+    _model_param.to(device=device, dtype=dtype)
+    logger.info(f"Model loaded on {device} with dtype {dtype}")
+
+    # Initialize inference engine with separate model and tokenizer
+    _engine = InferenceEngine(
+        model=_model_param,
+        tokenizer=tokenizer,
+        max_batch_size=max_batch_size,
+    )
+    logger.info(f"Inference engine initialized with max_batch_size={max_batch_size}")
+
+
+# Pydantic models for API request/response
+class ChatMessage(BaseModel):
+    role: str  # "user", "assistant", "system"
+    content: str
+
+
+class ChatCompletionRequest(BaseModel):
+    messages: List[ChatMessage]
+    temperature: float = Field(0.8, ge=0.0, le=2.0)
+    top_p: float = Field(0.95, ge=0.0, le=1.0)
+    top_k: int = Field(50, ge=0)
+    max_tokens: int = Field(2048, ge=1)
+    stream: bool = False
+    system_prompt: Optional[str] = None
+
+
+class CompletionResponse(BaseModel):
+    id: str = "chatcmpl-default"
+    object: str = "chat.completion"
+    created: int = 0
+    model: str = "astrai"
+    choices: List[Dict[str, Any]]
+
+
+@app.get("/health")
+async def health():
+    return {
+        "status": "ok",
+        "model_loaded": _model_param is not None,
+        "engine_ready": _engine is not None,
+    }
+
+
+@app.get("/stats")
+async def get_stats():
+    """Get inference engine statistics."""
+    if _engine is None:
+        raise HTTPException(status_code=503, detail="Engine not initialized")
+    return _engine.get_stats()
+
+
+@app.post("/v1/chat/completions", response_model=CompletionResponse)
+async def chat_completion(request: ChatCompletionRequest):
+    """OpenAI-compatible chat completion endpoint.
+
+    Supports both streaming and non-streaming modes with continuous batching.
+    """
+    if _engine is None:
+        raise HTTPException(status_code=503, detail="Engine not initialized")
+
+    # Convert messages to prompt using engine's tokenizer
+    # Extract system prompt if present, then apply chat template
+    # Apply chat template directly with messages
+    prompt = _engine.tokenizer.apply_chat_template(
+        [{"role": m.role, "content": m.content} for m in request.messages],
+        tokenize=False,
+    )
+
+    if request.stream:
+        # Streaming response (use synchronous generator)
+        generator = _engine.generate(
+            prompt=prompt,
+            stream=True,
+            max_tokens=request.max_tokens,
+            temperature=request.temperature,
+            top_p=request.top_p,
+            top_k=request.top_k,
+        )
+
+        def generate_stream():
+            for token in generator:
+                if token == "[DONE]":
+                    break
+                yield f"data: {json.dumps({'choices': [{'delta': {'content': token}}]})}\n\n"
+            yield "data: [DONE]\n\n"
+
+        return StreamingResponse(
+            generate_stream(),
+            media_type="text/event-stream",
+            headers={"Cache-Control": "no-cache", "Connection": "keep-alive"},
+        )
+    else:
+        # Non-streaming response
+        result = _engine.generate(
+            prompt=prompt,
+            stream=False,
+            max_tokens=request.max_tokens,
+            temperature=request.temperature,
+            top_p=request.top_p,
+            top_k=request.top_k,
+        )
+
+        # Build OpenAI-style response
+        import time
+
+        resp = CompletionResponse(
+            id=f"chatcmpl-{int(time.time())}",
+            created=int(time.time()),
+            choices=[
+                {
+                    "index": 0,
+                    "message": {"role": "assistant", "content": result},
+                    "finish_reason": "stop",
+                }
+            ],
+        )
+        return resp
+
+
+@app.post("/generate")
+async def generate(
+    query: str,
+    history: Optional[List[List[str]]] = None,
+    temperature: float = 0.8,
+    top_p: float = 0.95,
+    top_k: int = 50,
+    max_len: int = 2048,
+    stream: bool = False,
+):
+    """Simple generation endpoint.
+
+    Args:
+        query: Input query string
+        history: Conversation history as list of [user, assistant] pairs
+        temperature: Sampling temperature
+        top_p: Top-p sampling parameter
+        top_k: Top-k sampling parameter
+        max_len: Maximum tokens to generate
+        stream: Enable streaming output
+
+    Returns:
+        dict: Generation result with response field
+    """
+    if _engine is None:
+        raise HTTPException(status_code=503, detail="Engine not initialized")
+
+    # Build messages for chat template
+    messages = []
+    if history:
+        # Convert history format: List[List[str]] -> List[Dict]
+        for h in history:
+            if len(h) >= 2:
+                messages.append({"role": "user", "content": h[0]})
+                messages.append({"role": "assistant", "content": h[1]})
+    messages.append({"role": "user", "content": query})
+
+    # Use tokenizer's chat template
+    prompt = _engine.tokenizer.apply_chat_template(messages, tokenize=False)
+
+    if stream:
+        # Synchronous streaming
+        result = _engine.generate(
+            prompt=prompt,
+            stream=True,
+            max_tokens=max_len,
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+        )
+
+        def stream_generator():
+            for token in result:
+                yield token + "\n"
+
+        return StreamingResponse(stream_generator(), media_type="text/plain")
+    else:
+        result = _engine.generate(
+            prompt=prompt,
+            stream=False,
+            max_tokens=max_len,
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+        )
+        return {"response": result}
+
+
+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,
+):
+    """Run the FastAPI server with uvicorn.
+
+    Args:
+        host: Server host address
+        port: Server port number
+        reload: Enable auto-reload for development
+        device: Device to load model on (e.g., "cuda", "cpu", "cuda:0")
+        dtype: Data type for model weights (e.g., torch.bfloat16, torch.float16)
+        param_path: Path to model parameters directory
+        max_batch_size: Maximum batch size for continuous batching
+    """
+    configure_server(
+        device=device,
+        dtype=dtype,
+        param_path=param_path,
+        max_batch_size=max_batch_size,
+    )
+    uvicorn.run(
+        "astrai.inference.server:app",
+        host=host,
+        port=port,
+        reload=reload,
+    )

astrai/model/__init__.py

@@ -1,18 +1,12 @@
 from astrai.model.automodel import AutoModel
-from astrai.model.components.attention import GQA
+from astrai.model.module import (
-from astrai.model.components.decoder_block import DecoderBlock
+    GQA,
-from astrai.model.components.linear import Linear
+    MLP,
-from astrai.model.components.lora import (
+    DecoderBlock,
-    LoRAConfig,
+    Linear,
-    inject_lora,
+    RMSNorm,
-    load_lora,
-    merge_lora,
-    save_lora,
 )
-from astrai.model.components.mlp import MLP
+from astrai.model.transformer import Transformer
-from astrai.model.components.norm import RMSNorm
-from astrai.model.encoder import EmbeddingEncoder
-from astrai.model.transformer import AutoRegressiveLM
 
 __all__ = [
     # Modules
@@ -22,13 +16,6 @@ __all__ = [
     "GQA",
     "DecoderBlock",
     # Models
-    "AutoRegressiveLM",
+    "Transformer",
-    "EmbeddingEncoder",
     "AutoModel",
-    # LoRA
-    "LoRAConfig",
-    "inject_lora",
-    "merge_lora",
-    "save_lora",
-    "load_lora",
 ]

astrai/model/automodel.py

@@ -4,22 +4,17 @@ AutoModel base class for model loading and saving.
 
 from contextlib import contextmanager
 from pathlib import Path
-from typing import Self, Union
+from typing import Dict, Self, Type, Union
 
+import safetensors.torch as st
 import torch.nn as nn
 
-from astrai.config.model_config import BaseModelConfig, ConfigFactory
+from astrai.config import ModelConfig
-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:
+    init_functions = [
-        yield
-        return
-
-    names = (
         "xavier_normal_",
         "xavier_uniform_",
         "kaiming_normal_",
@@ -29,66 +24,110 @@ def _disable_random_init(enable: bool = True):
         "constant_",
         "normal_",
         "uniform_",
-    )
+    ]
-    orig = {n: getattr(nn.init, n) for n in names if hasattr(nn.init, n)}
+    original_funcs = {}
-    for n in orig:
+    for name in init_functions:
-        setattr(nn.init, n, lambda *a, **kw: None)
+        if enable and hasattr(nn.init, name):
+            original_funcs[name] = getattr(nn.init, name)
+            setattr(nn.init, name, lambda *args, **kwargs: None)
     try:
         yield
     finally:
-        for n, fn in orig.items():
+        if enable:
-            setattr(nn.init, n, fn)
+            for name, orig_func in original_funcs.items():
+                setattr(nn.init, name, orig_func)
 
 
-class AutoModel(BaseFactory["AutoModel"], nn.Module):
+class AutoModel(nn.Module):
     """
     Autoregressive language model base class.
-    Provides model loading/saving, registration, and generation.
+    Provides model loading/saving and generation capabilities.
     """
 
-    def __init__(self, config: BaseModelConfig):
+    # Model registry - stored as class attribute
+    _registry: Dict[str, Type["AutoModel"]] = {}
+
+    def __init__(self, config: ModelConfig):
         super().__init__()
         self.config = config
 
+    @classmethod
+    def register(cls, model_type: str):
+        """
+        Class method decorator to register model type.
+
+        Usage:
+            @AutoModel.register('transformer')
+            class Transformer(AutoModel):
+                ...
+        """
+
+        def decorator(sub_cls: Type["AutoModel"]) -> Type["AutoModel"]:
+            cls._registry[model_type.lower()] = sub_cls
+            return sub_cls
+
+        return decorator
+
+    @classmethod
+    def get_model_class(cls, model_type: str) -> Type["AutoModel"]:
+        """Get model class by model_type string."""
+        model_type = model_type.lower()
+        if model_type not in cls._registry:
+            available = list(cls._registry.keys())
+            raise ValueError(
+                f"Unknown model_type: {model_type}. Available: {available}"
+            )
+        return cls._registry[model_type]
+
     @classmethod
     def from_pretrained(
         cls,
         path: Union[str, Path],
         disable_random_init: bool = True,
-        strict: bool = True,
     ) -> nn.Module:
 
         model_path = Path(path)
 
+        # Load config
+        config = ModelConfig()
         config_path = model_path / "config.json"
-        if not config_path.exists():
+        if config_path.exists():
+            config.load(str(config_path))
+        else:
             raise FileNotFoundError(f"Config file not found: {config_path}")
 
-        raw = load_model_config(str(model_path))
+        # If called from base class, use model_type to determine actual model class
-        config = ConfigFactory.load(raw)
+        if cls is AutoModel:
-        model_type = config.model_type or "autoregressive_lm"
+            model_type = config.model_type or "transformer"
-
+            actual_cls = cls.get_model_class(model_type)
-        actual_cls = AutoModel.get_component_class(model_type)
+        else:
+            raise ValueError(
+                f"Cannot call from_pretrained() on subclass {cls.__name__}"
+            )
 
         with _disable_random_init(enable=disable_random_init):
             model = actual_cls(config)
 
+        # Load weights
         weights_path = model_path / "model.safetensors"
         if weights_path.exists():
-            state_dict = load_model_weights(str(model_path))
+            state_dict = st.load_file(str(weights_path))
-            model.load_state_dict(state_dict, strict=strict)
+            model.load_state_dict(state_dict, strict=False)
 
         return model
 
     def save_pretrained(
         self,
         save_directory: Union[str, Path],
-    ):
+    ) -> None:
-        save_model(
+        save_path = Path(save_directory)
-            config=self.config.to_dict(),
+        save_path.mkdir(parents=True, exist_ok=True)
-            state_dict=self.state_dict(),
+
-            save_directory=str(save_directory),
+        # Save config
-        )
+        self.config.save(str(save_path / "config.json"))
+
+        # Save weights
+        st.save_file(self.state_dict(), str(save_path / "model.safetensors"))
 
     def to(self, *args, **kwargs) -> Self:
         """Move model to device/dtype."""

astrai/model/components/__init__.py

@@ -1,25 +0,0 @@
-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

@@ -1,212 +0,0 @@
-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

@@ -1,59 +0,0 @@
-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

@@ -1,16 +0,0 @@
-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

@@ -1,21 +0,0 @@
-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

@@ -1,194 +0,0 @@
-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

@@ -1,93 +0,0 @@
-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

@@ -1,15 +0,0 @@
-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

@@ -1,71 +0,0 @@
-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

@@ -1,99 +0,0 @@
-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/module.py

@@ -0,0 +1,382 @@
+from typing import Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+
+
+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: Optional[torch.device] = None,
+) -> Tuple[Tensor, 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 (optional): The device to create tensors on. Defaults to None.
+    Returns:
+        Tensor: The rotary embedding 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)
+
+    return torch.cos(freqs).float(), torch.sin(freqs).float()
+
+
+def apply_rotary_emb(x: torch.Tensor, rotary_emb: Tuple[Tensor, Tensor]) -> Tensor:
+    """
+    Apply rotary embedding to the input tensor using cos/sin form.
+    Args:
+        x (Tensor): The input tensor (shape [..., seq_len, dim]).
+        rotary_emb (Tuple[Tensor, Tensor]): The rotary embedding (shape [seq_len, dim//2]).
+    Returns:
+        Tensor: The output tensor (rotated, same shape as input).
+    """
+
+    dtype = x.dtype
+    cos, sin = rotary_emb
+
+    cos = cos.unsqueeze(0).unsqueeze(2)  # [1, seq_len, 1, dim//2]
+    sin = sin.unsqueeze(0).unsqueeze(2)  # [1, seq_len, 1, dim//2]
+
+    x_real = x[..., 0::2]  # [batch, seq_len, dim//2]
+    x_imag = x[..., 1::2]  # [batch, seq_len, dim//2]
+
+    x_real_rot = x_real * cos - x_imag * sin
+    x_imag_rot = x_real * sin + x_imag * cos
+
+    x_out = torch.stack([x_real_rot, x_imag_rot], dim=-1)  # [batch, seq_len, dim//2, 2]
+    x_out = x_out.view(*x_out.shape[:-2], -1)  # [batch, seq_len, dim]
+
+    return x_out.to(dtype)
+
+
+class RotaryEmbedding(nn.Module):
+    def __init__(self, dim: int, max_len: int, base: int = 10000):
+        super().__init__()
+        self.dim = dim
+        self.max_len = max_len
+        self.base = base
+        self.max_len_cached = None
+        self._set_rotary_buffer(self.max_len, None)
+
+    def _set_rotary_buffer(self, max_len: int, device: Optional[torch.device] = None):
+        cos_cached, sin_cached = get_rotary_emb(self.dim, max_len, self.base, device)
+        self.register_buffer("cos_cached", cos_cached, persistent=False)
+        self.register_buffer("sin_cached", sin_cached, persistent=False)
+        self.max_len_cached = max_len
+
+    def forward(self, x: Tensor, start_pos: int = 0) -> Tuple[Tensor, Tensor]:
+        seq_len = x.size(1)
+
+        if self.max_len_cached < seq_len + start_pos:
+            self._set_rotary_buffer(self.max_len_cached * 2, x.device)
+
+        cos = self.cos_cached[start_pos : start_pos + seq_len]
+        sin = self.sin_cached[start_pos : start_pos + seq_len]
+
+        return (cos, sin)
+
+
+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 forward(self, x: Tensor) -> Tensor:
+        return F.linear(x, self.weight, self.bias)
+
+
+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)
+
+
+class MLP(nn.Module):
+    def __init__(self, dim: int, dim_feed_forward: int):
+        super().__init__()
+        self.up = Linear(dim, dim_feed_forward)
+        self.gate = Linear(dim, dim_feed_forward)
+        self.down = Linear(dim_feed_forward, dim)
+
+    def forward(self, x: Tensor) -> Tensor:
+        gated = self.up(x) * F.silu(self.gate(x))
+        out = self.down(gated)
+        return out
+
+
+class 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: Tuple[Tensor, Tensor],
+        mask: Tensor = None,
+        kv_cache: Optional[Tuple[Tensor, Tensor]] = None,
+        start_pos: int = 0,
+    ) -> Tensor:
+        bsz, seq_len, _ = x.size()
+        is_causal = mask is None
+
+        # 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_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 kv_cache is not None:
+            k_cache, v_cache = kv_cache
+
+            # copy to cache
+            k_cache[:bsz, start_pos : start_pos + seq_len, self.layer_id] = k
+            v_cache[:bsz, start_pos : start_pos + seq_len, self.layer_id] = v
+
+            # get cache
+            k = k_cache[:bsz, : start_pos + seq_len, self.layer_id]
+            v = v_cache[:bsz, : start_pos + seq_len, self.layer_id]
+
+        k, v = repeat_kv(k, self.n_rep), repeat_kv(v, self.n_rep)
+
+        # (bsz, seq_len, n_heads, head_dim) -> (bsz, n_heads, seq_len, head_dim)
+        q, k, v = q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3)
+        # (bsz, n_heads, seq_len, head_dim) - > (bsz, seq_len, n_heads*head_dim)
+        sdqa_out = (
+            F.scaled_dot_product_attention(q, k, v, mask, is_causal=is_causal)
+            .permute(0, 2, 1, 3)
+            .contiguous()
+            .flatten(2)
+        )
+
+        if self.use_gated_attention:
+            sdqa_out = sdqa_out * F.sigmoid(self.gate(x))
+
+        out = self.o_proj(sdqa_out)
+
+        return out
+
+
+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_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_gated_attention = use_gated_attention
+
+        self.q_proj = Linear(dim, n_heads * self.head_dim, bias=False)
+        self.kv_a_proj = Linear(dim, kv_lora_rank, bias=False)
+        self.kv_norm = RMSNorm(kv_lora_rank, norm_eps)
+
+        # KV (k_nope, k_rope, v)
+        self.kv_b_proj = Linear(
+            kv_lora_rank,
+            n_kv_heads * (self.head_dim + qk_rope_head_dim + 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: Tuple[Tensor, Tensor],
+        mask: Tensor = None,
+        kv_cache: Optional[Tuple[Tensor, Tensor]] = None,
+        start_pos: int = 0,
+    ) -> Tensor:
+        bsz, seq_len, _ = x.size()
+        is_causal = 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_rope_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 kv_cache is not None:
+            k_cache, v_cache = kv_cache
+            k_cache[:bsz, start_pos : start_pos + seq_len, self.layer_id] = k
+            v_cache[:bsz, start_pos : start_pos + seq_len, self.layer_id] = v
+            k = k_cache[:bsz, : start_pos + seq_len, self.layer_id]
+            v = v_cache[:bsz, : start_pos + seq_len, 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, 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
+
+
+class DecoderBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        n_heads: int,
+        dim_ffn: int,
+        n_kv_heads: int,
+        norm_eps: int,
+        use_qk_norm: bool,
+        use_gated_attention: bool,
+        layer_id: int,
+    ):
+        super().__init__()
+        self.attention = GQA(
+            dim,
+            n_heads,
+            n_kv_heads,
+            use_qk_norm,
+            norm_eps,
+            use_gated_attention,
+            layer_id,
+        )
+        self.input_norm = RMSNorm(dim, norm_eps)
+        self.mlp = MLP(dim, dim_ffn)
+        self.post_attention_norm = RMSNorm(dim, norm_eps)
+
+    def forward(
+        self,
+        x: Tensor,
+        rotary_emb: Tuple[Tensor, Tensor],
+        attention_mask: Optional[Tensor] = None,
+        kv_cache: Optional[Tuple[Tensor, Tensor]] = None,
+        start_pos: int = 0,
+    ) -> Tensor:
+        # attention
+        attn_output = self.attention(
+            self.input_norm(x), rotary_emb, attention_mask, kv_cache, start_pos
+        )
+        x = attn_output + x
+
+        # feed forward
+        x = self.mlp(self.post_attention_norm(x)) + x
+
+        return x
+
+
+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 forward(self, x: Tensor) -> Tensor:
+        return F.embedding(x, self.weight)

astrai/model/transformer.py

@@ -1,63 +1,83 @@
-from typing import Any, Dict, Mapping, Optional
+from typing import Any, Mapping, Optional, Tuple
 
 import torch
 import torch.nn as nn
 from torch import Tensor
 
-from astrai.config.model_config import AutoRegressiveLMConfig
+from astrai.config.model_config import ModelConfig
-from astrai.inference.core.cache import KvcacheView
 from astrai.model.automodel import AutoModel
-from astrai.model.components.decoder_block import DecoderBlock
+from astrai.model.module import (
-from astrai.model.components.embedding import Embedding
+    DecoderBlock,
-from astrai.model.components.linear import Linear
+    Embedding,
-from astrai.model.components.norm import RMSNorm
+    Linear,
-from astrai.model.components.rope import RotaryEmbedding
+    RMSNorm,
+    RotaryEmbedding,
+)
 
 
 def process_attention_mask(
+    seq_mask: Tensor,
     input_tensor: Tensor,
-    position_ids: Optional[Tensor],
+    start_pos: int = 0,
-    input_mask: Optional[Tensor] = None,
     is_causal: bool = False,
-) -> Optional[Tensor]:
+) -> Tensor:
-    if position_ids is None:
+    """
-        return None
+    Create attention mask for GQA
-    if input_mask is not None and input_mask.dim() > 2:
+    Args:
-        return input_mask
+        seq_mask (Tensor): A tensor indicating whether each position is valid or not.
-
+        input_tensor (Tensor): The input tensor.
+        start_pos (int): The starting position of the sequence.
+        is_causal (bool): Whether the attention is causal or not.
+    Returns:
+        Tensor: The attention mask tensor.
+    """
     device = input_tensor.device
-    B = input_tensor.size(0)
+    dtype = input_tensor.dtype
-    T = position_ids.max().item() + 1
+    seq_len = input_tensor.size(1)
 
-    if input_mask is None:
+    if seq_mask is None:
-        if position_ids.min().item() == 0 and is_causal:
+        if start_pos != 0:
-            return None
+            # for single prompt chat
-        attend = torch.ones(B, 1, T, dtype=torch.bool, device=device)
+            seq_mask = torch.ones((1, seq_len), dtype=torch.bool, device=device)
         else:
-        attend = input_mask[:, :T].to(device=device, dtype=torch.bool).unsqueeze(1)
+            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 = position_ids.unsqueeze(-1) >= torch.arange(T, device=device)
+        expanded_mask = torch.tril(expanded_mask, diagonal=start_pos)
-        attend = attend & causal
 
-    return attend.unsqueeze(1)
+    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
 
 
-@AutoModel.register("autoregressive_lm")
+@AutoModel.register("transformer")
-class AutoRegressiveLM(AutoModel):
+class Transformer(AutoModel):
-    """Autoregressive language model with paged KV cache."""
+    """
+    Transformer language model.
+    """
 
-    def __init__(self, config: AutoRegressiveLMConfig):
+    def __init__(self, config: ModelConfig):
         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
+            config.dim // config.n_heads, config.max_len
         )
         self.embed_tokens = Embedding(config.vocab_size, config.dim)
 
@@ -72,15 +92,6 @@ class AutoRegressiveLM(AutoModel):
                     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)
             ]
@@ -89,28 +100,32 @@ class AutoRegressiveLM(AutoModel):
         self.norm = RMSNorm(config.dim, config.norm_eps)
         self.lm_head = Linear(config.dim, config.vocab_size)
 
-        if self.config.tie_weight is True:
+        if self.config.tie_weight:
             self.lm_head.weight = self.embed_tokens.weight
 
-        self.apply(self._init_weights)
+        self._init_weights()
 
-    def _init_weights(self, module):
+    def _init_weights(self):
-        if hasattr(module, "reset_parameters"):
+        for param in self.parameters():
-            module.reset_parameters()
+            if param.dim() > 1:
+                nn.init.normal_(param, mean=0.0, std=0.006)
 
     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"
 
+        # Make a copy to avoid modifying the original state_dict
         state_dict = dict(state_dict)
 
-        if self.config.tie_weight is True:
+        if self.config.tie_weight:
-            # same tensor for embed and lm_head
+            # same tensor
             if embed_key in state_dict:
                 state_dict[lm_head_key] = state_dict[embed_key]
         else:
+            # If lm_head.weight exists in checkpoint, use it directly
+            # If not, copy from embed_tokens.weight
             if lm_head_key not in state_dict and embed_key in state_dict:
-                # clone to avoid sharing gradients
+                # use clone to avoid sharing the same tensor
                 state_dict[lm_head_key] = torch.clone(state_dict[embed_key])
 
         return super().load_state_dict(state_dict, strict, assign)
@@ -120,7 +135,7 @@ class AutoRegressiveLM(AutoModel):
             destination=destination, prefix=prefix, keep_vars=keep_vars
         )
 
-        if self.config.tie_weight is True:
+        if self.config.tie_weight:
             lm_head_key = prefix + "lm_head.weight"
             if lm_head_key in state_dict:
                 del state_dict[lm_head_key]
@@ -131,17 +146,18 @@ class AutoRegressiveLM(AutoModel):
         self,
         input_ids: Tensor,
         input_mask: Optional[Tensor] = None,
-        paged_cache: Optional[KvcacheView] = None,
+        persistent_key_values: Optional[Tuple[Tensor, Tensor]] = None,
-        position_ids: Optional[Tensor] = None,
+        start_pos: int = 0,
-    ) -> Dict[str, Tensor]:
+    ) -> Tensor:
         assert input_ids.ndim == 2
 
         x = self.embed_tokens(input_ids)
-        rotary_emb = self.rotary_embedding(x, position_ids)
+        rotary_emb = self.rotary_embedding(x, start_pos)
-        attn_mask = process_attention_mask(x, position_ids, input_mask, is_causal=True)
+
+        attn_mask = process_attention_mask(input_mask, x, start_pos, is_causal=True)
 
         for layer in self.layers:
-            x = layer(x, rotary_emb, attn_mask, paged_cache)
+            x = layer(x, rotary_emb, attn_mask, persistent_key_values, start_pos)
 
         hidden_states = self.norm(x)
         logits = self.lm_head(hidden_states)

astrai/parallel/__init__.py

@@ -1,13 +1,3 @@
-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,
@@ -27,12 +17,4 @@ __all__ = [
     "spawn_parallel_fn",
     "RowParallelLinear",
     "ColumnParallelLinear",
-    "ExecutorFactory",
-    "BaseExecutor",
-    "GradientState",
-    "AccumOptimizer",
-    "AccumScheduler",
-    "NoneExecutor",
-    "DDPExecutor",
-    "FSDPExecutor",
 ]

astrai/parallel/executor.py

@@ -1,272 +0,0 @@
-"""Unified training executor — parallel strategy + gradient accumulation."""
-
-import contextlib
-import logging
-import os
-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 = int(os.environ.get("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/setup.py

@@ -1,8 +1,7 @@
 import os
-from abc import ABC, abstractmethod
 from contextlib import contextmanager
 from functools import wraps
-from typing import Callable
+from typing import Callable, List, Optional
 
 import torch
 import torch.distributed as dist
@@ -31,11 +30,11 @@ def get_rank() -> int:
 def setup_parallel(
     rank: int,
     world_size: int,
-    local_rank: int,
     backend: str = "nccl",
     master_addr: str = "localhost",
     master_port: str = "29500",
     device_type: str = "cuda",
+    device_ids: Optional[List[int]] = None,
 ):
 
     if dist.is_available() and dist.is_initialized():
@@ -43,18 +42,19 @@ def setup_parallel(
         return
 
     if world_size <= 1:
-        device_id = torch.device(device_type, local_rank)
-        os.environ["LOCAL_RANK"] = str(local_rank)
-        os.environ["WORLD_SIZE"] = "1"
-        os.environ["LOCAL_DEVICE"] = str(device_id)
         yield None
         return
 
-    device_id = torch.device(device_type, local_rank)
+    if device_ids is None:
+        device_ids = [i for i in range(world_size)]
+
+    rank = device_ids[rank % len(device_ids)]
+    device_id = torch.device(device_type, device_ids[rank])
 
     os.environ["MASTER_ADDR"] = master_addr
     os.environ["MASTER_PORT"] = master_port
-    os.environ["LOCAL_RANK"] = str(local_rank)
+
+    os.environ["LOCAL_RANK"] = str(rank)
     os.environ["WORLD_SIZE"] = str(world_size)
     os.environ["LOCAL_DEVICE"] = str(device_id)
 
@@ -96,120 +96,34 @@ def only_on_rank(rank, sync=False):
     return decorator
 
 
-def _run_single_rank(
+def wrapper_spawn_func(
     rank: int,
     world_size: int,
     backend: str,
     master_addr: str,
     master_port: str,
     device_type: str,
+    device_ids: List[int],
     func: Callable,
     kwargs: dict,
 ):
+    try:
         with setup_parallel(
             rank=rank,
             world_size=world_size,
-        local_rank=rank,
             backend=backend,
             master_addr=master_addr,
             master_port=master_port,
             device_type=device_type,
+            device_ids=device_ids,
         ):
             func(**kwargs)
 
-
+    except Exception as e:
-class LaunchStrategy(ABC):
+        print(f"Error in rank {rank}: {e}")
-    """Strategy for launching a function in a distributed context."""
-
-    def __init__(
-        self,
-        world_size: int,
-        backend: str,
-        master_addr: str,
-        master_port: str,
-        device_type: str,
-        start_method: str,
-    ):
-        self.world_size = world_size
-        self.backend = backend
-        self.master_addr = master_addr
-        self.master_port = master_port
-        self.device_type = device_type
-        self.start_method = start_method
-
-    @abstractmethod
-    def launch(self, func: Callable, **kwargs):
-        raise NotImplementedError
-
-
-class TorchrunStrategy(LaunchStrategy):
-    """External orchestrator (torchrun, SLURM, K8s) — env vars pre-set."""
-
-    def launch(self, func: Callable, **kwargs):
-        rank = int(os.environ["RANK"])
-        world_size = int(os.environ["WORLD_SIZE"])
-        local_rank = int(os.environ.get("LOCAL_RANK", rank))
-        with setup_parallel(
-            rank=rank,
-            world_size=world_size,
-            local_rank=local_rank,
-            backend=self.backend,
-            master_addr=os.environ.get("MASTER_ADDR", self.master_addr),
-            master_port=os.environ.get("MASTER_PORT", self.master_port),
-            device_type=self.device_type,
-        ):
-            func(**kwargs)
-
-
-class LocalStrategy(LaunchStrategy):
-    """Local launcher — single-process or mp.start_processes."""
-
-    def launch(self, func: Callable, **kwargs):
-        args = (
-            self.world_size,
-            self.backend,
-            self.master_addr,
-            self.master_port,
-            self.device_type,
-            func,
-            kwargs,
-        )
-
-        if self.world_size == 1:
-            _run_single_rank(0, *args)
-            return
-
-        ctx = mp.start_processes(
-            _run_single_rank,
-            args=args,
-            nprocs=self.world_size,
-            start_method=self.start_method,
-            join=False,
-        )
-        try:
-            while not ctx.join():
-                pass
-        except BaseException:
-            for p in ctx.processes:
-                p.terminate()
-            ctx.join()
         raise
 
 
-def _detect_launcher() -> str:
-    """Detect the distributed launcher from environment.
-
-    Returns one of: "torchelastic", "torchrun", "external", "local".
-    """
-    if dist.is_torchelastic_launched():
-        return "torchelastic"
-    if "LOCAL_WORLD_SIZE" in os.environ:
-        return "torchrun"
-    if "RANK" in os.environ and "WORLD_SIZE" in os.environ:
-        return "external"
-    return "local"
-
-
 def spawn_parallel_fn(
     func: Callable,
     world_size: int,
@@ -217,16 +131,40 @@ def spawn_parallel_fn(
     master_addr: str = "localhost",
     master_port: str = "29500",
     device_type: str = "cuda",
-    start_method: str = "spawn",
+    device_ids: Optional[List[int]] = None,
     **kwargs,
 ):
-    launcher = _detect_launcher()
+    # clear environment variables
-    if launcher in ("torchelastic", "torchrun", "external"):
+    for key in [
-        strategy = TorchrunStrategy(
+        "MASTER_ADDR",
-            world_size, backend, master_addr, master_port, device_type, start_method
+        "MASTER_PORT",
+        "RANK",
+        "WORLD_SIZE",
+        "LOCAL_RANK",
+        "LOCAL_DEVICE",
+    ]:
+        if key in os.environ:
+            del os.environ[key]
+
+    if world_size == 1:
+        device_ids = device_ids or [0]
+        device_id = torch.device(device_type, device_ids[0])
+        os.environ["LOCAL_DEVICE"] = str(device_id)
+
+        func(**kwargs)
+        return
+
+    wrapper_spawn_func_args = (
+        world_size,
+        backend,
+        master_addr,
+        master_port,
+        device_type,
+        device_ids,
+        func,
+        kwargs,
     )
-    else:
+
-        strategy = LocalStrategy(
+    mp.spawn(
-            world_size, backend, master_addr, master_port, device_type, start_method
+        wrapper_spawn_func, nprocs=world_size, args=wrapper_spawn_func_args, join=True
     )
-    strategy.launch(func, **kwargs)

astrai/preprocessing/__init__.py

@@ -1,14 +0,0 @@
-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

@@ -1,338 +0,0 @@
-"""Mask building strategies for preprocessing pipeline.
-
-The single :class:`SectionedMaskBuilder` handles all input formats
-(single-sequence / DPO / GRPO) via declarative config: ``input.sections``
-for single-output or ``input.sources`` for multi-output.
-"""
-
-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 supporting single and multi-output modes.
-
-    Single-output (backward-compatible)::
-
-        {"input": {"sections": [
-            {"field": "messages", "action": "$role", "template": true}
-        ]}}
-        → {"sequence": [...], "loss_mask": [...], "domain": "..."}
-
-    Multi-output (DPO / GRPO)::
-
-        {"input": {"sources": {
-            "chosen": {"sections": [
-                {"field": "chosen", "action": "$role", "template": true}
-            ]},
-            "rejected": {"sections": [
-                {"field": "rejected", "action": "$role", "template": true}
-            ]}
-        }}}
-        → {"chosen": [...], "chosen_mask": [...],
-           "rejected": [...], "rejected_mask": [...], "domain": "..."}
-
-    Output spec fields::
-
-        sections      – list of section specs (same format as single-output)
-        list_field    – True when the JSONL field holds a list of values to
-                        tokenise individually and concatenate (GRPO responses)
-        mask_key      – explicit output key for the loss mask
-                        (default: ``"{output_key}_mask"``)
-        dtype         – explicit tensor dtype for this output key
-                        (default: "int32")
-    """
-
-    def build(self, item: dict, config, tokenizer) -> Optional[dict]:
-        sources_spec = getattr(config.input, "sources", None)
-        if sources_spec:
-            return self._build_multi(item, sources_spec, config, tokenizer)
-        return self._build_single(item, config, tokenizer)
-
-    def _build_single(self, item: dict, config, tokenizer) -> Optional[dict]:
-        sections = config.input.sections
-        if not sections:
-            return None
-
-        ids, mask = self._process_sections(
-            item, sections, config, tokenizer, is_top_level=True
-        )
-        if ids is None:
-            return None
-
-        result: dict = {
-            "sequence": ids,
-            "domain": _extract_domain(item, config.output.domain_key),
-        }
-        if not all(m == 1 for m in mask):
-            result["loss_mask"] = mask
-        return result
-
-    def _build_multi(
-        self, item: dict, sources_spec: dict, config, tokenizer
-    ) -> Optional[dict]:
-        result: dict = {}
-        any_output = False
-
-        for output_key, spec in sources_spec.items():
-            sections = spec.get("sections", [])
-            if not sections:
-                continue
-
-            if self._is_value_section(sections):
-                ids = self._extract_raw_value(item, sections)
-                if ids is None:
-                    continue
-                result[output_key] = ids
-                any_output = True
-                continue
-
-            list_field = spec.get("list_field", False)
-            mask_key = spec.get("mask_key", f"{output_key}_mask")
-
-            if list_field:
-                ids, mask = self._process_list_field(item, sections, config, tokenizer)
-            else:
-                ids, mask = self._process_sections(
-                    item, sections, config, tokenizer, is_top_level=True
-                )
-
-            if ids is None:
-                continue
-
-            result[output_key] = ids
-            if not all(m == 1 for m in mask):
-                result[mask_key] = mask
-            elif "mask_key" in spec:
-                result[mask_key] = mask
-
-            any_output = True
-
-        if not any_output:
-            return None
-
-        result["domain"] = _extract_domain(item, config.output.domain_key)
-        return result
-
-    @staticmethod
-    def _is_value_section(sections: list) -> bool:
-        return len(sections) == 1 and sections[0].get("action") == "value"
-
-    @staticmethod
-    def _extract_raw_value(item: dict, sections: list):
-        """Extract a raw value from a JSONL field without tokenisation.
-
-        Used for GRPO rewards where the field contains float values.
-        """
-        sec = sections[0]
-        field = sec["field"]
-        raw = item.get(field)
-        if raw is None:
-            return None
-        if isinstance(raw, list):
-            return [float(v) for v in raw]
-        return [float(raw)]
-
-    def _process_sections(
-        self,
-        item: dict,
-        sections: list,
-        config,
-        tokenizer,
-        *,
-        is_top_level: bool = False,
-    ):
-        """Process a list of sections into ``(ids, loss_mask)``.
-
-        Returns ``(None, None)`` if the item should be skipped.
-        """
-        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 is_top_level and 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:
-                success = self._append_template_section(
-                    item, field, action, tokenizer, config, all_ids, loss_mask
-                )
-                if not success:
-                    continue
-            else:
-                success = self._append_text_section(
-                    item,
-                    field,
-                    action,
-                    tokenizer,
-                    add_special,
-                    is_text_config,
-                    config,
-                    all_ids,
-                    loss_mask,
-                )
-                if not success:
-                    continue
-
-            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, None
-
-        if is_top_level and has_template and len(all_ids) <= 1:
-            return None, None
-
-        return all_ids, loss_mask
-
-    def _append_template_section(
-        self, item, field, action, tokenizer, config, all_ids, loss_mask
-    ):
-        messages = item.get(field)
-        if not isinstance(messages, list) or not messages:
-            return False
-        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))
-        return True
-
-    def _append_text_section(
-        self,
-        item,
-        field,
-        action,
-        tokenizer,
-        add_special,
-        is_text_config,
-        config,
-        all_ids,
-        loss_mask,
-    ):
-        text = str(item.get(field, ""))
-        if not text.strip():
-            return False
-        if is_text_config:
-            pp = config.preprocessing
-            if pp.min_chars > 0 and len(text) < pp.min_chars:
-                return False
-            if len(text) > pp.max_chars:
-                return False
-        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))
-        return True
-
-    def _process_list_field(self, item: dict, sections: list, config, tokenizer):
-        all_ids: list[int] = []
-        loss_mask: list[int] = []
-
-        for sec in sections:
-            field = sec["field"]
-            action = sec["action"]
-            use_template = sec.get("template", False)
-
-            values = item.get(field)
-            if not isinstance(values, list):
-                continue
-
-            for val in values:
-                if use_template:
-                    if isinstance(val, list):
-                        wrapper = {field: val}
-                        self._append_template_section(
-                            wrapper,
-                            field,
-                            action,
-                            tokenizer,
-                            config,
-                            all_ids,
-                            loss_mask,
-                        )
-                else:
-                    wrapper = {field: str(val)}
-                    self._append_text_section(
-                        wrapper,
-                        field,
-                        action,
-                        tokenizer,
-                        False,
-                        False,
-                        config,
-                        all_ids,
-                        loss_mask,
-                    )
-
-        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, None
-        return all_ids, loss_mask

astrai/preprocessing/pipeline.py

@@ -1,257 +0,0 @@
-"""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 List, Optional, Tuple
-
-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
-
-
-def _truncate(seq: list, max_len: int, mode: str) -> list:
-    if len(seq) <= max_len:
-        return seq
-    if mode == "keep_end":
-        return seq[-max_len:]
-    return seq[:max_len]
-
-
-def pack_sequences(
-    sequences: List[list],
-    max_packed_len: int,
-    strategy: str,
-    truncation_mode: str,
-) -> List[Tuple[int, int]]:
-    """Pack *sequences* into bins and return a reorder plan.
-
-    Returns a list of ``(orig_idx, truncated_length)`` in flush order.
-    All keys (sequence, loss_mask, …) must be reordered and truncated
-    identically according to this plan.
-
-    Supported *strategy* values:
-
-    - ``"simple"``: sequential, no reordering.
-    - ``"bfd"``: best-fit decreasing bin packing.
-    """
-    n = len(sequences)
-    if strategy == "simple":
-        return [(i, min(len(sequences[i]), max_packed_len)) for i in range(n)]
-
-    order = sorted(range(n), key=lambda i: len(sequences[i]), reverse=True)
-    bins: List[List[int]] = []
-    bin_lengths: List[int] = []
-
-    for orig_idx in order:
-        seq_len = min(len(sequences[orig_idx]), max_packed_len)
-
-        best_bin = None
-        best_remain = max_packed_len + 1
-        for i, bl in enumerate(bin_lengths):
-            remain = max_packed_len - bl
-            if seq_len <= remain < best_remain:
-                best_remain = remain
-                best_bin = i
-
-        if best_bin is not None:
-            bins[best_bin].append(orig_idx)
-            bin_lengths[best_bin] += seq_len
-        else:
-            bins.append([orig_idx])
-            bin_lengths.append(seq_len)
-
-    plan: List[Tuple[int, int]] = []
-    for bin_indices in bins:
-        for orig_idx in bin_indices:
-            plan.append((orig_idx, min(len(sequences[orig_idx]), max_packed_len)))
-
-    return plan
-
-
-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
-
-            domain = result.pop("domain", "__default__")
-
-            is_multi = bool(getattr(self.config.input, "sources", None))
-            if is_multi:
-                ids = self._primary_ids(result)
-            else:
-                ids = result.pop("sequence")
-                result["sequence"] = ids
-
-            if not ids:
-                continue
-
-            bucket = domains[domain]
-            self._align_bucket(bucket, result, ids, is_multi)
-            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.")
-
-    @staticmethod
-    def _primary_ids(result: dict) -> list:
-        """Return the first list-valued entry in *result* as the primary id
-        sequence for token counting."""
-        for val in result.values():
-            if isinstance(val, list) and val and isinstance(val[0], int):
-                return val
-        return []
-
-    @staticmethod
-    def _align_bucket(bucket: dict, result: dict, ids: list, is_multi: bool):
-        """Pad previously-accumulated keys that are missing from *result*."""
-        for key in list(bucket.keys()):
-            if key in result:
-                continue
-            if is_multi:
-                pad = bucket[key][-1] if bucket[key] else [1] * len(ids)
-                bucket[key].append(pad)
-            else:
-                bucket[key].append([1] * len(ids))
-
-    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]
-            chunk_dir = os.path.join(self.output_dir, domain)
-
-            pp = self.config.preprocessing
-            if pp.packing_strategy != "simple" and "sequence" in keys:
-                plan = pack_sequences(
-                    keys["sequence"],
-                    pp.max_packed_len,
-                    pp.packing_strategy,
-                    pp.truncation_mode,
-                )
-                reordered = defaultdict(list)
-                for orig_idx, truncated_len in plan:
-                    for k, vals in keys.items():
-                        reordered[k].append(
-                            _truncate(
-                                vals[orig_idx], pp.max_packed_len, pp.truncation_mode
-                            )
-                        )
-                keys = reordered
-
-            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)
-                ]
-
-            pid_mode = self.config.output.position_ids_mode
-            if pid_mode and pid_mode != "none" and "sequence" in tensors:
-                pos_ids = []
-                if pid_mode == "doc_reset":
-                    for item in keys["sequence"]:
-                        pos_ids.extend(range(len(item)))
-                else:
-                    total = sum(len(item) for item in keys["sequence"])
-                    pos_ids = list(range(total))
-                tensors["position_ids"] = [torch.tensor(pos_ids, dtype=torch.int32)]
-
-            shard_path = os.path.join(chunk_dir, f"shard_{idx:04d}")
-            fmt = self.config.output.storage_format
-            if fmt == "bin":
-                save_bin(shard_path, tensors)
-            else:
-                save_h5(chunk_dir, f"data_{idx:04d}", tensors)
-            shard_idx[domain] = idx + 1
-            first_key = "sequence" if "sequence" in tensors else next(iter(tensors))
-            tqdm.tqdm.write(
-                f"  saved {domain}/shard_{idx:04d}  "
-                f"({tensors[first_key][0].numel():,} tokens)"
-            )

astrai/protocols.py

@@ -1,21 +0,0 @@
-"""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

@@ -1,182 +1,106 @@
-import io
 import json
-import time
+import os
-from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Any, Dict, Union
+from typing import Any, Dict, List
 
+import h5py
 import safetensors.torch as st
 import torch
 import torch.distributed as dist
+from torch import Tensor
 
 from astrai.parallel.setup import get_rank
 
-_META_FILE = "meta.json"
+
-_CONFIG_FILE = "config.json"
+def save_h5(file_path: str, file_name: str, tensor_group: Dict[str, List[Tensor]]):
-_WEIGHTS_FILE = "model.safetensors"
+    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 save_safetensors(state_dict: dict, path: Union[str, Path]):
+def load_h5(file_path: str, share_memory=True) -> Dict[str, List[Tensor]]:
-    st.save_file(state_dict, str(path))
+    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 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)
+    def __init__(
-    epoch: int = 0
+        self,
-    iteration: int = 0
+        state_dict: Dict[str, Any],
-    extra: Dict[str, Any] = field(default_factory=dict)
+        epoch: int = 0,
-    meta: Dict[str, Any] = field(default_factory=dict)
+        iteration: int = 0,
-    config: Dict[str, Any] = field(default_factory=dict)
+    ):
+        self.state_dict = state_dict
+        self.epoch = epoch
+        self.iteration = iteration
+
+    def save(
+        self,
+        save_dir: str,
+    ) -> None:
 
-    def save(self, save_dir: str):
         save_path = Path(save_dir)
         save_path.mkdir(parents=True, exist_ok=True)
 
-        if get_rank() != 0:
+        rank = get_rank()
-            return
+        if rank == 0:
-
             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)
+            with open(save_path / "meta.json", "w") as f:
-        save_json(self.config, save_path / _CONFIG_FILE)
+                json.dump(meta, f, indent=2)
-        save_safetensors(self.state_dict, save_path / _WEIGHTS_FILE)
+
-        for key, value in self.extra.items():
+            st.save_file(self.state_dict, save_path / "state_dict.safetensors")
-            save_torch(value, save_path / f"{key}.pt")
 
     @classmethod
-    def load(cls, save_dir: str, broadcast: bool = False) -> "Checkpoint":
+    def load(
+        cls,
+        save_dir: str,
+    ) -> "Checkpoint":
+
+        rank = get_rank()
         save_path = Path(save_dir)
 
-        meta = load_json(save_path / _META_FILE, broadcast)
+        meta = {}
-        config = load_json(save_path / _CONFIG_FILE, broadcast)
+        if rank == 0:
-        state_dict = load_state_dict(save_path / _WEIGHTS_FILE, broadcast=broadcast)
+            with open(Path(save_dir) / "meta.json", "r") as f:
+                meta = json.load(f)
 
-        extra = {}
+        if dist.is_initialized():
-        for f in sorted(save_path.iterdir()):
+            meta_list = [meta]
-            if f.suffix == ".pt":
+            dist.broadcast_object_list(meta_list, src=0)
-                extra[f.stem] = load_torch(f, broadcast=broadcast)
+            meta = meta_list[0]
+
+        state_dict = st.load_file(save_path / "state_dict.safetensors")
 
         return cls(
             state_dict=state_dict,
-            epoch=meta.get("epoch", 0),
+            epoch=meta["epoch"],
-            iteration=meta.get("iteration", 0),
+            iteration=meta["iteration"],
-            extra=extra,
-            config=config,
         )

astrai/tokenize/chat_template.py

@@ -1,10 +1,13 @@
+from dataclasses import dataclass
 from typing import Any, Dict, List, Optional
 
 from jinja2 import Template
 
+# Message type for chat messages
 type MessageType = Dict[str, Any]
 
 
+@dataclass
 class ChatTemplate:
     """A chat template with Jinja2 rendering support.
 
@@ -12,24 +15,23 @@ class ChatTemplate:
         name: Unique identifier for the template.
         template_str: Jinja2 template string.
         description: Optional description.
-        default_variables: Optional dictionary of default variable values.
+        default_variables: Optional dictionary of default variable values
+            that will be passed to the template if not overridden during rendering.
         special_tokens: Optional dictionary mapping token names to their string values.
+            These tokens are automatically added to the template variables.
     """
 
-    def __init__(
+    name: str
-        self,
+    template_str: str
-        name: str = "",
+    description: str = ""
-        template_str: str = "",
+    default_variables: Dict[str, Any] = None
-        description: str = "",
+    special_tokens: Dict[str, str] = None
-        default_variables: Optional[Dict[str, Any]] = None,
+
-        special_tokens: Optional[Dict[str, str]] = None,
+    def __post_init__(self):
-    ):
+        if self.default_variables is None:
-        self.name = name
+            self.default_variables = {}
-        self.template_str = template_str
+        if self.special_tokens is None:
-        self.description = description
+            self.special_tokens = {}
-        self.default_variables = default_variables or {}
-        self.special_tokens = special_tokens or {}
-        self._compiled: Template = Template(template_str)
 
     @classmethod
     def from_string(
@@ -41,7 +43,7 @@ class ChatTemplate:
     ) -> "ChatTemplate":
         """Create a ChatTemplate instance directly from a template string."""
         return cls(
-            name="",
+            name="",  # empty name for ad‑hoc templates
             template_str=template_str,
             description=description,
             default_variables=default_variables,
@@ -71,4 +73,5 @@ class ChatTemplate:
         if system_prompt is not None:
             variables["system_prompt"] = system_prompt
 
-        return self._compiled.render(**variables)
+        jinja_template = Template(self.template_str)
+        return jinja_template.render(**variables)

astrai/tokenize/tokenizer.py

@@ -51,26 +51,9 @@ class AutoTokenizer:
                 self.set_chat_template(config["chat_template"])
 
     @classmethod
-    def from_pretrained(cls, path: Union[str, Path]) -> "AutoTokenizer":
+    def from_pretrained(cls, path: Union[str, Path], **kwargs) -> "AutoTokenizer":
-        """Load tokenizer from pretrained directory.
+        """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):
@@ -81,11 +64,6 @@ class AutoTokenizer:
             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)
 

astrai/trainer/__init__.py

@@ -1,4 +1,3 @@
-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 (
@@ -10,8 +9,6 @@ from astrai.trainer.trainer import Trainer
 __all__ = [
     # Main trainer
     "Trainer",
-    # Optimizer
-    "Muon",
     # Strategy factory
     "StrategyFactory",
     "BaseStrategy",

astrai/trainer/metric_util.py

@@ -1,42 +1,75 @@
-from typing import Any, Callable, Dict
+from typing import 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)
+    """Compute gradient norm for each parameter in the model."""
+    norms = {}
+    for name, param in model.named_parameters():
+        norms[name] = 0.0
+        if param.grad:
+            norm = param.grad.data.norm(norm_type).item()
+            norms[name] = norm
+    return norms
 
 
 def grad_std(model: nn.Module) -> Dict[str, float]:
-    return _grad_stat(model, lambda g: g.std().item(), 0.0)
+    """Compute standard deviation of gradients for each parameter."""
+    stds = {}
+    for name, param in model.named_parameters():
+        stds[name] = 0.0
+        if param.grad:
+            std = param.grad.data.std().item()
+            stds[name] = std
+    return stds
 
 
 def grad_max(model: nn.Module) -> Dict[str, float]:
-    return _grad_stat(model, lambda g: g.max().item(), -float("inf"))
+    """Find the maximum absolute gradient value for each parameter."""
+    max_vals = {}
+    for name, param in model.named_parameters():
+        max_vals[name] = -float("inf")
+        if param.grad:
+            max_val = param.grad.data.max().item()
+            max_vals[name] = max_val
+
+    return max_vals
 
 
 def grad_min(model: nn.Module) -> Dict[str, float]:
-    return _grad_stat(model, lambda g: g.min().item(), float("inf"))
+    """Find the minimum absolute gradient value for each parameter."""
+    min_vals = {}
+    for name, param in model.named_parameters():
+        min_vals[name] = float("inf")
+        if param.grad:
+            min_val = param.grad.data.min().item()
+            min_vals[name] = min_val
+
+    return min_vals
 
 
 def grad_mean(model: nn.Module) -> Dict[str, float]:
-    return _grad_stat(model, lambda g: g.mean().item(), 0.0)
+    """Compute mean of gradients for each parameter."""
+    means = {}
+    for name, param in model.named_parameters():
+        means[name] = 0.0
+        if param.grad:
+            mean = param.grad.data.mean().item()
+            means[name] = mean
+
+    return means
 
 
 def grad_nan_num(model: nn.Module) -> Dict[str, int]:
-    return _grad_stat(model, lambda g: g.isnan().sum().item(), 0)
+    """Count the number of NaNs in gradients for each parameter."""
+    nan_nums = {}
+    for name, param in model.named_parameters():
+        nan_nums[name] = 0
+        if param.grad:
+            nan_num = param.grad.isnan().sum().item()
+            nan_nums[name] = nan_num
+    return nan_nums
 
 
 def ctx_get_loss(ctx):
@@ -47,10 +80,6 @@ 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)
 

astrai/trainer/optim.py

@@ -1,143 +0,0 @@
-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

@@ -42,7 +42,7 @@ class SchedulerFactory(BaseFactory["BaseScheduler"]):
     """
 
     @classmethod
-    def _validate_component(cls, scheduler_cls: Type[BaseScheduler]):
+    def _validate_component(cls, scheduler_cls: Type[BaseScheduler]) -> None:
         """Validate that the scheduler class inherits from BaseScheduler."""
         if not issubclass(scheduler_cls, BaseScheduler):
             raise TypeError(f"{scheduler_cls.__name__} must inherit from BaseScheduler")

astrai/trainer/strategy.py

@@ -1,5 +1,6 @@
 """Training strategy implementations with factory pattern."""
 
+import copy
 from abc import ABC, abstractmethod
 from typing import Any, Callable, Dict, Union
 
@@ -7,14 +8,26 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from torch import Tensor
+from torch.nn.parallel import DistributedDataParallel as DDP
 
 from astrai.factory import BaseFactory
 
 
-def create_ref_model(model_fn, state_dict: dict) -> nn.Module:
+def unwrap_model(model: nn.Module) -> nn.Module:
-    """Create a frozen reference model from model_fn + full state dict."""
+    """Unwrap DDP wrapper if present to get the original model."""
-    ref_model = model_fn()
+    if isinstance(model, DDP):
-    ref_model.load_state_dict(state_dict)
+        return model.module
+    return model
+
+
+def create_ref_model(model: nn.Module) -> nn.Module:
+    """Create a reference model for DPO/GRPO training.
+
+    Handles DDP-wrapped models safely by unwrapping first,
+    then creating a deep copy with frozen gradients.
+    """
+    original_model = unwrap_model(model)
+    ref_model = copy.deepcopy(original_model)
     ref_model.requires_grad_(False)
     ref_model.eval()
     return ref_model
@@ -68,22 +81,6 @@ def get_logprobs(
         return token_logprobs * shifted_mask
 
 
-def make_doc_boundary_mask(position_ids: Tensor) -> Tensor:
-    S = position_ids.size(1)
-    device = position_ids.device
-    boundaries = position_ids[:, 1:] <= position_ids[:, :-1]
-    doc_ids = torch.cat(
-        [
-            torch.zeros(position_ids.size(0), 1, dtype=torch.long, device=device),
-            boundaries.long().cumsum(dim=1),
-        ],
-        dim=1,
-    )
-    same_doc = doc_ids.unsqueeze(-1) == doc_ids.unsqueeze(-2)
-    causal = torch.tril(torch.ones(S, S, dtype=torch.bool, device=device))
-    return (same_doc & causal).unsqueeze(1)
-
-
 class BaseStrategy(ABC):
     """Abstract base class for training strategies."""
 
@@ -92,8 +89,6 @@ class BaseStrategy(ABC):
     ):
         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
@@ -128,7 +123,7 @@ class StrategyFactory(BaseFactory["BaseStrategy"]):
     """
 
     @classmethod
-    def _validate_component(cls, strategy_cls: type):
+    def _validate_component(cls, strategy_cls: type) -> None:
         """Validate that the strategy class inherits from BaseStrategy."""
         if not issubclass(strategy_cls, BaseStrategy):
             raise TypeError(f"{strategy_cls.__name__} must inherit from BaseStrategy")
@@ -196,19 +191,15 @@ class SFTStrategy(BaseStrategy):
 
     def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
         batch = move_to_device(batch, self.device)
-        input_ids, target_ids, position_ids, loss_mask = (
+        input_ids, target_ids, loss_mask = (
             batch["input_ids"],
             batch["target_ids"],
-            batch["position_ids"],
             batch["loss_mask"],
         )
 
         ignore_index = -100
-        input_mask = make_doc_boundary_mask(position_ids)
+        logits = self.model(input_ids=input_ids)["logits"]
         target_ids = target_ids.masked_fill(loss_mask == 0, ignore_index)
-        logits = self.model(
-            input_ids=input_ids, position_ids=position_ids, input_mask=input_mask
-        )["logits"]
 
         loss = F.cross_entropy(
             input=logits.flatten(0, 1).float(),
@@ -237,9 +228,7 @@ class DPOStrategy(BaseStrategy):
         **kwargs,
     ):
         super().__init__(model, device, **kwargs)
-        self.ref_model = create_ref_model(
+        self.ref_model = create_ref_model(model)
-            self.model_fn, self.executor.unwrap_model(model)
-        ).to(device=self.device)
         self.beta = beta
         self.reduction = reduction
 
@@ -276,9 +265,7 @@ class DPOStrategy(BaseStrategy):
 class GRPOStrategy(BaseStrategy):
     """Group Relative Policy Optimization strategy.
 
-    On-policy GRPO following DeepSeek-R1: the policy model is updated while
+    Implements GRPO with clipping and KL penalty.
-    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__(
@@ -289,29 +276,16 @@ class GRPOStrategy(BaseStrategy):
         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.ref_model = create_ref_model(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"]
@@ -323,6 +297,7 @@ class GRPOStrategy(BaseStrategy):
         masks_flat = masks.view(-1, response_len)
         prompt_expanded = prompts.unsqueeze(1).repeat(1, group_size, 1).flatten(0, 1)
 
+        # Shape: (batch_size * group_size, seq_len + response_len)
         full_sequences = torch.cat([prompt_expanded, responses_flat], dim=-1)
         full_masks = torch.cat([torch.ones_like(prompt_expanded), masks_flat], dim=-1)
 
@@ -337,13 +312,14 @@ class GRPOStrategy(BaseStrategy):
             )
             log_probs_ref = log_probs_ref.view(batch_size, group_size)
 
+        # Compute advantages from rewards with normalization
         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)
+        # PPO-style clipped surrogate objective
-
+        ratio = torch.exp(0)  # Off-policy: policy_model = old_model
         surr1 = ratio * advantages
         surr2 = torch.clamp(ratio, 1 - self.clip_eps, 1 + self.clip_eps) * advantages
 

astrai/trainer/train_callback.py

@@ -1,21 +1,15 @@
 import json
-import logging
 import os
-import sys
 import time
 from pathlib import Path
-from typing import IO, Callable, List, Optional, Protocol, runtime_checkable
+from typing import 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,
@@ -26,12 +20,9 @@ from astrai.trainer.metric_util import (
     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):
@@ -51,15 +42,18 @@ class TrainCallback(Protocol):
     def on_epoch_end(self, context: TrainContext):
         """Called at the end of each epoch."""
 
+    def on_step_begin(self, context: TrainContext):
+        """Called at the beginning of each step."""
+
+    def on_step_end(self, context: TrainContext):
+        """Called at the end of each step."""
+
     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."""
 
@@ -75,6 +69,12 @@ class CallbackFactory(BaseFactory[TrainCallback]):
         callback = CallbackFactory.create("my_callback", **kwargs)
     """
 
+    @classmethod
+    def _validate_component(cls, callback_cls: type) -> None:
+        """Validate that the callback class inherits from TrainCallback."""
+        if not issubclass(callback_cls, TrainCallback):
+            raise TypeError(f"{callback_cls.__name__} must inherit from TrainCallback")
+
 
 @CallbackFactory.register("gradient_clipping")
 class GradientClippingCallback(TrainCallback):
@@ -85,43 +85,28 @@ class GradientClippingCallback(TrainCallback):
     def __init__(self, max_grad_norm: float):
         self.max_grad_norm = max_grad_norm
 
-    def on_optimizer_step(self, context: TrainContext):
+    def on_step_begin(self, context: TrainContext):
+        _ = context
         clip_grad_norm_(context.model.parameters(), self.max_grad_norm)
 
 
-@CallbackFactory.register("gradient_checkpointing")
+@CallbackFactory.register("scheduler")
-class GradientCheckpointingCallback(TrainCallback):
+class SchedulerCallback(TrainCallback):
     """
-    Activation checkpointing callback — trades compute for memory
+    Scheduler callback for trainer.
-    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):
+    def __init__(self):
-        self.modules = tuple(modules) if modules else ()
+        pass
-
-    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)
+        for group in context.optimizer.param_groups:
-        logger.info("Gradient checkpointing enabled")
+            if "initial_lr" not in group:
+                group["initial_lr"] = group["lr"]
 
-    def on_train_end(self, context: TrainContext):
+    def on_batch_end(self, context: TrainContext):
-        context.model.apply(self._disable)
+        if context.scheduler:
+            context.scheduler.step()
 
 
 @CallbackFactory.register("checkpoint")
@@ -130,38 +115,36 @@ 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,
+        state_dict_fn: Optional[Callable[[nn.Module], 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.state_dict_fn = state_dict_fn
         self.last_ckpt_iter = 0
 
+    @only_on_rank(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)
+        state_dict = (
-            context.checkpoint = Checkpoint(
+            self.state_dict_fn(context.model)
-                state_dict=state_dict,
+            if self.state_dict_fn
-                epoch=context.epoch,
+            else context.model.state_dict()
-                iteration=context.iteration,
-                extra=extra,
-                config=context.model_config,
         )
+
+        context.checkpoint = Checkpoint(
+            state_dict=state_dict, epoch=context.epoch, iteration=context.iteration
+        )
+
         context.checkpoint.save(save_path)
+        self.last_ckpt_iter = context.iteration
 
     def on_batch_end(self, context: TrainContext):
         if context.iteration - self.last_ckpt_iter >= self.interval:
@@ -174,15 +157,6 @@ class CheckpointCallback(TrainCallback):
     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):
@@ -190,12 +164,8 @@ class ProgressBarCallback(TrainCallback):
     Progress bar callback for trainer.
     """
 
-    def __init__(
+    def __init__(self, num_epoch: int):
-        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)
@@ -204,18 +174,16 @@ class ProgressBarCallback(TrainCallback):
             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 = {
+        self.progress_bar.set_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)
@@ -247,7 +215,6 @@ class MetricLoggerCallback(TrainCallback):
         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,
@@ -258,7 +225,7 @@ class MetricLoggerCallback(TrainCallback):
 
     def _get_log_data(self, context: TrainContext):
         return {
-            "timestamp": time.strftime("%Y-%m-%dT%H:%M:%S"),
+            "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
             "epoch": context.epoch,
             "iter": context.iteration,
             **{m: self._metric_funcs[m](context) for m in self.metrics},
@@ -291,43 +258,3 @@ class MetricLoggerCallback(TrainCallback):
 
     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

@@ -1,18 +1,15 @@
 from dataclasses import dataclass, field
-from pathlib import Path
 from typing import Optional, Self
 
-import torch
 import torch.nn as nn
-from torch.utils.data import DataLoader, random_split
+from torch.optim import Optimizer
+from torch.optim.lr_scheduler import LRScheduler
+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
-from astrai.serialization import Checkpoint, load_json, load_model_weights
 from astrai.trainer.strategy import BaseStrategy, StrategyFactory
 
 
@@ -21,18 +18,13 @@ class TrainContext:
     model: nn.Module = field(default=None)
     strategy: BaseStrategy = field(default=None)
     dataloader: DataLoader = field(default=None)
-    optimizer: OptimizerProtocol = field(default=None)
+    optimizer: Optimizer = field(default=None)
-    scheduler: SchedulerProtocol = field(default=None)
+    scheduler: LRScheduler = 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)
@@ -40,144 +32,68 @@ class TrainContext:
 
 
 class TrainContextBuilder:
-    def __init__(
+    def __init__(self, config: TrainConfig):
-        self,
-        config: TrainConfig,
-    ):
         self.config = config
-        self._resume_dir: Optional[str] = None
+        self._context = TrainContext(
+            model=config.model,
+            world_size=get_world_size(),
+            rank=get_rank(),
+        )
 
-    def with_resume_dir(self, resume_dir: Optional[str]) -> Self:
+        device = get_current_device()
-        self._resume_dir = resume_dir
+        self._context.model = self._context.model.to(device=device)
+
+        if self.config.nprocs > 1:
+            fn = self.config.parallel_wrapper
+            self._context.model = fn(self._context.model)
+
+        self._context.optimizer = self.config.optimizer_fn(self._context.model)
+        self._context.scheduler = self.config.scheduler_fn(self._context.optimizer)
+
+    def with_checkpoint(self, checkpoint: Optional[Checkpoint]) -> Self:
+        if checkpoint is None:
+            checkpoint = Checkpoint(
+                state_dict=self._context.model.state_dict(),
+            )
+        else:
+            # resume from the assigned checkpoint or assigned iteration
+            self._context.epoch = max(checkpoint.epoch, self.config.start_epoch)
+            self._context.iteration = max(checkpoint.iteration, self.config.start_batch)
+            self._context.model.load_state_dict(checkpoint.state_dict)
+
+        self._context.checkpoint = checkpoint
+        return self
+
+    def with_dataloader(self) -> Self:
+        # fix: change batch level iteration to sample level offset
+        config = self.config
+        sampler_offset = self._context.iteration * config.batch_size
+        resumeable_sampler = ResumableDistributedSampler(
+            data_source=config.dataset,
+            start_epoch=self._context.epoch,
+            start_iter=sampler_offset,
+            seed=config.random_seed,
+        )
+
+        dataloader = DataLoader(
+            config.dataset,
+            batch_size=config.batch_size,
+            sampler=resumeable_sampler,
+            num_workers=config.num_workers,
+            pin_memory=config.pin_memory,
+            prefetch_factor=config.prefetch_factor,
+        )
+        self._context.dataloader = dataloader
+        return self
+
+    def with_strategy(self) -> Self:
+        self._context.strategy = StrategyFactory.create(
+            model=self._context.model,
+            train_type=self.config.strategy,
+            device=get_current_device(),
+            **self.config.extra_kwargs,
+        )
         return self
 
     def build(self) -> TrainContext:
-        cfg = self.config
+        return self._context
-        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)
-
-        train_dataset = cfg.dataset
-        val_dataset = cfg.val_dataset
-
-        if val_dataset is None and cfg.val_split is not None:
-            n_total = len(cfg.dataset)
-            n_val = max(1, int(n_total * cfg.val_split))
-            n_train = n_total - n_val
-            generator = torch.Generator().manual_seed(cfg.random_seed)
-            train_dataset, val_dataset = random_split(
-                cfg.dataset, [n_train, n_val], generator=generator
-            )
-
-        sampler_offset = context.iteration * cfg.batch_per_device
-        sampler = ResumableDistributedSampler(
-            data_source=train_dataset,
-            start_epoch=context.epoch,
-            start_iter=sampler_offset,
-            seed=cfg.random_seed,
-        )
-        context.dataloader = DataLoader(
-            train_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 val_dataset is not None:
-            val_sampler = ResumableDistributedSampler(
-                data_source=val_dataset,
-                start_epoch=0,
-                start_iter=0,
-                seed=cfg.random_seed,
-                shuffle=False,
-            )
-            context.val_dataloader = DataLoader(
-                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

@@ -3,6 +3,7 @@ from typing import List, Optional
 
 from astrai.config import TrainConfig
 from astrai.parallel.setup import spawn_parallel_fn
+from astrai.serialization import Checkpoint
 from astrai.trainer.train_callback import (
     CallbackFactory,
     TrainCallback,
@@ -24,28 +25,22 @@ class Trainer:
 
     def _get_default_callbacks(self) -> List[TrainCallback]:
         cfg = self.train_config
-        callbacks = [
+        return [
-            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("checkpoint", cfg.ckpt_dir, cfg.ckpt_interval),
+            CallbackFactory.create("metric_logger", cfg.ckpt_dir, cfg.ckpt_interval),
             CallbackFactory.create("gradient_clipping", cfg.max_grad_norm),
-            CallbackFactory.create("validation"),
+            CallbackFactory.create("scheduler"),
         ]
-        return callbacks
+
+    def _build_context(self, checkpoint: Optional[Checkpoint]) -> TrainContext:
+        return (
+            TrainContextBuilder(self.train_config)
+            .with_checkpoint(checkpoint)
+            .with_dataloader()
+            .with_strategy()
+            .build()
+        )
 
     def _call_callbacks(self, method_name: str, context: TrainContext):
         for callback in self.callbacks:
@@ -53,57 +48,55 @@ class Trainer:
             if method:
                 method(context)
 
-    def _trainer_loop(self, resume_dir: Optional[str] = None):
+    def train(self, checkpoint: Optional[Checkpoint] = None):
-        context = (
+        config = self.train_config
-            TrainContextBuilder(self.train_config).with_resume_dir(resume_dir).build()
+        spawn_parallel_fn(
+            self._train_impl,
+            backend=config.backend,
+            world_size=config.nprocs,
+            master_addr=config.master_addr,
+            master_port=config.master_port,
+            device_type=config.device_type,
+            device_ids=config.device_ids,
+            checkpoint=checkpoint,
         )
-        executor = context.executor
+
+    def _train_impl(self, checkpoint: Optional[Checkpoint] = None) -> Checkpoint:
+        context = self._build_context(checkpoint)
         self._call_callbacks("on_train_begin", context)
 
         try:
             context.model.train()
-
+            # 1.epoch
-            for epoch in range(context.epoch, context.config.n_epoch):
+            for epoch in range(context.epoch, self.train_config.n_epoch):
                 context.epoch = epoch
                 self._call_callbacks("on_epoch_begin", context)
 
                 for batch in context.dataloader:
-                    self._call_callbacks("on_batch_begin", context)
+                    if context.iteration % self.train_config.accumulation_steps == 0:
-
+                        # 2. step
-                    with executor.accumulate(context.model):
+                        self._call_callbacks("on_step_begin", context)
-                        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()
+                        self._call_callbacks("on_step_end", context)
 
-                            if context.scheduler:
+                    # 3. batch
-                                context.scheduler.step()
+                    self._call_callbacks("on_batch_begin", context)
+                    loss = context.strategy(batch)
+                    context.loss = loss.item()
+                    context.iteration += 1
+
+                    # to make the loss normalized by accumulation steps
+                    stand_loss = loss / self.train_config.accumulation_steps
+                    stand_loss.backward()
+
+                    self._call_callbacks("on_batch_end", context)
 
                 self._call_callbacks("on_epoch_end", context)
 
         except Exception as e:
-            logger.error("Training failed: %s", str(e), exc_info=True)
+            logger.error(f"Training failed: {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,
-        )

docker-compose.yml

@@ -1,44 +0,0 @@
-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

scripts/demo/generate_ar.py

@@ -11,6 +11,7 @@ PARAMETER_ROOT = Path(PROJECT_ROOT, "params")
 
 
 def generate_text():
+    # Load model from pretrained
     model = AutoModel.from_pretrained(PARAMETER_ROOT)
     tokenizer = AutoTokenizer.from_pretrained(PARAMETER_ROOT)
     model.to(device="cuda", dtype=torch.bfloat16)
@@ -21,15 +22,16 @@ def generate_text():
         model=model,
         tokenizer=tokenizer,
     )
-    for token in engine.generate(
+    response = engine.generate(
         prompt=query,
-        stream=True,
+        stream=False,
         max_tokens=2048,
         temperature=0.8,
         top_p=0.95,
         top_k=50,
-    ):
+    )
-        print(token, end="", flush=True)
+
+    print(response)
 
 
 if __name__ == "__main__":

scripts/demo/generate_batch.py

@@ -24,23 +24,12 @@ def batch_generate():
         "请问什么是显卡",
     ]
 
-    prompts = [
-        tokenizer.apply_chat_template(
-            [
-                {"role": "system", "content": "You are a helpful assistant."},
-                {"role": "user", "content": q},
-            ],
-            tokenize=False,
-        )
-        for q in inputs
-    ]
-
     engine = InferenceEngine(
         model=model,
         tokenizer=tokenizer,
     )
     responses = engine.generate(
-        prompt=prompts,
+        prompt=inputs,
         stream=False,
         max_tokens=2048,
         temperature=0.8,

scripts/demo/stream_chat.py

@@ -15,7 +15,7 @@ def chat():
     tokenizer = AutoTokenizer.from_pretrained(PARAMETER_ROOT)
     model.to(device="cuda", dtype=torch.bfloat16)
 
-    messages = [{"role": "system", "content": "You are a helpful assistant."}]
+    messages = []
     engine = InferenceEngine(model=model, tokenizer=tokenizer)
 
     while True:

scripts/docker.sh

@@ -16,7 +16,6 @@ NC='\033[0m' # No Color
 IMAGE_NAME="astrai"
 IMAGE_TAG="latest"
 REGISTRY=""
-CONTAINER_ID=""
 
 # Print colored messages
 print_info() {
@@ -176,10 +175,6 @@ main() {
                 PORT="$2"
                 shift 2
                 ;;
-            --container)
-                CONTAINER_ID="$2"
-                shift 2
-                ;;
             --gpu)
                 GPU=true
                 shift
@@ -202,7 +197,6 @@ main() {
                 echo "  --dockerfile FILE  Dockerfile path (default: Dockerfile)"
                 echo "  --context PATH     Build context (default: .)"
                 echo "  --port PORT        Port for run (default: 8000)"
-                echo "  --container ID     Container ID for logs"
                 echo "  --gpu              Enable GPU support"
                 echo "  --help             Show this help message"
                 echo ""
@@ -211,7 +205,6 @@ main() {
                 echo "  $0 build --tag v1.0.0"
                 echo "  $0 run --port 8080"
                 echo "  $0 run --gpu"
-                echo "  $0 logs --container abc123"
                 echo "  $0 push --registry ghcr.io/username"
                 exit 0
                 ;;
@@ -244,7 +237,7 @@ main() {
             show_info
             ;;
         logs)
-            show_logs "$CONTAINER_ID"
+            show_logs "$2"
             ;;
         "")
             print_error "No command specified. Use --help for usage"

scripts/tools/benchmark.py

@@ -1,13 +1,9 @@
-"""Benchmark AutoRegressiveLM with KVCache"""
-
 from dataclasses import dataclass
 from typing import Any, Dict
 
 import torch
 
-from astrai.config import AutoRegressiveLMConfig
+from astrai.model.transformer import ModelConfig, Transformer
-from astrai.inference import KVCache
-from astrai.model.transformer import AutoRegressiveLM
 
 
 @dataclass
@@ -21,27 +17,29 @@ class BenchmarkResult:
 class GenerationBenchmark:
     def __init__(
         self,
-        config: AutoRegressiveLMConfig,
+        config: ModelConfig,
         device: str = "cuda",
-        dtype: torch.dtype = torch.bfloat16,
+        dtype: torch.dtype = torch.float16,
-        page_size: int = 128,
     ):
         self.config = config
         self.device = device
         self.dtype = dtype
-        self.model = AutoRegressiveLM(config).to(device=device, dtype=dtype)
+        self.model = Transformer(config).to(device=device, dtype=dtype)
         self.model.eval()
-        head_dim = config.dim // config.n_heads
+
-        n_pages = (config.max_len * 4 + page_size - 1) // page_size
+    def _initialize_kv_cache(self, batch_size: int) -> list:
-        self._page_cache = KVCache(
+        """初始化KV缓存"""
+        config = self.config
+        shape = (
+            batch_size,
+            config.max_len,
             config.n_layers,
-            n_pages,
-            page_size,
             config.n_kv_heads,
-            head_dim,
+            config.dim // config.n_heads,
-            device,
-            dtype,
         )
+        k_cache = torch.zeros(shape, device=self.device, dtype=self.dtype)
+        v_cache = torch.zeros(shape, device=self.device, dtype=self.dtype)
+        return (k_cache, v_cache)
 
     def _prepare_inputs(self, batch_size: int, prompt_length: int, total_length: int):
         prompt_ids = torch.randint(
@@ -51,6 +49,7 @@ class GenerationBenchmark:
             device=self.device,
             dtype=torch.long,
         )
+
         gen_ids = torch.randint(
             low=0,
             high=self.config.vocab_size,
@@ -58,6 +57,7 @@ class GenerationBenchmark:
             device=self.device,
             dtype=torch.long,
         )
+
         return prompt_ids, gen_ids
 
     @torch.inference_mode()
@@ -67,11 +67,13 @@ class GenerationBenchmark:
         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
@@ -81,20 +83,20 @@ class GenerationBenchmark:
             prompt_ids, _ = self._prepare_inputs(
                 batch_size, prompt_length, prompt_length
             )
-            start = torch.cuda.Event(enable_timing=True)
+            start_event = torch.cuda.Event(enable_timing=True)
-            end = torch.cuda.Event(enable_timing=True)
+            end_event = torch.cuda.Event(enable_timing=True)
 
-            start.record()
+            start_event.record()
             _ = self.model(prompt_ids)
-            end.record()
+            end_event.record()
             torch.cuda.synchronize()
 
-            trial_time = start.elapsed_time(end) / 1000
+            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} tok/s)"
+                f"({prompt_length / trial_time:.1f} tokens/s)"
             )
 
         return BenchmarkResult(
@@ -105,7 +107,7 @@ class GenerationBenchmark:
                 "benchmark_type": "prefill",
                 "batch_size": batch_size,
                 "prompt_length": prompt_length,
-                "dtype": str(self.dtype),
+                "dtype": self.dtype,
                 "device": self.device,
             },
         )
@@ -118,74 +120,41 @@ class GenerationBenchmark:
         gen_length: int = 128,
         num_trials: int = 5,
     ) -> BenchmarkResult:
+
         total_time = 0.0
         total_tokens = batch_size * gen_length * num_trials
-        page_size = self._page_cache.page_size
 
         for trial in range(num_trials):
             prompt_ids, gen_ids = self._prepare_inputs(
-                batch_size,
+                batch_size, prompt_length, prompt_length + gen_length
-                prompt_length,
-                prompt_length + gen_length,
-            )
-
-            n_pages = (prompt_length + gen_length + page_size - 1) // page_size
-            total = n_pages * batch_size
-            pages = []
-            for _ in range(total):
-                p = self._page_cache._pool.alloc()
-                assert p >= 0, "OOM"
-                pages.append(p)
-            page_table = torch.tensor(
-                [pages[i * n_pages : (i + 1) * n_pages] for i in range(batch_size)],
-                dtype=torch.long,
-                device=self.device,
-            )
-
-            cv = self._page_cache.bind(page_table, total_len=prompt_length)
-            _ = self.model(
-                prompt_ids,
-                paged_cache=cv,
-                position_ids=torch.arange(
-                    prompt_length, dtype=torch.long, device=self.device
-                )
-                .unsqueeze(0)
-                .expand(batch_size, -1),
             )
+            kv_cache = self._initialize_kv_cache(batch_size)
+            _ = self.model(prompt_ids, persistent_key_values=kv_cache, start_pos=0)
 
             torch.cuda.synchronize()
 
-            start = torch.cuda.Event(enable_timing=True)
+            start_event = torch.cuda.Event(enable_timing=True)
-            end = torch.cuda.Event(enable_timing=True)
+            end_event = torch.cuda.Event(enable_timing=True)
+
+            start_event.record()
 
-            start.record()
             current_pos = prompt_length
             for i in range(gen_length):
                 input_token = gen_ids[:, i : i + 1]
-                cv = self._page_cache.bind(page_table, total_len=current_pos + 1)
                 _ = self.model(
-                    input_token,
+                    input_token, persistent_key_values=kv_cache, start_pos=current_pos
-                    paged_cache=cv,
-                    position_ids=torch.full(
-                        (batch_size, 1),
-                        current_pos,
-                        dtype=torch.long,
-                        device=self.device,
-                    ),
                 )
                 current_pos += 1
-            end.record()
+
+            end_event.record()
             torch.cuda.synchronize()
 
-            trial_time = start.elapsed_time(end) / 1000
+            trial_time = start_event.elapsed_time(end_event) / 1000
             total_time += trial_time
 
-            for idx in pages:
-                self._page_cache._pool.free(idx)
-
             print(
                 f"Trial {trial + 1}/{num_trials}: {gen_length} tokens in {trial_time:.3f}s "
-                f"({gen_length / trial_time:.1f} tok/s)"
+                f"({gen_length / trial_time:.1f} tokens/s)"
             )
 
         return BenchmarkResult(
@@ -197,26 +166,36 @@ class GenerationBenchmark:
                 "batch_size": batch_size,
                 "prompt_length": prompt_length,
                 "gen_length": gen_length,
-                "dtype": str(self.dtype),
+                "dtype": self.dtype,
                 "device": self.device,
             },
         )
 
 
 def print_benchmark_result(result: BenchmarkResult):
-    btype = result.metadata["benchmark_type"]
+    """打印基准测试结果"""
-    print(f"\n{' ' + btype.upper() + ' Benchmark ':-^80}")
+    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} tok/s")
+    print(f"Throughput: {result.tokens_per_second:,.1f} tokens/s")
-    for k, v in result.metadata.items():
+
-        if k != "benchmark_type":
+    if benchmark_type == "prefill":
-            print(f"{k.replace('_', ' ').title()}: {v}")
+        print(
+            f"Batch Size: {result.metadata['batch_size']} | Prompt Length: {result.metadata['prompt_length']}"
+        )
+    elif benchmark_type == "decoding":
+        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 = AutoRegressiveLMConfig(
+    config = ModelConfig(
         vocab_size=10000,
         dim=1536,
         n_heads=24,
@@ -230,20 +209,15 @@ if __name__ == "__main__":
     benchmark = GenerationBenchmark(config)
 
     print("=" * 80)
-    print("Running AutoRegressiveLM Generation Benchmark (KVCache)")
+    print("Running Transformer Generation Benchmark")
     print("=" * 80)
 
     prefill_result = benchmark.run_prefill_benchmark(
-        batch_size=4,
+        batch_size=4, prompt_length=512, num_trials=5
-        prompt_length=512,
-        num_trials=5,
     )
     print_benchmark_result(prefill_result)
 
     gen_result = benchmark.run_decoding_benchmark(
-        batch_size=4,
+        batch_size=4, prompt_length=512, gen_length=128, num_trials=5
-        prompt_length=512,
-        gen_length=128,
-        num_trials=5,
     )
     print_benchmark_result(gen_result)

scripts/tools/evaluate_humaneval.py

@@ -1,336 +0,0 @@
-"""HumanEval code generation benchmark.
-
-Generates n completions per problem, extracts function bodies, executes
-against hidden tests, and computes pass@k.
-
-Usage::
-
-    python scripts/tools/evaluate_humaneval.py --param_path ./params \
-        --data_path HumanEval.jsonl.gz --output results.json \
-        --num_samples 200 --temperature 0.8 --max_tokens 512
-"""
-
-import argparse
-import json
-import os
-import re
-import signal
-import sys
-from math import prod
-from multiprocessing import Process, Queue
-from typing import Dict, List, Optional, Tuple
-
-import numpy as np
-import torch
-import tqdm
-
-from astrai.inference import InferenceEngine
-from astrai.model import AutoModel
-from astrai.tokenize import AutoTokenizer
-
-HUMANEVAL_URL = (
-    "https://github.com/openai/human-eval/raw/master/data/HumanEval.jsonl.gz"
-)
-
-_STOP_SEQUENCES = [
-    "\nclass ",
-    "\ndef ",
-    "\n# ",
-    "\nif __name__",
-    "\nprint(",
-    "\n\n\n",
-]
-
-
-def _download_humaneval(data_path: str):
-    if os.path.exists(data_path):
-        return
-    import gzip
-    import urllib.request
-
-    os.makedirs(os.path.dirname(data_path) or ".", exist_ok=True)
-    print(f"Downloading HumanEval from {HUMANEVAL_URL} ...")
-    tmp = data_path + ".tmp"
-    urllib.request.urlretrieve(HUMANEVAL_URL, tmp)
-    with gzip.open(tmp, "rb") as f_in:
-        with open(data_path, "wb") as f_out:
-            f_out.write(f_in.read())
-    os.remove(tmp)
-    print(f"  saved to {data_path}")
-
-
-def _load_problems(data_path: str) -> List[dict]:
-    problems = []
-    with open(data_path, "r", encoding="utf-8") as f:
-        for line in f:
-            line = line.strip()
-            if line:
-                problems.append(json.loads(line))
-    return problems
-
-
-def _extract_function_body(code: str, entry_point: str) -> Optional[str]:
-    """Extract the function body from a completion."""
-    pattern = rf"def\s+{re.escape(entry_point)}\b[^:]*:"
-    match = re.search(pattern, code)
-    if not match:
-        # Use the full code as-is if we can't find the function
-        return code
-
-    body_start = match.end()
-    lines = code[body_start:].split("\n")
-    body_lines = []
-    started = False
-
-    for line in lines:
-        stripped = line.rstrip()
-        if not stripped and not started:
-            continue
-        if not stripped and started:
-            body_lines.append("")
-            continue
-        if not started:
-            started = True
-        if stripped.lstrip() == stripped and started:
-            break
-        body_lines.append(stripped)
-
-    body = "\n".join(body_lines)
-    if not body.strip():
-        return None
-    return body
-
-
-def _trim_stop_sequences(text: str) -> str:
-    for stop in _STOP_SEQUENCES:
-        idx = text.find(stop)
-        if idx != -1:
-            text = text[:idx]
-    return text
-
-
-def _execute_code(problem: dict, completion: str, timeout: float = 3.0) -> bool:
-    """Run the completion against hidden tests in a subprocess."""
-
-    def _worker(queue, full_code):
-        try:
-            namespace = {}
-            exec(full_code, namespace)
-            check = namespace.get("check")
-            if check is None:
-                queue.put(False)
-                return
-            check(namespace.get(problem["entry_point"]))
-            queue.put(True)
-        except Exception:
-            queue.put(False)
-
-    full_code = problem["prompt"] + completion + "\n" + problem["test"]
-
-    queue: Queue = Queue()
-    proc = Process(target=_worker, args=(queue, full_code))
-    proc.start()
-    proc.join(timeout)
-
-    if proc.is_alive():
-        proc.terminate()
-        proc.join()
-        return False
-
-    try:
-        return queue.get_nowait()
-    except Exception:
-        return False
-
-
-def _pass_at_k(n: int, c: int, k: int) -> float:
-    """Unbiased estimator of pass@k."""
-    if n - c < k:
-        return 1.0
-    return 1.0 - float(prod(1.0 - k / np.arange(n - c + 1, n + 1)))
-
-
-def _deduplicate(completions: List[str]) -> List[str]:
-    seen = set()
-    unique = []
-    for c in completions:
-        if c not in seen:
-            seen.add(c)
-            unique.append(c)
-    return unique
-
-
-def _generate(
-    engine: InferenceEngine,
-    prompt: str,
-    num_samples: int,
-    max_tokens: int,
-    temperature: float,
-    top_p: float,
-    top_k: int,
-    batch_size: int,
-) -> List[str]:
-    batches = [prompt] * min(batch_size, num_samples)
-    completions = []
-    remaining = num_samples
-
-    while remaining > 0:
-        current = min(batch_size, remaining)
-        batch_prompts = batches[:current]
-        outputs = engine.generate(
-            prompt=batch_prompts,
-            stream=False,
-            max_tokens=max_tokens,
-            temperature=temperature,
-            top_p=top_p,
-            top_k=top_k,
-        )
-        if isinstance(outputs, str):
-            outputs = [outputs]
-        completions.extend(outputs)
-        remaining -= current
-
-    return _deduplicate(completions)
-
-
-def evaluate(
-    engine: InferenceEngine,
-    problems: List[dict],
-    num_samples: int,
-    max_tokens: int,
-    temperature: float,
-    top_p: float,
-    top_k: int,
-    batch_size: int,
-    k_values: Tuple[int, ...] = (1, 10, 100),
-) -> Dict:
-    results = {}
-    all_pass_at_k = {k: [] for k in k_values}
-
-    for problem in tqdm.tqdm(problems, desc="HumanEval", unit="problem"):
-        task_id = problem["task_id"]
-        prompt = problem["prompt"]
-        entry_point = problem["entry_point"]
-
-        raw_completions = _generate(
-            engine,
-            prompt,
-            num_samples,
-            max_tokens,
-            temperature,
-            top_p,
-            top_k,
-            batch_size,
-        )
-
-        completions = []
-        for raw in raw_completions:
-            trimmed = _trim_stop_sequences(raw)
-            body = _extract_function_body(trimmed, entry_point)
-            if body:
-                completions.append(body)
-
-        passed = 0
-        for comp in completions:
-            if _execute_code(problem, comp):
-                passed += 1
-
-        n = len(completions)
-        c = passed
-        result = {"task_id": task_id, "n": n, "passed": c}
-        for k in k_values:
-            result[f"pass@{k}"] = round(_pass_at_k(n, c, k), 4)
-            all_pass_at_k[k].append(_pass_at_k(n, c, k))
-        results[task_id] = result
-
-    summary = {}
-    for k in k_values:
-        vals = all_pass_at_k[k]
-        summary[f"pass@{k}"] = round(float(np.mean(vals)), 4)
-    results["_summary"] = summary
-
-    return results
-
-
-def main():
-    parser = argparse.ArgumentParser(description="HumanEval benchmark")
-    parser.add_argument(
-        "--param_path", type=str, default="./params", help="Model directory"
-    )
-    parser.add_argument(
-        "--data_path",
-        type=str,
-        default="./humaneval/HumanEval.jsonl",
-        help="HumanEval JSONL file (auto-download if missing)",
-    )
-    parser.add_argument("--output", type=str, default=None, help="Output JSON path")
-    parser.add_argument(
-        "--num_samples",
-        type=int,
-        default=200,
-        help="Completions per problem",
-    )
-    parser.add_argument(
-        "--max_tokens", type=int, default=512, help="Max generation tokens"
-    )
-    parser.add_argument(
-        "--temperature", type=float, default=0.8, help="Sampling temperature"
-    )
-    parser.add_argument("--top_p", type=float, default=0.95, help="Top-p sampling")
-    parser.add_argument("--top_k", type=int, default=50, help="Top-k sampling")
-    parser.add_argument(
-        "--batch_size", type=int, default=1, help="Inference batch size"
-    )
-    parser.add_argument(
-        "--problems",
-        type=int,
-        nargs="+",
-        default=None,
-        help="Specific problem indices (0-based)",
-    )
-    args = parser.parse_args()
-
-    _download_humaneval(args.data_path)
-    problems = _load_problems(args.data_path)
-    if args.problems:
-        problems = [problems[i] for i in args.problems if i < len(problems)]
-
-    model = AutoModel.from_pretrained(args.param_path)
-    tokenizer = AutoTokenizer.from_pretrained(args.param_path)
-    model.to(device="cuda", dtype=torch.bfloat16)
-
-    engine = InferenceEngine(
-        model=model,
-        tokenizer=tokenizer,
-        max_batch_size=args.batch_size,
-    )
-
-    results = evaluate(
-        engine=engine,
-        problems=problems,
-        num_samples=args.num_samples,
-        max_tokens=args.max_tokens,
-        temperature=args.temperature,
-        top_p=args.top_p,
-        top_k=args.top_k,
-        batch_size=args.batch_size,
-        k_values=(1, 10, 100),
-    )
-
-    summary = results.pop("_summary")
-    print(f"\n{'=' * 60}")
-    for k, v in summary.items():
-        print(f"  {k}: {v:.2%}")
-    print(f"{'=' * 60}")
-
-    if args.output:
-        results["_summary"] = summary
-        with open(args.output, "w", encoding="utf-8") as f:
-            json.dump(results, f, indent=2, ensure_ascii=False)
-        print(f"Results saved to {args.output}")
-
-    engine.shutdown()
-
-
-if __name__ == "__main__":
-    main()

scripts/tools/evaluate_mmlu.py

@@ -1,319 +0,0 @@
-"""MMLU evaluation via log-likelihood ranking."""
-
-import argparse
-import csv
-import json
-import os
-import shutil
-import tarfile
-
-import requests
-import torch
-import torch.nn.functional as F
-import tqdm
-
-from astrai.model import AutoModel
-from astrai.tokenize import AutoTokenizer
-
-MMLU_URL = "https://people.eecs.berkeley.edu/~hendrycks/data.tar"
-MMLU_SUBJECTS = [
-    "abstract_algebra",
-    "anatomy",
-    "astronomy",
-    "business_ethics",
-    "clinical_knowledge",
-    "college_biology",
-    "college_chemistry",
-    "college_computer_science",
-    "college_mathematics",
-    "college_medicine",
-    "college_physics",
-    "computer_security",
-    "conceptual_physics",
-    "econometrics",
-    "electrical_engineering",
-    "elementary_mathematics",
-    "formal_logic",
-    "global_facts",
-    "high_school_biology",
-    "high_school_chemistry",
-    "high_school_computer_science",
-    "high_school_european_history",
-    "high_school_geography",
-    "high_school_government_and_politics",
-    "high_school_macroeconomics",
-    "high_school_mathematics",
-    "high_school_microeconomics",
-    "high_school_physics",
-    "high_school_psychology",
-    "high_school_statistics",
-    "high_school_us_history",
-    "high_school_world_history",
-    "human_aging",
-    "human_sexuality",
-    "international_law",
-    "jurisprudence",
-    "logical_fallacies",
-    "machine_learning",
-    "management",
-    "marketing",
-    "medical_genetics",
-    "miscellaneous",
-    "moral_disputes",
-    "moral_scenarios",
-    "nutrition",
-    "philosophy",
-    "prehistory",
-    "professional_accounting",
-    "professional_law",
-    "professional_medicine",
-    "professional_psychology",
-    "public_relations",
-    "security_studies",
-    "sociology",
-    "us_foreign_policy",
-    "virology",
-    "world_religions",
-]
-
-
-def _download_and_extract(url: str, data_dir: str):
-    tar_path = os.path.join(data_dir, "data.tar")
-    os.makedirs(data_dir, exist_ok=True)
-    print(f"Downloading MMLU data from {url}...")
-    resp = requests.get(url, stream=True, timeout=300)
-    resp.raise_for_status()
-    total = int(resp.headers.get("content-length", 0))
-    with tqdm.tqdm(total=total, unit="B", unit_scale=True, desc="  Download") as bar:
-        with open(tar_path, "wb") as f:
-            for chunk in resp.iter_content(chunk_size=8192):
-                f.write(chunk)
-                bar.update(len(chunk))
-    print("Extracting...")
-    with tarfile.open(tar_path, "r") as tf:
-        tf.extractall(data_dir)
-    os.remove(tar_path)
-
-
-def download_mmlu(data_dir: str):
-    _download_and_extract(MMLU_URL, data_dir)
-    src = os.path.join(data_dir, "data")
-    if os.path.exists(src):
-        for item in os.listdir(src):
-            src_item = os.path.join(src, item)
-            dst_item = os.path.join(data_dir, item)
-            if os.path.exists(dst_item):
-                if os.path.isdir(dst_item):
-                    shutil.rmtree(dst_item)
-                else:
-                    os.remove(dst_item)
-            os.rename(src_item, dst_item)
-        os.rmdir(src)
-    print(f"MMLU data saved to {data_dir}")
-
-
-def _strip_prefix(text: str, prefix: str) -> str:
-    if text.startswith(prefix):
-        return text[len(prefix) :].strip()
-    return text
-
-
-def load_csv(path: str) -> list[dict]:
-    data = []
-    with open(path, "r", encoding="utf-8") as f:
-        for row in csv.reader(f):
-            if len(row) < 6:
-                continue
-            if row[0].strip().lower() == "question":
-                continue
-            data.append(
-                {
-                    "question": row[0].strip(),
-                    "A": _strip_prefix(row[1].strip(), "A)"),
-                    "B": _strip_prefix(row[2].strip(), "B)"),
-                    "C": _strip_prefix(row[3].strip(), "C)"),
-                    "D": _strip_prefix(row[4].strip(), "D)"),
-                    "answer": row[5].strip(),
-                }
-            )
-    return data
-
-
-def build_prompt(
-    question: str, choices: dict, subject: str, n_shot: int, dev_data: list[dict]
-) -> str:
-    prompt = ""
-    if n_shot > 0 and dev_data:
-        prompt = f"The following are multiple choice questions (with answers) about {subject}.\n\n"
-        for item in dev_data[:n_shot]:
-            prompt += f"Question: {item['question']}\n"
-            for k in ("A", "B", "C", "D"):
-                prompt += f"{k}. {item[k]}\n"
-            prompt += f"Answer: {item['answer']}\n\n"
-    prompt += f"Question: {question}\n"
-    for k in ("A", "B", "C", "D"):
-        prompt += f"{k}. {choices[k]}\n"
-    prompt += "Answer:"
-    return prompt
-
-
-def apply_chat(
-    tokenizer, raw_prompt: str, n_shot: int, dev_data: list[dict] | None
-) -> str:
-    """Wrap raw MMLU prompt in the model's chat template format.
-
-    For few-shot, prepend example Q&A pairs as a second user/assistant exchange.
-    """
-    messages = []
-    if n_shot > 0 and dev_data:
-        for item in dev_data[:n_shot]:
-            q = f"Question: {item['question']}\n"
-            for k in ("A", "B", "C", "D"):
-                q += f"{k}. {item[k]}\n"
-            q += "Answer:"
-            messages.append({"role": "user", "content": q})
-            messages.append({"role": "assistant", "content": item["answer"]})
-    messages.append({"role": "user", "content": raw_prompt})
-    return tokenizer.apply_chat_template(
-        messages, tokenize=False, add_generation_prompt=True
-    )
-
-
-def choice_logprob(
-    model, tokenizer, context_ids: list[int], choice_letter: str, device: str
-) -> float:
-    choice_text = choice_letter
-    choice_ids = tokenizer.encode(choice_text, add_special_tokens=False)
-    input_ids = context_ids + choice_ids
-    max_len = model.config.max_len
-    if len(input_ids) > max_len:
-        overflow = len(input_ids) - max_len
-        input_ids = input_ids[overflow:]
-        ctx_len = len(input_ids) - len(choice_ids)
-    else:
-        ctx_len = len(context_ids)
-
-    input_tensor = torch.tensor([input_ids], device=device, dtype=torch.long)
-    with torch.inference_mode():
-        logits = model(input_tensor)["logits"][0]
-
-    score = 0.0
-    for i, tid in enumerate(choice_ids):
-        pos = ctx_len - 1 + i
-        if pos >= len(logits):
-            break
-        score += F.log_softmax(logits[pos], dim=-1)[tid].item()
-    return score
-
-
-def evaluate_subject(
-    model,
-    tokenizer,
-    subject: str,
-    test_data: list[dict],
-    dev_data: list[dict] | None,
-    device: str,
-    n_shot: int,
-) -> tuple[float, int, int]:
-    correct = 0
-    total = 0
-    for item in tqdm.tqdm(test_data, desc=f"{subject:40s}", leave=False):
-        raw_prompt = build_prompt(
-            item["question"], item, subject, n_shot, dev_data or []
-        )
-        context = apply_chat(tokenizer, raw_prompt, n_shot, dev_data or [])
-        context_ids = tokenizer.encode(context)
-        scores = {
-            c: choice_logprob(model, tokenizer, context_ids, c, device)
-            for c in ("A", "B", "C", "D")
-        }
-        if max(scores, key=scores.get) == item["answer"]:
-            correct += 1
-        total += 1
-    return correct / total, correct, total
-
-
-def main():
-    parser = argparse.ArgumentParser(description="MMLU evaluation")
-    parser.add_argument(
-        "--param_path", type=str, default="./params", help="Model directory"
-    )
-    parser.add_argument(
-        "--data_dir", type=str, default="./mmlu_data", help="MMLU data directory"
-    )
-    parser.add_argument("--download", action="store_true", help="Download MMLU data")
-    parser.add_argument(
-        "--n_shot", type=int, default=5, help="Few-shot examples (0 for zero-shot)"
-    )
-    parser.add_argument(
-        "--subjects", type=str, nargs="+", help="Specific subjects (default: all)"
-    )
-    parser.add_argument("--output", type=str, help="Output JSON path")
-    parser.add_argument("--split", type=str, default="test", choices=["test", "val"])
-    parser.add_argument(
-        "--device",
-        type=str,
-        default="cuda" if torch.cuda.is_available() else "cpu",
-        help="Device",
-    )
-    parser.add_argument(
-        "--dtype",
-        type=str,
-        default="bfloat16" if torch.cuda.is_available() else "float32",
-        help="Torch dtype",
-    )
-    args = parser.parse_args()
-
-    if args.download or not os.path.exists(args.data_dir):
-        download_mmlu(args.data_dir)
-
-    model = AutoModel.from_pretrained(args.param_path)
-    tokenizer = AutoTokenizer.from_pretrained(args.param_path)
-    device = args.device
-    dtype = getattr(torch, args.dtype)
-    model.to(device=device, dtype=dtype)
-    model.eval()
-
-    subjects = args.subjects or MMLU_SUBJECTS
-    results = {}
-    total_correct = 0
-    total_questions = 0
-
-    for subject in subjects:
-        dev_path = os.path.join(args.data_dir, "dev", f"{subject}_dev.csv")
-        test_path = os.path.join(
-            args.data_dir, args.split, f"{subject}_{args.split}.csv"
-        )
-
-        if not os.path.exists(test_path):
-            print(f"  Skipping {subject}: test file not found")
-            continue
-
-        dev_data = load_csv(dev_path) if os.path.exists(dev_path) else None
-        test_data = load_csv(test_path)
-
-        acc, corr, tot = evaluate_subject(
-            model, tokenizer, subject, test_data, dev_data, device, args.n_shot
-        )
-        results[subject] = {"accuracy": round(acc, 4), "correct": corr, "total": tot}
-        total_correct += corr
-        total_questions += tot
-        print(f"  {subject:40s}  {acc:.2%}  ({corr}/{tot})")
-
-    overall = total_correct / total_questions if total_questions else 0
-    print(f"\n{'=' * 70}")
-    print(f"  Overall: {overall:.2%}  ({total_correct}/{total_questions})")
-    results["_overall"] = {
-        "accuracy": round(overall, 4),
-        "correct": total_correct,
-        "total": total_questions,
-    }
-
-    if args.output:
-        with open(args.output, "w", encoding="utf-8") as f:
-            json.dump(results, f, indent=2)
-        print(f"Results saved to {args.output}")
-
-
-if __name__ == "__main__":
-    main()

scripts/tools/generate.py

@@ -9,7 +9,7 @@ from astrai.tokenize import AutoTokenizer
 
 
 def processor(
-    param_path: str,
+    model_dir: str,
     input_json_file: str,
     output_json_file: str,
     temperature: float,
@@ -18,17 +18,14 @@ def processor(
     question_key: str,
     response_key: str,
     max_tokens: int,
-    batch_size: int,
 ):
     # Load model and tokenizer
-    model = AutoModel.from_pretrained(param_path)
+    model = AutoModel.from_pretrained(model_dir)
-    tokenizer = AutoTokenizer.from_pretrained(param_path)
+    tokenizer = AutoTokenizer.from_pretrained(model_dir)
     model.to(device="cuda", dtype=torch.bfloat16)
 
     # Create inference engine
-    engine = InferenceEngine(
+    engine = InferenceEngine(model=model, tokenizer=tokenizer)
-        model=model, tokenizer=tokenizer, max_batch_size=batch_size
-    )
 
     with open(input_json_file, "r", encoding="utf-8") as f:
         input_data = [json.loads(line) for line in f]
@@ -75,7 +72,7 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Run generate with a Khaosz model.")
 
     parser.add_argument(
-        "--param_path", type=str, required=True, help="Path to the model directory."
+        "--model_dir", type=str, required=True, help="Path to the model directory."
     )
     parser.add_argument(
         "--input_json_file",

scripts/tools/perplexity.py

@@ -10,11 +10,11 @@ from astrai.tokenize import AutoTokenizer
 
 
 def process_file(
-    param_path: str, input_file: str, output_file: str, batch_size: int, text_key: str
+    model_dir: str, input_file: str, output_file: str, batch_size: int, text_key: str
 ):
     # Load model and tokenizer
-    model = AutoModel.from_pretrained(param_path)
+    model = AutoModel.from_pretrained(model_dir)
-    tokenizer = AutoTokenizer.from_pretrained(param_path)
+    tokenizer = AutoTokenizer.from_pretrained(model_dir)
     model.to(device="cuda", dtype=torch.bfloat16)
 
     with open(input_file, "r", encoding="utf-8") as f:
@@ -44,8 +44,8 @@ def process_file(
 
         for seq in batch_encoded:
             pad_len = max_len - len(seq)
-            padded_seq = seq + [tokenizer.pad_id] * pad_len
+            padded_seq = [tokenizer.pad_id] * pad_len + seq
-            mask = [True] * len(seq) + [False] * pad_len
+            mask = [False] * pad_len + [True] * len(seq)
             padded_ids.append(padded_seq)
             masks.append(mask)
 
@@ -88,7 +88,7 @@ def process_file(
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Run perplexity with a Khaosz model.")
     parser.add_argument(
-        "--param_path", type=str, required=True, help="Path to the model directory."
+        "--model_dir", type=str, required=True, help="Path to the model directory."
     )
     parser.add_argument(
         "--input_file", type=str, required=True, help="Path to the input file."

scripts/tools/preprocess.py

@@ -1,38 +0,0 @@
-"""CLI: JSONL → tokenized .h5/.bin via config-driven Pipeline."""
-
-import argparse
-
-from astrai.config.preprocess_config import PipelineConfig
-from astrai.preprocessing.pipeline import Pipeline
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Raw JSONL → tokenized .h5/.bin via config-driven Pipeline"
-    )
-    parser.add_argument(
-        "inputs", nargs="+", metavar="JSONL", help="One or more JSONL files"
-    )
-    parser.add_argument("--output_dir", "-o", required=True, help="Output directory")
-    parser.add_argument(
-        "--config", "-c", required=True, help="Path to pipeline config JSON"
-    )
-    parser.add_argument(
-        "--tokenizer_path",
-        default="params",
-        help="Path to tokenizer directory (default: params)",
-    )
-    args = parser.parse_args()
-
-    config = PipelineConfig.from_json(args.config)
-
-    Pipeline(
-        config=config,
-        input_paths=args.inputs,
-        output_dir=args.output_dir,
-        tokenizer_path=args.tokenizer_path,
-    ).run()
-
-
-if __name__ == "__main__":
-    main()

scripts/tools/server.py

@@ -3,7 +3,7 @@ from pathlib import Path
 
 import torch
 
-from astrai.inference import run_server
+from astrai.inference.server import run_server
 
 
 def main():
@@ -18,7 +18,7 @@ def main():
         "--reload", action="store_true", help="Enable auto-reload for development"
     )
     parser.add_argument(
-        "--param_path",
+        "--param-path",
         type=Path,
         default=None,
         help="Path to model parameters (default: project_root/params)",

scripts/tools/train.py

@@ -2,25 +2,28 @@ import argparse
 import os
 from functools import partial
 
+import safetensors.torch as st
 import torch
+import torch.nn as nn
 import torch.optim as optim
+from torch.nn.parallel import DistributedDataParallel as DDP
 
-from astrai.config import AutoRegressiveLMConfig, TrainConfig
+from astrai.config import ModelConfig, TrainConfig
 from astrai.dataset import DatasetFactory
-from astrai.model import AutoRegressiveLM
+from astrai.model import Transformer
-from astrai.model.components.decoder_block import DecoderBlock
+from astrai.parallel import get_rank
 from astrai.trainer import SchedulerFactory, Trainer
 
 
 def parse_args() -> argparse.Namespace:
 
-    parser = argparse.ArgumentParser(description="Train the AutoRegressiveLM model.")
+    parser = argparse.ArgumentParser(description="Train the Transformer model.")
 
     parser.add_argument(
         "--train_type",
         type=str,
         required=True,
-        choices=["seq", "sft", "dpo", "grpo"],
+        choices=["seq", "sft", "dpo"],
         help="Train type.",
     )
     parser.add_argument(
@@ -40,19 +43,19 @@ def parse_args() -> argparse.Namespace:
         "--n_epoch", type=int, default=1, help="Number of epochs to train."
     )
     parser.add_argument(
-        "--batch_per_device", type=int, default=1, help="Batch size per GPU."
+        "--batch_size", type=int, default=1, help="Batch size for training."
     )
     parser.add_argument(
-        "--grad_accum_steps",
+        "--accumulation_steps",
         type=int,
         default=1,
         help="Number of iterations between each optimizer step.",
     )
     parser.add_argument(
-        "--warmup_ratio",
+        "--warmup_steps",
-        type=float,
+        type=int,
-        default=0.05,
+        default=1000,
-        help="Fraction of total steps used for LR warmup.",
+        help="Number of iters between warnings.",
     )
     parser.add_argument(
         "--max_lr", type=float, default=3e-4, help="Max learning rate for training."
@@ -67,13 +70,13 @@ def parse_args() -> argparse.Namespace:
         "--adamw_beta1",
         type=float,
         default=0.9,
-        help="Beta1 for AdamW optimizer.",
+        help="Beta values for AdamW optimizer.",
     )
     parser.add_argument(
         "--adamw_beta2",
         type=float,
         default=0.95,
-        help="Beta2 for AdamW optimizer.",
+        help="Beta values for AdamW optimizer.",
     )
     parser.add_argument(
         "--adamw_weight_decay",
@@ -97,31 +100,18 @@ def parse_args() -> argparse.Namespace:
         "--window_size",
         type=int,
         default=None,
-        help="Max length of the input sequence.",
+        help="the max length of the input sequence.",
     )
     parser.add_argument(
-        "--stride", type=int, default=None, help="Step size of the input sequence."
+        "--stride", type=int, default=None, help="the step size of the input sequence."
     )
     parser.add_argument("--dpo_beta", type=float, default=0.1, help="DPO beta value.")
-    parser.add_argument("--group_size", type=int, default=4, help="GRPO group size.")
-    parser.add_argument(
-        "--grpo_clip_eps", type=float, default=0.2, help="GRPO clipping epsilon."
-    )
-    parser.add_argument(
-        "--grpo_kl_coef", type=float, default=0.01, help="GRPO KL penalty coefficient."
-    )
     parser.add_argument(
         "--label_smoothing",
         type=float,
-        default=0.05,
+        default=0.1,
         help="cross_entropy function label smoothing parameter",
     )
-    parser.add_argument(
-        "--gradient_checkpointing",
-        action=argparse.BooleanOptionalAction,
-        default=False,
-        help="Enable activation checkpointing for DecoderBlock modules.",
-    )
 
     parser.add_argument(
         "--ckpt_interval",
@@ -135,42 +125,6 @@ def parse_args() -> argparse.Namespace:
         default="checkpoint",
         help="Directory to save checkpoints.",
     )
-    parser.add_argument(
-        "--val_split",
-        type=float,
-        default=None,
-        help="Ratio to split from training dataset for validation (e.g. 0.05).",
-    )
-    parser.add_argument(
-        "--val_step",
-        type=int,
-        default=1000,
-        help="Number of optimizer steps between validation runs.",
-    )
-    parser.add_argument(
-        "--metrics",
-        nargs="*",
-        default=["loss", "lr"],
-        help="Metrics to log (e.g. --metrics loss lr val_loss). Default: loss lr.",
-    )
-    parser.add_argument(
-        "--log_dir",
-        type=str,
-        default="checkpoint/logs",
-        help="Directory for metric logs.",
-    )
-    parser.add_argument(
-        "--log_interval",
-        type=int,
-        default=100,
-        help="Number of batch iterations between metric logs.",
-    )
-    parser.add_argument(
-        "--grpo_sync_interval",
-        type=int,
-        default=200,
-        help="GRPO ref model sync interval (steps).",
-    )
     parser.add_argument(
         "--start_epoch", type=int, default=0, help="Start epoch for training."
     )
@@ -178,54 +132,30 @@ def parse_args() -> argparse.Namespace:
         "--start_batch", type=int, default=0, help="Start batch for training."
     )
 
-    parser.add_argument(
-        "--master_addr",
-        type=str,
-        default="localhost",
-        help="Master node address for distributed training.",
-    )
-    parser.add_argument(
-        "--master_port",
-        type=str,
-        default="29500",
-        help="Master node port for distributed training.",
-    )
-    parser.add_argument(
-        "--backend",
-        type=str,
-        default="nccl",
-        help="Distributed training backend.",
-    )
     parser.add_argument("--nprocs", type=int, default=1, help="Number of GPUs to use.")
-    parser.add_argument(
-        "--parallel_mode",
-        type=str,
-        default="none",
-        choices=["none", "ddp", "fsdp"],
-        help="Parallel training strategy (none, ddp, fsdp).",
-    )
     parser.add_argument(
         "--device_type", type=str, default="cuda", help="Device type to use."
     )
-    parser.add_argument(
-        "--start_method",
-        type=str,
-        default="spawn",
-        choices=["spawn", "fork", "forkserver"],
-        help="Multiprocessing start method.",
-    )
 
     args = parser.parse_args()
 
     return args
 
 
-def create_model(config):
+def ddp_wrap(model: nn.Module):
-    return AutoRegressiveLM(config).to(dtype=torch.bfloat16)
+    local_rank = get_rank()
+    model = model.to(device=f"cuda:{local_rank}", dtype=torch.bfloat16)
+    ddp_model = DDP(
+        model,
+        device_ids=[local_rank],
+        output_device=local_rank,
+        find_unused_parameters=False,
+    )
+    return ddp_model
 
 
-def create_optimizer(model, **kwargs) -> optim.Optimizer:
+def create_optimizer(model: nn.Module, **kwargs) -> optim.Optimizer:
-    return optim.AdamW(model.parameters(), fused=True, **kwargs)
+    return optim.AdamW(model.parameters(), **kwargs)
 
 
 def create_scheduler(
@@ -234,21 +164,8 @@ def create_scheduler(
     return SchedulerFactory.create(optimizer, **kwargs)
 
 
-def compute_total_steps(
+def prepare_checkpoint(model: nn.Module) -> dict:
-    dataset_len: int,
+    return model.module.state_dict()
-    n_epoch: int,
-    batch_per_device: int,
-    nprocs: int,
-    grad_accum_steps: int,
-) -> int:
-
-    def ceil_div(a: int, b: int) -> int:
-        return (a + b - 1) // b
-
-    samples_per_replica = ceil_div(dataset_len, nprocs)
-    batches_per_replica = ceil_div(samples_per_replica, batch_per_device)
-    total_steps = (batches_per_replica // grad_accum_steps) * n_epoch
-    return total_steps
 
 
 def train(
@@ -257,23 +174,14 @@ def train(
     data_root_path: str,
     max_lr: float,
     n_epoch: int,
-    batch_per_device: int,
+    batch_size: int,
     start_epoch: int,
     start_batch: int,
-    grad_accum_steps: int,
+    accumulation_steps: int,
-    warmup_ratio: float,
+    warmup_steps: int,
     ckpt_interval: int,
     ckpt_dir: str,
-    val_split: float,
-    val_step: int,
-    metrics: list[str],
-    log_dir: str,
-    log_interval: int,
     dpo_beta: float,
-    grpo_clip_eps: float,
-    grpo_kl_coef: float,
-    group_size: int,
-    grpo_sync_interval: int,
     adamw_beta1: float,
     adamw_beta2: float,
     adamw_weight_decay: float,
@@ -282,44 +190,34 @@ def train(
     random_seed: int,
     num_workers: int,
     pin_memory: bool,
-    gradient_checkpointing: bool,
     window_size: int,
     stride: int,
     nprocs: int,
-    parallel_mode: str,
     device_type: str,
-    backend: str,
-    master_addr: str,
-    master_port: str,
-    start_method: str,
 ):
-    assert train_type in ["seq", "sft", "dpo", "grpo"]
+    assert train_type in ["seq", "sft", "dpo"]
     assert os.path.exists(param_path)
-    if nprocs > 1 and parallel_mode == "none":
-        raise ValueError("--nprocs > 1 requires --parallel_mode to be 'ddp' or 'fsdp'")
 
     # Load config
+    config = ModelConfig()
     config_path = os.path.join(param_path, "config.json")
-    config = AutoRegressiveLMConfig.from_file(config_path)
+    if os.path.exists(config_path):
+        config.load(config_path)
 
     if window_size is None:
         window_size = config.max_len
 
-    strategy_kwargs = {
+    # Create bare Transformer (for training, no tokenizer needed)
-        "beta": dpo_beta,
+    model = Transformer(config)
-        "label_smoothing": label_smoothing,
-        "clip_eps": grpo_clip_eps,
-        "kl_coef": grpo_kl_coef,
-        "group_size": group_size,
-        "sync_interval": grpo_sync_interval,
-    }
 
-    executor_kwargs = {
+    # Load weights if available
-        "gradient_as_bucket_view": True,
+    weights_path = os.path.join(param_path, "model.safetensors")
-        "broadcast_buffers": False,
+    if os.path.exists(weights_path):
-    }
+        state_dict = st.load_file(weights_path)
+        model.load_state_dict(state_dict, strict=False)
+
+    strategy_kwargs = {"dpo_beta": dpo_beta, "label_smoothing": label_smoothing}
 
-    model_fn = partial(create_model, config)
     dataset = DatasetFactory.load(
         train_type=train_type,
         load_path=data_root_path,
@@ -336,58 +234,42 @@ def train(
         },
     )
 
-    total_steps = compute_total_steps(
+    total_steps = len(dataset) * n_epoch // (batch_size * nprocs)
-        len(dataset), n_epoch, batch_per_device, nprocs, grad_accum_steps
-    )
-    warmup_steps = int(warmup_ratio * total_steps)
-
     scheduler_fn = partial(
         create_scheduler,
         **{
             "schedule_type": "cosine",
-            "warmup_steps": min(warmup_steps, total_steps),
+            "warmup_steps": warmup_steps,
-            "lr_decay_steps": total_steps - min(warmup_steps, total_steps),
+            "lr_decay_steps": total_steps - warmup_steps,
         },
     )
 
-    grad_ckpt_modules = [DecoderBlock] if gradient_checkpointing else []
-
     train_config = TrainConfig(
-        model_fn=model_fn,
+        model=model,
         strategy=train_type,
         dataset=dataset,
         optimizer_fn=optimizer_fn,
         scheduler_fn=scheduler_fn,
         ckpt_dir=ckpt_dir,
         n_epoch=n_epoch,
-        batch_per_device=batch_per_device,
+        batch_size=batch_size,
         start_epoch=start_epoch,
         start_batch=start_batch,
         ckpt_interval=ckpt_interval,
-        grad_accum_steps=grad_accum_steps,
+        accumulation_steps=accumulation_steps,
         max_grad_norm=max_grad_norm,
         random_seed=random_seed,
         num_workers=num_workers,
         pin_memory=pin_memory,
         nprocs=nprocs,
-        backend=backend,
+        parallel_wrapper=ddp_wrap,
-        master_addr=master_addr,
+        state_dict_fn=prepare_checkpoint,
-        master_port=master_port,
-        parallel_mode=parallel_mode,
         device_type=device_type,
-        start_method=start_method,
-        val_split=val_split,
-        val_step=val_step,
-        metrics=metrics,
-        log_dir=log_dir,
-        log_interval=log_interval,
-        gradient_checkpointing_modules=grad_ckpt_modules,
-        executor_kwargs=executor_kwargs,
         extra_kwargs=strategy_kwargs,
     )
 
     trainer = Trainer(train_config)
-    trainer.train(resume_dir=param_path)
+    trainer.train()
 
 
 if __name__ == "__main__":

tests/conftest.py

@@ -3,22 +3,18 @@ import os
 import shutil
 import tempfile
 
+import numpy as np
 import pytest
+import safetensors.torch as st
 import torch
 from tokenizers import Tokenizer, models, pre_tokenizers, trainers
 from torch.utils.data import Dataset
 
-from astrai.config.model_config import AutoRegressiveLMConfig
+from astrai.config.model_config import ModelConfig
-from astrai.model.transformer import AutoRegressiveLM
+from astrai.model.transformer import Transformer
 from astrai.tokenize import AutoTokenizer
 
 
-def pytest_configure(config):
-    config.addinivalue_line("markers", "slow: marks tests as slow")
-    config.addinivalue_line("markers", "integration: integration tests")
-    config.addinivalue_line("markers", "unit: fast unit tests")
-
-
 def create_test_tokenizer(vocab_size: int = 1000) -> AutoTokenizer:
     """Create a simple tokenizer for testing purposes."""
     tokenizer = Tokenizer(models.BPE())
@@ -26,6 +22,7 @@ def create_test_tokenizer(vocab_size: int = 1000) -> AutoTokenizer:
     trainer = trainers.BpeTrainer(
         vocab_size=vocab_size, min_frequency=1, special_tokens=["<unk>", "<pad>"]
     )
+    # Train on empty iterator with single character
     tokenizer.train_from_iterator([chr(i) for i in range(256)], trainer)
     auto_tokenizer = AutoTokenizer()
     auto_tokenizer._tokenizer = tokenizer
@@ -37,7 +34,7 @@ class RandomDataset(Dataset):
     """Random dataset for testing purposes."""
 
     def __init__(self, length=None, max_length=64, vocab_size=1000):
-        self.length = length or int(torch.randint(100, 200, (1,)).item())
+        self.length = length or int(np.random.randint(100, 200))
         self.max_length = max_length
         self.vocab_size = vocab_size
 
@@ -55,7 +52,7 @@ class MultiTurnDataset(Dataset):
     """Multi-turn dataset with loss mask for SFT training tests."""
 
     def __init__(self, length=None, max_length=64, vocab_size=1000):
-        self.length = length or int(torch.randint(100, 200, (1,)).item())
+        self.length = length or int(np.random.randint(100, 200))
         self.max_length = max_length
         self.vocab_size = vocab_size
 
@@ -96,65 +93,46 @@ class EarlyStoppingDataset(Dataset):
         }
 
 
-@pytest.fixture(scope="session")
+@pytest.fixture
-def test_tokenizer():
+def base_test_env(request: pytest.FixtureRequest):
-    """Session-scoped tokenizer, created once for the entire test run."""
+    """Create base test environment with randomly configured model and tokenizer"""
-    return create_test_tokenizer()
+    func_name = request.function.__name__
+    test_dir = tempfile.mkdtemp(prefix=f"{func_name}_")
+    config_path = os.path.join(test_dir, "config.json")
 
+    n_dim_choices = [8, 16, 32]
+    n_head_choices = [2, 4]
 
-@pytest.fixture(scope="session")
+    dim = int(np.random.choice(n_dim_choices))
-def test_model():
+    n_heads = int(np.random.choice(n_head_choices))
-    """Session-scoped small AutoRegressiveLM model, created once."""
+    n_kv_heads = n_heads // 2
-    config = AutoRegressiveLMConfig(
+    dim_ffn = dim * 2
-        vocab_size=1000,
-        dim=8,
-        n_heads=2,
-        n_kv_heads=1,
-        dim_ffn=16,
-        max_len=64,
-        n_layers=2,
-        norm_eps=1e-5,
-    )
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    model = AutoRegressiveLM(config).to(device=device)
 
-    return {
+    config = {
-        "model": model,
+        "vocab_size": 1000,
-        "device": device,
+        "dim": dim,
-        "config": config,
+        "n_heads": n_heads,
+        "n_kv_heads": n_kv_heads,
+        "dim_ffn": dim_ffn,
+        "max_len": 1024,
+        "n_layers": 4,
+        "norm_eps": 1e-5,
     }
 
-
-@pytest.fixture
-def base_test_env(test_model, test_tokenizer):
-    """Function-scoped test environment with isolated temp directory.
-
-    Composes session-scoped model and tokenizer with a per-test temp dir.
-    """
-    test_dir = tempfile.mkdtemp()
-    config_path = os.path.join(test_dir, "config.json")
     with open(config_path, "w") as f:
-        json.dump(
+        json.dump(config, f)
-            {
+    device = "cuda" if torch.cuda.is_available() else "cpu"
-                "vocab_size": 1000,
+    transformer_config = ModelConfig().load(config_path)
-                "dim": 8,
+    model = Transformer(transformer_config).to(device=device)
-                "n_heads": 2,
+    tokenizer = create_test_tokenizer()
-                "n_kv_heads": 1,
-                "dim_ffn": 16,
-                "max_len": 64,
-                "n_layers": 2,
-                "norm_eps": 1e-5,
-            },
-            f,
-        )
 
     yield {
-        "device": test_model["device"],
+        "device": device,
         "test_dir": str(test_dir),
         "config_path": config_path,
-        "transformer_config": test_model["config"],
+        "transformer_config": transformer_config,
-        "model": test_model["model"],
+        "model": model,
-        "tokenizer": test_tokenizer,
+        "tokenizer": tokenizer,
     }
 
     shutil.rmtree(test_dir)
@@ -176,3 +154,43 @@ def multi_turn_dataset():
 def early_stopping_dataset():
     dataset = EarlyStoppingDataset()
     yield dataset
+
+
+@pytest.fixture
+def test_env(request: pytest.FixtureRequest):
+    """Create a test environment with saved model and tokenizer files."""
+
+    func_name = request.function.__name__
+    test_dir = tempfile.mkdtemp(prefix=f"{func_name}_")
+    config_path = os.path.join(test_dir, "config.json")
+    tokenizer_path = os.path.join(test_dir, "tokenizer.json")
+    model_path = os.path.join(test_dir, "model.safetensors")
+
+    config = {
+        "vocab_size": 1000,
+        "dim": 128,
+        "n_heads": 4,
+        "n_kv_heads": 2,
+        "dim_ffn": 256,
+        "max_len": 64,
+        "n_layers": 2,
+        "norm_eps": 1e-5,
+    }
+    with open(config_path, "w") as f:
+        json.dump(config, f)
+
+    tokenizer = create_test_tokenizer(vocab_size=config["vocab_size"])
+    tokenizer.save(tokenizer_path)
+
+    transformer_config = ModelConfig().load(config_path)
+    model = Transformer(transformer_config)
+    st.save_file(model.state_dict(), model_path)
+
+    yield {
+        "test_dir": test_dir,
+        "model": model,
+        "tokenizer": tokenizer,
+        "transformer_config": transformer_config,
+    }
+
+    shutil.rmtree(test_dir)

tests/data/conftest.py

@@ -1,202 +0,0 @@
-import tempfile
-
-import pytest
-from tokenizers import Tokenizer, models, pre_tokenizers, trainers
-
-from astrai.config.preprocess_config import (
-    InputConfig,
-    PipelineConfig,
-    ProcessingConfig,
-)
-from astrai.tokenize import AutoTokenizer
-
-_SPECIAL_TOKENS_CONFIG = {
-    "bos_token": "<|begin_of_sentence|>",
-    "eos_token": "<|end_of_sentence|>",
-    "pad_token": "<|_pad_|>",
-    "unk_token": "<|_unk_|>",
-    "im_start": "<|im_start|>",
-    "im_end": "<|im_end|>",
-}
-
-_SPECIAL_TOKENS = list(_SPECIAL_TOKENS_CONFIG.values())
-
-_CHAT_TEMPLATE = (
-    "{% for message in messages %}"
-    "{% if message['role'] == 'system' %}"
-    "<|im_start|>system\n{{ message['content'] }}<|im_end|>\n"
-    "{% elif message['role'] == 'user' %}"
-    "<|im_start|>user\n{{ message['content'] }}<|im_end|>\n"
-    "{% elif message['role'] == 'assistant' %}"
-    "<|im_start|>assistant\n{{ message['content'] }}<|im_end|>\n"
-    "{% endif %}"
-    "{% endfor %}"
-    "{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
-)
-
-_CHAT_SECTIONS = [{"field": "messages", "action": "$role", "template": True}]
-
-_INSTRUCTION_SECTIONS = [
-    {"field": "prompt", "action": "mask", "add_special_tokens": True},
-    {"field": "response", "action": "train"},
-]
-
-_TEXT_SECTIONS = [{"field": "text", "action": "train"}]
-
-_GRPO_RESPONSE_SECTIONS = [{"field": "responses", "action": "train"}]
-
-
-def _build_chat_tokenizer():
-    tok = Tokenizer(models.BPE())
-    tok.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)
-    tr = trainers.BpeTrainer(
-        vocab_size=512,
-        min_frequency=1,
-        special_tokens=_SPECIAL_TOKENS,
-    )
-    train_data = [
-        "hello world",
-        "Hi there!",
-        "You are helpful.",
-        "What is 2+2?",
-        "Tell me a story about dragons and knights.",
-        "Sure, here is a tale.",
-        "Translate to French: Hello",
-        "Bonjour",
-        "Artificial Intelligence is a field of computer science.",
-        "system",
-        "user",
-        "assistant",
-        "<|im_start|>",
-        "<|im_end|>",
-        *[chr(i) for i in range(32, 127)],
-    ]
-    tok.train_from_iterator(train_data, tr)
-
-    auto_tok = AutoTokenizer()
-    auto_tok._tokenizer = tok
-    auto_tok._special_token_map = {
-        "bos_token": "<|begin_of_sentence|>",
-        "eos_token": "<|end_of_sentence|>",
-        "pad_token": "<|_pad_|>",
-        "unk_token": "<|_unk_|>",
-    }
-    auto_tok.set_chat_template(_CHAT_TEMPLATE)
-    return auto_tok
-
-
-@pytest.fixture(scope="session")
-def chat_tokenizer():
-    return _build_chat_tokenizer()
-
-
-@pytest.fixture
-def temp_dir():
-    d = tempfile.mkdtemp()
-    yield d
-    import shutil
-
-    shutil.rmtree(d, ignore_errors=True)
-
-
-def make_chat_config():
-    return PipelineConfig(
-        input=InputConfig(sections=_CHAT_SECTIONS),
-        mask={"system": "mask", "user": "mask", "assistant": "train"},
-        mask_default="mask",
-        preprocessing=ProcessingConfig(max_seq_len=2048),
-    )
-
-
-def make_instruction_config():
-    return PipelineConfig(
-        input=InputConfig(sections=_INSTRUCTION_SECTIONS),
-        mask={"prompt": "mask", "response": "train"},
-        mask_default="mask",
-        preprocessing=ProcessingConfig(max_seq_len=2048),
-    )
-
-
-def make_text_config():
-    return PipelineConfig(
-        input=InputConfig(sections=_TEXT_SECTIONS),
-        preprocessing=ProcessingConfig(
-            max_seq_len=2048, min_chars=1, max_chars=2_000_000
-        ),
-    )
-
-
-def make_dpo_chat_config():
-    return PipelineConfig(
-        input=InputConfig(
-            sources={
-                "chosen": {
-                    "sections": [
-                        {"field": "chosen", "action": "$role", "template": True}
-                    ]
-                },
-                "rejected": {
-                    "sections": [
-                        {"field": "rejected", "action": "$role", "template": True}
-                    ]
-                },
-            }
-        ),
-        mask={"user": "mask", "assistant": "train"},
-        mask_default="mask",
-        preprocessing=ProcessingConfig(max_seq_len=2048),
-    )
-
-
-def make_grpo_config():
-    return PipelineConfig(
-        input=InputConfig(
-            sources={
-                "prompts": {
-                    "sections": [
-                        {"field": "prompt", "action": "mask", "template": True}
-                    ]
-                },
-                "responses": {
-                    "sections": _GRPO_RESPONSE_SECTIONS,
-                    "list_field": True,
-                    "mask_key": "masks",
-                },
-                "rewards": {
-                    "sections": [{"field": "rewards", "action": "value"}],
-                },
-            }
-        ),
-        mask={"user": "mask", "assistant": "train"},
-        mask_default="mask",
-        preprocessing=ProcessingConfig(max_seq_len=2048),
-    )
-
-
-def make_grpo_no_template_config():
-    return PipelineConfig(
-        input=InputConfig(
-            sources={
-                "prompts": {
-                    "sections": [
-                        {
-                            "field": "prompt",
-                            "action": "mask",
-                            "add_special_tokens": True,
-                        }
-                    ]
-                },
-                "responses": {
-                    "sections": _GRPO_RESPONSE_SECTIONS,
-                    "list_field": True,
-                    "mask_key": "masks",
-                },
-                "rewards": {
-                    "sections": [{"field": "rewards", "action": "value"}],
-                },
-            }
-        ),
-        mask={"user": "mask", "assistant": "train"},
-        mask_default="mask",
-        preprocessing=ProcessingConfig(max_seq_len=2048),
-    )

tests/data/test_checkpoint.py

@@ -1,4 +1,3 @@
-import os
 import tempfile
 
 import torch
@@ -36,30 +35,6 @@ def test_single_process():
         assert loaded_checkpoint.iteration == 30
 
 
-def test_checkpoint_with_extra():
-    model = torch.nn.Linear(10, 5)
-    optimizer = AdamW(model.parameters(), lr=1e-3)
-    optimizer.step()
-
-    extra = {
-        "optimizer": optimizer.state_dict(),
-        "scheduler": {"last_epoch": 5},
-    }
-    checkpoint = Checkpoint(
-        state_dict=model.state_dict(), epoch=1, iteration=10, extra=extra
-    )
-
-    with tempfile.TemporaryDirectory() as tmpdir:
-        checkpoint.save(tmpdir)
-
-        assert os.path.exists(os.path.join(tmpdir, "optimizer.pt"))
-        assert os.path.exists(os.path.join(tmpdir, "scheduler.pt"))
-
-        loaded = Checkpoint.load(tmpdir)
-        assert loaded.extra["scheduler"]["last_epoch"] == 5
-        assert "state" in loaded.extra["optimizer"]
-
-
 def simple_training():
     model = torch.nn.Linear(10, 5)
     optimizer = AdamW(model.parameters(), lr=1e-3)

tests/data/test_dataset.py

@@ -1,18 +1,8 @@
-import os
-
 import numpy as np
-import pytest
 import torch
 
-from astrai.dataset.dataset import DatasetFactory, SEQDataset
+from astrai.dataset.dataset import DatasetFactory
-from astrai.dataset.storage import (
+from astrai.serialization import save_h5
-    H5Store,
-    StoreFactory,
-    detect_format,
-    load_bin,
-    save_bin,
-    save_h5,
-)
 
 
 def test_dataset_loader_random_paths(base_test_env):
@@ -74,7 +64,7 @@ def test_dpo_strategy_with_random_data(base_test_env):
     )
 
     assert dpo_dataset is not None
-    assert dpo_dataset.storage is not None
+    assert hasattr(dpo_dataset, "fetcher")
     assert len(dpo_dataset) > 0
 
     # Test that we can get DPO items without errors
@@ -98,7 +88,6 @@ def test_sft_dataset_with_random_data(base_test_env):
     dummy_data = {
         "sequence": [torch.randint(0, 1000, (seq_length,), dtype=torch.int64)],
         "loss_mask": [torch.ones(seq_length, dtype=torch.bool)],
-        "position_ids": [torch.arange(seq_length, dtype=torch.int32)],
     }
 
     save_h5(test_dir, "sft_data", dummy_data)
@@ -111,7 +100,7 @@ def test_sft_dataset_with_random_data(base_test_env):
     )
 
     assert sft_dataset is not None
-    assert sft_dataset.storage is not None
+    assert hasattr(sft_dataset, "fetcher")
     assert len(sft_dataset) > 0
 
     # Test that we can get SFT items without errors
@@ -154,291 +143,3 @@ def test_dataset_with_custom_stride(base_test_env):
     )
 
     assert len(dataset) > len(default_stride_dataset)
-
-
-def test_dataset_count_property(base_test_env):
-    """Test the count property returns correct raw token count"""
-    test_dir = base_test_env["test_dir"]
-
-    seq_length = 200
-    dummy_data = {
-        "sequence": [torch.randint(0, 1000, (seq_length,), dtype=torch.int64)],
-    }
-
-    save_h5(test_dir, "count_test_data", dummy_data)
-
-    dataset = DatasetFactory.load(
-        train_type="seq",
-        load_path=test_dir,
-        window_size=64,
-    )
-
-    assert dataset.count == seq_length
-    assert dataset.count > len(dataset)  # raw tokens > windows
-    assert len(dataset) == (seq_length - 1 - 64) // 64 + 1
-
-
-def test_empty_dataset_count():
-    """Test count returns 0 when no data is loaded"""
-    dataset = SEQDataset(window_size=64, stride=32)
-    assert dataset.count == 0
-    assert dataset.keys == []
-
-
-def test_dataset_too_short_for_window(base_test_env):
-    """Dataset shorter than window_size returns __len__ == 0"""
-    test_dir = base_test_env["test_dir"]
-    seq_length = 30
-    save_h5(
-        test_dir,
-        "short",
-        {"sequence": [torch.randint(0, 1000, (seq_length,), dtype=torch.int64)]},
-    )
-    dataset = DatasetFactory.load("seq", test_dir, window_size=64)
-    assert len(dataset) == 0
-    assert dataset.count == seq_length
-
-
-def test_unloaded_dataset_getitem_raises():
-    """__getitem__ without load() should fail clearly"""
-    dataset = SEQDataset(window_size=64, stride=32)
-    with pytest.raises(RuntimeError, match="not loaded"):
-        dataset.get_index(0)
-
-
-def test_unloaded_dataset_len():
-    """__len__ without load() returns 0"""
-    dataset = SEQDataset(window_size=64, stride=32)
-    assert len(dataset) == 0
-
-
-def test_store_unloaded_len():
-    """Unloaded Store has __len__ == 0"""
-    store = H5Store()
-    assert len(store) == 0
-    assert store.keys == []
-
-
-def test_store_fetch_begin_equals_end(base_test_env):
-    """Store.fetch with begin == end returns empty tensor"""
-    test_dir = base_test_env["test_dir"]
-    dummy = {"sequence": [torch.randint(0, 1000, (100,), dtype=torch.int64)]}
-    save_h5(test_dir, "empty_fetch", dummy)
-
-    dataset = DatasetFactory.load("seq", test_dir, window_size=32)
-    result = dataset.storage.fetch(10, 10, "sequence")
-    assert result.numel() == 0
-
-
-def test_store_fetch_before_load():
-    """Store.fetch before load raises RuntimeError"""
-    store = H5Store()
-    with pytest.raises(RuntimeError, match="not loaded"):
-        store.fetch(0, 10, "sequence")
-
-
-def test_detect_format_nonexistent_path():
-    """detect_format raises FileNotFoundError for bad path"""
-    with pytest.raises(FileNotFoundError, match="No supported"):
-        detect_format("/nonexistent/path/xyz")
-
-
-def test_detect_format_unsupported_file(base_test_env):
-    """detect_format raises ValueError for unsupported file extension"""
-    test_dir = base_test_env["test_dir"]
-    path = os.path.join(test_dir, "data.txt")
-    with open(path, "w") as f:
-        f.write("hello")
-    with pytest.raises(ValueError, match="Unsupported"):
-        detect_format(path)
-
-
-def test_create_store_invalid_type():
-    """StoreFactory.create raises ValueError for unknown type"""
-    with pytest.raises(ValueError, match="Unknown component"):
-        StoreFactory.create("parquet")
-
-
-def test_store_multi_segment_concat(base_test_env):
-    """Multi-segment H5 data is concatenated into single tensor at load time"""
-    import os
-
-    test_dir = base_test_env["test_dir"]
-    data_dir = os.path.join(test_dir, "multi_seg")
-    os.makedirs(data_dir, exist_ok=True)
-
-    segs = [
-        torch.tensor([1, 2, 3]),
-        torch.tensor([4, 5, 6, 7]),
-        torch.tensor([8, 9]),
-    ]
-    save_h5(data_dir, "data", {"sequence": segs})
-
-    store = StoreFactory.create("h5")
-    store.load(data_dir)
-    assert len(store) == 9
-    result = store.fetch(2, 7, "sequence")
-    assert result.tolist() == [3, 4, 5, 6, 7]
-
-
-def test_save_load_bin_roundtrip(base_test_env):
-    """save_bin + load_bin roundtrip preserves data"""
-    test_dir = base_test_env["test_dir"]
-
-    data = {
-        "sequence": [torch.tensor([1, 2, 3, 4, 5], dtype=torch.int64)],
-        "loss_mask": [torch.tensor([0, 1, 1, 0, 1], dtype=torch.int64)],
-    }
-    save_bin(test_dir, data)
-    result = load_bin(test_dir)
-
-    assert "sequence" in result
-    assert "loss_mask" in result
-    assert result["sequence"][0].tolist() == [1, 2, 3, 4, 5]
-    assert result["loss_mask"][0].tolist() == [0, 1, 1, 0, 1]
-
-
-def test_mmap_store_load_and_fetch(base_test_env):
-    """MmapStore loads bin data and fetches correctly"""
-    test_dir = base_test_env["test_dir"]
-
-    data = {
-        "sequence": [torch.randint(0, 1000, (200,), dtype=torch.int64)],
-    }
-    save_bin(test_dir, data)
-
-    store = StoreFactory.create("bin")
-    store.load(test_dir)
-    assert len(store) == 200
-    assert "sequence" in store.keys
-
-    result = store.fetch(10, 20, "sequence")
-    assert result.tolist() == data["sequence"][0][10:20].tolist()
-
-
-def test_mmap_dataset_load(base_test_env):
-    """DatasetFactory.load auto-detects bin format"""
-    test_dir = base_test_env["test_dir"]
-
-    data = {
-        "sequence": [torch.randint(0, 1000, (200,), dtype=torch.int64)],
-    }
-    save_bin(test_dir, data)
-
-    dataset = DatasetFactory.load("seq", test_dir, window_size=64)
-    assert len(dataset) > 0
-    assert dataset.count == 200
-    assert dataset[0]["input_ids"].shape[0] == 64
-
-
-def test_normalize_empty_key():
-    """_normalize with empty tensor list does not crash"""
-    store = H5Store()
-    store._normalize({"sequence": []})
-    assert len(store) == 0
-    assert store.keys == ["sequence"]
-
-
-def test_normalize_mixed_empty_key():
-    """_normalize with empty + non-empty keys returns min=0"""
-    store = H5Store()
-    store._normalize({"sequence": [torch.tensor([1, 2, 3])], "loss_mask": []})
-    assert len(store) == 0
-    assert set(store.keys) == {"sequence", "loss_mask"}
-
-
-def test_grpo_dataset_dtype(base_test_env):
-    """GRPODataset returns correct dtypes"""
-    test_dir = base_test_env["test_dir"]
-
-    seq_len = 100
-    data = {
-        "prompts": [torch.randint(0, 100, (seq_len,), dtype=torch.int32)],
-        "responses": [torch.randint(0, 100, (seq_len,), dtype=torch.int32)],
-        "masks": [torch.ones(seq_len, dtype=torch.int32)],
-        "rewards": [torch.ones(seq_len, dtype=torch.float32)],
-    }
-    save_h5(test_dir, "grpo_dtype", data)
-
-    dataset = DatasetFactory.load("grpo", test_dir, window_size=32)
-    item = dataset[0]
-
-    assert item["prompts"].dtype == torch.long
-    assert item["responses"].dtype == torch.long
-    assert item["masks"].dtype == torch.bool
-    assert item["rewards"].dtype == torch.float32
-
-
-def test_grpo_dataset_load(base_test_env):
-    """GRPODataset loads and returns correct keys"""
-    test_dir = base_test_env["test_dir"]
-    seq_len = 200
-    data = {
-        "prompts": [torch.randint(0, 1000, (seq_len,), dtype=torch.int64)],
-        "responses": [torch.randint(0, 1000, (seq_len,), dtype=torch.int64)],
-        "masks": [torch.ones(seq_len, dtype=torch.int64)],
-        "rewards": [torch.rand(seq_len, dtype=torch.float32)],
-    }
-    save_h5(test_dir, "grpo_test", data)
-
-    dataset = DatasetFactory.load("grpo", test_dir, window_size=64)
-    assert len(dataset) > 0
-    item = dataset[0]
-    assert "prompts" in item
-    assert "responses" in item
-    assert "masks" in item
-    assert "rewards" in item
-    assert item["prompts"].shape[0] == 64
-    assert item["responses"].shape[0] == 64
-
-
-def test_detect_format_bin_dir(base_test_env):
-    """detect_format returns 'bin' for directory with .bin + meta.json"""
-    test_dir = base_test_env["test_dir"]
-    save_bin(test_dir, {"sequence": [torch.randint(0, 100, (10,))]})
-    assert detect_format(test_dir) == "bin"
-
-
-def test_store_fetch_multi_key(base_test_env):
-    """Store.fetch with List[str] returns Dict[str, Tensor]"""
-    test_dir = base_test_env["test_dir"]
-    save_h5(
-        test_dir,
-        "multi_key",
-        {
-            "sequence": [torch.randint(0, 100, (100,), dtype=torch.int64)],
-            "loss_mask": [torch.ones(100, dtype=torch.int64)],
-        },
-    )
-
-    store = StoreFactory.create("h5")
-    store.load(test_dir)
-    result = store.fetch(10, 20, ["sequence", "loss_mask"])
-    assert isinstance(result, dict)
-    assert result["sequence"].shape[0] == 10
-    assert result["loss_mask"].shape[0] == 10
-
-
-def test_store_fetch_out_of_bounds(base_test_env):
-    """Store.fetch raises ValueError for out-of-bounds indices"""
-    test_dir = base_test_env["test_dir"]
-    save_h5(test_dir, "bounds", {"sequence": [torch.randint(0, 100, (50,))]})
-
-    store = StoreFactory.create("h5")
-    store.load(test_dir)
-    with pytest.raises(ValueError, match="out of bounds"):
-        store.fetch(-1, 10, "sequence")
-    with pytest.raises(ValueError, match="out of bounds"):
-        store.fetch(0, 51, "sequence")
-    with pytest.raises(ValueError, match="out of bounds"):
-        store.fetch(50, 50, "sequence")
-
-
-def test_dataset_load_explicit_storage_type(base_test_env):
-    """DatasetFactory.load with explicit storage_type bypasses auto-detect"""
-    test_dir = base_test_env["test_dir"]
-    save_h5(test_dir, "explicit", {"sequence": [torch.randint(0, 100, (200,))]})
-
-    dataset = DatasetFactory.load("seq", test_dir, window_size=64, storage_type="h5")
-    assert len(dataset) > 0
-    assert dataset.count == 200

tests/data/test_preprocess_builder.py

@@ -1,396 +0,0 @@
-from astrai.config.preprocess_config import (
-    InputConfig,
-    OutputConfig,
-    PipelineConfig,
-    ProcessingConfig,
-)
-from astrai.preprocessing.builder import (
-    MaskBuilderFactory,
-    SectionedMaskBuilder,
-)
-from tests.data.conftest import (
-    _CHAT_SECTIONS,
-    _INSTRUCTION_SECTIONS,
-    _TEXT_SECTIONS,
-    make_chat_config,
-    make_dpo_chat_config,
-    make_grpo_config,
-    make_instruction_config,
-    make_text_config,
-)
-
-
-def test_chat_simple(chat_tokenizer):
-    config = make_chat_config()
-    builder = SectionedMaskBuilder()
-    item = {
-        "messages": [
-            {"role": "system", "content": "You are helpful."},
-            {"role": "user", "content": "Hello."},
-            {"role": "assistant", "content": "Hi there!"},
-        ]
-    }
-    result = builder.build(item, config, chat_tokenizer)
-    assert result is not None
-    assert "sequence" in result
-    assert "loss_mask" in result
-    assert len(result["sequence"]) == len(result["loss_mask"])
-
-    ids = chat_tokenizer.decode(result["sequence"], skip_special_tokens=False)
-    assert "system" in ids.lower() or "<|im_start|>system" in ids
-    assert "assistant" in ids.lower() or "<|im_start|>assistant" in ids
-
-    total = len(result["sequence"])
-    trained = sum(result["loss_mask"])
-    assert trained > 0
-    assert trained < total
-
-
-def test_chat_mask_only_assistant(chat_tokenizer):
-    config = make_chat_config()
-    builder = SectionedMaskBuilder()
-    item = {
-        "messages": [
-            {"role": "user", "content": "What is 2+2?"},
-            {"role": "assistant", "content": "4"},
-        ]
-    }
-    result = builder.build(item, config, chat_tokenizer)
-    mask = result["loss_mask"]
-    ids = result["sequence"]
-    assert len(ids) == len(mask)
-
-    trained = [i for i, m in enumerate(mask) if m == 1]
-    masked = [i for i, m in enumerate(mask) if m == 0]
-    assert len(trained) > 0
-    assert len(masked) > 0
-
-
-def test_chat_all_masked(chat_tokenizer):
-    config = PipelineConfig(
-        input=InputConfig(sections=_CHAT_SECTIONS),
-        mask={"system": "mask", "user": "mask", "assistant": "mask"},
-        mask_default="mask",
-        preprocessing=ProcessingConfig(max_seq_len=2048),
-    )
-    builder = SectionedMaskBuilder()
-    item = {
-        "messages": [
-            {"role": "system", "content": "You are helpful."},
-            {"role": "assistant", "content": "Hi there!"},
-        ]
-    }
-    result = builder.build(item, config, chat_tokenizer)
-    assert sum(result["loss_mask"]) == 0
-
-
-def test_chat_all_trained(chat_tokenizer):
-    config = PipelineConfig(
-        input=InputConfig(sections=_CHAT_SECTIONS),
-        mask={},
-        mask_default="train",
-        preprocessing=ProcessingConfig(max_seq_len=2048),
-    )
-    builder = SectionedMaskBuilder()
-    item = {
-        "messages": [
-            {"role": "system", "content": "You are helpful."},
-            {"role": "assistant", "content": "Hi there!"},
-        ]
-    }
-    result = builder.build(item, config, chat_tokenizer)
-    assert sum(result["loss_mask"]) == len(result["sequence"]) - 1
-
-
-def test_chat_empty_messages(chat_tokenizer):
-    config = make_chat_config()
-    builder = SectionedMaskBuilder()
-    assert builder.build({"messages": []}, config, chat_tokenizer) is None
-    assert builder.build({}, config, chat_tokenizer) is None
-
-
-def test_chat_domain_extraction(chat_tokenizer):
-    config = PipelineConfig(
-        input=InputConfig(sections=_CHAT_SECTIONS),
-        mask={"assistant": "train"},
-        mask_default="mask",
-        preprocessing=ProcessingConfig(max_seq_len=2048),
-        output=OutputConfig(domain_key="source"),
-    )
-    builder = SectionedMaskBuilder()
-    item = {
-        "messages": [
-            {"role": "user", "content": "Hi"},
-            {"role": "assistant", "content": "Hello"},
-        ],
-        "source": "wiki",
-    }
-    result = builder.build(item, config, chat_tokenizer)
-    assert result["domain"] == "wiki"
-
-
-def test_chat_truncation(chat_tokenizer):
-    config = PipelineConfig(
-        input=InputConfig(sections=_CHAT_SECTIONS),
-        mask={"assistant": "train"},
-        mask_default="mask",
-        preprocessing=ProcessingConfig(max_seq_len=10),
-    )
-    builder = SectionedMaskBuilder()
-    item = {
-        "messages": [
-            {
-                "role": "user",
-                "content": "Tell me a very long story about dragons and knights and magic.",
-            },
-            {"role": "assistant", "content": "Sure! Here is a tale..."},
-        ]
-    }
-    result = builder.build(item, config, chat_tokenizer)
-    assert len(result["sequence"]) <= 10
-    assert len(result["loss_mask"]) == len(result["sequence"])
-
-
-def test_instruction_basic(test_tokenizer):
-    config = make_instruction_config()
-    builder = SectionedMaskBuilder()
-    item = {"prompt": "Translate to French: Hello", "response": "Bonjour"}
-    result = builder.build(item, config, test_tokenizer)
-    assert result is not None
-    assert len(result["sequence"]) == len(result["loss_mask"])
-
-
-def test_instruction_prompt_masked(test_tokenizer):
-    config = make_instruction_config()
-    builder = SectionedMaskBuilder()
-    item = {"prompt": "hello", "response": "world"}
-    result = builder.build(item, config, test_tokenizer)
-    mask = result["loss_mask"]
-    ids = result["sequence"]
-
-    prompt_ids = test_tokenizer.encode("hello", add_special_tokens=True)
-    p_len = min(len(prompt_ids), len(ids))
-    assert all(m == 0 for m in mask[:p_len])
-    if p_len < len(ids):
-        assert all(m == 1 for m in mask[p_len:])
-
-
-def test_instruction_train_on_prompt(test_tokenizer):
-    config = PipelineConfig(
-        input=InputConfig(
-            sections=[
-                {"field": "prompt", "action": "train", "add_special_tokens": True},
-                {"field": "response", "action": "mask"},
-            ]
-        ),
-        preprocessing=ProcessingConfig(max_seq_len=2048),
-    )
-    builder = SectionedMaskBuilder()
-    item = {"prompt": "hello", "response": "world"}
-    result = builder.build(item, config, test_tokenizer)
-    mask = result["loss_mask"]
-    ids = result["sequence"]
-
-    prompt_ids = test_tokenizer.encode("hello", add_special_tokens=True)
-    p_len = min(len(prompt_ids), len(ids))
-    assert all(m == 1 for m in mask[:p_len])
-
-
-def test_text_basic(test_tokenizer):
-    config = make_text_config()
-    builder = SectionedMaskBuilder()
-    item = {"text": "Hello world. This is a test document."}
-    result = builder.build(item, config, test_tokenizer)
-    assert result is not None
-    assert "sequence" in result
-    assert len(result["sequence"]) > 0
-    assert "loss_mask" not in result
-
-
-def test_text_empty(test_tokenizer):
-    config = make_text_config()
-    builder = SectionedMaskBuilder()
-    assert builder.build({"text": ""}, config, test_tokenizer) is None
-    assert builder.build({"text": "   "}, config, test_tokenizer) is None
-
-
-def test_text_too_short(test_tokenizer):
-    config = PipelineConfig(
-        input=InputConfig(sections=_TEXT_SECTIONS),
-        preprocessing=ProcessingConfig(min_chars=100),
-    )
-    builder = SectionedMaskBuilder()
-    assert builder.build({"text": "short"}, config, test_tokenizer) is None
-
-
-def test_text_truncation(test_tokenizer):
-    config = PipelineConfig(
-        input=InputConfig(sections=_TEXT_SECTIONS),
-        preprocessing=ProcessingConfig(max_seq_len=3, min_chars=1),
-    )
-    builder = SectionedMaskBuilder()
-    item = {"text": "This is a very long text that should be truncated"}
-    result = builder.build(item, config, test_tokenizer)
-    assert len(result["sequence"]) <= 3
-
-
-def test_sectioned_chat(chat_tokenizer):
-    config = PipelineConfig(
-        input=InputConfig(sections=_CHAT_SECTIONS),
-        mask={"system": "mask", "user": "mask", "assistant": "train"},
-        mask_default="mask",
-        preprocessing=ProcessingConfig(max_seq_len=2048),
-    )
-    builder = SectionedMaskBuilder()
-    item = {
-        "messages": [
-            {"role": "user", "content": "What is 2+2?"},
-            {"role": "assistant", "content": "4"},
-        ]
-    }
-    result = builder.build(item, config, chat_tokenizer)
-    assert result is not None
-    assert len(result["sequence"]) == len(result["loss_mask"])
-    assert sum(result["loss_mask"]) > 0
-    assert 0 in result["loss_mask"]
-
-
-def test_sectioned_instruction(test_tokenizer):
-    config = PipelineConfig(
-        input=InputConfig(sections=_INSTRUCTION_SECTIONS),
-        preprocessing=ProcessingConfig(max_seq_len=2048, min_chars=0),
-    )
-    builder = SectionedMaskBuilder()
-    item = {"prompt": "Q: Why?", "response": "A: Because."}
-    result = builder.build(item, config, test_tokenizer)
-    assert result is not None
-    mask = result["loss_mask"]
-    assert mask[0] == 0
-    assert mask[-1] == 1
-
-
-def test_sectioned_text(test_tokenizer):
-    config = PipelineConfig(
-        input=InputConfig(sections=_TEXT_SECTIONS),
-        preprocessing=ProcessingConfig(max_seq_len=2048, min_chars=1),
-    )
-    builder = SectionedMaskBuilder()
-    item = {"text": "Hello world, this is a test."}
-    result = builder.build(item, config, test_tokenizer)
-    assert result is not None
-    assert "loss_mask" not in result
-
-
-def test_sectioned_text_too_short(test_tokenizer):
-    config = PipelineConfig(
-        input=InputConfig(sections=_TEXT_SECTIONS),
-        preprocessing=ProcessingConfig(max_seq_len=2048, min_chars=100),
-    )
-    builder = SectionedMaskBuilder()
-    assert builder.build({"text": "short"}, config, test_tokenizer) is None
-
-
-def test_factory_registered():
-    names = MaskBuilderFactory._registry.list_names()
-    assert "sectioned" in names
-
-
-def test_factory_create():
-    builder = MaskBuilderFactory.create("sectioned")
-    assert isinstance(builder, SectionedMaskBuilder)
-
-
-def test_dpo_chat_basic(chat_tokenizer):
-    config = make_dpo_chat_config()
-    builder = SectionedMaskBuilder()
-    item = {
-        "chosen": [
-            {"role": "user", "content": "What is 2+2?"},
-            {"role": "assistant", "content": "4"},
-        ],
-        "rejected": [
-            {"role": "user", "content": "What is 2+2?"},
-            {"role": "assistant", "content": "5"},
-        ],
-    }
-    result = builder.build(item, config, chat_tokenizer)
-    assert result is not None
-    assert "chosen" in result
-    assert "rejected" in result
-    assert "chosen_mask" in result
-    assert "rejected_mask" in result
-    assert "domain" in result
-    assert len(result["chosen"]) == len(result["chosen_mask"])
-    assert len(result["rejected"]) == len(result["rejected_mask"])
-    assert sum(result["chosen_mask"]) > 0
-    assert sum(result["rejected_mask"]) > 0
-
-
-def test_dpo_chosen_only_trained(chat_tokenizer):
-    config = make_dpo_chat_config()
-    builder = SectionedMaskBuilder()
-    item = {
-        "chosen": [
-            {"role": "user", "content": "Hi"},
-            {"role": "assistant", "content": "Hello"},
-        ],
-        "rejected": [
-            {"role": "user", "content": "Hi"},
-            {"role": "assistant", "content": "Go away"},
-        ],
-    }
-    result = builder.build(item, config, chat_tokenizer)
-    assert 0 in result["chosen_mask"]
-    assert 1 in result["chosen_mask"]
-    assert 0 in result["rejected_mask"]
-    assert 1 in result["rejected_mask"]
-
-
-def test_dpo_missing_field_is_none(chat_tokenizer):
-    config = make_dpo_chat_config()
-    builder = SectionedMaskBuilder()
-    assert builder.build({"chosen": [], "rejected": []}, config, chat_tokenizer) is None
-
-
-def test_grpo_basic(chat_tokenizer):
-    config = make_grpo_config()
-    builder = SectionedMaskBuilder()
-    item = {
-        "prompt": [{"role": "user", "content": "What is 2+2?"}],
-        "responses": ["4", "The answer is four", "Four", "2+2=4"],
-        "rewards": [1.0, 0.5, 0.8, 0.2],
-    }
-    result = builder.build(item, config, chat_tokenizer)
-    assert result is not None
-    assert "prompts" in result
-    assert "responses" in result
-    assert "masks" in result
-    assert "rewards" in result
-    assert len(result["responses"]) == len(result["masks"])
-    assert result["rewards"] == [1.0, 0.5, 0.8, 0.2]
-
-
-def test_grpo_response_tokens_all_trained(chat_tokenizer):
-    config = make_grpo_config()
-    builder = SectionedMaskBuilder()
-    item = {
-        "prompt": [{"role": "user", "content": "Q"}],
-        "responses": ["A", "B"],
-        "rewards": [0.8, 0.2],
-    }
-    result = builder.build(item, config, chat_tokenizer)
-    masks = result["masks"]
-    assert all(m == 1 for m in masks)
-    assert len(masks) == len(result["responses"])
-
-
-def test_grpo_single_reward(chat_tokenizer):
-    config = make_grpo_config()
-    builder = SectionedMaskBuilder()
-    item = {
-        "prompt": [{"role": "user", "content": "Q"}],
-        "responses": ["A"],
-        "rewards": 0.9,
-    }
-    result = builder.build(item, config, chat_tokenizer)
-    assert result["rewards"] == [0.9]

tests/data/test_preprocess_config.py

@@ -1,77 +0,0 @@
-import os
-
-from astrai.config.preprocess_config import (
-    InputConfig,
-    PipelineConfig,
-)
-from tests.data.conftest import (
-    _INSTRUCTION_SECTIONS,
-    _TEXT_SECTIONS,
-    make_dpo_chat_config,
-)
-
-
-def test_default_values():
-    config = PipelineConfig()
-    assert config.version == 1
-    assert config.mask == {}
-    assert config.mask_default == "mask"
-    assert config.preprocessing.max_seq_len == 2048
-    assert config.output.storage_format == "bin"
-    assert config.input.sections is None
-
-
-def test_from_dict_flat():
-    data = {
-        "version": 1,
-        "input": {
-            "sections": [{"field": "messages", "action": "$role", "template": True}]
-        },
-        "mask": {"system": "mask", "assistant": "train"},
-        "mask_default": "mask",
-        "preprocessing": {"max_seq_len": 1024},
-        "output": {"storage_format": "h5"},
-    }
-    config = PipelineConfig.from_dict(data)
-    assert config.input.sections == [
-        {"field": "messages", "action": "$role", "template": True}
-    ]
-    assert config.mask == {"system": "mask", "assistant": "train"}
-    assert config.preprocessing.max_seq_len == 1024
-    assert config.output.storage_format == "h5"
-
-
-def test_to_dict_roundtrip():
-    config = PipelineConfig(
-        input=InputConfig(sections=_INSTRUCTION_SECTIONS),
-        mask={"prompt": "mask", "response": "train"},
-        mask_default="mask",
-    )
-    d = config.to_dict()
-    config2 = PipelineConfig.from_dict(d)
-    assert config2.input.sections == _INSTRUCTION_SECTIONS
-    assert config2.mask == {"prompt": "mask", "response": "train"}
-
-
-def test_to_json_from_json(temp_dir):
-    config = PipelineConfig(
-        input=InputConfig(sections=_TEXT_SECTIONS),
-        mask={"text": "train"},
-        mask_default="mask",
-    )
-    path = os.path.join(temp_dir, "config.json")
-    config.to_json(path)
-    loaded = PipelineConfig.from_json(path)
-    assert loaded.input.sections == _TEXT_SECTIONS
-    assert loaded.mask == {"text": "train"}
-
-
-def test_dpo_config_roundtrip(temp_dir):
-    config = make_dpo_chat_config()
-    path = os.path.join(temp_dir, "config.json")
-    config.to_json(path)
-    loaded = PipelineConfig.from_json(path)
-    assert loaded.input.sources is not None
-    assert "chosen" in loaded.input.sources
-    assert "rejected" in loaded.input.sources
-    assert loaded.input.sections is None

tests/data/test_preprocess_pipeline.py

@@ -1,349 +0,0 @@
-import json
-import os
-
-from astrai.config.preprocess_config import (
-    InputConfig,
-    OutputConfig,
-    PipelineConfig,
-    ProcessingConfig,
-)
-from astrai.preprocessing.pipeline import Pipeline, filter_by_length
-from tests.data.conftest import (
-    _CHAT_SECTIONS,
-    _CHAT_TEMPLATE,
-    _INSTRUCTION_SECTIONS,
-    _SPECIAL_TOKENS_CONFIG,
-    _TEXT_SECTIONS,
-    make_dpo_chat_config,
-    make_grpo_no_template_config,
-)
-
-
-def test_filter_by_length():
-    assert filter_by_length("hello world", min_len=5)
-    assert not filter_by_length("hi", min_len=5)
-    assert not filter_by_length("x" * 100, max_len=50)
-    assert filter_by_length("just right", min_len=5, max_len=20)
-
-
-def test_full_chat_pipeline(temp_dir, chat_tokenizer):
-    tokenizer_dir = os.path.join(temp_dir, "tok")
-    os.makedirs(tokenizer_dir, exist_ok=True)
-    chat_tokenizer._tokenizer.save(os.path.join(tokenizer_dir, "tokenizer.json"))
-    with open(os.path.join(tokenizer_dir, "tokenizer_config.json"), "w") as f:
-        json.dump(
-            {
-                "special_tokens": _SPECIAL_TOKENS_CONFIG,
-                "chat_template": _CHAT_TEMPLATE,
-            },
-            f,
-        )
-
-    jsonl_path = os.path.join(temp_dir, "chat.jsonl")
-    with open(jsonl_path, "w", encoding="utf-8") as f:
-        f.write(
-            json.dumps(
-                {
-                    "messages": [
-                        {"role": "system", "content": "You are helpful."},
-                        {"role": "user", "content": "Hi."},
-                        {"role": "assistant", "content": "Hello!"},
-                    ]
-                }
-            )
-            + "\n"
-        )
-        f.write(
-            json.dumps(
-                {
-                    "messages": [
-                        {"role": "user", "content": "What is 2+2?"},
-                        {"role": "assistant", "content": "4"},
-                    ]
-                }
-            )
-            + "\n"
-        )
-
-    config = PipelineConfig(
-        input=InputConfig(sections=_CHAT_SECTIONS),
-        mask={"system": "mask", "user": "mask", "assistant": "train"},
-        mask_default="mask",
-        preprocessing=ProcessingConfig(max_seq_len=2048),
-        output=OutputConfig(storage_format="bin", domain_key=None),
-    )
-
-    out_dir = os.path.join(temp_dir, "output")
-    Pipeline(
-        config=config,
-        input_paths=[jsonl_path],
-        output_dir=out_dir,
-        tokenizer_path=tokenizer_dir,
-    ).run()
-
-    meta_path = os.path.join(out_dir, "__default__", "shard_0000", "meta.json")
-    assert os.path.exists(meta_path)
-    with open(meta_path, "r") as f:
-        meta = json.load(f)
-    assert "sequence" in meta
-    assert "loss_mask" in meta
-    assert meta["sequence"]["dtype"] == "int32"
-    assert meta["loss_mask"]["dtype"] == "int32"
-
-
-def test_full_text_pipeline(temp_dir, test_tokenizer):
-    tokenizer_dir = os.path.join(temp_dir, "tok")
-    os.makedirs(tokenizer_dir, exist_ok=True)
-    test_tokenizer._tokenizer.save(os.path.join(tokenizer_dir, "tokenizer.json"))
-    with open(os.path.join(tokenizer_dir, "tokenizer_config.json"), "w") as f:
-        json.dump(
-            {
-                "special_tokens": {
-                    "pad_token": "<|_pad_|>",
-                    "unk_token": "<|_unk_|>",
-                }
-            },
-            f,
-        )
-
-    jsonl_path = os.path.join(temp_dir, "text.jsonl")
-    with open(jsonl_path, "w", encoding="utf-8") as f:
-        f.write(
-            json.dumps(
-                {
-                    "text": "Hello world this is a test document with enough characters to pass the minimum length filter."
-                }
-            )
-            + "\n"
-        )
-        f.write(
-            json.dumps(
-                {
-                    "text": "Another document for testing purposes with sufficient length to be processed."
-                }
-            )
-            + "\n"
-        )
-
-    config = PipelineConfig(
-        input=InputConfig(sections=_TEXT_SECTIONS),
-        preprocessing=ProcessingConfig(max_seq_len=2048, min_chars=10),
-        output=OutputConfig(storage_format="bin"),
-    )
-
-    out_dir = os.path.join(temp_dir, "output")
-    Pipeline(
-        config=config,
-        input_paths=[jsonl_path],
-        output_dir=out_dir,
-        tokenizer_path=tokenizer_dir,
-    ).run()
-
-    meta_path = os.path.join(out_dir, "__default__", "shard_0000", "meta.json")
-    assert os.path.exists(meta_path)
-    with open(meta_path, "r") as f:
-        meta = json.load(f)
-    assert "sequence" in meta
-    assert "loss_mask" not in meta
-    assert meta["sequence"]["dtype"] == "int32"
-
-
-def test_full_instruction_pipeline(temp_dir, test_tokenizer):
-    tokenizer_dir = os.path.join(temp_dir, "tok")
-    os.makedirs(tokenizer_dir, exist_ok=True)
-    test_tokenizer._tokenizer.save(os.path.join(tokenizer_dir, "tokenizer.json"))
-    with open(os.path.join(tokenizer_dir, "tokenizer_config.json"), "w") as f:
-        json.dump(
-            {
-                "special_tokens": {
-                    "pad_token": "<|_pad_|>",
-                    "unk_token": "<|_unk_|>",
-                }
-            },
-            f,
-        )
-
-    jsonl_path = os.path.join(temp_dir, "instruct.jsonl")
-    with open(jsonl_path, "w", encoding="utf-8") as f:
-        f.write(
-            json.dumps(
-                {
-                    "prompt": "Tell me a joke",
-                    "response": "Why did the chicken cross the road?",
-                }
-            )
-            + "\n"
-        )
-        f.write(
-            json.dumps(
-                {
-                    "prompt": "What is AI?",
-                    "response": "Artificial Intelligence is a field of computer science.",
-                }
-            )
-            + "\n"
-        )
-
-    config = PipelineConfig(
-        input=InputConfig(sections=_INSTRUCTION_SECTIONS),
-        mask={"prompt": "mask", "response": "train"},
-        mask_default="mask",
-        preprocessing=ProcessingConfig(max_seq_len=2048),
-        output=OutputConfig(storage_format="bin"),
-    )
-
-    out_dir = os.path.join(temp_dir, "output")
-    Pipeline(
-        config=config,
-        input_paths=[jsonl_path],
-        output_dir=out_dir,
-        tokenizer_path=tokenizer_dir,
-    ).run()
-
-    meta_path = os.path.join(out_dir, "__default__", "shard_0000", "meta.json")
-    assert os.path.exists(meta_path)
-    with open(meta_path, "r") as f:
-        meta = json.load(f)
-    assert "sequence" in meta
-    assert "loss_mask" in meta
-    assert meta["sequence"]["dtype"] == "int32"
-    assert meta["loss_mask"]["dtype"] == "int32"
-
-
-def test_dtype_override(temp_dir, test_tokenizer):
-    tokenizer_dir = os.path.join(temp_dir, "tok")
-    os.makedirs(tokenizer_dir, exist_ok=True)
-    test_tokenizer._tokenizer.save(os.path.join(tokenizer_dir, "tokenizer.json"))
-    with open(os.path.join(tokenizer_dir, "tokenizer_config.json"), "w") as f:
-        json.dump(
-            {
-                "special_tokens": {
-                    "pad_token": "<|_pad_|>",
-                    "unk_token": "<|_unk_|>",
-                }
-            },
-            f,
-        )
-
-    jsonl_path = os.path.join(temp_dir, "data.jsonl")
-    with open(jsonl_path, "w", encoding="utf-8") as f:
-        f.write(json.dumps({"prompt": "Q", "response": "A"}) + "\n")
-
-    config = PipelineConfig(
-        input=InputConfig(sections=_INSTRUCTION_SECTIONS),
-        mask={"prompt": "mask", "response": "train"},
-        mask_default="mask",
-        preprocessing=ProcessingConfig(max_seq_len=2048),
-        output=OutputConfig(storage_format="bin", dtype={"loss_mask": "bool"}),
-    )
-
-    out_dir = os.path.join(temp_dir, "output")
-    Pipeline(
-        config=config,
-        input_paths=[jsonl_path],
-        output_dir=out_dir,
-        tokenizer_path=tokenizer_dir,
-    ).run()
-
-    meta_path = os.path.join(out_dir, "__default__", "shard_0000", "meta.json")
-    with open(meta_path, "r") as f:
-        meta = json.load(f)
-    assert meta["sequence"]["dtype"] == "int32"
-    assert meta["loss_mask"]["dtype"] == "bool"
-
-
-def test_dpo_pipeline(temp_dir, chat_tokenizer):
-    tokenizer_dir = os.path.join(temp_dir, "tok")
-    os.makedirs(tokenizer_dir, exist_ok=True)
-    chat_tokenizer._tokenizer.save(os.path.join(tokenizer_dir, "tokenizer.json"))
-    with open(os.path.join(tokenizer_dir, "tokenizer_config.json"), "w") as f:
-        json.dump(
-            {
-                "special_tokens": _SPECIAL_TOKENS_CONFIG,
-                "chat_template": _CHAT_TEMPLATE,
-            },
-            f,
-        )
-
-    jsonl_path = os.path.join(temp_dir, "dpo.jsonl")
-    with open(jsonl_path, "w", encoding="utf-8") as f:
-        f.write(
-            json.dumps(
-                {
-                    "chosen": [
-                        {"role": "user", "content": "Hi."},
-                        {"role": "assistant", "content": "Hello!"},
-                    ],
-                    "rejected": [
-                        {"role": "user", "content": "Hi."},
-                        {"role": "assistant", "content": "Go away."},
-                    ],
-                }
-            )
-            + "\n"
-        )
-
-    out_dir = os.path.join(temp_dir, "output")
-    Pipeline(
-        config=make_dpo_chat_config(),
-        input_paths=[jsonl_path],
-        output_dir=out_dir,
-        tokenizer_path=tokenizer_dir,
-    ).run()
-
-    meta_path = os.path.join(out_dir, "__default__", "shard_0000", "meta.json")
-    assert os.path.exists(meta_path)
-    with open(meta_path, "r") as f:
-        meta = json.load(f)
-    assert "chosen" in meta
-    assert "rejected" in meta
-    assert "chosen_mask" in meta
-    assert "rejected_mask" in meta
-    assert "sequence" not in meta
-
-
-def test_grpo_pipeline(temp_dir, test_tokenizer):
-    tokenizer_dir = os.path.join(temp_dir, "tok")
-    os.makedirs(tokenizer_dir, exist_ok=True)
-    test_tokenizer._tokenizer.save(os.path.join(tokenizer_dir, "tokenizer.json"))
-    with open(os.path.join(tokenizer_dir, "tokenizer_config.json"), "w") as f:
-        json.dump(
-            {
-                "special_tokens": {
-                    "pad_token": "<|_pad_|>",
-                    "unk_token": "<|_unk_|>",
-                }
-            },
-            f,
-        )
-
-    jsonl_path = os.path.join(temp_dir, "grpo.jsonl")
-    with open(jsonl_path, "w", encoding="utf-8") as f:
-        f.write(
-            json.dumps(
-                {
-                    "prompt": "Question?",
-                    "responses": ["Answer A", "Answer B"],
-                    "rewards": [0.8, 0.3],
-                }
-            )
-            + "\n"
-        )
-
-    out_dir = os.path.join(temp_dir, "output")
-    Pipeline(
-        config=make_grpo_no_template_config(),
-        input_paths=[jsonl_path],
-        output_dir=out_dir,
-        tokenizer_path=tokenizer_dir,
-    ).run()
-
-    meta_path = os.path.join(out_dir, "__default__", "shard_0000", "meta.json")
-    assert os.path.exists(meta_path)
-    with open(meta_path, "r") as f:
-        meta = json.load(f)
-    assert "prompts" in meta
-    assert "responses" in meta
-    assert "masks" in meta
-    assert "rewards" in meta
-    assert "sequence" not in meta

tests/inference/conftest.py

@@ -5,50 +5,46 @@ from unittest.mock import MagicMock
 import pytest
 from fastapi.testclient import TestClient
 
-from astrai.inference import get_app
+from astrai.inference.server import app
 
 
 @pytest.fixture
 def client():
     """Provide a test client for the FastAPI app."""
-    _app = get_app()
+    return TestClient(app)
-    _app.state.server_config = {
+
-        "device": "cpu",
+
-        "dtype": "bfloat16",
+@pytest.fixture
-        "param_path": None,
+def mock_model_param():
-        "max_batch_size": 1,
+    """Create a mock ModelParameter."""
-        "_test": True,
+    mock_param = MagicMock()
-    }
+    mock_param.model = MagicMock()
-    _app.state.engine = None
+    mock_param.tokenizer = MagicMock()
-    return TestClient(_app)
+    mock_param.config = MagicMock()
+    mock_param.config.max_len = 100
+    mock_param.tokenizer.encode = MagicMock(return_value=[1, 2, 3])
+    mock_param.tokenizer.decode = MagicMock(return_value="mock response")
+    mock_param.tokenizer.stop_ids = []
+    mock_param.tokenizer.pad_id = 0
+    return mock_param
 
 
 @pytest.fixture
 def mock_engine():
     """Create a mock InferenceEngine."""
-
-    async def _async_gen():
-        yield "chunk1"
-        yield "chunk2"
-        yield "[DONE]"
-
     mock = MagicMock()
     mock.generate.return_value = "mock response"
-    mock.generate_async.return_value = _async_gen()
     mock.get_stats.return_value = {
         "total_tasks": 0,
         "total_tokens": 0,
         "active_tasks": 0,
         "waiting_queue": 0,
     }
-    mock.tokenizer.encode.return_value = [1, 2, 3]
-    mock.tokenizer.decode.return_value = "mock response"
-    mock.tokenizer.apply_chat_template.return_value = "mock prompt"
     return mock
 
 
 @pytest.fixture
-def loaded_model(client, mock_engine):
+def loaded_model(mock_model_param, monkeypatch):
-    """Simulate that the engine is loaded."""
+    """Simulate that the model is loaded."""
-    get_app().state.engine = mock_engine
+    monkeypatch.setattr("astrai.inference.server._model_param", mock_model_param)
-    return mock_engine
+    return mock_model_param

tests/inference/test_cache.py

@@ -1,279 +0,0 @@
-"""Unit tests for inference cache components."""
-
-import torch
-
-from astrai.inference import (
-    Allocator,
-    KVCache,
-    PagePool,
-    PrefixCache,
-    Storage,
-    TaskTable,
-    page_hash,
-)
-
-
-def make_pool(n_pages: int, page_size: int) -> PagePool:
-    return PagePool(Allocator(n_pages), PrefixCache(page_size))
-
-
-def test_page_hash_full_page():
-    token_ids = list(range(256))
-    h = page_hash(token_ids, 0, 64)
-    assert isinstance(h, int)
-    assert h >= 0
-
-
-def test_page_hash_different_page_differs():
-    token_ids = list(range(256))
-    assert page_hash(token_ids, 0, 64) != page_hash(token_ids, 1, 64)
-
-
-def test_page_pool_alloc_free_cycle():
-    pool = make_pool(4, 64)
-    a = pool.alloc()
-    b = pool.alloc()
-    assert a != b
-    pool.free(a)
-    pool.free(b)
-    c = pool.alloc()
-    assert c in (a, b)
-
-
-def test_page_pool_alloc_when_full():
-    pool = make_pool(2, 64)
-    pool.alloc()
-    pool.alloc()
-    assert pool.alloc() == -1
-
-
-def test_page_pool_lru_eviction():
-    pool = make_pool(2, 64)
-    p0 = pool.alloc()
-    p1 = pool.alloc()
-    pool.record(p0, list(range(64)), 0)
-    pool.record(p1, list(range(64, 128)), 0)
-    pool.free(p0)
-    pool.free(p1)
-    pool.alloc()
-    assert p0 in pool._alloc._lru or p1 in pool._alloc._lru
-
-
-def test_page_pool_inc_ref_and_free():
-    pool = make_pool(2, 64)
-    p = pool.alloc()
-    pool.inc_ref(p)
-    assert pool._alloc._refs[p] == 2
-    pool.free(p)
-    assert pool._alloc._refs[p] == 1
-    pool.free(p)
-    assert pool._alloc._refs[p] == 0
-
-
-def test_page_pool_keep_cached_realloc():
-    """Free mask has priority over LRU; cached page returned only when no free pages."""
-    pool = make_pool(3, 64)
-    p0 = pool.alloc()
-    p1 = pool.alloc()
-    p2 = pool.alloc()
-    for p in (p0, p1, p2):
-        pool.record(p, [p] * 64, 0)
-    pool.free(p0)
-    pool.free(p1)
-    pool.free(p2)
-    assert pool.alloc() == p0
-
-
-def test_prefix_cache_lookup_returns_hits():
-    token_ids = list(range(256))
-    pool = make_pool(16, 64)
-    pages = [pool.alloc() for _ in range(4)]
-    for i, p in enumerate(pages):
-        pool.record(p, token_ids, i)
-        pool.free(p)
-    hits = pool.lookup(token_ids)
-    assert hits == pages
-
-
-def test_prefix_cache_lookup_stops_at_first_miss():
-    token_ids = list(range(256))
-    pool = make_pool(16, 64)
-    p0 = pool.alloc()
-    pool.record(p0, token_ids, 0)
-    pool.free(p0)
-    p1 = pool.alloc()
-    pool.record(p1, [99] * 64, 1)
-    pool.free(p1)
-    hits = pool.lookup(token_ids)
-    assert len(hits) == 1
-    assert hits[0] == p0
-
-
-def test_prefix_cache_ignores_partial_last_page():
-    token_ids = list(range(100))
-    pool = make_pool(16, 64)
-    p = pool.alloc()
-    pool.record(p, token_ids, 0)
-    pool.free(p)
-    hits = pool.lookup(token_ids)
-    assert len(hits) == 1
-
-
-def test_prefix_cache_on_evict_clears_mappings():
-    pool = make_pool(4, 64)
-    p = pool.alloc()
-    pool.record(p, list(range(64)), 0)
-    pool.free(p)
-    assert p in pool._prefix._page_to_hash
-    pool._prefix.evict(p)
-    assert p not in pool._prefix._page_to_hash
-
-
-def test_prefix_cache_has_page():
-    pool = make_pool(4, 64)
-    p = pool.alloc()
-    assert p not in pool._prefix._page_to_hash
-    pool.record(p, list(range(64)), 0)
-    pool.free(p)
-    assert p in pool._prefix._page_to_hash
-
-
-def test_task_table_set_get():
-    table = TaskTable(page_size=64)
-    table.set("task1", [0, 1, 2], 128)
-    assert table.get("task1") == [0, 1, 2]
-    assert table.get_cached("task1") == 128
-
-
-def test_task_table_get_missing():
-    table = TaskTable(page_size=64)
-    assert table.get("nonexistent") == []
-    assert table.get_cached("nonexistent") == 0
-
-
-def test_task_table_pop():
-    table = TaskTable(page_size=64)
-    table.set("task1", [0, 1], 64)
-    pages, cached = table.pop("task1")
-    assert pages == [0, 1]
-    assert cached == 64
-    assert table.get("task1") == []
-
-
-def test_kv_cache_task_extend_allocates():
-    cache = KVCache(
-        n_layers=1,
-        n_pages=8,
-        page_size=64,
-        n_kv_heads=2,
-        head_dim=8,
-        device=torch.device("cpu"),
-        dtype=torch.float32,
-    )
-    cache._table.set("task1", [], 0)
-    ok = cache.task_extend("task1", 200)
-    assert ok
-    assert len(cache._table.get("task1")) == 4
-
-
-def test_kv_cache_task_extend_fails_when_pool_full():
-    cache = KVCache(
-        n_layers=1,
-        n_pages=2,
-        page_size=64,
-        n_kv_heads=2,
-        head_dim=8,
-        device=torch.device("cpu"),
-        dtype=torch.float32,
-    )
-    cache._table.set("task1", [0, 1], 0)
-    ok = cache.task_extend("task1", 300)
-    assert not ok
-
-
-def test_task_table_table_tensor():
-    table = TaskTable(page_size=64)
-    table.set("a", [0, 1], 0)
-    table.set("b", [2, 3, 4], 0)
-    t = table.table_tensor(["a", "b"], torch.device("cpu"))
-    assert t.shape == (2, 3)
-    assert t[0].tolist() == [0, 1, -1]
-    assert t[1].tolist() == [2, 3, 4]
-
-
-def test_task_table_table_tensor_empty_input():
-    table = TaskTable(page_size=64)
-    t = table.table_tensor([], torch.device("cpu"))
-    assert t.numel() == 0
-
-
-def test_storage_write_gather_single_page():
-    storage = Storage(
-        n_layers=2,
-        n_pages=8,
-        page_size=4,
-        n_kv_heads=2,
-        head_dim=8,
-        device=torch.device("cpu"),
-        dtype=torch.float32,
-    )
-    page_table = torch.tensor([[0]], dtype=torch.long)
-    k = torch.randn(1, 2, 2, 8)
-    v = torch.randn(1, 2, 2, 8)
-
-    storage.write(0, page_table, 0, k, v)
-    gk, gv = storage.gather(0, page_table, 2)
-    assert torch.allclose(gk, k)
-
-
-def test_storage_write_cross_page():
-    storage = Storage(
-        n_layers=1,
-        n_pages=8,
-        page_size=4,
-        n_kv_heads=2,
-        head_dim=8,
-        device=torch.device("cpu"),
-        dtype=torch.float32,
-    )
-    page_table = torch.tensor([[0, 1]], dtype=torch.long)
-    k = torch.randn(1, 8, 2, 8)
-    v = torch.randn(1, 8, 2, 8)
-
-    storage.write(0, page_table, 0, k, v)
-    gk, gv = storage.gather(0, page_table, 8)
-    assert torch.allclose(gk, k)
-
-
-def test_storage_gather_truncates_to_total_len():
-    storage = Storage(
-        n_layers=1,
-        n_pages=8,
-        page_size=4,
-        n_kv_heads=2,
-        head_dim=8,
-        device=torch.device("cpu"),
-        dtype=torch.float32,
-    )
-    page_table = torch.tensor([[0, 1]], dtype=torch.long)
-    k = torch.randn(1, 6, 2, 8)
-    v = torch.randn(1, 6, 2, 8)
-    storage.write(0, page_table, 0, k, v)
-
-    gk, gv = storage.gather(0, page_table, 5)
-    assert gk.shape == (1, 5, 2, 8)
-
-
-def test_storage_gather_clamps_negative_padding():
-    storage = Storage(
-        n_layers=1,
-        n_pages=8,
-        page_size=4,
-        n_kv_heads=2,
-        head_dim=8,
-        device=torch.device("cpu"),
-        dtype=torch.float32,
-    )
-    page_table = torch.tensor([[0, -1]], dtype=torch.long)
-    gk, gv = storage.gather(0, page_table, 4)
-    assert gk.shape == (1, 4, 2, 8)

tests/inference/test_engine.py

@@ -1,181 +0,0 @@
-"""Unit tests for GenerateResult accumulator and InferenceEngine.generate()."""
-
-import threading
-from unittest.mock import MagicMock, patch
-
-from astrai.inference import STOP
-from astrai.inference.engine import GenerateResult
-
-
-def test_result_append_single():
-    r = GenerateResult(count=1)
-    r.append("hello", 0)
-    assert r.results[0] == "hello"
-
-
-def test_result_append_multiple_tasks():
-    r = GenerateResult(count=3)
-    r.append("a", 0)
-    r.append("b", 1)
-    r.append("c", 2)
-    assert r.results[0] == "a"
-    assert r.results[1] == "b"
-    assert r.results[2] == "c"
-
-
-def test_result_stop_marks_complete():
-    r = GenerateResult(count=2)
-    r.append("text", 0)
-    r.append(STOP, 0)
-    r.append("more", 1)
-    assert r._done[0] is True
-    assert r._done[1] is False
-    assert r._completed == 1
-
-
-def test_result_stop_does_not_double_count():
-    r = GenerateResult(count=1)
-    r.append(STOP, 0)
-    r.append(STOP, 0)
-    assert r._completed == 1
-
-
-def test_result_pop_all_returns_and_clears():
-    r = GenerateResult(count=2)
-    r.append("a", 0)
-    r.append("b", 1)
-    out = r.pop_all()
-    assert len(out) == 2
-    assert out[0] == (0, "a")
-    assert out[1] == (1, "b")
-    assert r.pop_all() == []
-
-
-def test_result_wait_blocks_until_data():
-    r = GenerateResult(count=1)
-
-    def delayed_append():
-        import time
-
-        time.sleep(0.05)
-        r.append("delayed", 0)
-
-    t = threading.Thread(target=delayed_append)
-    t.start()
-    ok = r.wait(timeout=5.0)
-    t.join()
-    assert ok
-    assert r.results[0] == "delayed"
-
-
-def test_result_wait_timeout():
-    r = GenerateResult(count=1)
-    ok = r.wait(timeout=0.01)
-    assert not ok
-
-
-def test_result_wait_completion_non_streaming():
-    r = GenerateResult(count=2)
-
-    def finish_later():
-        import time
-
-        time.sleep(0.05)
-        r.append(STOP, 0)
-        time.sleep(0.05)
-        r.append(STOP, 1)
-
-    t = threading.Thread(target=finish_later)
-    t.start()
-    r.wait_completion()
-    t.join()
-    assert r._completed == 2
-
-
-def test_result_get_results():
-    r = GenerateResult(count=2)
-    r.append("hello", 0)
-    r.append("world", 1)
-    results = r.get_results()
-    assert results == ["hello", "world"]
-
-
-def test_engine_generate_non_streaming_single():
-    from astrai.inference.engine import InferenceEngine
-
-    mock_model = MagicMock()
-    mock_tokenizer = MagicMock()
-    mock_tokenizer.encode.return_value = [1, 2, 3]
-    mock_tokenizer.decode.return_value = "response"
-    mock_tokenizer.stop_ids = [0]
-
-    with patch("astrai.inference.engine.InferenceScheduler") as MockSched:
-        instance = MockSched.return_value
-
-        def fake_add(prompt, **kw):
-            cb = kw["stream_callback"]
-            cb("response")
-            cb(STOP)
-
-        instance.add_task.side_effect = fake_add
-        instance.remove_task.return_value = []
-
-        eng = InferenceEngine(mock_model, mock_tokenizer, max_batch_size=1)
-        result = eng.generate("hello")
-        assert result == "response"
-
-
-def test_engine_generate_streaming_yields_tokens():
-    from astrai.inference.engine import InferenceEngine
-
-    mock_model = MagicMock()
-    mock_tokenizer = MagicMock()
-    mock_tokenizer.encode.return_value = [1, 2, 3]
-    mock_tokenizer.decode.return_value = "tok"
-    mock_tokenizer.stop_ids = [0]
-
-    callbacks_saved = []
-
-    def capture_cb(prompt, **kw):
-        callbacks_saved.append(kw.get("stream_callback"))
-
-    with patch("astrai.inference.engine.InferenceScheduler") as MockSched:
-        instance = MockSched.return_value
-        instance.add_task.side_effect = capture_cb
-        instance.remove_task.return_value = []
-
-        eng = InferenceEngine(mock_model, mock_tokenizer, max_batch_size=1)
-        gen = eng.generate("hello", stream=True)
-
-        cb = callbacks_saved[0]
-        cb("t1")
-        cb("t2")
-        cb(STOP)
-
-        tokens = list(gen)
-        assert tokens == ["t1", "t2"]
-
-
-def test_engine_generate_non_streaming_batch():
-    from astrai.inference.engine import InferenceEngine
-
-    mock_model = MagicMock()
-    mock_tokenizer = MagicMock()
-    mock_tokenizer.encode.return_value = [1, 2, 3]
-    mock_tokenizer.decode.return_value = "r"
-    mock_tokenizer.stop_ids = [0]
-
-    with patch("astrai.inference.engine.InferenceScheduler") as MockSched:
-        instance = MockSched.return_value
-
-        def fake_add(prompt, **kw):
-            cb = kw["stream_callback"]
-            cb("r")
-            cb(STOP)
-
-        instance.add_task.side_effect = fake_add
-        instance.remove_task.return_value = []
-
-        eng = InferenceEngine(mock_model, mock_tokenizer, max_batch_size=2)
-        results = eng.generate(["hello", "world"])
-        assert results == ["r", "r"]

tests/inference/test_protocol.py

@@ -1,286 +0,0 @@
-"""Unit tests for protocol builders, StopChecker, GenContext, StopInfo."""
-
-import json
-from unittest.mock import MagicMock
-
-import pytest
-
-from astrai.inference.api.anthropic import AnthropicResponseBuilder
-from astrai.inference.api.openai import OpenAIResponseBuilder
-from astrai.inference.api.protocol import GenContext, StopChecker, StopInfo
-from astrai.inference.engine import GenerationRequest
-
-
-def _make_ctx(**kwargs):
-    defaults = {
-        "resp_id": "test-123",
-        "created": 1000,
-        "model": "test-model",
-        "prompt_tokens": 10,
-        "completion_tokens": 5,
-    }
-    defaults.update(kwargs)
-    return GenContext(**defaults)
-
-
-def _sse_payloads(events):
-    payloads = []
-    for chunk in events:
-        for line in chunk.strip().split("\n"):
-            if line.startswith("data: "):
-                try:
-                    payloads.append(json.loads(line[6:]))
-                except json.JSONDecodeError:
-                    pass
-    return payloads
-
-
-class TestStopChecker:
-    def test_check_finds_match(self):
-        sc = StopChecker(["stop", "end"])
-        assert sc.check("hello stop world") == "stop"
-
-    def test_check_returns_none_when_no_match(self):
-        sc = StopChecker(["stop"])
-        assert sc.check("hello world") is None
-
-    def test_check_empty_sequences(self):
-        sc = StopChecker([])
-        assert sc.check("hello") is None
-
-
-class TestGenContext:
-    def test_defaults(self):
-        ctx = GenContext(resp_id="a", created=1, model="m", prompt_tokens=10)
-        assert ctx.completion_tokens == 0
-
-    def test_fields_mutable(self):
-        ctx = GenContext(resp_id="a", created=1, model="m", prompt_tokens=10)
-        ctx.completion_tokens = 42
-        assert ctx.completion_tokens == 42
-
-
-class TestStopInfo:
-    def test_defaults(self):
-        s = StopInfo()
-        assert s.matched is None
-        assert s.body == ""
-        assert s.yielded == ""
-
-    def test_with_values(self):
-        s = StopInfo(matched="stop", body="hello stop", yielded="hello ")
-        assert s.matched == "stop"
-        assert s.body == "hello stop"
-        assert s.yielded == "hello "
-
-
-class TestOpenAIResponseBuilder:
-    @pytest.fixture
-    def builder(self):
-        builder = OpenAIResponseBuilder()
-        req = MagicMock()
-        req.messages = [MagicMock(role="user", content="Hello")]
-        req.stop = None
-        req.model = "astrai"
-        engine = MagicMock()
-        engine.tokenizer.apply_chat_template.return_value = "Hello"
-        builder.prepare(req, engine)
-        return builder
-
-    def test_prepare_returns_prompt_ctx_stops(self, builder):
-        req = MagicMock()
-        req.messages = [MagicMock(role="user", content="Hi")]
-        req.stop = ["END"]
-        req.model = "gpt"
-        engine = MagicMock()
-        engine.tokenizer.apply_chat_template.return_value = "Hi"
-        prompt, ctx, stops = builder.prepare(req, engine)
-        assert prompt == "Hi"
-        assert ctx.model == "gpt"
-        assert ctx.prompt_tokens == 0
-        assert stops == ["END"]
-
-    def test_prepare_no_stop_returns_empty_list(self, builder):
-        req = MagicMock()
-        req.messages = []
-        req.stop = None
-        req.model = "x"
-        engine = MagicMock()
-        engine.tokenizer.apply_chat_template.return_value = ""
-        _, _, stops = builder.prepare(req, engine)
-        assert stops == []
-
-    def test_format_stream_start(self, builder):
-        ctx = _make_ctx()
-        events = builder.format_stream_start(ctx)
-        payloads = _sse_payloads(events)
-        assert len(payloads) == 1
-        p = payloads[0]
-        assert p["object"] == "chat.completion.chunk"
-        assert p["choices"][0]["delta"]["role"] == "assistant"
-        assert p["choices"][0]["finish_reason"] is None
-
-    def test_format_chunk(self, builder):
-        event = builder.format_chunk("hello")
-        payload = json.loads(event.split("data: ", 1)[1])
-        assert payload["choices"][0]["delta"]["content"] == "hello"
-        assert payload["choices"][0]["finish_reason"] is None
-
-    def test_format_stream_end(self, builder):
-        ctx = _make_ctx(completion_tokens=5)
-        stop = StopInfo(matched="stop")
-        events = builder.format_stream_end(ctx, stop)
-        payloads = _sse_payloads(events)
-        finish = payloads[0]
-        assert finish["choices"][0]["finish_reason"] == "stop"
-        usage = payloads[1]
-        assert usage["completion_tokens"] == 5
-        assert usage["total_tokens"] == 15
-
-    def test_format_response(self, builder):
-        ctx = _make_ctx()
-        stop = StopInfo()
-        resp = builder.format_response(ctx, "hello", stop)
-        assert resp["object"] == "chat.completion"
-        assert resp["choices"][0]["message"]["content"] == "hello"
-        assert resp["usage"]["prompt_tokens"] == 10
-
-
-class TestAnthropicResponseBuilder:
-    @pytest.fixture
-    def builder(self):
-        builder = AnthropicResponseBuilder()
-        req = MagicMock()
-        req.messages = [MagicMock(role="user", content="Hello")]
-        req.model = "claude"
-        engine = MagicMock()
-        engine.tokenizer.apply_chat_template.return_value = "Hello"
-        req.system = None
-        builder.prepare(req, engine)
-        return builder
-
-    def test_prepare_messages(self, builder):
-        req = MagicMock()
-        req.messages = [MagicMock(role="user", content="Hi")]
-        req.model = "claude"
-        req.system = None
-        req.stop_sequences = None
-        engine = MagicMock()
-        engine.tokenizer.apply_chat_template.return_value = "Hi"
-        prompt, ctx, stops = builder.prepare(req, engine)
-        assert prompt == "Hi"
-        assert stops == []
-
-    def test_prepare_with_stop_sequences(self, builder):
-        req = MagicMock()
-        req.messages = []
-        req.model = "x"
-        req.stop_sequences = ["stop", "end"]
-        req.system = None
-        engine = MagicMock()
-        engine.tokenizer.apply_chat_template.return_value = ""
-        _, _, stops = builder.prepare(req, engine)
-        assert stops == ["stop", "end"]
-
-    def test_format_stream_start(self, builder):
-        ctx = _make_ctx(prompt_tokens=3)
-        events = builder.format_stream_start(ctx)
-        payloads = _sse_payloads(events)
-        assert len(payloads) == 2
-        assert payloads[0]["type"] == "message_start"
-        assert payloads[0]["message"]["usage"]["input_tokens"] == 3
-        assert payloads[1]["type"] == "content_block_start"
-
-    def test_format_chunk(self, builder):
-        event = builder.format_chunk("tok")
-        payload = json.loads(event.split("data: ", 1)[1])
-        assert payload["type"] == "content_block_delta"
-        assert payload["delta"]["text"] == "tok"
-
-    def test_format_stream_end_no_stop(self, builder):
-        ctx = _make_ctx(completion_tokens=3)
-        stop = StopInfo()
-        events = builder.format_stream_end(ctx, stop)
-        payloads = _sse_payloads(events)
-        # content_block_stop, message_delta, message_stop
-        types = [p["type"] for p in payloads]
-        assert types == ["content_block_stop", "message_delta", "message_stop"]
-        assert payloads[1]["delta"]["stop_reason"] == "end_turn"
-
-    def test_format_stream_end_with_stop_trims_and_emits_remaining(self, builder):
-        ctx = _make_ctx(completion_tokens=7)
-        stop = StopInfo(
-            matched="END",
-            body="Hello world END extra",
-            yielded="Hello ",
-        )
-        events = builder.format_stream_end(ctx, stop)
-        payloads = _sse_payloads(events)
-        # unyielded delta, content_block_stop, message_delta, message_stop
-        types = [p["type"] for p in payloads]
-        assert types == [
-            "content_block_delta",
-            "content_block_stop",
-            "message_delta",
-            "message_stop",
-        ]
-        assert payloads[0]["delta"]["text"] == "world "
-        assert payloads[2]["delta"]["stop_reason"] == "stop_sequence"
-        assert payloads[2]["delta"]["stop_sequence"] == "END"
-
-    def test_format_stream_end_stop_trimmed_already_yielded(self, builder):
-        ctx = _make_ctx()
-        stop = StopInfo(
-            matched="END",
-            body="Hello END",
-            yielded="Hello ",
-        )
-        events = builder.format_stream_end(ctx, stop)
-        payloads = _sse_payloads(events)
-        # No unyielded delta (everything already sent)
-        types = [p["type"] for p in payloads]
-        assert types == ["content_block_stop", "message_delta", "message_stop"]
-
-    def test_format_response_with_stop_trims_content(self, builder):
-        ctx = _make_ctx()
-        stop = StopInfo(matched="STOP", body="text STOP extra", yielded="text ")
-        resp = builder.format_response(ctx, "text STOP extra", stop)
-        assert resp["content"][0]["text"] == "text "
-        assert resp["stop_reason"] == "stop_sequence"
-        assert resp["stop_sequence"] == "STOP"
-
-    def test_format_response_no_stop(self, builder):
-        ctx = _make_ctx()
-        stop = StopInfo()
-        resp = builder.format_response(ctx, "full text", stop)
-        assert resp["content"][0]["text"] == "full text"
-        assert resp["stop_reason"] == "end_turn"
-
-
-class TestGenerationRequestValidation:
-    def test_valid_params(self):
-        gr = GenerationRequest(
-            messages=[{"role": "user", "content": "hi"}],
-            top_k=50,
-            top_p=0.9,
-            temperature=0.7,
-        )
-        assert gr.top_k == 50
-
-    def test_invalid_top_p_raises(self):
-        with pytest.raises(ValueError, match="top_p"):
-            GenerationRequest(messages=[{"role": "user", "content": "hi"}], top_p=1.5)
-
-    def test_invalid_top_k_raises(self):
-        with pytest.raises(ValueError, match="top_k"):
-            GenerationRequest(messages=[{"role": "user", "content": "hi"}], top_k=-1)
-
-    def test_invalid_temperature_raises(self):
-        with pytest.raises(ValueError, match="temperature"):
-            GenerationRequest(
-                messages=[{"role": "user", "content": "hi"}], temperature=-0.1
-            )
-
-    def test_top_k_zero_valid(self):
-        gr = GenerationRequest(messages=[{"role": "user", "content": "hi"}], top_k=0)
-        assert gr.top_k == 0

tests/inference/test_sample.py

@@ -1,127 +0,0 @@
-"""Unit tests for inference sampling strategies."""
-
-import torch
-
-from astrai.inference.sample import (
-    SamplingPipeline,
-    TemperatureStrategy,
-    TopKStrategy,
-    TopPStrategy,
-    sample,
-)
-
-
-def test_temperature_scalar():
-    logits = torch.tensor([[1.0, 2.0, 3.0]])
-    s = TemperatureStrategy(0.5)
-    result = s.apply(logits.clone())
-    assert torch.allclose(result, logits / 0.5)
-
-
-def test_temperature_skip_when_one():
-    logits = torch.tensor([[1.0, 2.0, 3.0]])
-    s = TemperatureStrategy(1.0)
-    result = s.apply(logits.clone())
-    assert torch.equal(result, logits)
-
-
-def test_temperature_per_sample_tensor():
-    logits = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
-    s = TemperatureStrategy(torch.tensor([0.5, 0.5]))
-    result = s.apply(logits.clone())
-    assert torch.allclose(result, logits / 0.5)
-
-
-def test_top_k_keeps_top():
-    logits = torch.tensor([[0.1, 0.5, 0.3, 0.9, 0.2]])
-    s = TopKStrategy(top_k=2)
-    result = s.apply(logits.clone(), filter_value=-1e9)
-    kept = (result > -1e9).sum().item()
-    assert kept == 2
-
-
-def test_top_k_skip_when_zero():
-    logits = torch.tensor([[1.0, 2.0, 3.0]])
-    s = TopKStrategy(top_k=0)
-    result = s.apply(logits.clone())
-    assert torch.equal(result, logits)
-
-
-def test_top_k_batch_tensor():
-    """Each row respects its own top_k."""
-    logits = torch.tensor([[0.1, 0.5, 0.3], [0.9, 0.2, 0.1]])
-    s = TopKStrategy(top_k=torch.tensor([2, 1]))
-    result = s.apply(logits.clone(), filter_value=-1e9)
-    assert (result[0] > -1e9).sum() == 2
-    assert (result[1] > -1e9).sum() == 1
-
-
-def test_top_p_nucleus_filtering():
-    logits = torch.tensor([[10.0, 1.0, 1.0, 1.0, 1.0]])
-    s = TopPStrategy(top_p=0.5)
-    result = s.apply(logits.clone(), filter_value=-1e9)
-    kept = (result > -1e9).sum().item()
-    assert kept >= 1
-
-
-def test_top_p_skip_when_one():
-    logits = torch.tensor([[1.0, 2.0, 3.0]])
-    s = TopPStrategy(top_p=1.0)
-    result = s.apply(logits.clone())
-    assert torch.equal(result, logits)
-
-
-def test_top_p_filter_all_except_max_when_zero():
-    logits = torch.tensor([[0.1, 0.5, 0.3, 0.9, 0.2]])
-    s = TopPStrategy(top_p=0.0)
-    result = s.apply(logits.clone(), filter_value=-1e9)
-    kept = (result > -1e9).sum().item()
-    assert kept == 1
-
-
-def test_sampling_pipeline_composes_strategies():
-    logits = torch.tensor([[1.0, 2.0, 3.0, 4.0, 5.0]])
-    pipeline = SamplingPipeline(
-        [
-            TemperatureStrategy(0.8),
-            TopKStrategy(3),
-            TopPStrategy(0.95),
-        ]
-    )
-    result = pipeline.apply(logits.clone(), filter_value=-1e9)
-    kept = (result > -1e9).sum().item()
-    assert 1 <= kept <= 3
-
-
-def test_sampling_pipeline_sample_returns_valid_token():
-    logits = torch.tensor([[1.0, 2.0, 3.0, 4.0, 5.0]])
-    pipeline = SamplingPipeline(
-        [
-            TemperatureStrategy(0.8),
-            TopKStrategy(3),
-            TopPStrategy(0.95),
-        ]
-    )
-    tokens = pipeline.sample(logits)
-    assert tokens.shape == (1,)
-    assert 0 <= tokens[0] < logits.size(-1)
-
-
-def test_module_sample_shortcut():
-    logits = torch.tensor([[1.0, 2.0, 3.0, 4.0, 5.0]])
-    tokens = sample(logits, temperature=0.8, top_k=3, top_p=0.95)
-    assert tokens.shape == (1,)
-    assert 0 <= tokens[0] < logits.size(-1)
-
-
-def test_module_sample_batch():
-    logits = torch.tensor(
-        [
-            [1.0, 2.0, 3.0, 4.0, 5.0],
-            [5.0, 4.0, 3.0, 2.0, 1.0],
-        ]
-    )
-    tokens = sample(logits, temperature=0.8, top_k=3, top_p=0.95)
-    assert tokens.shape == (2,)
-    for t in tokens:
-        assert 0 <= t < logits.size(-1)

tests/inference/test_scheduler.py

@@ -1,193 +0,0 @@
-"""Tests for scheduler concurrency."""
-
-import threading
-from unittest.mock import MagicMock, patch
-
-import pytest
-import torch
-
-from astrai.inference import InferenceScheduler
-
-
-@pytest.fixture
-def mock_model_and_tokenizer():
-    """Create mock model and tokenizer."""
-    mock_model = MagicMock()
-    mock_model.config = MagicMock()
-    mock_model.config.n_kv_heads = 8
-    mock_model.config.n_heads = 8
-    mock_model.config.dim = 128
-    mock_model.config.n_layers = 2
-    mock_model.config.max_len = 100
-    mock_model.parameters.return_value = iter(
-        [MagicMock(dtype=torch.float32, device=torch.device("cpu"))]
-    )
-
-    mock_tokenizer = MagicMock()
-    mock_tokenizer.encode.return_value = [1, 2, 3, 4, 5]
-    mock_tokenizer.decode.return_value = "token"
-    mock_tokenizer.stop_ids = [0]
-    mock_tokenizer.pad_id = None
-
-    return mock_model, mock_tokenizer
-
-
-def test_scheduler_concurrent_add_task(mock_model_and_tokenizer):
-    """Test concurrent add_task operations."""
-    mock_model, mock_tokenizer = mock_model_and_tokenizer
-
-    with patch("astrai.inference.core.scheduler.AutoModel"):
-        with patch("astrai.inference.core.scheduler.AutoTokenizer"):
-            scheduler = InferenceScheduler(
-                model=mock_model,
-                tokenizer=mock_tokenizer,
-                max_batch_size=4,
-                device="cpu",
-            )
-
-    results = {"task_ids": [], "errors": []}
-    lock = threading.Lock()
-
-    def add_task_worker(worker_id):
-        try:
-            for i in range(10):
-                task_id = scheduler.add_task(f"prompt from worker {worker_id}-{i}")
-                with lock:
-                    results["task_ids"].append(task_id)
-        except Exception as e:
-            results["errors"].append(str(e))
-
-    threads = [threading.Thread(target=add_task_worker, args=(i,)) for i in range(5)]
-
-    for t in threads:
-        t.start()
-
-    for t in threads:
-        t.join()
-
-    scheduler.stop()
-
-    assert len(results["errors"]) == 0, f"Errors: {results['errors']}"
-    assert len(results["task_ids"]) == 50
-
-
-def test_scheduler_concurrent_add_remove_task(mock_model_and_tokenizer):
-    """Test concurrent add and remove task operations."""
-    mock_model, mock_tokenizer = mock_model_and_tokenizer
-
-    with patch("astrai.inference.core.scheduler.AutoModel"):
-        with patch("astrai.inference.core.scheduler.AutoTokenizer"):
-            scheduler = InferenceScheduler(
-                model=mock_model,
-                tokenizer=mock_tokenizer,
-                max_batch_size=4,
-                device="cpu",
-            )
-
-    results = {"added": [], "removed": [], "errors": []}
-    add_ready = threading.Event()
-
-    def add_worker():
-        try:
-            for i in range(20):
-                task_id = scheduler.add_task(f"prompt {i}")
-                results["added"].append(task_id)
-                if len(results["added"]) >= 10:
-                    add_ready.set()
-        except Exception as e:
-            results["errors"].append(f"Add: {str(e)}")
-
-    def remove_worker():
-        try:
-            add_ready.wait(timeout=5.0)
-            for task_id in results["added"][:10]:
-                scheduler.remove_task(task_id)
-                results["removed"].append(task_id)
-        except Exception as e:
-            results["errors"].append(f"Remove: {str(e)}")
-
-    add_thread = threading.Thread(target=add_worker)
-    remove_thread = threading.Thread(target=remove_worker)
-
-    add_thread.start()
-    remove_thread.start()
-
-    add_thread.join()
-    remove_thread.join()
-    scheduler.stop()
-
-    assert len(results["errors"]) == 0, f"Errors: {results['errors']}"
-    assert len(results["added"]) == 20
-
-
-def test_scheduler_concurrent_get_stats(mock_model_and_tokenizer):
-    """Test concurrent get_stats operations."""
-    mock_model, mock_tokenizer = mock_model_and_tokenizer
-
-    with patch("astrai.inference.core.scheduler.AutoModel"):
-        with patch("astrai.inference.core.scheduler.AutoTokenizer"):
-            scheduler = InferenceScheduler(
-                model=mock_model,
-                tokenizer=mock_tokenizer,
-                max_batch_size=4,
-                device="cpu",
-            )
-
-    results = {"stats": [], "errors": []}
-    started = threading.Event()
-    stats_done = threading.Event()
-
-    def add_tasks():
-        try:
-            for i in range(20):
-                scheduler.add_task(f"prompt {i}")
-                started.set()
-        except Exception as e:
-            results["errors"].append(f"Add: {str(e)}")
-
-    def get_stats():
-        try:
-            started.wait(timeout=5.0)
-            for _ in range(50):
-                stats = scheduler.get_stats()
-                results["stats"].append(stats)
-            stats_done.set()
-        except Exception as e:
-            results["errors"].append(f"Get stats: {str(e)}")
-
-    add_thread = threading.Thread(target=add_tasks)
-    stats_thread = threading.Thread(target=get_stats)
-
-    add_thread.start()
-    stats_thread.start()
-
-    add_thread.join()
-    stats_done.wait(timeout=5.0)
-    scheduler.stop()
-
-    stats_thread.join()
-
-    assert len(results["errors"]) == 0, f"Errors: {results['errors']}"
-    assert len(results["stats"]) == 50
-
-    for stats in results["stats"]:
-        assert "total_tasks" in stats
-        assert stats["total_tasks"] >= 0
-
-
-def test_prefill_skips_fully_cached_tasks(mock_model_and_tokenizer):
-    """Tasks whose entire prompt is cached skip the prefill phase."""
-    mock_model, mock_tokenizer = mock_model_and_tokenizer
-
-    with patch("astrai.inference.core.scheduler.AutoModel"):
-        with patch("astrai.inference.core.scheduler.AutoTokenizer"):
-            scheduler = InferenceScheduler(
-                model=mock_model,
-                tokenizer=mock_tokenizer,
-                max_batch_size=4,
-                device="cpu",
-            )
-
-    task_id = scheduler.add_task("short prompt", stream_callback=lambda t: None)
-    scheduler.stop()
-    assert task_id.startswith("task_")

tests/inference/test_scheduler_concurrency.py

@@ -0,0 +1,320 @@
+"""Tests for scheduler concurrency."""
+
+import threading
+import time
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from astrai.inference.scheduler import (
+    InferenceScheduler,
+    PrefixCacheManager,
+)
+
+
+def test_prefix_cache_concurrent_insert_find():
+    """Test concurrent insert and find operations."""
+    cache = PrefixCacheManager(max_capacity=100)
+
+    results = {"errors": [], "inserts": 0, "finds": 0}
+
+    def insert_worker():
+        try:
+            for i in range(50):
+                cache.insert((i,), slot=i % 10)
+                results["inserts"] += 1
+        except Exception as e:
+            results["errors"].append(str(e))
+
+    def find_worker():
+        try:
+            for i in range(50):
+                cache.find_longest_prefix([i])
+                results["finds"] += 1
+        except Exception as e:
+            results["errors"].append(str(e))
+
+    threads = [threading.Thread(target=insert_worker) for _ in range(3)]
+    threads += [threading.Thread(target=find_worker) for _ in range(3)]
+
+    for t in threads:
+        t.start()
+    for t in threads:
+        t.join()
+
+    assert len(results["errors"]) == 0, f"Errors: {results['errors']}"
+    assert results["inserts"] == 150
+    assert results["finds"] == 150
+
+
+def test_prefix_cache_concurrent_release():
+    """Test concurrent release operations."""
+    cache = PrefixCacheManager(max_capacity=100)
+
+    # Insert some prefixes
+    for i in range(10):
+        cache.insert((i,), slot=i)
+
+    results = {"errors": []}
+
+    def release_worker():
+        try:
+            for i in range(10):
+                cache.release((i,))
+        except Exception as e:
+            results["errors"].append(str(e))
+
+    threads = [threading.Thread(target=release_worker) for _ in range(3)]
+
+    for t in threads:
+        t.start()
+    for t in threads:
+        t.join()
+
+    assert len(results["errors"]) == 0, f"Errors: {results['errors']}"
+
+
+def test_prefix_cache_concurrent_insert_release_find():
+    """Test mixed concurrent operations."""
+    cache = PrefixCacheManager(max_capacity=50)
+
+    results = {"errors": []}
+
+    def worker(worker_id):
+        try:
+            for i in range(20):
+                token_ids = (worker_id * 100 + i,)
+                cache.insert(token_ids, slot=worker_id)
+
+                # Find after insert
+                cache.find_longest_prefix(list(token_ids))
+
+                # Release
+                cache.release(token_ids)
+        except Exception as e:
+            results["errors"].append(f"Worker {worker_id}: {str(e)}")
+
+    threads = [threading.Thread(target=worker, args=(i,)) for i in range(5)]
+
+    for t in threads:
+        t.start()
+    for t in threads:
+        t.join()
+
+    assert len(results["errors"]) == 0, f"Errors: {results['errors']}"
+
+
+@pytest.fixture
+def mock_model_and_tokenizer():
+    """Create mock model and tokenizer."""
+    mock_model = MagicMock()
+    mock_model.config = MagicMock()
+    mock_model.config.n_kv_heads = 8
+    mock_model.config.n_heads = 8
+    mock_model.config.dim = 128
+    mock_model.config.n_layers = 2
+    mock_model.config.max_len = 100
+
+    mock_tokenizer = MagicMock()
+    mock_tokenizer.encode.return_value = [1, 2, 3, 4, 5]
+    mock_tokenizer.decode.return_value = "token"
+    mock_tokenizer.stop_ids = [0]
+    mock_tokenizer.pad_id = None
+
+    return mock_model, mock_tokenizer
+
+
+def test_scheduler_concurrent_add_task(mock_model_and_tokenizer):
+    """Test concurrent add_task operations."""
+    mock_model, mock_tokenizer = mock_model_and_tokenizer
+
+    with patch("astrai.inference.scheduler.AutoModel"):
+        with patch("astrai.inference.scheduler.AutoTokenizer"):
+            scheduler = InferenceScheduler(
+                model=mock_model,
+                tokenizer=mock_tokenizer,
+                max_batch_size=4,
+                device="cpu",
+            )
+
+    results = {"task_ids": [], "errors": []}
+    lock = threading.Lock()
+
+    def add_task_worker(worker_id):
+        try:
+            for i in range(10):
+                task_id = scheduler.add_task(f"prompt from worker {worker_id}-{i}")
+                with lock:
+                    results["task_ids"].append(task_id)
+        except Exception as e:
+            results["errors"].append(str(e))
+
+    threads = [threading.Thread(target=add_task_worker, args=(i,)) for i in range(5)]
+
+    for t in threads:
+        t.start()
+
+    # Let some tasks be processed
+    time.sleep(0.1)
+
+    scheduler.stop()
+
+    for t in threads:
+        t.join()
+
+    assert len(results["errors"]) == 0, f"Errors: {results['errors']}"
+    assert len(results["task_ids"]) == 50
+
+
+def test_scheduler_concurrent_add_remove_task(mock_model_and_tokenizer):
+    """Test concurrent add and remove task operations."""
+    mock_model, mock_tokenizer = mock_model_and_tokenizer
+
+    with patch("astrai.inference.scheduler.AutoModel"):
+        with patch("astrai.inference.scheduler.AutoTokenizer"):
+            scheduler = InferenceScheduler(
+                model=mock_model,
+                tokenizer=mock_tokenizer,
+                max_batch_size=4,
+                device="cpu",
+            )
+
+    results = {"added": [], "removed": [], "errors": []}
+
+    def add_worker():
+        try:
+            for i in range(20):
+                task_id = scheduler.add_task(f"prompt {i}")
+                results["added"].append(task_id)
+                time.sleep(0.001)
+        except Exception as e:
+            results["errors"].append(f"Add: {str(e)}")
+
+    def remove_worker():
+        try:
+            time.sleep(0.05)  # Wait for some tasks to be added
+            for task_id in results["added"][:10]:
+                scheduler.remove_task(task_id)
+                results["removed"].append(task_id)
+        except Exception as e:
+            results["errors"].append(f"Remove: {str(e)}")
+
+    add_thread = threading.Thread(target=add_worker)
+    remove_thread = threading.Thread(target=remove_worker)
+
+    add_thread.start()
+    remove_thread.start()
+
+    time.sleep(0.2)
+    scheduler.stop()
+
+    add_thread.join()
+    remove_thread.join()
+
+    assert len(results["errors"]) == 0, f"Errors: {results['errors']}"
+    assert len(results["added"]) == 20
+
+
+def test_scheduler_concurrent_get_stats(mock_model_and_tokenizer):
+    """Test concurrent get_stats operations."""
+    mock_model, mock_tokenizer = mock_model_and_tokenizer
+
+    with patch("astrai.inference.scheduler.AutoModel"):
+        with patch("astrai.inference.scheduler.AutoTokenizer"):
+            scheduler = InferenceScheduler(
+                model=mock_model,
+                tokenizer=mock_tokenizer,
+                max_batch_size=4,
+                device="cpu",
+            )
+
+    results = {"stats": [], "errors": []}
+
+    def add_tasks():
+        try:
+            for i in range(20):
+                scheduler.add_task(f"prompt {i}")
+                time.sleep(0.001)
+        except Exception as e:
+            results["errors"].append(f"Add: {str(e)}")
+
+    def get_stats():
+        try:
+            for _ in range(50):
+                stats = scheduler.get_stats()
+                results["stats"].append(stats)
+                time.sleep(0.001)
+        except Exception as e:
+            results["errors"].append(f"Get stats: {str(e)}")
+
+    add_thread = threading.Thread(target=add_tasks)
+    stats_thread = threading.Thread(target=get_stats)
+
+    add_thread.start()
+    stats_thread.start()
+
+    time.sleep(0.3)
+    scheduler.stop()
+
+    add_thread.join()
+    stats_thread.join()
+
+    assert len(results["errors"]) == 0, f"Errors: {results['errors']}"
+    assert len(results["stats"]) == 50
+
+    # Verify stats are consistent
+    for stats in results["stats"]:
+        assert "total_tasks" in stats
+        assert stats["total_tasks"] >= 0
+
+
+def test_prefix_cache_insert_same_prefix_concurrently():
+    """Test inserting the same prefix concurrently."""
+    cache = PrefixCacheManager(max_capacity=100)
+
+    results = {"slot_values": [], "errors": []}
+
+    def insert_worker():
+        try:
+            # All workers try to insert the same prefix
+            cache.insert((1, 2, 3), slot=threading.current_thread().name)
+            node = cache.root.children.get(1)
+            if node:
+                node = node.children.get(2)
+                if node:
+                    node = node.children.get(3)
+                    if node:
+                        results["slot_values"].append(node.slot)
+        except Exception as e:
+            results["errors"].append(str(e))
+
+    threads = [threading.Thread(target=insert_worker) for _ in range(10)]
+
+    for t in threads:
+        t.start()
+    for t in threads:
+        t.join()
+
+    # All inserts should succeed, final slot should be one of the values
+    assert len(results["errors"]) == 0, f"Errors: {results['errors']}"
+    # Check ref_count is correct (should be 10)
+    node = cache.root.children.get(1).children.get(2).children.get(3)
+    assert node.ref_count == 10, f"Expected ref_count=10, got {node.ref_count}"
+
+
+def test_prefix_cache_ref_count_underflow_prevention():
+    """Test that ref_count doesn't go negative."""
+    cache = PrefixCacheManager(max_capacity=100)
+
+    # Insert a prefix
+    cache.insert((1, 2, 3), slot=0)
+
+    # Release multiple times
+    for _ in range(5):
+        cache.release((1, 2, 3))
+
+    # Try to find it - should return None since ref_count would be negative
+    # or handle it gracefully
+    node = cache.root.children.get(1).children.get(2).children.get(3)
+    # The ref_count should be 0, not negative
+    assert node.ref_count >= 0, f"ref_count went negative: {node.ref_count}"