perf: batch tokenizer preprocessing

- Wrap each child module individually, skip root (CPython layout
  conflict between ABC+Generic and FSDP2 __class__ assignment)
- Remove manual unshard in clip_grad_norm (DTensor compatible)
- Fix _no_sync to iterate modules() instead of checking root
- Add reshard after unwrap_model
- Guard __init_subclass__ type resolution against dynamic subclasses
- Add fsdp2 to --parallel_mode CLI choices

.dockerignore

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

.gitattributes

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

.github/ISSUE_TEMPLATE/bug_report.md

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

.github/ISSUE_TEMPLATE/custom.md

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

.github/ISSUE_TEMPLATE/feature_request.md

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

.github/PULL_REQUEST_TEMPLATE.md

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

.github/workflows/docker.yml

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

.github/workflows/lint.yml

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

.github/workflows/release.yml

@@ -0,0 +1,92 @@
+name: Release
+
+on:
+  push:
+    tags:
+      - "v*"
+
+jobs:
+  build-pure:
+    name: Build pure-Python wheel
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+
+      - name: Build wheel (no CUDA)
+        run: |
+          pip wheel . --no-deps -w dist/
+
+      - uses: actions/upload-artifact@v4
+        with:
+          name: pure-wheel
+          path: dist/*.whl
+          if-no-files-found: error
+
+  build-cuda-linux:
+    name: Build CUDA wheel (Linux)
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+
+      - name: Install torch (CUDA 12.8)
+        run: |
+          pip install torch --index-url https://download.pytorch.org/whl/cu128
+
+      - name: Setup CUDA
+        uses: Jimver/cuda-toolkit@v0.2.35
+        with:
+          cuda: "12.8.0"
+
+      - name: Build wheel (with CUDA kernels)
+        run: |
+          CSRC_KERNELS=true pip wheel . --no-deps --no-build-isolation -w dist/
+
+      - uses: actions/upload-artifact@v4
+        with:
+          name: cuda-wheel-linux
+          path: dist/*.whl
+          if-no-files-found: error
+
+  release:
+    name: Attach wheels to release
+    needs: [build-pure, build-cuda-linux]
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+    steps:
+      - name: Download pure-Python wheel
+        uses: actions/download-artifact@v4
+        with:
+          name: pure-wheel
+          path: release-assets/pure
+
+      - name: Download CUDA wheel
+        uses: actions/download-artifact@v4
+        with:
+          name: cuda-wheel-linux
+          path: release-assets/cuda
+
+      - name: Verify release assets
+        shell: bash
+        run: |
+          set -euo pipefail
+          pure_wheels=(release-assets/pure/*.whl)
+          cuda_wheels=(release-assets/cuda/*.whl)
+          test "${#pure_wheels[@]}" -eq 1
+          test "${#cuda_wheels[@]}" -eq 1
+          test "$(basename "${pure_wheels[0]}")" != "$(basename "${cuda_wheels[0]}")"
+
+      - name: Create release & upload assets
+        uses: softprops/action-gh-release@v2
+        with:
+          files: |
+            release-assets/pure/*.whl
+            release-assets/cuda/*.whl
+          tag_name: ${{ github.ref_name }}
+          generate_release_notes: true

.github/workflows/tests.yml

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

.gitignore

@@ -1,13 +1,36 @@
-# cache
+# Ignore everything
-__pycache__
+*
-.pytest_cache
 
-# params
+# Allow directories to be traversed
-params/*
+!*/
 
-# vscode file
+# Allow specific file types and root files
-.vscode
+!astrai/**/*.py
+!scripts/**/*.py
+!tests/**/*.py
+!csrc/**/*.py
 
-# build file
+!csrc/**/*.cu
-build
+!csrc/**/*.h
-*.egg-info
+!csrc/**/*.cuh
+
+!scripts/**/*.sh
+
+# Allow GitHub files
+!/.github/**
+
+# Allow root files
+!/.gitattributes
+!/.dockerignore
+!/Dockerfile
+!/docker-compose.yml
+!/assets/**
+!/CONTRIBUTING.md
+!/LICENSE
+!/pyproject.toml
+!/README.md
+# Allow extension modules (only source .py)
+!/astrai/extension/**/*.py
+
+# Allow build files
+!/setup.py

CONTRIBUTING.md

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

Dockerfile

@@ -0,0 +1,55 @@
+# AstrAI Dockerfile - Multi-stage Build (Optimized)
+
+# Build stage - use base image with minimal build tools
+FROM ubuntu:24.04 AS builder
+
+WORKDIR /app
+
+# Install Python 3.12 and minimal build dependencies
+RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
+    python3.12 \
+    python3.12-dev \
+    python3.12-venv \
+    gcc \
+    g++ \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create isolated virtual environment
+RUN python3.12 -m venv --copies /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# Copy source code and install (deps read from pyproject.toml)
+COPY astrai/ ./astrai/
+COPY pyproject.toml .
+RUN pip install --no-cache-dir --upgrade pip \
+    && pip install --no-cache-dir . \
+    --extra-index-url https://download.pytorch.org/whl/cu128
+
+# Production stage
+FROM ubuntu:24.04 AS production
+
+WORKDIR /app
+
+# Install Python 3.12 runtime and healthcheck dependency
+RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
+    python3.12 \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy virtual environment from builder
+COPY --from=builder /opt/venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# Copy application code
+COPY astrai/ ./astrai/
+COPY scripts/ ./scripts/
+COPY assets/ ./assets/
+COPY pyproject.toml .
+COPY README.md .
+
+# Create non-root user
+RUN useradd -m astrai && chown -R astrai:astrai /app
+USER astrai
+
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1

LICENSE

@@ -1,201 +1,674 @@
-                                 Apache License
+                    GNU GENERAL PUBLIC LICENSE
-                           Version 2.0, January 2004
+                       Version 3, 29 June 2007
-                        http://www.apache.org/licenses/
 
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
 
-   1. Definitions.
+                            Preamble
 
-      "License" shall mean the terms and conditions for use, reproduction,
+  The GNU General Public License is a free, copyleft license for
-      and distribution as defined by Sections 1 through 9 of this document.
+software and other kinds of works.
 
-      "Licensor" shall mean the copyright owner or entity authorized by
+  The licenses for most software and other practical works are designed
-      the copyright owner that is granting the License.
+to take away your freedom to share and change the works.  By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.  We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
+your programs, too.
 
-      "Legal Entity" shall mean the union of the acting entity and all
+  When we speak of free software, we are referring to freedom, not
-      other entities that control, are controlled by, or are under common
+price.  Our General Public Licenses are designed to make sure that you
-      control with that entity. For the purposes of this definition,
+have the freedom to distribute copies of free software (and charge for
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+want it, that you can change the software or use pieces of it in new
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-      "You" (or "Your") shall mean an individual or Legal Entity
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+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -1,333 +1,254 @@
-![image-20250306182014120](/assets/images/project_logo_clipped.png)
+<div align="center">
-
-<div style="display: flex; flex-direction: column; align-items: center; justify-content: center; text-align: center; font-size: 16px; font-weight: bold; margin-top: 50px;">
   
-  <div>
+  <img src="assets/images/logo.png" width="auto" alt="Logo">
-    <a href="#english" style="text-decoration: none; margin: 0 10px; color: blue;">English</a> | 
+  <p>
-    <a href="#chinese" style="text-decoration: none; margin: 0 10px; color: blue;">中文</a>
+    <strong>A lightweight Transformer training & inference framework</strong>
-  </div>
+  </p>
-
-  <h1 style="margin: 20px 0 0 0; font-size: 2.5em; font-weight: bold;">KHAOSZ </h1>
 </div>
 
-<h2 id="english">English Version</h2>
+<div align="center">
+  <img src="https://img.shields.io/badge/python-3.12+-blue.svg" alt="python">
+  <img src="https://img.shields.io/badge/license-GPL--3.0-blue.svg" alt="license">
+  <img src="https://img.shields.io/github/v/tag/ViperEkura/AstrAI?label=Release&color=76bad9" alt="release">
+  <img src="https://img.shields.io/github/stars/ViperEkura/AstrAI?style=flat&label=Stars&color=76bad9" alt="stars">
+  <img src="https://img.shields.io/github/forks/ViperEkura/AstrAI?style=flat&label=Forks&color=76bad9" alt="forks">
+</div>
+<br>
 
-This is a Chinese-English bilingual Transformer model supporting both languages. It contains model configurations and training workflows, completing training by loading parameters defined in `param_path/config.json`. The training script `train.py` parses command-line arguments, including dataset root directory, number of training epochs, batch size, checkpoint interval, and checkpoint directory.
+<div align="center">
+  <a href="#english">English</a> •
+  <a href="assets/docs/README-zh-CN.md">中文</a> •
+  <a href="https://github.com/ViperEkura/AstrAI/issues">Issue Tracker</a> •
+  <a href="https://github.com/ViperEkura/AstrAI/discussions">Discussions</a> •
+  <a href="https://huggingface.co/ViperEkura">HuggingFace</a>
+</div>
 
-**Model Download Options (Choose One):**
+<br>
 
-1. Visit [HuggingFace](https://huggingface.co/ViperEk/KHAOSZ) to access **Files and versions**
+## 📖 Table of Contents
-2. Run `scripts/download.py` to download parameters
 
-**Demo Video:** [bilibili](https://www.bilibili.com/video/BV1z5RPYHEkd)
+- [Features](#features)
+- [Getting Started](#getting-started)
+- [Demo](#demo)
+- [Documentation](#documentation)
+- [Contributing](#contributing)
+- [Community](#community)
+- [License](#license)
 
-Training dataset sources are listed in the **Model Card** section of the HuggingFace download link.
+---
 
-**License:** Code follows Apache-2.0 protocol. Please credit the source code when used.
+<a id="english"></a>
+## English
 
-- **📊 Device Selection:** Code defaults to CUDA training
+### Features
-- **🌐 Performance Optimization:** `dtype=torch.bfloat16` is enabled to accelerate training and reduce memory usage. Ensure hardware supports this feature.
-- **🤖 Language Support:** Model supports Chinese and English training. The BBPE tokenizer was trained without multilingual text, so OOV (out-of-vocabulary) issues are minimized for these languages but may exist for others.
 
-### 📌 Training Guide
+- 🚀 **High Performance**: Optimized for both training and inference with efficient parallelization.
+- 🔧 **Flexible**: Support for seq/sft/dpo/grpo training, customizable model architectures.
+- 💡 **Easy to Use**: Simple API with comprehensive examples and demos.
+- 📦 **Lightweight**: Minimal dependencies, easy to deploy.
+- 🔬 **Research‑Friendly**: Modular design, easy to experiment with new ideas.
+- 🤗 **HuggingFace-Style API**: AutoModel/AutoTokenizer APIs inspired by HuggingFace for easy model and tokenizer loading.
+- 🔌 **Dual API Compatibility**: Supports both OpenAI and Anthropic chat completion APIs out of the box.
 
-To train this Transformer model, follow these steps:
+### Getting Started
 
-**(1). Prepare Dataset:**
+End-to-end walkthrough in 5 steps:
 
-Place datasets in the designated root directory. Files should be text documents in Chinese, English, or mixed. Format should align with model input requirements - preferably pre-tokenized token_ids stored as `torch.Tensor` (using `torch.Tensor` saves memory compared to Python lists, which default to 64-bit precision).
+**1. Install**
-
-**(2). Install Dependencies:**
 
 ```bash
-pip install -r requirements.txt
+git clone https://github.com/ViperEkura/AstrAI.git
-pip install .
+cd AstrAI
+pip install -e .                                          # pure PyTorch (no CUDA kernels)
+# CSRC_KERNELS=true pip install -e . --no-build-isolation  # optional: fused CUDA kernels
+# pip install -e ".[dev]"                                  # dev dependencies (pytest, ruff)
 ```
 
-**(3). Run Training Script:**
+**2. Download model**
 
 ```bash
-python train.py \
+python scripts/demo/download.py    # downloads 1B checkpoint to params/
---train_type=train_type[seq, sft, dpo] \
---data_root_path=/path/to/dataset \
---param_path=/path/to/param_path \
---n_epoch=5 \
---batch_size=8 \
---max_lr=2e-4 \
---checkpoint_interval=10000 \
---checkpoint_dir=checkpoints
 ```
 
-**Parameters Explanation:**
+**3. Preprocess data**
-- `--train_type`: Training type (seq, sft, dpo)
-- `--data_root_path`: Root directory of the dataset
-- `--param_path`: Path to the model training parameters
-- `--n_epoch`: Total number of training epochs
-- `--batch_size`: Batch size
-- `--accumulation_steps`: Number of batches per training step
-- `--warmup_steps`: Number of warmup steps
-- `--max_lr`: Maximum learning rate (using warmup + cosine decay)
-- `--checkpoint_interval`: Checkpoint saving interval
-- `--checkpoint_dir`: Directory to save checkpoints
-- `--resume_dir`: Resume training from the specified path
 
-Training logs will be saved in `train_log.txt`. Checkpoints will be saved in the specified directory for resuming training or evaluation.
+Create `pretrain.json` (preprocessing config for `seq` strategy):
 
-### 👉 Usage Guide
+```json
-
+{
-**(1). Chatting with the Model:**
+    "version": 1,
-
+    "input": {"sections": [{"field": "text", "action": "train"}]},
-Open `chat.py` or use streaming/non-streaming interfaces:
+    "preprocessing": {"max_seq_len": 2048},
-
+    "output": {"storage_format": "bin"}
-**Streaming Output:**
+}
-```python
-import torch
-from khaosz import Khaosz
-
-model_dir = "your_model_parameter_dir"
-model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
-history = []
-
-while True:
-    query = input(">> ")
-    if query == "!exit":
-        break
-    
-    response_size = 0
-    for response, history in model.stream_generate(
-        query=query, 
-        history=history,
-        temperature=0.85,
-        top_p=0.95,
-        top_k=50
-    ):
-        print(response[response_size:], end="")
-        response_size = len(response)       
 ```
 
-**Non-streaming Output:**
-```python
-import torch
-from khaosz import Khaosz
-
-model_dir = "your_model_parameter_dir"
-model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
-history = []
-
-while True:
-    query = input(">> ")
-    if query == "!exit":
-        break
-    
-    response = model.generate(
-        query=query, 
-        history=history,
-        temperature=0.85,
-        top_p=0.95,
-        top_k=50
-    )
-    print(response)
-```
-
-**(2) Retrieval-Augmented Generation (RAG):**
-
-```python
-import torch
-from khaosz import Khaosz
-
-model_dir = "your_model_parameter_dir"
-model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
-
-retrieved_content = model.retrieve_generate(
-    query=query,
-    retrieve_top_k=5,
-    temperature=0.6,
-    top_k=30,
-    top_p=0.95
-)
-print(retrieved_content)
-```
-
-### 📌 Model Specifications
-
-This model is based on a 24-layer Transformer with parameters defined in `config.json`, totaling approximately 1.0 billion (1.0B) parameters.
-
-**Key Design Choices:**
-- Weight tying between embedding and final linear layers (standard for small models to save parameters)
-- Embedding layer optimization: Without weight tying, a 10,000-word vocabulary would consume ~102M parameters (0.1B)
-
-**Limitations:**
-- May struggle with complex language phenomena due to smaller parameter size
-- Prone to overfitting on specialized datasets
-- Limited multilingual capabilities
-
-**Advantages:**
-- Runs efficiently on lower-spec hardware
-- Shorter training time compared to larger models
-
-**Training Pipeline:** 
-The model has completed pre-training + SFT (Supervised Fine-Tuning) + DPO (Direct Preference Optimization) workflows. All corresponding training code is included in the repository.
-
-
-<h2 id="chinese">中文版本</h2>
-这是一个支持中英文双语的 Transformer 模型，能够处理两种语言。模型包含配置文件和训练流程，通过加载 `param_path/config.json` 中定义的参数完成训练。训练脚本 `train.py` 支持命令行参数解析，包括数据集根目录、训练轮数（epochs）、批量大小（batch size）、检查点保存间隔、检查点目录等。
-
-**模型下载选项（任选其一）：**
-
-1. 访问 [HuggingFace](https://huggingface.co/ViperEk/KHAOSZ) 查看 **Files and versions**
-2. 运行 `scripts/download.py` 下载模型参数
-
-**演示视频：** [bilibili](https://www.bilibili.com/video/BV1z5RPYHEkd)
-
-训练数据来源请参见 HuggingFace 下载页面中的 **Model Card** 部分。
-
-**许可证：** 代码遵循 Apache-2.0 协议，使用时请注明出处。
-
-- **📊 设备选择：** 默认使用 CUDA 进行训练
-- **🌐 性能优化：** 启用 `dtype=torch.bfloat16` 以加速训练并减少内存占用，请确保硬件支持该特性
-- **🤖 语言支持：** 模型支持中文和英文训练。由于 BBPE 分词器未使用多语言文本训练，因此中英文的 OOV（未登录词）问题较少，其他语言可能存在 OOV 问题
-
-
-
-### 📌 训练指南
-
-要训练该 Transformer 模型，请按照以下步骤操作：
-
-#### **(1). 准备数据集：**
-
-将数据集放置在指定的根目录下。文件应为包含中文、英文或混合文本的文本文档。格式应符合模型输入要求——建议使用预分词后的 `token_ids` 并以 `torch.Tensor` 格式保存（使用 `torch.Tensor` 相比 Python 列表更节省内存，列表默认为 64 位精度）。
-
-#### **(2). 安装依赖：**
-
 ```bash
-pip install -r requirements.txt
+python scripts/tools/preprocess.py data/*.jsonl -o output/ -c pretrain.json
-pip install .
 ```
 
-#### **(3). 运行训练脚本：**
+**4. Train**
 
 ```bash
-python train.py \
+export CUDA_VISIBLE_DEVICES=0,1,2,3
---train_type=train_type[seq, sft, dpo] \
+
---data_root_path=/path/to/dataset \
+nohup python scripts/tools/train.py \
---param_path=/path/to/param_path \
+    --nprocs=4 \
---n_epoch=5 \
+    --parallel_mode=ddp \
---batch_size=8 \
+    --train_type=seq \
---max_lr=2e-4 \
+    --data_root_path=/path/to/dataset \
---checkpoint_interval=10000 \
+    --param_path=/path/to/model \
---checkpoint_dir=checkpoints 
+    --batch_per_device=4 \
+    --grad_accum_steps=8 \
+    --warmup_ratio=0.05 \
+    --max_lr=1e-4 \
+    --max_grad_norm=1.0 \
+    --weight_decay=0.1 \
+    --window_size=2048 \
+    --ckpt_interval=10000 \
+    --ckpt_dir=./checkpoint \
+    --random_seed=3407 \
+    --label_smoothing=0.05 \
+    > out.log 2> err.log &
 ```
 
-**参数说明：**
+**5. Serve & query**
-- `--train_type`: 训练类型（seq, sft, dpo）
-- `--data_root_path`: 数据集根目录
-- `--param_path`: 模型训练参数路径
-- `--n_epoch`: 总训练轮数
-- `--batch_size`: 批量大小
-- `--accumulation_steps`: 每个训练步骤的 batch 数量
-- `--warmup_steps`: 预热步数（warmup steps）
-- `--max_lr`: 最大学习率（使用预热 + 余弦衰减）
-- `--checkpoint_interval`: 检查点保存间隔
-- `--checkpoint_dir`: 检查点保存目录
-- `--resume_dir`: 从指定路径恢复训练
 
-训练日志将保存在 `train_log.txt` 中。检查点将保存在指定目录，用于恢复训练或评估。
+```bash
+# Terminal 1: start server
+python scripts/tools/server.py --param_path ./params --device cuda
 
-
+# Terminal 2: query
-
+curl http://localhost:8000/v1/chat/completions \
-### 👉 使用指南
+  -H "Content-Type: application/json" \
-
+  -d '{"messages":[{"role":"user","content":"Hello"}],"max_tokens":512}'
-#### **(1). 与模型对话：**
-
-打开 `chat.py` 或使用流式/非流式接口：
-
-**流式输出：**
-```python
-import torch
-from khaosz import Khaosz
-
-model_dir = "your_model_parameter_dir"
-model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
-history = []
-
-while True:
-    query = input(">> ")
-    if query == "!exit":
-        break
-    
-    response_size = 0
-    for response, history in model.stream_generate(
-        query=query, 
-        history=history,
-        temperature=0.85,
-        top_p=0.95,
-        top_k=50
-    ):
-        print(response[response_size:], end="")
-        response_size = len(response)       
 ```
 
-**非流式输出：**
+### Demo
-```python
-import torch
-from khaosz import Khaosz
 
-model_dir = "your_model_parameter_dir"
+Check out the demos in the `scripts/demo/` folder:
-model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
-history = []
 
-while True:
+```bash
-    query = input(">> ")
+# Download model weights (required before running demos)
-    if query == "!exit":
+python scripts/demo/download.py                      # model → params/
-        break
+
-    
+# Interactive streaming chat (multi-turn, maintains history)
-    response = model.generate(
+python scripts/demo/stream_chat.py
-        query=query, 
+# Type your message after >>, type !exit to quit
-        history=history,
+
-        temperature=0.85,
+# Batch generation (5 hardcoded prompts, non-streaming)
-        top_p=0.95,
+python scripts/demo/generate_batch.py
-        top_k=50
+
-    )
+# Single-prompt autoregressive streaming
-    print(response)
+python scripts/demo/generate_ar.py
 ```
 
-#### **(2). 基于检索的生成（RAG）：**
+All generation demos use `temperature=0.8`, `top_p=0.95`, `top_k=50`, `max_tokens=2048` by default and require `params/` to contain model weights (run `download.py` first).
 
-```python
+Watch a video walkthrough on [bilibili](https://www.bilibili.com/video/BV1fuLB6yEj6).
-import torch
-from khaosz import Khaosz
 
-model_dir = "your_model_parameter_dir"
+---
-model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
 
-retrieved_content = model.retrieve_generate(
+See [Documentation](#documentation) for full references beyond the examples above.
-    query=query,
+
-    retrieve_top_k=5,
+#### Text Generation
-    temperature=0.6,
+
-    top_k=30,
+Batch generation from a JSONL file:
-    top_p=0.95
+
-)
+```bash
-print(retrieved_content)
+python scripts/tools/generate.py \
+    --param_path ./params \
+    --input_json_file input.jsonl \
+    --output_json_file output.jsonl
 ```
 
+#### Docker
 
+Build and run with Docker (recommended for GPU environments):
 
-### 📌 模型规格说明（重复部分）
+```bash
+# Build image
+docker build -t astrai:latest .
 
-该模型基于一个 24 层的 Transformer 架构，参数配置定义在 `config.json` 中，总参数量约为 10 亿（1.0B）。
+# Run with GPU support
+docker run --gpus all -it astrai:latest
 
-**关键设计选择：**
+# Run inference server
-- 在嵌入层（embedding）与最终线性层之间进行权重绑定（weight tying），这是小型模型中常见的节省参数量的做法
+docker run --gpus all -p 8000:8000 astrai:latest \
-- 嵌入层优化：若不进行权重绑定，一个包含 10,000 个词的词汇表将消耗约 1.02 亿（0.1B）参数
+  python -m scripts.tools.server --port 8000 --device cuda
 
-**局限性：**
+# Run with volume mount for data
-- 由于参数规模较小，可能在处理复杂语言现象时表现受限
+docker run --gpus all -v /path/to/data:/data -it astrai:latest
-- 在特定领域的数据集上容易出现过拟合
-- 多语言能力有限
 
-**优势：**
+# Docker Compose (GPU, default)
-- 可在低配置硬件上高效运行
+docker compose up -d
-- 相较于大型模型，训练时间更短
 
-**训练流程：**  
+# Docker Compose (CPU only)
-该模型已完成预训练（pre-training）+ 监督微调（SFT, Supervised Fine-Tuning）+ 直接偏好优化（DPO, Direct Preference Optimization）的全流程。所有相关的训练代码均已包含在代码库中。
+docker compose --profile cpu up -d
+```
+
+> **Note**: `--gpus all` is required for CUDA support. Without it, `torch.cuda.is_available()` will return `False`.
+
+#### HTTP API Examples
+
+Additional request examples beyond the [Getting Started](#getting-started) flow:
+
+```bash
+# OpenAI-compatible streaming
+curl -X POST http://localhost:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{"messages":[{"role":"user","content":"Tell a story"}],"stream":true,"max_tokens":500}'
+
+# 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
+```
+
+See [Inference Guide](assets/docs/inference.md) for SSE streaming format, error codes, and stats endpoint.
+
+### Documentation
+
+| Document | Description |
+|----------|-------------|
+| [CLI Reference](./assets/docs/params.md) | Parameters for all CLI tools (train, server, generate, preprocess) |
+| [Architecture](./assets/docs/architecture.md) | System architecture, class diagram & design patterns |
+| [Training](./assets/docs/training.md) | Training loop, strategies & formulas |
+| [Inference](./assets/docs/inference.md) | KVCache, continuous batching, sampling & HTTP API |
+| [Data Flow](./assets/docs/dataflow.md) | Data pipeline, storage backends & dataset architecture |
+| [Preprocessing](./assets/docs/preprocessing.md) | Declarative JSON-driven data preprocessing |
+
+### Contributing
+
+We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.
+
+1. Fork the repository.
+2. Create a feature branch.
+3. Commit your changes.
+4. Open a Pull Request.
+
+For major changes, please open an issue first to discuss what you would like to change.
+
+### Community
+
+- **GitHub Issues**: [Issue Tracker](https://github.com/ViperEkura/AstrAI/issues)
+- **Discussions**: [GitHub Discussions](https://github.com/ViperEkura/AstrAI/discussions)
+- **HuggingFace**: [Model Hub](https://huggingface.co/ViperEkura)
+
+### License
+
+This project is licensed under the [GPL-3.0 License](LICENSE).
+
+---
+
+<div align="center">
+  <em>A lightweight Transformer framework designed for both high performance and ease of use.</em>
+</div>

assets/docs/README-zh-CN.md

@@ -0,0 +1,260 @@
+<div align="center">
+  
+  <img src="../images/logo.png" width="auto" alt="Logo">
+  
+  <div>
+    <a href="../../README.md">English</a> • 
+    <a href="#chinese">中文</a>
+  </div>
+  
+  <p>
+    <strong>轻量级 Transformer 训练与推理框架</strong>
+  </p>
+</div>
+
+<div align="center">
+  <img src="https://img.shields.io/badge/python-3.12+-blue.svg" alt="python">
+  <img src="https://img.shields.io/badge/license-GPL--3.0-blue.svg" alt="license">
+  <img src="https://img.shields.io/github/v/tag/ViperEkura/AstrAI?label=Release&color=76bad9" alt="release">
+  <img src="https://img.shields.io/github/stars/ViperEkura/AstrAI?style=flat&label=Stars&color=76bad9" alt="stars">
+  <img src="https://img.shields.io/github/forks/ViperEkura/AstrAI?style=flat&label=Forks&color=76bad9" alt="forks">
+</div>
+
+<br>
+
+<div align="center">
+  <a href="../../README.md">English</a> •
+  <a href="#chinese">中文</a> •
+  <a href="https://github.com/ViperEkura/AstrAI/issues">问题追踪</a> •
+  <a href="https://github.com/ViperEkura/AstrAI/discussions">讨论区</a> •
+  <a href="https://huggingface.co/ViperEkura">HuggingFace</a>
+</div>
+<br>
+
+## 📖 目录
+
+- [特性](#特性)
+- [快速上手](#快速上手)
+- [演示](#演示)
+- [文档](#文档)
+- [贡献](#贡献)
+- [社区](#社区)
+- [许可证](#许可证)
+
+---
+
+<a id="chinese"></a>
+## 中文
+
+### 特性
+
+- 🚀 **高性能**: 训练与推理双向优化，高效并行。
+- 🔧 **灵活**: 支持 seq/sft/dpo/grpo 多种训练方式，可定制模型架构。
+- 💡 **易用**: 简洁的 API 与丰富的示例、演示。
+- 📦 **轻量**: 依赖少，部署简单。
+- 🔬 **研究友好**: 模块化设计，便于实验新想法。
+- 🤗 **HuggingFace 风格 API**: 类 HuggingFace 的 AutoModel/AutoTokenizer 接口，方便加载模型和分词器。
+- 🔌 **双 API 兼容**: 同时支持 OpenAI 和 Anthropic 聊天补全 API，开箱即用。
+
+### 快速上手
+
+端到端演示，只需 5 步：
+
+**1. 安装**
+
+```bash
+git clone https://github.com/ViperEkura/AstrAI.git
+cd AstrAI
+pip install -e .                                          # 纯 PyTorch（不含 CUDA 内核）
+# CSRC_KERNELS=true pip install -e . --no-build-isolation  # 可选：融合 CUDA 内核加速
+# pip install -e ".[dev]"                                  # 可选：开发依赖（pytest, ruff）
+```
+
+**2. 下载模型**
+
+```bash
+python scripts/demo/download.py    # 下载 1B 检查点到 params/
+```
+
+**3. 预处理数据**
+
+创建 `pretrain.json`（`seq` 策略的预处理配置）：
+
+```json
+{
+    "version": 1,
+    "input": {"sections": [{"field": "text", "action": "train"}]},
+    "preprocessing": {"max_seq_len": 2048},
+    "output": {"storage_format": "bin"}
+}
+```
+
+```bash
+python scripts/tools/preprocess.py data/*.jsonl -o output/ -c pretrain.json
+```
+
+**4. 训练**
+
+```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 \
+    --weight_decay=0.1 \
+    --window_size=2048 \
+    --ckpt_interval=10000 \
+    --ckpt_dir=./checkpoint \
+    --random_seed=3407 \
+    --label_smoothing=0.05 \
+    > out.log 2> err.log &
+```
+
+**5. 启动服务并调用**
+
+```bash
+# 终端 1：启动服务
+python scripts/tools/server.py --param_path ./params --device cuda
+
+# 终端 2：发起请求
+curl http://localhost:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{"messages":[{"role":"user","content":"你好"}],"max_tokens":512}'
+```
+
+### 演示
+
+查看 `scripts/demo/` 文件夹中的演示：
+
+```bash
+# 下载模型权重（运行演示前必需）
+python scripts/demo/download.py                      # model → params/
+
+# 交互式流式聊天（多轮对话，保持历史记录）
+python scripts/demo/stream_chat.py
+# 在 >> 后输入消息，输入 !exit 退出
+
+# 批量生成（5 条硬编码提示词，非流式）
+python scripts/demo/generate_batch.py
+
+# 单条提示词自回归流式生成
+python scripts/demo/generate_ar.py
+```
+
+所有生成演示默认使用 `temperature=0.8`、`top_p=0.95`、`top_k=50`、`max_tokens=2048`，需要 `params/` 目录包含模型权重（请先运行 `download.py`）。
+
+观看 [bilibili](https://www.bilibili.com/video/BV1fuLB6yEj6) 上的视频演示。
+
+---
+
+更多选项请参考[文档](#文档)。
+
+#### 文本生成
+
+从 JSONL 文件批量生成：
+
+```bash
+python scripts/tools/generate.py \
+    --param_path ./params \
+    --input_json_file input.jsonl \
+    --output_json_file output.jsonl
+```
+
+#### Docker
+
+使用 Docker 构建和运行（推荐用于 GPU 环境）：
+
+```bash
+# 构建镜像
+docker build -t astrai:latest .
+
+# 启用 GPU 运行
+docker run --gpus all -it astrai:latest
+
+# 运行推理服务
+docker run --gpus all -p 8000:8000 astrai:latest \
+  python -m scripts.tools.server --port 8000 --device cuda
+
+# 挂载数据卷
+docker run --gpus all -v /path/to/data:/data -it astrai:latest
+
+# Docker Compose（GPU，默认）
+docker compose up -d
+
+# Docker Compose（仅 CPU）
+docker compose --profile cpu up -d
+```
+
+> **注意**: 必须使用 `--gpus all` 才能启用 CUDA 支持，否则 `torch.cuda.is_available()` 将返回 `False`。
+
+#### HTTP API 示例
+
+除[快速上手](#快速上手)流程外，更多请求示例：
+
+```bash
+# OpenAI 兼容流式
+curl -X POST http://localhost:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{"messages":[{"role":"user","content":"讲个故事"}],"stream":true,"max_tokens":500}'
+
+# Anthropic 兼容
+curl -X POST http://localhost:8000/v1/messages \
+  -H "Content-Type: application/json" \
+  -d '{"model":"astrai","system":"你是一个乐于助人的助手。","messages":[{"role":"user","content":"你好"}],"max_tokens":512}'
+
+# Anthropic 兼容流式并设置停止序列
+curl -X POST http://localhost:8000/v1/messages \
+  -H "Content-Type: application/json" \
+  -d '{"model":"astrai","messages":[{"role":"user","content":"写个故事"}],"max_tokens":500,"stream":true,"stop_sequences":["结束"]}'
+
+# 健康检查
+curl http://localhost:8000/health
+```
+
+SSE 流式格式、错误码和统计端点详见[推理文档](./inference.md)。
+
+### 文档
+
+| 文档 | 说明 |
+|------|------|
+| [CLI 参考](./params.md) | 所有 CLI 工具参数（训练、服务、生成、预处理） |
+| [架构文档](./architecture.md) | 系统架构、类图与设计模式 |
+| [训练文档](./training.md) | 训练循环、策略与公式 |
+| [推理文档](./inference.md) | KVCache、连续批处理、采样与 HTTP API |
+| [数据流程](./dataflow.md) | 数据管道、存储后端与数据集架构 |
+| [数据预处理](./preprocessing.md) | 声明式 JSON 驱动数据预处理 |
+
+### 贡献
+
+我们欢迎贡献！请参阅[贡献指南](../../CONTRIBUTING.md)了解详情。
+
+1. Fork 本仓库。
+2. 创建功能分支。
+3. 提交更改。
+4. 发起 Pull Request。
+
+重大更改请先开 issue 讨论。
+
+### 社区
+
+- **GitHub Issues**: [问题追踪](https://github.com/ViperEkura/AstrAI/issues)
+- **Discussions**: [GitHub 讨论区](https://github.com/ViperEkura/AstrAI/discussions)
+- **HuggingFace**: [模型中心](https://huggingface.co/ViperEkura)
+
+### 许可证
+
+本项目采用 [GPL-3.0 许可证](../../LICENSE)。
+
+---
+
+<div align="center">
+  <em>专为高性能与易用性设计的轻量级 Transformer 框架。</em>
+</div>

assets/docs/dataflow.md

@@ -0,0 +1,130 @@
+# Data Flow
+
+This document describes the data pipeline: from raw text to model input tensors. For creating preprocessing configs, see [Preprocessing Guide](preprocessing.md).
+
+## Contents
+
+- [Overview](#overview)
+- [Data Preparation](#data-preparation) — tokenization, format detection, backends
+- [Data Keys by Training Type](#data-keys-by-training-type)
+- [Dataset Architecture](#dataset-architecture)
+- [Sampler](#sampler)
+- [DataLoader](#dataloader)
+
+## Overview
+
+```
+JSONL Lines → Pipeline (mask builder) → Tokenized Tensors
+                                              ↓
+                                      .h5 or .bin storage
+                                              ↓
+                                      Store.load()
+                                              ↓
+                                      Store.fetch(begin, end, keys)
+                                              ↓
+                                      BaseDataset.__getitem__(idx)
+                                              ↓
+                                      Sampler → DataLoader → Training / Inference
+```
+
+## Data Preparation
+
+Raw text is tokenized via `AutoTokenizer.encode()` and saved as HDF5 (`.h5`) or binary (`.bin` + `meta.json`) files with keyed tensor groups.
+
+### Tokenization
+
+The `Pipeline` reads JSONL lines, applies the mask builder (see [Preprocessing](preprocessing.md)), and produces flat token sequences:
+
+```python
+# Per JSONL line: messages → chat template → token IDs + loss mask
+tokens = tokenizer.encode(rendered_text)        # List[int]
+loss_mask = [0, 0, 0, 1, 1, 1, 1, 1, 1]        # 0=masked, 1=train
+# Stored as flat tensors, packed with other lines by packing strategy
+```
+
+The output `meta.json` records the storage format, key names, dtype, total token count, and tensor shapes for each shard.
+
+### Format Detection
+
+`detect_format(load_path)` inspects the path:
+
+- If `load_path` is a file: checks suffix — `.h5`/`.hdf5` → `"h5"`, `.jsonl` → `"jsonl"`, unknown suffix raises `ValueError`
+- If `load_path` is a directory: recursively globs for `*.h5`/`*.hdf5` files → `"h5"`, `*.bin` + `**/meta.json` → `"bin"`, or `*.jsonl` + `dataset_config.json` → `"jsonl"`
+
+### Store Backends
+
+Storage format is auto-detected by `detect_format()`; backends are dispatched via registry:
+
+```
+StoreFactory.create("h5")    → H5Store
+StoreFactory.create("bin")   → MmapStore
+StoreFactory.create("jsonl") → JsonlStore
+```
+
+All three inherit `Store` (base, owns `_data`/`_cum`/`_offsets`/`_normalize`) plus the `Streamable` and `Recordable` mixins, so every backend supports both `fetch(begin, end, keys)` (stream) and `fetch_record(index, keys)` (record) APIs.
+
+**H5Store**: Reads HDF5 files. Tensors are loaded into host memory and normalized into segmented storage. `segments_are_records=True` — each `data_i` dataset is one record.
+
+**MmapStore**: Memory-maps `.bin` files. OS page cache sharing is native — no explicit `share_memory_()` needed. Uses `torch.from_numpy(np.memmap(...))`. `segments_are_records=False` — bin segments are contiguous streams; record access is driven by `_offsets` (written when `save_bin(..., record_keys=...)` was used at preprocessing time).
+
+**JsonlStore**: On-the-fly tokenization of raw JSONL files at load time. Requires a `dataset_config.json` alongside the `.jsonl` files following the same `PipelineConfig` schema with an additional `tokenizer_path` field. Two modes: eager (default, applies `TokenizeTransform` to all records at load) and lazy (`processor=fn` given, defers tokenisation to `fetch_record` — used by DPO/GRPO).
+
+All backends normalise tensors into `Store._data[Dict[str, List[Tensor]]]` + `Store._cum[Dict[str, List[int]]]` (cumulative lengths for bisect-based stream indexing) + `Store._offsets[Dict[str, List[int]]]` (per-record offsets for record-mode indexing). Nested keys (GRPO `responses`/`masks` as `List[List[Tensor]]`) are stored as-is and excluded from both bookkeepings — they are only accessed record-by-record.
+
+## Data Keys by Training Type
+
+| Type | Storage Keys | Access Mode |
+|------|-------------|-------------|
+| `seq` | `sequence` (→ input_ids, target_ids via offset-by-1) | stream (`fetch`) |
+| `sft` | `sequence`, `loss_mask`, `position_ids` | stream (`fetch`) |
+| `dpo` | `chosen`, `rejected`, `chosen_mask`, `rejected_mask` | record (`fetch_record`) |
+| `grpo` | `prompts`, `responses`, `masks`, `rewards` | record (`fetch_record`) |
+
+## Dataset Architecture
+
+```
+DatasetFactory.load(train_type, load_path, window_size, stride=None,
+                    storage_type=None, tokenizer_path=None,
+                    max_position_embeddings=2048, store=None)
+  → BaseDataset.load(load_path, storage_type=None)
+    → detect_format(load_path)
+    → StoreFactory.create(storage_type)
+    → Store.load(load_path)
+      → _normalize(raw)  # base Store, shared by both backends
+        → Store._data[Dict[str, List[Tensor]]]
+          + _cum[Dict[str, List[int]]]   (stream mode)
+          + _offsets[Dict[str, List[int]]]  (record mode)
+
+Stream datasets (SEQ/SFT):
+  BaseDataset.__getitem__(idx)
+    → get_index(idx) → [begin, end)
+    → Store.fetch(begin, end, keys) → Tensor / Dict[str, Tensor]
+
+Record datasets (DPO/GRPO via RecordDataset):
+  RecordDataset.__getitem__(idx)
+    → Store.fetch_record(idx, keys) → Tensor / Dict[str, Tensor]
+```
+
+Class hierarchy: `BaseDataset` ← `SEQDataset` / `SFTDataset` (stream); `BaseDataset` ← `RecordDataset` ← `DPODataset` / `GRPODataset` (record).
+
+`window_size` = max input length, `stride` = step between consecutive samples (defaults to `window_size`, optional). Only meaningful for stream datasets — record datasets ignore both. `storage_type` defaults to `None` (auto-detect via `detect_format`).
+
+`tokenizer_path` triggers lazy on-the-fly tokenisation for record datasets on raw JSONL (DPO builds a `dpo_processor`; SEQ/SFT/pre-tokenised backends ignore it). `store` (pre-built `Store`) bypasses `load_path`/`storage_type`/`tokenizer_path` entirely — the caller controls Store construction.
+
+`Store.fetch(begin, end, keys)` (stream mode, on `Streamable`): 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()`.
+
+`Store.fetch_record(index, keys)` (record mode, on `Recordable`): same key API. Uses `_offsets[key]` when present (bin layout with per-record offsets), otherwise indexes `_data[key]` directly (H5/JSONL where each segment is one record).
+
+## Sampler
+
+`ResumableDistributedSampler` supports checkpoint-aware distributed sampling:
+
+- Tracks `start_epoch` / `start_iter` for resume
+- Shuffle via `torch.Generator(seed + epoch)`
+- Per-replica index slicing for DDP
+
+## DataLoader
+
+Standard PyTorch `DataLoader` with configurable `batch_size`, `num_workers`, `pin_memory`, `prefetch_factor`. Sampler produces indices; dataloader fetches tensor batches via `__getitem__`.
+
+> Document Update Time: 2026-07-19

assets/docs/inference.md

@@ -0,0 +1,252 @@
+# Inference
+
+## Contents
+
+- [KV Cache](#kv-cache)
+- [KVCache System](#kvcache-system)
+- [Continuous Batching](#continuous-batching)
+- [Sampling](#sampling-strategy-pattern)
+- [Protocol Handlers](#protocol-handlers-strategy-pattern)
+- [Engine & GenerateResult](#engine--generateresult)
+- [HTTP API](#http-api) — endpoints, SSE, errors, stats
+- [Engine API](#engine-api)
+
+## 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
+
+Seven classes working together, with two concrete cache implementations:
+
+### ContiguousCache (default)
+
+```
+ContiguousCache (simple contiguous per-slot cache)
+  ├── ContiguousCacheView  bundles k/v tensors + slot indices for attention layers
+```
+
+Created by default when no cache is passed to `InferenceScheduler`. Each task occupies a fixed slot of `[max_seq_len, num_key_value_heads, head_dim]`. Simple and efficient for small-to-medium batch sizes.
+
+### PageCache (paged with prefix sharing)
+
+```
+PageCache (paged KV cache with prefix sharing, alternative)
+  ├── PagePool               orchestrates page allocation + prefix matching
+  │     ├── Allocator         bitmask-based page allocator + ref-count + LRU
+  │     └── PrefixCache       hash-based prefix matching (page_hash via polynomial hash)
+  ├── TaskTable              maps task_id → page_table + cached token count
+  ├── Storage                k_cache / v_cache tensors (num_hidden_layers × n_pages × page_size × num_key_value_heads × head_dim)
+  └── PageCacheView          bundles Storage + page_table + total_len for attention layers
+```
+
+`isinstance(cache, KVCache)` checks dispatch to the correct view. Both implement the abstract `KVCache` interface used by `Executor` and `InferenceScheduler`.
+
+## Continuous Batching
+
+`InferenceScheduler` runs a daemon thread with a 4-phase loop:
+
+```
+1. Cleanup → Remove finished tasks, free KV cache slots/pages
+2. Refill  → Pop from waiting_queue, task_alloc resources, activate
+3. Prefill → Group by (prompt_len, start_pos), run full forward
+4. Decode  → Run single-token forward for each same-position group
+```
+
+## 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 |
+
+### SSE Streaming Format
+
+**OpenAI** (`/v1/chat/completions`, `stream=true`):
+
+```
+data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":...,"model":"astrai",
+       "choices":[{"index":0,"delta":{"role":"assistant"},"finish_reason":null}]}
+
+data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":0,"model":"astrai",
+       "choices":[{"index":0,"delta":{"content":"Hello"},"finish_reason":null}]}
+
+data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":...,"model":"astrai",
+       "choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}
+
+data: {"prompt_tokens":5,"completion_tokens":1,"total_tokens":6}
+
+data: [DONE]
+```
+
+**Anthropic** (`/v1/messages`, `stream=true`):
+
+```
+event: message_start
+data: {"type":"message_start","message":{"id":"msg_...","model":"astrai","role":"assistant",
+       "content":[],"usage":{"input_tokens":0}}}
+
+event: content_block_start
+data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""}}
+
+event: content_block_delta
+data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"Hello"}}
+
+event: content_block_stop
+data: {"type":"content_block_stop","index":0}
+
+event: message_delta
+data: {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null},"usage":{...}}
+
+event: message_stop
+data: {"type":"message_stop"}
+```
+
+### Error Responses
+
+The server returns standard HTTP status codes. Pydantic validation errors (e.g. missing required fields)
+are handled automatically by FastAPI with 422 status. The only application-level error is engine initialization:
+
+| Status | Meaning |
+|--------|---------|
+| 200 | Success |
+| 422 | Unprocessable entity (Pydantic validation) |
+| 503 | Service unavailable (model not loaded, engine not ready) |
+
+Error response body (503):
+
+```json
+{
+    "detail": "Engine not initialized"
+}
+```
+
+### Stats Endpoint
+
+```
+GET /stats
+```
+
+Response:
+
+```json
+{
+    "total_tasks": 128,
+    "total_tokens": 10240,
+    "active_tasks": 3,
+    "waiting_queue": 2
+}
+```
+
+## 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: 2026-07-09

assets/docs/introduction.md

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

assets/docs/kvcache.md

@@ -1,27 +0,0 @@
-## kv_cache 实现
-
-根据注意力的计算公式
-
-$$
-\begin{align*}
-o_i &= \sum_j s_{ij} v_{j} \\
-s_{ij} &= \text{softmax}\left( \sum_n \frac{q_{i,n} k_{j,n}}{\sqrt{d_k}} \right)
-\end{align*}
-$$
-
-由于模型是自回归模型, 我们只用求序列最后一个部分，也就是说 $ i $ 的下标是确定的, 是序列最后一个元素, 我们求的是 $o_{n} $ 
-
-$$
-\begin{align*}
-o_n &= \sum_j s_{j}v_{j,n} \\
-s_j &= \text{softmax}\left(\sum_n\frac{q_n k_{j,n}}{\sqrt{d_k}} \right)
-\end{align*}
-$$
-
-如果我们把式子展开
-
-$$
-o_n = \sum_j \sum_n \text{softmax}\left(\frac{q_n k_{j,n}}{\sqrt{d_k}}\right)v_{j,n}
-$$
-
-以上表达式只有k和v存在长度下标, 而 $q$ 没有， 所以计算过程中 $q$ 的输入是确定的上次输入的最后一个token, 而 $k,  v$ 是需要对不同长度的部分进行缓存的，同时缓存的时候应该注意位置编码的计算应该在kvcache的计算之前进行，否则会存在位置编码的计算错误

assets/docs/params.md

@@ -0,0 +1,217 @@
+# CLI Parameter Reference
+
+## Contents
+
+- [Training Parameters](#training-parameters)
+- [Inference Server](#inference-server-serverpy)
+- [Generate](#generate-generatepy)
+- [Preprocess](#preprocess-preprocesspy)
+
+## Training Parameters
+
+### Basic Parameters
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--train_type` | Training type (`seq`, `sft`, `dpo`, `grpo`, `online_grpo`, `online_dpo`) | required |
+| `--data_root_path` | Dataset root directory | required |
+| `--param_path` | Model parameters or checkpoint path | required |
+| `--n_epoch` | Total training epochs | 1 |
+| `--batch_per_device` | Batch size per device | 1 |
+| `--grad_accum_steps` | Gradient accumulation steps between optimizer steps | 1 |
+
+### Learning Rate Scheduling
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--warmup_ratio` | Fraction of total steps used for LR warmup | 0.05 |
+| `--max_lr` | Maximum learning rate (cosine decay after warmup) | 3e-4 |
+| `--max_grad_norm` | Maximum gradient norm for clipping (None disables) | 1.0 |
+
+### Optimizer (MuonMix)
+
+Combined optimizer: matrix parameters via **Muon**, non-matrix via **AdamW** (`fused=True`).
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--weight_decay` | Weight decay (applied to Muon matrix params; non-matrix use 0) | 0.1 |
+| `--muon_momentum` | Muon momentum factor | 0.95 |
+| `--muon_nesterov` | Enable Nesterov momentum for Muon | True |
+| `--muon_ns_steps` | Newton-Schulz iteration steps for Muon | 5 |
+| `--muon_adjust_lr` | Muon LR adjustment strategy (`original`, `match_rms_adamw`) | `match_rms_adamw` |
+
+### Data Loading
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--window_size` | Max input sequence length | model config `max_position_embeddings` |
+| `--stride` | Stride for sliding window over sequences | None |
+| `--random_seed` | Random seed for reproducibility | 3407 |
+| `--num_workers` | DataLoader worker processes | 4 |
+| `--no_pin_memory` | Disable pin_memory (enabled by default) | (flag) |
+
+### Checkpoint & Resume
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--ckpt_interval` | Iterations between checkpoints | 5000 |
+| `--ckpt_dir` | Checkpoint save directory | checkpoint |
+| `--start_epoch` | Resume from epoch (0 = from scratch) | 0 |
+| `--start_samples` | Resume from sample count per rank | 0 |
+
+### Validation
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--val_split` | Ratio to split from training dataset for validation (e.g. 0.05) | None |
+| `--val_step` | Number of optimizer steps between validation runs | 1000 |
+
+### Logging
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--log_dir` | Directory for metric logs | checkpoint/logs |
+| `--metrics` | Metrics to log (e.g. --metrics loss lr val_loss) | ["loss", "lr", "grad_norm"] |
+
+### Gradient Checkpointing
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--gradient_checkpointing` | Enable activation checkpointing for DecoderBlock modules | False |
+
+### Distributed Training
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--nprocs` | Number of GPUs / processes | 1 |
+| `--parallel_mode` | Parallel strategy (`none`, `ddp`, or `fsdp`) | none |
+| `--device_type` | Device type | cuda |
+| `--start_method` | Multiprocessing start method (`spawn`, `fork`, `forkserver`) | spawn |
+| `--backend` | Distributed training backend | nccl |
+| `--master_addr` | Master node address | localhost |
+| `--master_port` | Master node port | 29500 |
+
+### Strategy-specific
+
+| Parameter | Description | Default | Used by |
+|-----------|-------------|---------|---------|
+| `--dpo_beta` | DPO beta value | 0.1 | `dpo` |
+| `--label_smoothing` | Label smoothing for cross-entropy loss | 0.0 | `seq`, `sft` |
+| `--group_size` | GRPO group size | 4 | `grpo` |
+| `--grpo_clip_eps` | GRPO clipping epsilon | 0.2 | `grpo` |
+| `--grpo_kl_coef` | GRPO KL penalty coefficient | 0.01 | `grpo` |
+| `--neftune_alpha` | NEFTune noise alpha (0=disabled, typical: 5.0) | 0.0 | `sft` |
+
+### Online Rollout
+
+These options apply to `online_grpo` and `online_dpo`. Online strategies require
+a `BaseRewardModel` factory in `TrainConfig`; `train.py` does not currently
+provide a command-line option for configuring one.
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--rollout_interval` | Optimizer steps between rollout refreshes | 512 |
+| `--rollout_temperature` | Rollout sampling temperature | 0.7 |
+| `--rollout_top_k` | Rollout top-k filtering (`0` disables) | 0 |
+| `--rollout_top_p` | Rollout nucleus sampling threshold | 0.9 |
+| `--rollout_max_tokens` | Maximum generated tokens per response | 1024 |
+
+### Scheduler
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--schedule_type` | LR scheduler type (`cosine`, `sgdr`, `wsd`) | cosine |
+| `--min_rate` | Minimum LR as fraction of base LR | None (scheduler default: 0.05 for cosine/SGDR, 0.0 for WSD) |
+| `--cycle_length` | SGDR first cycle length in steps | None (total_steps - warmup_steps) |
+| `--t_mult` | SGDR cycle length multiplier per restart | 2 |
+| `--stable_steps` | WSD stable plateau steps | None (80% of post-warmup steps) |
+| `--decay_steps` | WSD decay steps | None (total_steps - warmup_steps - stable_steps) |
+
+### Usage Example
+
+```bash
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+
+nohup python scripts/tools/train.py \
+    --nprocs=4 \
+    --parallel_mode=ddp \
+    --train_type=seq \
+    --data_root_path=/path/to/dataset \
+    --param_path=/path/to/model \
+    --batch_per_device=4 \
+    --grad_accum_steps=8 \
+    --warmup_ratio=0.05 \
+    --max_lr=1e-4 \
+    --max_grad_norm=1.0 \
+    --weight_decay=0.1 \
+    --window_size=2048 \
+    --ckpt_interval=10000 \
+    --ckpt_dir=./checkpoint \
+    --random_seed=3407 \
+    --label_smoothing=0.05 \
+    > out.log 2> err.log &
+```
+
+---
+
+## Inference Server (`server.py`)
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `--host` | str | `0.0.0.0` | Host address |
+| `--port` | int | `8000` | Port number |
+| `--param_path` | path | `project_root/params` | Path to model parameters |
+| `--device` | str | `cuda` | Device to load model on |
+| `--dtype` | str | `bfloat16` | Model weights dtype (`bfloat16`, `float16`, `float32`) |
+| `--max_batch_size` | int | `16` | Maximum batch size for continuous batching |
+| `--reload` | flag | `False` | Enable auto-reload for development |
+
+Usage:
+```bash
+python scripts/tools/server.py --param_path ./params --device cuda --dtype bfloat16
+```
+
+See [Inference Guide](inference.md) for HTTP API documentation.
+
+## Generate (`generate.py`)
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `--param_path` | str | required | Path to the model directory |
+| `--input_json_file` | str | required | Path to the input JSONL file |
+| `--output_json_file` | str | required | Path to the output JSONL file |
+| `--question_key` | str | `question` | Key for the question in input JSON |
+| `--response_key` | str | `response` | Key for the response in output JSON |
+| `--temperature` | float | `0.60` | Sampling temperature |
+| `--top_k` | int | `30` | Top-k filtering |
+| `--top_p` | float | `0.95` | Nucleus sampling threshold |
+| `--batch_size` | int | `1` | Batch size for generation |
+| `--max_tokens` | int | model config `max_position_embeddings` | Maximum tokens to generate |
+
+Usage:
+```bash
+python scripts/tools/generate.py \
+    --param_path ./params \
+    --input_json_file input.jsonl \
+    --output_json_file output.jsonl
+```
+
+## Preprocess (`preprocess.py`)
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `input_files` | path(s) | required | Input JSONL file(s), supports glob (`data/*.jsonl`) |
+| `--output_dir`, `-o` | path | required | Output directory for processed data |
+| `--config`, `-c` | path | required | Preprocessing pipeline config (JSON) |
+| `--tokenizer_path` | str | `params` | Path to tokenizer directory |
+
+Usage:
+```bash
+python scripts/tools/preprocess.py data/*.jsonl -o output/ -c sft.json
+```
+
+See [Preprocessing Guide](preprocessing.md) for config file format and examples.
+
+---
+
+> Document Update Time: 2026-07-20

assets/docs/preprocessing.md

@@ -0,0 +1,364 @@
+# Preprocessing Pipeline
+
+Declarative JSON-driven data preprocessing. `MaskBuilderFactory` supports three registered builders: `"single"` (single-output via `input.sections`), `"multi"` (multi-output via `input.sources`), and `"sectioned"` (façade dispatching to `single` or `multi` based on config).
+
+## Contents
+
+- [Philosophy](#philosophy)
+- [Config Structure](#config-structure)
+- [Quick Start](#quick-start) — SFT Chat, SFT Instruction, Pretrain, DPO, GRPO examples
+- [Configuration Reference](#configuration-reference) — all fields
+- [Mask Algorithm](#mask-algorithm)
+- [Output Layout](#output-layout)
+- [CLI](#cli)
+- [Python API](#python-api)
+
+## 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
+{
+  "version":       1,
+  "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`, `prompts_mask`, `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`)
+- `prompts_mask` is auto-generated (all masked) and unused by GRPOStrategy
+
+---
+
+## 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 |
+| `packing_strategy` | str | `"simple"` | Packing strategy: `"simple"`, `"bfd"`, `"bfd_split"` |
+| `max_packed_len` | int | `8192` | Maximum length of a packed bin |
+| `truncation_mode` | str | `"keep_start"` | How to truncate sequences: `"keep_start"` or `"keep_end"` |
+
+### `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"}`) |
+| `position_ids_mode` | str | `"doc_reset"` | How to compute position_ids: `"none"`, `"doc_reset"`, `"continuous"` |
+
+---
+
+## Mask Algorithm
+
+### Template mode (`template: true`)
+
+1. Prepend BOS token (masked)
+2. For each message in the field's array:
+   1. Render through `chat_template` for that single message
+   2. Encode rendered text
+   3. 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 omits `loss_mask` from the output — all tokens are trained.
+
+---
+
+## Output Layout
+
+### Single-Shard (`bin`)
+
+```
+output/
+  __default__/
+    shard_0000/
+      meta.json
+      sequence.bin
+      loss_mask.bin
+  wiki/
+    shard_0000/
+      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
+```
+
+For `bin` format, `MmapStore` discovers all shards under the domain directory via `rglob("meta.json")`. For `h5` format, `H5Store` discovers `.h5`/`.hdf5` files via recursive glob.
+
+---
+
+## 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_file("sft.json")
+Pipeline(
+    config,
+    ["data_part1.jsonl", "data_part2.jsonl"],
+    output_dir="output/",
+    tokenizer_path="params",
+).run()
+```
+
+> Document Update Time: 2026-07-09

assets/docs/training.md

@@ -0,0 +1,235 @@
+# Training
+
+## Contents
+
+- [Autoregression](#autoregression)
+- [Causal Mask](#causal-mask)
+- [Rotary Position Embedding (RoPE)](#rotary-position-embedding-rope)
+- [Training Loop](#training-loop)
+- [Strategies](#strategies) — SEQ, SFT, DPO, GRPO, online rollout
+- [LR Schedulers](#lr-schedulers)
+- [Gradient Checkpointing](#gradient-checkpointing)
+- [Checkpoint](#checkpoint)
+- [TrainContextBuilder](#traincontextbuilder-builder-pattern)
+- [Training CLI](#training-cli)
+
+### 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.consumed_samples += (
+            context.config.batch_per_device * context.world_size
+        )
+        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`, `MetricCallback`, `ProgressBarCallback` |
+| `on_batch_end` | Every batch | `CheckpointCallback` |
+| `on_epoch_end` | End of each epoch | `MetricCallback`, `ProgressBarCallback` |
+| `on_error` | On exception during training | `CheckpointCallback`, `MetricCallback` |
+| `on_train_end` | Training ends (always via finally) | `CheckpointCallback`, `MetricCallback`, `GradientCheckpointingCallback` |
+
+Default callbacks (in order): `gradient_checkpointing` (activation checkpointing, optional), `checkpoint` (safetensors, rank-0), `metric` (JSONL + validation, rank-0), `progress_bar` (tqdm), `gradient_clipping` (always registered; computes grad norm, clips only when `max_grad_norm` is not `None`).
+
+## 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`, `position_ids`. 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="sum"`. Keys: `chosen`, `rejected`, `chosen_mask`, `rejected_mask`.
+
+### GRPO (Group Relative Policy Optimization)
+
+Token-level PPO with group-normalized advantages. Advantages are derived from
+scalar per-response rewards, group-normalized, and broadcast across all response
+tokens. Only response tokens contribute to the loss (prompt tokens are masked
+out):
+
+$$
+\text{Advantage}_i = \frac{r_i - \mu}{\sigma + \epsilon}
+$$
+
+$$
+L_{\text{GRPO}} = -\mathbb{E}_t\left[\min\left(\rho_t A,\; \text{clip}\left(\rho_t, 1-\epsilon, 1+\epsilon\right)A\right)\right] + \lambda \cdot \mathbb{E}_t\left[\frac{\pi_{\text{ref}}}{\pi_\theta} - \log\frac{\pi_{\text{ref}}}{\pi_\theta} - 1\right]
+$$
+
+where $\rho_t = \pi_\theta(a_t|s_t) / \pi_{\text{old}}(a_t|s_t)$ is the
+per-token importance sampling ratio against the behaviour policy
+(`old_model`, synced externally between data-generation rounds) and the
+expectations are over valid response tokens. The KL term regularises
+$\pi_\theta$ towards a frozen reference model (`ref_model`, typically
+the SFT checkpoint).
+
+Parameters: `group_size=4`, `clip_eps=0.2`, `kl_coef=0.01`. External sync of `old_model` weights via `sync_old_model()` between data-generation rounds.
+
+Keys: `prompts`, `responses`, `masks`, `rewards`.
+
+### Online Rollout
+
+`online_grpo` and `online_dpo` use the respective GRPO and DPO strategies with
+a `RolloutRunner`. The runner renders prompts through the tokenizer chat
+template, generates grouped responses through `InferenceScheduler`, then scores
+them with a `BaseRewardModel`. It refreshes cached rollouts every
+`rollout_interval` optimizer steps. `online_grpo` synchronizes `old_model` when
+a fresh rollout is produced.
+
+Online strategies require `TrainConfig.reward_model_fn`. `train.py` exposes the
+rollout sampling parameters but does not yet offer a CLI argument for the reward
+model factory.
+
+## LR Schedulers
+
+| Type | Class | Description |
+|------|-------|-------------|
+| Cosine | `CosineScheduler` | Linear warmup → cosine decay to `min_rate` |
+| SGDR | `SGDRScheduler` | Cosine annealing with warm restarts (`t_mult=2`) |
+| WSD | `WSDScheduler` | Warmup-Stable-Decay with sqrt cooldown |
+
+Created by `SchedulerFactory.create(schedule_type, optimizer, **kwargs)`. Valid types: `"cosine"`, `"sgdr"`, `"wsd"`. 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, consumed_samples, extra, meta, config)
+  ├── save(save_dir)    rank-0 only: meta.json (epoch/consumed_samples/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_param_path(param_path, resume=True).build()
+# Returns TrainContext with model, strategy, optimizer, scheduler, dataloader, checkpoint
+```
+
+- Loads checkpoint weights before the model is wrapped
+- Creates executor via `ExecutorFactory.create(cfg.parallel_mode, grad_accum_steps=cfg.grad_accum_steps, **cfg.executor_kwargs)`
+- Calls `executor.prepare(model_fn, optimizer_fn, scheduler_fn, before_wrap=...)`; the executor creates, wraps, then builds the optimizer and scheduler for the wrapped model
+- Creates `RDSampler` 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 \
+    --weight_decay=0.1 \
+    --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: 2026-07-20

astrai/__init__.py

@@ -0,0 +1,98 @@
+__version__ = "1.3.10"
+__author__ = "ViperEkura"
+
+from astrai.config import (
+    AutoRegressiveLMConfig,
+    BaseModelConfig,
+    ConfigFactory,
+    EncoderConfig,
+    PipelineConfig,
+    TrainConfig,
+)
+from astrai.dataset import (
+    BaseDataset,
+    DatasetFactory,
+    RDSampler,
+    Store,
+    StoreFactory,
+)
+from astrai.factory import BaseFactory
+from astrai.inference import (
+    GenerationRequest,
+    InferenceEngine,
+    ProtocolHandler,
+    SamplingPipeline,
+    get_app,
+    run_server,
+    sample,
+)
+from astrai.model import (
+    AutoModel,
+    AutoRegressiveLM,
+    EmbeddingEncoder,
+    LoRAConfig,
+    inject_lora,
+)
+from astrai.parallel import (
+    ExecutorFactory,
+    get_rank,
+    get_world_size,
+    only_on_rank,
+    spawn_parallel_fn,
+)
+from astrai.preprocessing import Pipeline, filter_by_length
+from astrai.serialization import Checkpoint
+from astrai.tokenize import AutoTokenizer, ChatTemplate
+from astrai.trainer import (
+    BaseScheduler,
+    BaseStrategy,
+    CallbackFactory,
+    SchedulerFactory,
+    StrategyFactory,
+    TrainCallback,
+    Trainer,
+)
+
+__all__ = [
+    "AutoRegressiveLM",
+    "AutoRegressiveLMConfig",
+    "AutoModel",
+    "AutoTokenizer",
+    "BaseDataset",
+    "BaseFactory",
+    "BaseModelConfig",
+    "BaseScheduler",
+    "BaseStrategy",
+    "CallbackFactory",
+    "ChatTemplate",
+    "Checkpoint",
+    "ConfigFactory",
+    "DatasetFactory",
+    "EmbeddingEncoder",
+    "EncoderConfig",
+    "ExecutorFactory",
+    "GenerationRequest",
+    "InferenceEngine",
+    "LoRAConfig",
+    "Pipeline",
+    "PipelineConfig",
+    "ProtocolHandler",
+    "RDSampler",
+    "SamplingPipeline",
+    "SchedulerFactory",
+    "Store",
+    "StoreFactory",
+    "StrategyFactory",
+    "TrainCallback",
+    "TrainConfig",
+    "Trainer",
+    "filter_by_length",
+    "get_app",
+    "get_rank",
+    "get_world_size",
+    "inject_lora",
+    "only_on_rank",
+    "run_server",
+    "sample",
+    "spawn_parallel_fn",
+]

astrai/config/__init__.py

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

astrai/config/base.py

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

astrai/config/model_config.py

@@ -0,0 +1,82 @@
+from dataclasses import dataclass
+from typing import Any, Dict, Optional
+
+from astrai.config.base import BaseConfig
+from astrai.factory import BaseFactory
+
+
+class ConfigFactory(BaseFactory[BaseConfig]):
+    """Factory that dispatches config classes by ``model_type``."""
+
+    @classmethod
+    def load(cls, raw: Dict[str, Any]) -> BaseConfig:
+        model_type = raw.get("model_type") or "autoregressive_lm"
+        config_cls = cls.get_component_class(model_type)
+        return config_cls.from_dict(raw)
+
+
+@dataclass
+class BaseModelConfig(BaseConfig):
+    """Base config with ``model_type`` dispatch and file I/O."""
+
+    model_type: Optional[str] = None
+    neftune_alpha: float = 0.0
+
+
+@dataclass
+@ConfigFactory.register("autoregressive_lm")
+class AutoRegressiveLMConfig(BaseModelConfig):
+    """Configuration for autoregressive language model."""
+
+    vocab_size: Optional[int] = None
+    hidden_size: Optional[int] = None
+    num_hidden_layers: Optional[int] = None
+    rms_norm_eps: Optional[float] = None
+    intermediate_size: Optional[int] = None
+    tie_word_embeddings: Optional[bool] = None
+
+    max_position_embeddings: Optional[int] = None
+    rope_theta: Optional[float] = None
+    rope_scaling: Optional[dict] = None
+
+    attn_type: str = "gqa"
+    num_attention_heads: Optional[int] = None
+    num_key_value_heads: Optional[int] = None
+    use_qk_norm: Optional[bool] = None
+    use_gated_attention: Optional[bool] = None
+
+    kv_lora_rank: Optional[int] = None
+    qk_nope_head_dim: Optional[int] = None
+    qk_rope_head_dim: Optional[int] = None
+
+    ffn_type: str = "mlp"
+    n_routed_experts: Optional[int] = None
+    n_shared_experts: Optional[int] = None
+    n_activated_experts: Optional[int] = None
+    topk_method: Optional[str] = None
+
+
+@dataclass
+@ConfigFactory.register("embedding")
+class EncoderConfig(BaseModelConfig):
+    """Configuration for embedding encoder model."""
+
+    vocab_size: Optional[int] = None
+    hidden_size: Optional[int] = None
+    num_hidden_layers: Optional[int] = None
+    rms_norm_eps: Optional[float] = None
+    intermediate_size: Optional[int] = None
+
+    max_position_embeddings: Optional[int] = None
+    rope_theta: Optional[float] = None
+    rope_scaling: Optional[dict] = None
+
+    attn_type: str = "gqa"
+    num_attention_heads: Optional[int] = None
+    num_key_value_heads: Optional[int] = None
+    use_qk_norm: Optional[bool] = None
+    use_gated_attention: Optional[bool] = None
+
+    ffn_type: str = "mlp"
+    pooling_type: Optional[str] = None
+    normalize_embeddings: Optional[bool] = None

astrai/config/preprocess_config.py

@@ -0,0 +1,112 @@
+"""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).
+    batch_size : int
+        Number of records tokenized together (default: 256).
+    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
+    batch_size: int = 256
+    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"``: do not generate (default).
+        - ``"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: str = "doc_reset"
+
+
+@dataclass
+class PipelineConfig(BaseConfig):
+    version: int = 1
+    input: InputConfig = field(default_factory=InputConfig)
+    mask: Dict[str, str] = field(default_factory=dict)
+    mask_default: str = "mask"
+    preprocessing: ProcessingConfig = field(default_factory=ProcessingConfig)
+    output: OutputConfig = field(default_factory=OutputConfig)

astrai/config/train_config.py

@@ -0,0 +1,181 @@
+from dataclasses import dataclass, field, fields
+from typing import Any, Callable, Dict, List, Optional
+
+import torch.nn as nn
+from torch.optim import Optimizer
+from torch.optim.lr_scheduler import LRScheduler
+from torch.utils.data import Dataset
+
+from astrai.config.base import BaseConfig
+from astrai.model.components.lora import LoRAConfig
+
+
+def required(**kw):
+    return {"required": True, **kw}
+
+
+@dataclass
+class TrainConfig(BaseConfig):
+    # basic setting
+    model_fn: Callable[[], nn.Module] = field(
+        default=None, metadata=required(help="Model factory for training.")
+    )
+    strategy: str = field(default=None, metadata=required(help="Training strategy."))
+    dataset: Dataset = field(
+        default=None, metadata=required(help="Dataset for training.")
+    )
+    optimizer_fn: Callable[[nn.Module], Optimizer] = field(
+        default=None, metadata=required(help="Optimizer factory for training.")
+    )
+    scheduler_fn: Callable[[Optimizer], LRScheduler] = field(
+        default=None, metadata=required(help="Scheduler factory for training.")
+    )
+    n_epoch: int = field(default=1, metadata={"help": "Number of epochs for training."})
+    batch_per_device: int = field(
+        default=4, metadata={"help": "Batch size per device."}
+    )
+    grad_accum_steps: int = field(
+        default=1, metadata={"help": "Number of iterations between steps."}
+    )
+    max_grad_norm: Optional[float] = field(
+        default=1.0,
+        metadata={"help": "Maximum gradient norm. None disables clipping."},
+    )
+    gradient_checkpointing_modules: List[str] = field(
+        default_factory=list,
+        metadata={"help": "Module types to enable activation checkpointing for."},
+    )
+
+    # checkpoint setting
+    start_epoch: int = field(default=0, metadata={"help": "Start epoch for training."})
+    start_samples: int = field(
+        default=0,
+        metadata={
+            "help": "Start samples count (per rank). Superseded by checkpoint consumed_samples."
+        },
+    )
+    ckpt_dir: str = field(
+        default="./checkpoint", metadata={"help": "Checkpoint directory."}
+    )
+    ckpt_interval: int = field(
+        default=5000,
+        metadata={"help": "Number of optimizer steps 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."}
+    )
+    metrics: List[str] = field(
+        default_factory=lambda: ["loss", "lr", "grad_norm"],
+        metadata={"help": "Metrics to record during training."},
+    )
+
+    # dataloader setting
+    random_seed: int = field(default=3407, metadata={"help": "Random seed."})
+    num_workers: int = field(
+        default=0, metadata={"help": "Number of workers for dataloader."}
+    )
+    prefetch_factor: Optional[int] = field(
+        default=None, metadata={"help": "Prefetch factor for dataloader."}
+    )
+    pin_memory: bool = field(
+        default=False, metadata={"help": "Pin memory for dataloader."}
+    )
+    collate_fn: Optional[Callable[[List[Any]], Any]] = field(
+        default=None,
+        metadata={"help": "Collate function for dataloader (e.g. dpo_collate_fn)."},
+    )
+
+    # distributed training
+    nprocs: int = field(
+        default=1, metadata={"help": "Number of processes for distributed training."}
+    )
+    backend: str = field(
+        default="nccl", metadata={"help": "Distributed training backend."}
+    )
+    master_addr: str = field(
+        default="localhost",
+        metadata={"help": "Master address for distributed training."},
+    )
+    master_port: str = field(
+        default="29500", metadata={"help": "Master port for distributed training."}
+    )
+    parallel_mode: str = field(
+        default="none",
+        metadata={"help": "Parallel strategy: none, ddp, fsdp."},
+    )
+    start_method: str = field(
+        default="spawn",
+        metadata={"help": "Multiprocessing start method (spawn/fork/forkserver)."},
+    )
+
+    # others
+    device_type: str = field(
+        default="cuda", metadata={"help": "Device type for distributed training."}
+    )
+    val_dataset: Optional[Dataset] = field(
+        default=None, metadata={"help": "Dataset for validation."}
+    )
+    val_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."},
+    )
+    neftune_alpha: float = field(
+        default=0.0,
+        metadata={"help": "NEFTune noise alpha (0=disabled, typical: 5.0)."},
+    )
+
+    # online rollout
+    rollout_interval: int = field(
+        default=512,
+        metadata={"help": "Number of optimizer steps between online rollouts."},
+    )
+    rollout_temperature: float = field(
+        default=0.7, metadata={"help": "Sampling temperature for online rollout."}
+    )
+    rollout_top_k: int = field(
+        default=0, metadata={"help": "Top-k filtering for online rollout (0=disable)."}
+    )
+    rollout_top_p: float = field(
+        default=0.9,
+        metadata={"help": "Top-p (nucleus) filtering for online rollout."},
+    )
+    rollout_max_tokens: int = field(
+        default=1024,
+        metadata={"help": "Maximum generated tokens per response in rollout."},
+    )
+    reward_model_fn: Optional[Callable] = field(
+        default=None,
+        metadata={
+            "help": "Factory for reward model (required for online RL strategies)."
+        },
+    )
+
+    executor_kwargs: Dict[str, Any] = field(
+        default_factory=dict,
+        metadata={"help": "Extra kwargs passed to ExecutorFactory.create()."},
+    )
+    extra_kwargs: Dict[str, Any] = field(
+        default_factory=dict, metadata={"help": "Other arguments."}
+    )
+
+    def __post_init__(self):
+        self.validate()
+
+    def validate(self):
+        for fld in fields(self):
+            if fld.metadata.get("required") and getattr(self, fld.name) is None:
+                raise ValueError(f"TrainConfig.{fld.name} is required but got None.")

astrai/dataset/__init__.py

@@ -0,0 +1,43 @@
+from astrai.dataset.dataset import (
+    BaseDataset,
+    DatasetFactory,
+    dpo_collate_fn,
+    grpo_collate_fn,
+)
+from astrai.dataset.sampler import RDSampler
+from astrai.dataset.storage import (
+    H5Store,
+    JsonlStore,
+    MmapStore,
+    Recordable,
+    Store,
+    StoreFactory,
+    Streamable,
+    detect_format,
+)
+from astrai.serialization import (
+    load_bin,
+    load_h5,
+    save_bin,
+    save_h5,
+)
+
+__all__ = [
+    "BaseDataset",
+    "DatasetFactory",
+    "dpo_collate_fn",
+    "grpo_collate_fn",
+    "Store",
+    "Streamable",
+    "Recordable",
+    "StoreFactory",
+    "H5Store",
+    "MmapStore",
+    "JsonlStore",
+    "detect_format",
+    "save_h5",
+    "load_h5",
+    "save_bin",
+    "load_bin",
+    "RDSampler",
+]

astrai/dataset/dataset.py

@@ -0,0 +1,502 @@
+"""Dataset implementations for training.
+
+Composition over inheritance — every dataset is a thin wrapper that
+binds a :class:`Store` to a particular train-type's key mapping.  All
+sample-id → token/record indexing lives on the Store; datasets never
+know about window/stride math or segment layouts.
+
+Class hierarchy:
+
+    BaseDataset (ABC)         — holds a Store, exposes __len__/keys,
+                                 overrides __getitem__
+    ├── SEQDataset            — next-token prediction (stream)
+    ├── SFTDataset            — loss-mask + position_ids (stream)
+    ├── DPODataset            — chosen/rejected pairs (record)
+    └── GRPODataset           — prompt + response group (record)
+
+``DatasetFactory.load(train_type, load_path, window_size, stride, …)``
+builds the Store (auto-detecting format) before constructing the
+matching dataset.  Passing ``store=`` skips Store construction.
+
+When a record dataset (DPO) reads from raw JSONL, a *processor*
+function (pure ``record -> Dict[str, Tensor]``) is forwarded to
+:class:`JsonlStore` so tokenisation happens on the fly.
+"""
+
+from abc import ABC, abstractmethod
+from functools import partial
+from typing import Callable, Dict, List, Optional
+
+import torch
+from torch import Tensor
+from torch.utils.data import Dataset
+
+from astrai.dataset.storage import (
+    Store,
+    StoreFactory,
+    detect_format,
+)
+from astrai.factory import BaseFactory
+from astrai.tokenize import AutoTokenizer
+
+
+def dpo_tokenize(
+    record: dict,
+    tokenizer,
+    max_len: int = 2048,
+) -> Optional[dict]:
+    """Tokenize one DPO record into chosen/rejected + masks.
+
+    Applies the tokenizer's chat template so token sequences match the
+    SFT checkpoint's format.  Prompt is rendered with
+    ``add_generation_prompt=True``; chosen/rejected are appended as a
+    single assistant turn.
+
+    Accepts:
+
+    - Flat:   ``{"prompt": str, "chosen": str, "rejected": str}``
+    - Conv:   ``{"prompt": [{role, content}, ...], "chosen": [...], ...}``
+    - Legacy: ``{"input": str, "chosen": str, "rejected": str}``
+
+    No packing, no ``position_ids`` — DPO sequences are independent.
+    """
+    prompt = record.get("prompt") or record.get("input")
+    chosen = record.get("chosen")
+    rejected = record.get("rejected")
+    if prompt is None or chosen is None or rejected is None:
+        return None
+
+    prompt_messages = _to_messages(prompt)
+    chosen_text = _extract_text(chosen)
+    rejected_text = _extract_text(rejected)
+    if chosen_text is None or rejected_text is None:
+        return None
+    chosen_messages = prompt_messages + [{"role": "assistant", "content": chosen_text}]
+    rejected_messages = prompt_messages + [
+        {"role": "assistant", "content": rejected_text}
+    ]
+
+    prompt_ids = tokenizer.apply_chat_template(
+        prompt_messages, tokenize=True, add_generation_prompt=True
+    )
+    ch_ids = tokenizer.apply_chat_template(
+        chosen_messages, tokenize=True, add_generation_prompt=False
+    )
+    re_ids = tokenizer.apply_chat_template(
+        rejected_messages, tokenize=True, add_generation_prompt=False
+    )
+
+    full_ch = ch_ids[:max_len]
+    full_re = re_ids[:max_len]
+
+    prompt_len = min(len(prompt_ids), max_len)
+    ch_mask = [0] * prompt_len + [1] * max(0, len(full_ch) - prompt_len)
+    ch_mask = ch_mask[:max_len]
+    re_mask = [0] * prompt_len + [1] * max(0, len(full_re) - prompt_len)
+    re_mask = re_mask[:max_len]
+
+    return {
+        "chosen": full_ch,
+        "rejected": full_re,
+        "chosen_mask": ch_mask,
+        "rejected_mask": re_mask,
+    }
+
+
+def _to_messages(value) -> list:
+    """Accept str or conversation list; return message list."""
+    if isinstance(value, str):
+        return [{"role": "user", "content": value}]
+    if isinstance(value, list):
+        return value
+    return [{"role": "user", "content": str(value)}]
+
+
+def _extract_text(value) -> Optional[str]:
+    """Accept str or conversation list; return plain text."""
+    if value is None:
+        return None
+    if isinstance(value, str):
+        return value
+    if isinstance(value, list):
+        return "".join(m.get("content", "") for m in value if isinstance(m, dict))
+    return None
+
+
+def dpo_processor(
+    record: dict,
+    tokenizer,
+    max_len: int = 2048,
+) -> Dict[str, Tensor]:
+    """DPO processor: wraps :func:`dpo_tokenize` and returns tensors."""
+    result = dpo_tokenize(record, tokenizer, max_len=max_len)
+    if result is None:
+        raise ValueError(f"Malformed DPO record: {list(record.keys())}")
+    return {
+        "chosen": torch.tensor(result["chosen"], dtype=torch.int32),
+        "rejected": torch.tensor(result["rejected"], dtype=torch.int32),
+        "chosen_mask": torch.tensor(result["chosen_mask"], dtype=torch.bool),
+        "rejected_mask": torch.tensor(result["rejected_mask"], dtype=torch.bool),
+    }
+
+
+def dpo_collate_fn(batch: List[Dict[str, Tensor]]) -> Dict[str, Tensor]:
+    """Collate variable-length DPO samples into padded 2-D tensors.
+
+    Input: list of dicts, each with:
+      - chosen:        [C_i]
+      - rejected:      [R_i]
+      - chosen_mask:   [C_i]
+      - rejected_mask: [R_i]
+
+    Output (padded to the max length across chosen/rejected within the batch):
+      - chosen:        [B, S_max]
+      - rejected:      [B, S_max]
+      - chosen_mask:   [B, S_max]
+      - rejected_mask: [B, S_max]
+    """
+    B = len(batch)
+    S_max = max(b["chosen"].size(0) for b in batch)
+    S_max = max(S_max, max(b["rejected"].size(0) for b in batch))
+
+    chosen = torch.zeros(B, S_max, dtype=torch.long)
+    rejected = torch.zeros(B, S_max, dtype=torch.long)
+    chosen_mask = torch.zeros(B, S_max, dtype=torch.bool)
+    rejected_mask = torch.zeros(B, S_max, dtype=torch.bool)
+
+    for i, b in enumerate(batch):
+        c_len = b["chosen"].size(0)
+        r_len = b["rejected"].size(0)
+        chosen[i, :c_len] = b["chosen"]
+        rejected[i, :r_len] = b["rejected"]
+        chosen_mask[i, :c_len] = b["chosen_mask"]
+        rejected_mask[i, :r_len] = b["rejected_mask"]
+
+    return {
+        "chosen": chosen,
+        "rejected": rejected,
+        "chosen_mask": chosen_mask,
+        "rejected_mask": rejected_mask,
+    }
+
+
+def grpo_collate_fn(batch: List[Dict[str, Tensor]]) -> Dict[str, Tensor]:
+    """Collate variable-length GRPO samples into padded 3-D tensors.
+
+    Input: list of dicts, each with:
+      - prompts:  [P_i]
+      - responses: list of G tensors, each [R_ij]
+      - masks:     list of G tensors, each [R_ij]
+      - rewards:  [G]
+
+    Output:
+      - prompts:   [B, P_max]
+      - responses: [B, G, R_max]
+      - masks:     [B, G, R_max]
+      - rewards:   [B, G]
+    """
+    B = len(batch)
+    G = len(batch[0]["responses"])
+    P_max = max(b["prompts"].size(0) for b in batch)
+    R_max = max(r.size(0) for b in batch for r in b["responses"])
+
+    prompts = torch.zeros(B, P_max, dtype=torch.long)
+    responses = torch.zeros(B, G, R_max, dtype=torch.long)
+    masks = torch.zeros(B, G, R_max, dtype=torch.bool)
+    rewards = torch.zeros(B, G, dtype=torch.float32)
+
+    for i, b in enumerate(batch):
+        p_len = b["prompts"].size(0)
+        prompts[i, :p_len] = b["prompts"]
+        rewards[i, : b["rewards"].size(0)] = b["rewards"]
+        for g in range(min(G, len(b["responses"]))):
+            r_len = b["responses"][g].size(0)
+            responses[i, g, :r_len] = b["responses"][g]
+            if g < len(b["masks"]):
+                masks[i, g, :r_len] = b["masks"][g]
+
+    return {
+        "prompts": prompts,
+        "responses": responses,
+        "masks": masks,
+        "rewards": rewards,
+    }
+
+
+def validate_keys(store: Store, required: List[str]) -> None:
+    """Raise ``KeyError`` if *store* is missing any *required* key."""
+    if not required:
+        return
+    actual = set(store.keys)
+    missing = [k for k in required if k not in actual]
+    if missing:
+        raise KeyError(
+            f"Store at {getattr(store, '_load_path', '?')} is missing required "
+            f"keys {missing}; available keys are {sorted(actual)}."
+        )
+
+
+class BaseDataset(Dataset, ABC):
+    """Abstract base class for dataset types.
+
+    Holds a :class:`Store`.  All sample-id indexing is delegated to the
+    store — this class exposes ``__len__`` as ``len(store)`` and the
+    ``keys`` property as ``store.keys``.  Subclasses implement
+    ``__getitem__`` with the train-type-specific key mapping and any
+    training-only index arithmetic (e.g. the next-token ``+1`` shift).
+    """
+
+    required_keys: List[str] = []
+
+    def __init__(self, store: Store):
+        super().__init__()
+        self.store: Store = store
+        validate_keys(store, self.required_keys)
+
+    def __len__(self) -> int:
+        return len(self.store)
+
+    @property
+    def keys(self) -> List[str]:
+        return self.store.keys
+
+    @property
+    def token_count(self) -> int:
+        return self.store.token_count
+
+    @abstractmethod
+    def __getitem__(self, index: int) -> Dict[str, Tensor]:
+        raise NotImplementedError
+
+
+class DatasetFactory(BaseFactory["BaseDataset"]):
+    """Factory for creating dataset instances by train-type.
+
+    Use :meth:`DatasetFactory.register("custom")` to register new
+    dataset classes; they must inherit from :class:`BaseDataset`.
+    """
+
+    @classmethod
+    def load(
+        cls,
+        train_type: str,
+        load_path: Optional[str] = None,
+        window_size: int = 0,
+        stride: Optional[int] = None,
+        storage_type: Optional[str] = None,
+        tokenizer_path: Optional[str] = None,
+        max_len: int = 2048,
+        store: Optional[Store] = None,
+        **kwargs,
+    ) -> "BaseDataset":
+        """Create and load a dataset in one step.
+
+        Two entry points:
+
+        - **store given**: bind it directly — the caller fully controls
+          Store construction and processor setup.  *load_path*,
+          *storage_type*, *tokenizer_path*, *window_size*, *stride* are
+          ignored.
+        - **store is None**: build a Store from *load_path*, auto-detecting
+          format and constructing a processor when *tokenizer_path* is
+          given for a record dataset on JSONL.
+
+        Args:
+            train_type: Registered dataset name ("seq", "sft", "dpo",
+                "grpo", …).
+            load_path: Path to the data file or directory (ignored if
+                *store* is given).
+            window_size: Stream window length — only meaningful for
+                stream datasets (SEQ/SFT).  Record datasets ignore it.
+            stride: Stride between consecutive stream samples
+                (default: same as *window_size*).
+            storage_type: Storage backend ("h5", "bin", "jsonl") or
+                None for auto-detection.
+            tokenizer_path: Path to tokenizer for lazy JSONL
+                tokenisation (record datasets only).
+            max_len: Max sequence length forwarded to processors.
+            store: Pre-built, already-loaded Store instance.
+            **kwargs: Extra arguments forwarded to ``store.load()``.
+
+        Returns:
+            Loaded dataset instance.
+        """
+        if store is not None:
+            return cls.create(train_type, store=store)
+
+        if load_path is None:
+            raise ValueError("Either load_path or store must be provided")
+
+        if storage_type is None:
+            storage_type = detect_format(load_path)
+
+        if stride is None:
+            stride = window_size
+
+        processor = cls._maybe_build_processor(
+            train_type, storage_type, tokenizer_path, max_len
+        )
+
+        store_window = cls._store_window_for(train_type, window_size)
+        store = StoreFactory.create(
+            storage_type,
+            window_size=store_window,
+            stride=stride if stride else store_window,
+        )
+        if processor is not None:
+            store.load(load_path, processor=processor, **kwargs)
+        else:
+            load_kwargs = dict(kwargs)
+            if (
+                tokenizer_path is not None
+                and storage_type == "jsonl"
+                and train_type in ("seq", "sft")
+                and "tokenizer_path" not in load_kwargs
+            ):
+                load_kwargs["tokenizer_path"] = tokenizer_path
+            store.load(load_path, **load_kwargs)
+
+        return cls.create(train_type, store=store)
+
+    @staticmethod
+    def _store_window_for(train_type: str, window_size: int) -> int:
+        """Stream datasets consume ``window_size``; record datasets ignore it.
+
+        Record datasets (dpo/grpo) treat each record as an independent
+        training unit and never window, so the store is built with
+        ``window_size=0`` and ``len(store)`` returns the record count.
+        """
+        if train_type in ("seq", "sft"):
+            return window_size
+        return 0
+
+    @staticmethod
+    def _maybe_build_processor(
+        train_type: str,
+        storage_type: str,
+        tokenizer_path: Optional[str],
+        max_len: int,
+    ) -> Optional[Callable[[dict], Dict[str, Tensor]]]:
+        """Build an on-the-fly tokenisation processor if applicable.
+
+        Only raw JSONL + record datasets (DPO/GRPO) need a processor;
+        pre-tokenised backends (H5/bin) and stream datasets (SEQ/SFT)
+        return ``None`` so no tokenizer is loaded.
+        """
+        if tokenizer_path is None or storage_type != "jsonl":
+            return None
+        if train_type == "dpo":
+            tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
+            return partial(dpo_processor, tokenizer=tokenizer, max_len=max_len)
+        return None
+
+
+@DatasetFactory.register("seq")
+class SEQDataset(BaseDataset):
+    """Dataset for sequential next-token prediction training.
+
+    Stream mode: ``store.fetch(begin, end, "sequence")`` returns the
+    input window; the +1 shifted call returns the next-token target.
+    """
+
+    required_keys = ["sequence"]
+
+    def __getitem__(self, index: int):
+        begin, end = self.store.sample_window(index)
+        x = self.store.fetch(begin, end, "sequence")
+        y = self.store.fetch(begin + 1, end + 1, "sequence")
+        return {
+            "input_ids": x.to(dtype=torch.long),
+            "target_ids": y.to(dtype=torch.long),
+        }
+
+
+@DatasetFactory.register("sft")
+class SFTDataset(BaseDataset):
+    """Dataset for supervised fine-tuning with loss masking.
+
+    Stream mode: ``sequence``/``loss_mask``/``position_ids`` are sliced
+    to the window.  ``loss_mask`` and ``target_ids`` use the +1 shifted
+    slice so they align with the predicted positions.
+    """
+
+    required_keys = ["sequence", "loss_mask", "position_ids"]
+
+    def __getitem__(self, index: int):
+        begin, end = self.store.sample_window(index)
+        x = self.store.fetch(begin, end, "sequence")
+        y = self.store.fetch(begin + 1, end + 1, "sequence")
+        position_ids = self.store.fetch(begin, end, "position_ids")
+        loss_mask = self.store.fetch(begin + 1, end + 1, "loss_mask")
+        return {
+            "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")
+class DPODataset(BaseDataset):
+    """Record-structured dataset for Direct Preference Optimization.
+
+    Each sample is one preference pair (chosen + rejected) and is an
+    independent training unit — no windowing, stride, or cross-record
+    concatenation.  This keeps each sequence self-contained so attention
+    never leaks across preference pairs.
+
+    Two loading paths (handled by :class:`DatasetFactory`):
+
+    - **Pre-tokenized** (H5/bin): ``store.load(path)`` reads per-record
+      tensors; ``__getitem__`` returns them directly.
+    - **Raw JSONL** (``tokenizer_path=...``): builds a lazy processor
+      via :func:`dpo_processor` that tokenises on the fly — no packing,
+      no ``position_ids``.
+    """
+
+    required_keys = ["chosen", "rejected", "chosen_mask", "rejected_mask"]
+
+    def make_processor(self, tokenizer, max_len: int):
+        return partial(dpo_processor, tokenizer=tokenizer, max_len=max_len)
+
+    def __getitem__(self, index: int) -> Dict[str, Tensor]:
+        return {
+            "chosen": self.store.fetch_record(index, "chosen").to(dtype=torch.long),
+            "rejected": self.store.fetch_record(index, "rejected").to(dtype=torch.long),
+            "chosen_mask": self.store.fetch_record(index, "chosen_mask").to(
+                dtype=torch.bool
+            ),
+            "rejected_mask": self.store.fetch_record(index, "rejected_mask").to(
+                dtype=torch.bool
+            ),
+        }
+
+
+@DatasetFactory.register("grpo")
+class GRPODataset(BaseDataset):
+    """Dataset for offline Group Relative Policy Optimization.
+
+    Each sample is one prompt with its group of responses and scalar
+    rewards — an independent training unit with no windowing or stride.
+
+    Expected storage layout (produced by JsonlStore or pre-tokenized):
+
+    - ``prompts``:   List[Tensor]  — one 1-D token tensor per record
+    - ``responses``: List[List[Tensor]] — G response tensors per record
+    - ``masks``:     List[List[Tensor]] — G mask tensors per record
+    - ``rewards``:   List[Tensor]  — one 1-D float tensor (len G) per record
+    """
+
+    required_keys = ["prompts", "responses", "masks", "rewards"]
+
+    def __getitem__(self, index: int) -> Dict[str, Tensor]:
+        prompts = self.store.fetch_record(index, "prompts")
+        responses = self.store.fetch_record(index, "responses")
+        masks = self.store.fetch_record(index, "masks")
+        rewards = self.store.fetch_record(index, "rewards")
+        return {
+            "prompts": prompts.to(dtype=torch.long),
+            "responses": [r.to(dtype=torch.long) for r in responses],
+            "masks": [m.to(dtype=torch.bool) for m in masks],
+            "rewards": rewards.to(dtype=torch.float32),
+        }

astrai/dataset/sampler.py

@@ -0,0 +1,93 @@
+from typing import Optional
+
+import torch
+import torch.distributed as dist
+from torch.utils.data import Dataset, Sampler
+
+
+class RDSampler(Sampler[int]):
+    """Resumable Distributed Sampler.
+
+    A distributed sampler that supports checkpoint-based resume: iteration
+    state (epoch, position) is tracked so training can continue from the
+    exact sample after a restart.  Shards the dataset across
+    ``dist.world_size`` replicas with optional shuffling.
+    """
+
+    def __init__(
+        self,
+        data_source: Dataset,
+        start_epoch: int = 0,
+        start_iter: int = 0,
+        seed: int = 42,
+        drop_last: bool = False,
+        shuffle: bool = True,
+        process_group: Optional[dist.ProcessGroup] = None,
+    ):
+        self.epoch = start_epoch
+        self.iter = start_iter
+        self.seed = seed
+        self.num_samples = len(data_source)
+
+        if process_group is not None:
+            # input process group
+            self.rank = dist.get_rank(process_group)
+            self.num_replicas = dist.get_world_size(process_group)
+
+        elif dist.is_available() and dist.is_initialized():
+            # use default process group
+            process_group = dist.group.WORLD
+            self.rank = dist.get_rank()
+            self.num_replicas = dist.get_world_size()
+
+        else:
+            # single process
+            self.rank = 0
+            self.num_replicas = 1
+
+        self.drop_last = drop_last
+        self.shuffle = shuffle
+
+        offset = 0 if drop_last else self.num_replicas - 1
+        self.num_samples_per_replica = (self.num_samples + offset) // self.num_replicas
+        self.total_size = self.num_samples_per_replica * self.num_replicas
+        self.iter = self.iter % self.num_samples_per_replica
+
+        self._indices = None
+
+    def _get_indices(self):
+        if self.shuffle:
+            generator = torch.Generator()
+            generator.manual_seed(self.seed + self.epoch)
+            indices = torch.randperm(self.num_samples, generator=generator).tolist()
+        else:
+            indices = torch.arange(self.num_samples).tolist()
+
+        if not self.drop_last and self.num_samples < self.total_size:
+            padding_size = self.total_size - len(indices)
+            indices += indices[:padding_size]
+
+        local_indices = indices[self.rank : self.total_size : self.num_replicas]
+
+        self.iter = self.iter % self.num_samples_per_replica
+        self._indices = local_indices[self.iter :]
+
+    def __iter__(self):
+        if self._indices is None:
+            self._get_indices()
+
+        for i in self._indices:
+            self.iter += 1
+            yield i
+
+        self.epoch += 1
+        self._indices = None
+        self.iter = self.iter % self.num_samples_per_replica
+
+    @property
+    def _remaining(self):
+        remaining = self.num_samples_per_replica - self.iter
+        return max(remaining, 0)
+
+    def __len__(self):
+        return self._remaining

astrai/dataset/storage.py

@@ -0,0 +1,664 @@
+"""Storage backends for different data formats.
+
+Architecture (composition over inheritance):
+
+    Store (ABC)               — owns _data/_cum/_offsets bookkeeping
+                                  + window_size/stride for sample-id
+                                  indexing.  __getitem__/__len__ produce
+                                  the smallest iterable unit so Dataset
+                                  classes are pure delegators.
+    Streamable (mixin)        — raw token slice  fetch(begin, end, keys)
+    Recordable (mixin)        — raw record slice fetch_record(idx, keys)
+
+    H5Store(Store, Streamable, Recordable)
+    MmapStore(Store, Streamable, Recordable)
+    JsonlStore(Store, Streamable, Recordable)
+
+Each mixin is a stateless trait that relies on ``self._data`` etc.
+provided by :class:`Store`.  Concrete stores mix in whichever access
+primitives they support — ``Store`` is the sole base class, so there is
+no diamond inheritance or MRO ambiguity.
+
+Sample-id indexing lives on :class:`Store`, not on the dataset:
+
+- **Stream mode** (``window_size > 0``):  ``len(store)`` returns the number
+  of ``(window_size, stride)`` windows that fit in the token river;
+  ``store[i]`` returns the *i*-th window as a dict of per-key tensors;
+  ``store.sample_window(i)`` exposes the underlying ``(begin, end)``
+  token slice for callers (e.g. next-token trainers) that need a +1
+  shifted companion window.
+- **Record mode** (``num_records > 0``):  ``len(store)`` returns the
+  record count; ``store[i]`` returns the *i*-th record dict.
+
+Raw token/record access via :meth:`fetch` / :meth:`fetch_record`
+remains available for low-level callers that want explicit index
+control.  ``store.token_count`` is the total stream token count (what
+``len(store)`` used to mean in the legacy stream-only API).
+
+``segments_are_records`` (class attribute on each Store subclass)
+tells ``_normalize`` whether segments are inherently per-record (H5/
+JSONL) or opaque shards (bin).  Record access for bin relies on
+``_offsets`` instead.
+
+:class:`JsonlStore` supports a lazy mode (``processor=fn``) that keeps
+raw records and defers tokenisation to ``fetch_record`` — used by DPO
+to train directly from a ``.jsonl`` file without a pre-tokenised copy.
+"""
+
+import bisect
+import glob
+import json
+import logging
+from abc import ABC, abstractmethod
+from pathlib import Path
+from typing import Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+from torch import Tensor
+
+from astrai.config.preprocess_config import PipelineConfig
+from astrai.factory import BaseFactory
+from astrai.preprocessing.transform import TokenizeTransform
+from astrai.serialization import (
+    load_bin,
+    load_bin_offsets,
+    load_h5,
+)
+
+logger = logging.getLogger(__name__)
+
+
+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", "bin", "jsonl", or "processed")
+
+    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"
+        if suffix == ".jsonl":
+            return "jsonl"
+        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"
+    jsonl_files = [
+        Path(p) for p in glob.glob(str(root / "**" / "*.jsonl"), recursive=True)
+    ]
+    if jsonl_files:
+        return "jsonl"
+    raise FileNotFoundError(f"No supported data files found at {load_path}")
+
+
+class Store(ABC):
+    """Common base for all storage backends.
+
+    A Store owns both its data layout AND its sample-id → token/record
+    index translation.  Datasets are thin wrappers that bind a Store
+    to a particular train-type's key mapping; they never know about
+    window/stride math.
+
+    Two iteration modes:
+
+    - **Stream** (``window_size > 0``): data is treated as one long
+      token river.  ``len(store)`` returns the number of windows;
+      ``store[i]`` slices every stream-compatible key to window ``i``;
+      ``store.sample_window(i)`` returns the ``(begin, end)`` token
+      slice for callers needing a +1 shifted companion window.
+    - **Record** (``num_records > 0``): data is per-record.
+      ``len(store)`` returns ``num_records``; ``store[i]`` returns
+      the *i*-th record as a dict.
+
+    Raw token slicing is still available via :meth:`fetch` (mixed in
+    by :class:`Streamable`) when a store has stream support configured.
+    Raw record slicing via :meth:`fetch_record` (mixed in by
+    :class:`Recordable`) when a store has record support.
+
+    ``token_count`` exposes the raw total stream length — this is what
+    ``len(store)`` returned in the legacy stream-only API and what
+    stream-bound ``fetch`` uses for its bounds check.
+    """
+
+    segments_are_records: bool = False
+
+    def __init__(
+        self,
+        window_size: int = 0,
+        stride: Optional[int] = None,
+    ):
+        self._data: Dict[str, List[Tensor]] = {}
+        self._cum: Dict[str, List[int]] = {}
+        self._offsets: Dict[str, List[int]] = {}
+        self._length: int = 0
+        self._num_records: int = 0
+        self._window_size: int = int(window_size)
+        self._stride: int = int(stride) if stride is not None else int(window_size)
+
+    @abstractmethod
+    def load(self, path: str, **kwargs) -> None:
+        raise NotImplementedError
+
+    @property
+    def keys(self) -> List[str]:
+        return list(self._data.keys())
+
+    @property
+    def window_size(self) -> int:
+        return self._window_size
+
+    @property
+    def stride(self) -> int:
+        return self._stride
+
+    @property
+    def token_count(self) -> int:
+        """Total tokens across all stream segments.
+
+        Useful for the bounds-checked raw :meth:`fetch` and as the
+        legacy ``len(store)`` value.
+        """
+        return self._length
+
+    @property
+    def num_records(self) -> int:
+        """Number of records available via :meth:`fetch_record`.
+
+        Non-zero only when the backing layout provides per-record
+        indexing (H5/JSONL segments or bin ``_offsets``).
+        """
+        return self._num_records
+
+    @property
+    def num_samples(self) -> int:
+        """Number of items produced by ``__getitem__``.
+
+        Stream-mode wins when ``window_size > 0`` and there are tokens
+        to slice; otherwise falls back to ``num_records``.
+        """
+        if self._window_size > 0 and self._length > 0:
+            total = self._length
+            w = self._window_size
+            if total <= w:
+                return 0
+            return (total - 1 - w) // self._stride + 1
+        return self._num_records
+
+    def __len__(self) -> int:
+        return self.num_samples
+
+    def __getitem__(self, index: int) -> Dict[str, Tensor]:
+        if index < 0:
+            index += self.num_samples
+        if not 0 <= index < self.num_samples:
+            raise IndexError(
+                f"Store index out of range: {index}, num_samples={self.num_samples}"
+            )
+        if self._window_size > 0 and self._length > 0:
+            begin, end = self.sample_window(index)
+            keys = self._stream_keys()
+            return {k: self.fetch(begin, end, k) for k in keys}
+        return self.fetch_record(index, self._record_keys())
+
+    def sample_window(self, index: int) -> Tuple[int, int]:
+        """Return ``(begin, end)`` token positions for stream sample *index*.
+
+        The clipped tail keeps the last reachable window inside the
+        token river instead of overshooting.  Caller is responsible
+        for staying within :attr:`num_samples`: an out-of-range index
+        raises ``IndexError``.
+        """
+        if self._window_size <= 0:
+            raise RuntimeError("sample_window() requires window_size > 0 (stream mode)")
+        if self._window_size <= 0 or self._length <= self._window_size:
+            raise IndexError(
+                f"Data too short for window: token_count={self._length}, "
+                f"window_size={self._window_size}"
+            )
+        if not 0 <= index < self.num_samples:
+            raise IndexError(
+                f"Sample index out of range: {index}, num_samples={self.num_samples}"
+            )
+        total = self._length
+        begin = min(index * self._stride, total - 1 - self._window_size)
+        end = min(begin + self._window_size, total - 1)
+        return begin, end
+
+    def _stream_keys(self) -> List[str]:
+        out: List[str] = []
+        for k, tensors in self._data.items():
+            if tensors and isinstance(tensors[0], list):
+                continue
+            out.append(k)
+        return out
+
+    def _record_keys(self) -> List[str]:
+        return list(self._data.keys())
+
+    def _normalize(
+        self,
+        raw: Dict[str, list],
+        offsets: Optional[Dict[str, List[int]]] = None,
+    ):
+        """Register segments and pre-compute indices for both access modes.
+
+        Stream mode: ``_cum[key]`` accumulates per-segment lengths so
+        ``Streamable._fetch_stream_key`` can bisect across segments
+        without concatenation.
+
+        Record mode: if *offsets* is provided (bin layout),
+        ``_offsets[key]`` stores cumulative per-record offsets into the
+        single concatenated segment.  Otherwise, when
+        ``segments_are_records`` is True (H5/JSONL), ``_data[key]`` is
+        a per-record list and ``fetch_record`` indexes it directly.
+
+        Nested keys (GRPO ``responses``/``masks`` as
+        ``List[List[Tensor]]``) are stored as-is and excluded from both
+        cumulative bookkeepings — they are only accessed record-by-record.
+        """
+        flat_lengths = []
+        for key, tensors in raw.items():
+            self._data[key] = tensors
+            if not tensors:
+                self._cum[key] = []
+                flat_lengths.append(0)
+                continue
+            if isinstance(tensors[0], list):
+                self._cum[key] = []
+                continue
+            cum = []
+            total = 0
+            for t in tensors:
+                total += t.shape[0]
+                cum.append(total)
+            self._cum[key] = cum
+            flat_lengths.append(cum[-1] if cum else 0)
+        self._length = min(flat_lengths) if flat_lengths else 0
+
+        valid_offsets: Dict[str, List[int]] = {}
+        if offsets:
+            for key, off in offsets.items():
+                segs = self._data.get(key, [])
+                if len(segs) == 1 and len(off) > 1:
+                    valid_offsets[key] = off
+                elif len(segs) > 1:
+                    logger.warning(
+                        "Key '%s' has %d segments with offsets — record mode "
+                        "disabled for this key (multi-shard bin+offsets not "
+                        "supported). Merge shards or use H5/JSONL.",
+                        key,
+                        len(segs),
+                    )
+        self._offsets = valid_offsets
+        if valid_offsets:
+            record_counts = [len(v) - 1 for v in valid_offsets.values()]
+            self._num_records = min(record_counts) if record_counts else 0
+        elif self.segments_are_records:
+            per_record_counts = []
+            for key, tensors in self._data.items():
+                if tensors and isinstance(tensors[0], list):
+                    continue
+                per_record_counts.append(len(tensors))
+            self._num_records = min(per_record_counts) if per_record_counts else 0
+        else:
+            self._num_records = 0
+
+
+class Streamable:
+    """Mixin granting raw token-stream access via :meth:`fetch`.
+
+    Stateless trait relying on ``self._data``, ``self._cum``,
+    ``self._length`` maintained by :class:`Store`.  Stream mode is
+    active when the owning store has ``window_size > 0``; for stores
+    that can also serve record access (H5/JSONL/bin+offsets), the
+    ``fetch_record`` API from :class:`Recordable` is used instead.
+    """
+
+    def fetch(
+        self,
+        begin: int,
+        end: int,
+        keys: Union[str, List[str]],
+    ):
+        return _stream_fetch(self, begin, end, keys)
+
+
+def _stream_fetch(self, begin: int, end: int, keys: Union[str, List[str]]):
+    if not getattr(self, "_data", None):
+        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 _fetch_stream_key(self, keys, begin, end)
+    return {k: _fetch_stream_key(self, k, begin, end) for k in keys}
+
+
+def _fetch_stream_key(self, key: str, begin: int, end: int) -> Tensor:
+    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)
+
+
+class Recordable:
+    """Mixin granting raw record access via :meth:`fetch_record`.
+
+    Stateless trait relying on ``self._data``, ``self._offsets``,
+    ``self._num_records`` maintained by :class:`Store`.
+    """
+
+    def fetch_record(
+        self,
+        index: int,
+        keys: Union[str, List[str]],
+    ):
+        return _record_fetch(self, index, keys)
+
+
+def _record_fetch(self, index: int, keys: Union[str, List[str]]):
+    if not getattr(self, "_data", None) and self._num_records == 0:
+        raise RuntimeError("Store not loaded")
+    if not 0 <= index < self._num_records:
+        raise ValueError(
+            f"Record index out of bounds: {index}, num_records={self._num_records}"
+        )
+    if isinstance(keys, str):
+        return _fetch_record_key(self, keys, index)
+    return {k: _fetch_record_key(self, k, index) for k in keys}
+
+
+def _fetch_record_key(self, key: str, index: int):
+    offsets = self._offsets.get(key)
+    if offsets:
+        start = offsets[index]
+        end = (
+            offsets[index + 1]
+            if index + 1 < len(offsets)
+            else self._data[key][0].shape[0]
+        )
+        return self._data[key][0][start:end]
+    return self._data[key][index]
+
+
+class StoreFactory(BaseFactory["Store"]):
+    """Factory for creating Store instances by type name."""
+
+
+@StoreFactory.register("h5")
+class H5Store(Store, Streamable, Recordable):
+    """HDF5-based storage backend (pre-tokenized data).
+
+    Each key is stored as a group of per-record datasets (``data_0``,
+    ``data_1``, …).  Supports both access modes:
+
+    - **Stream**: ``fetch(begin, end, key)`` and ``store[i]`` slice
+      across concatenated records via ``_cum`` — used by SEQ/SFT.
+    - **Record**: ``fetch_record(i, key)`` and ``store[i]`` (when
+      ``window_size == 0``) index ``_data[key]`` directly — used by
+      DPO/GRPO.
+    """
+
+    segments_are_records = True
+
+    def __init__(
+        self,
+        window_size: int = 0,
+        stride: Optional[int] = None,
+    ):
+        super().__init__(window_size=window_size, stride=stride)
+
+    def load(self, path: str, **kwargs):
+        self._normalize(load_h5(path))
+
+
+@StoreFactory.register("bin")
+class MmapStore(Store, Streamable, Recordable):
+    """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.
+
+    Supports both access modes:
+
+    - **Stream**: always available via :meth:`fetch`.
+    - **Record** (``fetch_record(i, key)``): only when ``meta.json``
+      contains per-record ``offsets`` (written via
+      ``save_bin(..., record_keys=...)``).  Legacy bin files without
+      offsets have ``num_records == 0`` and ``len(store)`` reflects the
+      windowed sample count when ``window_size > 0``.
+
+    ``segments_are_records`` is ``False`` here (bin segments are
+    contiguous streams, not per-record) — record access is driven
+    purely by ``_offsets``.
+    """
+
+    segments_are_records = False
+
+    def __init__(
+        self,
+        window_size: int = 0,
+        stride: Optional[int] = None,
+    ):
+        super().__init__(window_size=window_size, stride=stride)
+        self._mmap_refs: List[Tensor] = []
+
+    def load(self, path: str, **kwargs):
+        self._mmap_refs = []
+        root = Path(path)
+        all_raw: Dict[str, List[Tensor]] = {}
+        all_offsets: Dict[str, List[int]] = {}
+        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))
+            off = load_bin_offsets(str(meta_path.parent))
+            for key, tensors in raw.items():
+                if key not in all_raw:
+                    all_raw[key] = []
+                all_raw[key].extend(tensors)
+            for key, o in off.items():
+                if key not in all_offsets:
+                    all_offsets[key] = []
+                all_offsets[key].extend(o)
+        if not meta_paths:
+            raise FileNotFoundError(f"No meta.json found under {path}")
+        self._normalize(all_raw, offsets=all_offsets or None)
+        for tensors in self._data.values():
+            self._mmap_refs.extend(tensors)
+
+
+class JsonlSource:
+    """Read raw JSON records from a ``.jsonl`` file or directory.
+
+    A thin reader used by :class:`JsonlStore` in processor mode — holds
+    no tokenizer, performs no tokenisation, just yields dicts.
+    """
+
+    def __init__(self, path: str):
+        self.path = Path(path)
+        self._records: Optional[List[dict]] = None
+
+    def load(self) -> List[dict]:
+        if self._records is None:
+            self._records = self._read(self.path)
+        return self._records
+
+    @staticmethod
+    def _read(root: Path) -> List[dict]:
+        if root.is_file():
+            return JsonlSource._read_file(root)
+        return JsonlSource._read_dir(root)
+
+    @staticmethod
+    def _read_file(path: Path) -> List[dict]:
+        records: List[dict] = []
+        with open(path, "r", encoding="utf-8") as f:
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                try:
+                    records.append(json.loads(line))
+                except json.JSONDecodeError:
+                    logger.warning("Failed to parse JSON line in %s, skipping", path)
+        return records
+
+    @staticmethod
+    def _read_dir(root: Path) -> List[dict]:
+        records: List[dict] = []
+        for jsonl_path in sorted(root.glob("*.jsonl")):
+            records.extend(JsonlSource._read_file(jsonl_path))
+        return records
+
+
+@StoreFactory.register("jsonl")
+class JsonlStore(Store, Streamable, Recordable):
+    """JSONL reader with eager/lazy tokenisation modes.
+
+    A JSONL dataset is a ``.jsonl`` file or a directory of ``*.jsonl``
+    files plus (optionally) a ``dataset_config.json`` describing the
+    tokenization pipeline.
+
+    Three ways to supply an eager transform (first match wins):
+
+    - **Explicit** (``transform=``): caller-built
+      :class:`TokenizeTransform` applied eagerly.
+    - **Config file**: ``dataset_config.json`` alongside the ``*.jsonl``
+      files — loaded via :meth:`TokenizeTransform.from_config_file`.
+    - **Default messages** (``tokenizer_path=`` given, no config file):
+      a built-in chatml config that tokenises the ``messages`` field,
+      masking every role except ``assistant`` (loss on assistant only).
+      Lets SFT/SEQ train straight from a chat-style JSONL directory
+      without a hand-written config.
+
+    Two tokenisation modes, selected at :meth:`load` time:
+
+    - **Eager** (default): applies the transform to every record at load
+      time and registers per-key tensors via ``_normalize``.  Both
+      ``fetch`` (stream) and ``fetch_record`` (record) work.
+    - **Lazy** (``processor=fn`` passed): keeps raw records and defers
+      tokenisation to ``fetch_record``.  Only record access works —
+      ``len(store)`` returns ``num_records``; stream primitives raise.
+    """
+
+    CONFIG_NAME = "dataset_config.json"
+    segments_are_records = True
+
+    _DEFAULT_MESSAGES_CONFIG = {
+        "version": 1,
+        "input": {
+            "sections": [{"field": "messages", "action": "$role", "template": True}]
+        },
+        "mask": {"system": "mask", "user": "mask", "assistant": "train"},
+        "mask_default": "mask",
+        "output": {"position_ids_mode": "doc_reset"},
+    }
+
+    def __init__(
+        self,
+        window_size: int = 0,
+        stride: Optional[int] = None,
+    ):
+        super().__init__(window_size=window_size, stride=stride)
+        self._source: Optional[JsonlSource] = None
+        self._processor: Optional[Callable[[dict], Dict[str, Tensor]]] = None
+        self._keys_cache: Optional[List[str]] = None
+
+    def load(self, path: str, transform=None, processor=None, **kwargs):
+        self._source = JsonlSource(path)
+        records = self._source.load()
+
+        if processor is not None:
+            self._processor = processor
+            self._num_records = len(records)
+            return
+
+        if transform is None:
+            root = Path(path)
+            config_path = root / self.CONFIG_NAME if root.is_dir() else None
+            if config_path is not None and config_path.exists():
+                transform = TokenizeTransform.from_config_file(str(config_path))
+            else:
+                tokenizer_path = kwargs.get("tokenizer_path")
+                if not tokenizer_path:
+                    raise FileNotFoundError(
+                        f"JSONL dataset config not found. Expected "
+                        f"{self.CONFIG_NAME} alongside *.jsonl files, pass an "
+                        f"explicit transform, pass processor= for lazy "
+                        f"on-the-fly tokenisation, or pass tokenizer_path= to "
+                        f"use the built-in messages config."
+                    )
+                config = PipelineConfig.from_dict(self._DEFAULT_MESSAGES_CONFIG)
+                transform = TokenizeTransform(config, tokenizer_path)
+
+        transformed = transform.apply(records)
+        self._normalize(transformed)
+
+    @property
+    def keys(self) -> List[str]:
+        if self._processor is not None:
+            if self._keys_cache is None and self._num_records > 0:
+                sample = self._processor(self._source.load()[0])
+                self._keys_cache = list(sample.keys())
+            return self._keys_cache or []
+        return list(self._data.keys())
+
+    def fetch_record(self, index: int, keys: Union[str, List[str]]):
+        if self._processor is not None:
+            if not 0 <= index < self._num_records:
+                raise ValueError(
+                    f"Record index out of bounds: {index}, "
+                    f"num_records={self._num_records}"
+                )
+            record = self._source.load()[index]
+            data = self._processor(record)
+            if isinstance(keys, str):
+                return data[keys]
+            return {k: data[k] for k in keys}
+        return _record_fetch(self, index, keys)
+
+    def fetch(self, begin: int, end: int, keys: Union[str, List[str]]):
+        if self._processor is not None:
+            raise RuntimeError(
+                "JsonlStore in lazy (processor) mode does not support "
+                "stream fetch(); use fetch_record() instead."
+            )
+        return _stream_fetch(self, begin, end, keys)
+
+    def __getitem__(self, index: int) -> Dict[str, Tensor]:
+        if self._processor is not None:
+            return self.fetch_record(index, self._record_keys())
+        return super().__getitem__(index)

astrai/extension/__init__.py

@@ -0,0 +1,30 @@
+"""CUDA attention kernel wrappers with torch fallback.
+
+Public API:
+    - ``attn_decode`` — single-query decode attention
+    - ``attn_prefill`` — multi-query prefill attention
+    - ``attn_paged_decode`` — paged decode attention (direct page-table access)
+
+Interface (shared by all wrappers):
+    causal_offset: -1 = non-causal; >=0 = absolute position of first Q token
+    mask:       2D [batch, kv_len] or 3D [batch, q_len, kv_len] (bool, True = keep)
+    scale:      0.0 = auto (1/sqrt(head_dim)); >0 = explicit
+    layout:     "bhld" (default) or "blhd"
+
+Causal and mask can coexist — both are applied simultaneously.
+
+Each wrapper dispatches to its compiled CUDA kernel (``astrai.extension.attn_*``)
+when available, otherwise falls back to ``torch.nn.functional.scaled_dot_product_attention``.
+"""
+
+from astrai.extension.loader import KERNEL_NAMES, is_available
+from astrai.extension.ops import attention, attn_decode, attn_paged_decode, attn_prefill
+
+__all__ = [
+    "attn_decode",
+    "attn_paged_decode",
+    "attn_prefill",
+    "attention",
+    "is_available",
+    "KERNEL_NAMES",
+]

astrai/extension/loader.py

@@ -0,0 +1,36 @@
+"""Dynamic discovery and loading of compiled CUDA kernel modules.
+
+Each kernel is registered in ``csrc/build.py`` and built into a ``.so`` placed
+in this package directory. On import we try to load each one; kernels that
+failed to build (or are running on a CPU-only machine) are marked unavailable
+so the wrapper functions can fall back to ``torch`` SDPA.
+"""
+
+import importlib
+import logging
+
+logger = logging.getLogger(__name__)
+
+KERNEL_NAMES = ["attn_decode", "attn_prefill", "attn_paged_decode"]
+
+_available: dict[str, bool] = {}
+_modules: dict[str, object] = {}
+
+for _name in KERNEL_NAMES:
+    try:
+        _mod = importlib.import_module(f".{_name}", package=__package__)
+        _available[_name] = True
+        _modules[_name] = _mod
+    except ImportError:
+        _available[_name] = False
+        _modules[_name] = None
+
+
+def is_available(name: str) -> bool:
+    """Return ``True`` if the compiled kernel ``name`` was loaded."""
+    return _available.get(name, False)
+
+
+def get_module(name: str) -> object:
+    """Return the loaded kernel module for ``name``, or ``None`` if unavailable."""
+    return _modules.get(name)

astrai/extension/ops.py

@@ -0,0 +1,298 @@
+"""GQA attention wrapper functions — one entry point per compiled kernel.
+
+Each wrapper dispatches to its CUDA kernel (loaded in ``loader.py``) when
+available, otherwise falls back to ``torch`` SDPA.
+
+Interface (all functions):
+    causal_offset: -1 = non-causal; >=0 = absolute position of first Q token
+    mask:       2D [batch, kv_len] or 3D [batch, q_len, kv_len] (bool)
+    scale:      0.0 = auto (1/sqrt(head_dim)); >0 = explicit
+    layout:     "bhld" (default) or "blhd"
+
+Add new kernel wrappers here; split into per-variant files only if this file
+grows large.
+"""
+
+import math
+
+import torch
+import torch.nn.functional as F
+
+from astrai.extension.loader import _available, _modules
+
+_LAYOUT_CODES: dict[str, int] = {"bhld": 0, "blhd": 1}
+
+
+def _parse_layout(layout: str | int) -> int:
+    if isinstance(layout, int):
+        return layout
+    code = _LAYOUT_CODES.get(layout.lower())
+    if code is None:
+        raise ValueError(
+            f"unknown layout '{layout}', expected one of {list(_LAYOUT_CODES)}"
+        )
+    return code
+
+
+def _to_bhld(t: torch.Tensor, layout: int) -> torch.Tensor:
+    """Normalize to b h l d view. Zero-copy transpose if layout==1 (b l h d)."""
+    if layout == 1:
+        return t.transpose(1, 2)
+    return t
+
+
+def _expand_kv_heads(
+    k: torch.Tensor, v: torch.Tensor, q_head: int
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Expand K/V heads to match Q heads for GQA fallback."""
+    kv_head = k.size(1)
+    if kv_head == q_head:
+        return k, v
+    group = q_head // kv_head
+    k = k.repeat_interleave(group, dim=1)
+    v = v.repeat_interleave(group, dim=1)
+    return k, v
+
+
+def _build_attn_mask(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    mask: torch.Tensor | None,
+    causal_offset: int,
+    scale: float,
+) -> tuple[torch.Tensor | None, float]:
+    """Build SDPA-compatible attn_mask + resolved scale.
+
+    q and k must already be in b h l d layout.
+    Causal and mask can coexist: causal sets -inf above the diagonal, mask
+    sets -inf for padded positions. Both are OR'd into a single bool mask.
+    """
+    q_len = q.size(2)
+    kv_len = k.size(2)
+    head_dim = q.size(3)
+    resolved_scale = scale if scale and scale > 0 else 1.0 / math.sqrt(head_dim)
+
+    attn_mask = None
+
+    if mask is not None:
+        if mask.dim() == 2:
+            # [batch, kv_len] → [batch, 1, 1, kv_len]
+            attn_mask = mask[:, None, None, :]
+        elif mask.dim() == 3:
+            # [batch, q_len, kv_len] → [batch, 1, q_len, kv_len]
+            attn_mask = mask[:, None, :, :]
+        else:
+            raise ValueError(f"mask must be 2D or 3D, got {mask.dim()}D")
+
+    if causal_offset >= 0:
+        batch = q.size(0)
+        # q row i attends to kv cols 0..(causal_offset + i)
+        q_idx = torch.arange(q_len, device=q.device).unsqueeze(1)  # [q_len, 1]
+        kv_idx = torch.arange(kv_len, device=q.device).unsqueeze(0)  # [1, kv_len]
+        causal_bool = kv_idx > (causal_offset + q_idx)  # True = masked out
+        causal_mask = causal_bool.unsqueeze(0).expand(
+            batch, -1, -1
+        )  # [batch, q_len, kv_len]
+        causal_mask = causal_mask[:, None, :, :]  # [batch, 1, q_len, kv_len]
+
+        if attn_mask is not None:
+            attn_mask = attn_mask | causal_mask
+        else:
+            attn_mask = causal_mask
+
+    return attn_mask, resolved_scale
+
+
+def _torch_fallback(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    mask: torch.Tensor | None,
+    causal_offset: int,
+    scale: float,
+    q_layout: int,
+    kv_layout: int | None = None,
+) -> torch.Tensor:
+    """Reference attention via ``scaled_dot_product_attention``.
+
+    q_layout / kv_layout: 0 = b h l d, 1 = b l h d.
+    If kv_layout is None, uses q_layout (Q and K/V share the same layout).
+    """
+    if kv_layout is None:
+        kv_layout = q_layout
+    q = _to_bhld(q, q_layout)
+    k = _to_bhld(k, kv_layout)
+    v = _to_bhld(v, kv_layout)
+    k, v = _expand_kv_heads(k, v, q.size(1))
+    attn_mask, resolved_scale = _build_attn_mask(q, k, mask, causal_offset, scale)
+    out = F.scaled_dot_product_attention(
+        q, k, v, attn_mask=attn_mask, is_causal=False, scale=resolved_scale
+    )
+    # Restore Q's original layout
+    if q_layout == 1:
+        out = out.transpose(1, 2)
+    return out
+
+
+def _gather_kv_from_pages(
+    page_table: torch.Tensor,
+    k_cache: torch.Tensor,
+    v_cache: torch.Tensor,
+    page_size: int,
+    kv_len: int,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Gather contiguous K/V from paged cache for torch SDPA fallback.
+
+    Shapes:
+        page_table : [batch, max_pages] (int64)
+        k_cache    : [n_pages, page_size, n_kv_heads, head_dim]
+        v_cache    : same as k_cache
+    Returns:
+        k, v : [batch, kv_len, n_kv_heads, head_dim]  (b l h d)
+    """
+    batch, max_pages = page_table.shape
+    _, ps, n_kv_heads, head_dim = k_cache.shape
+    if ps != page_size:
+        raise ValueError(f"k_cache page_size mismatch: {ps} vs {page_size}")
+
+    # Vectorized gather: build physical page + offset indices, then advanced-index
+    positions = torch.arange(kv_len, device=page_table.device)
+    logical_pages = positions // page_size  # [kv_len]
+    page_offsets = positions % page_size  # [kv_len]
+
+    phys_pages = page_table[:, logical_pages]  # [batch, kv_len]
+    # k_cache[phys_pages, page_offsets] → [batch, kv_len, n_kv_heads, head_dim] (b l h d)
+    k = k_cache[phys_pages, page_offsets]
+    v = v_cache[phys_pages, page_offsets]
+    return k, v
+
+
+def attn_decode(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    mask: torch.Tensor | None = None,
+    causal_offset: int = -1,
+    scale: float = 0.0,
+    layout: str = "bhld",
+) -> torch.Tensor:
+    li = _parse_layout(layout)
+    if _available["attn_decode"]:
+        return _modules["attn_decode"].attn_decode(
+            q,
+            k,
+            v,
+            mask=mask,
+            causal_offset=causal_offset,
+            scale=scale,
+            layout=li,
+        )
+    return _torch_fallback(q, k, v, mask, causal_offset, scale, q_layout=li)
+
+
+def attn_prefill(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    mask: torch.Tensor | None = None,
+    causal_offset: int = -1,
+    scale: float = 0.0,
+    layout: str = "bhld",
+) -> torch.Tensor:
+    li = _parse_layout(layout)
+    if _available["attn_prefill"]:
+        return _modules["attn_prefill"].attn_prefill(
+            q,
+            k,
+            v,
+            mask=mask,
+            causal_offset=causal_offset,
+            scale=scale,
+            layout=li,
+        )
+    return _torch_fallback(q, k, v, mask, causal_offset, scale, q_layout=li)
+
+
+def attn_paged_decode(
+    q: torch.Tensor,
+    page_table: torch.Tensor,
+    k_cache: torch.Tensor,
+    v_cache: torch.Tensor,
+    page_size: int,
+    kv_len: int,
+    mask: torch.Tensor | None = None,
+    causal_offset: int = -1,
+    scale: float = 0.0,
+    layout: str = "bhld",
+) -> torch.Tensor:
+    li = _parse_layout(layout)
+    if _available["attn_paged_decode"]:
+        return _modules["attn_paged_decode"].attn_paged_decode(
+            q,
+            page_table,
+            k_cache,
+            v_cache,
+            page_size,
+            kv_len,
+            mask=mask,
+            causal_offset=causal_offset,
+            scale=scale,
+            layout=li,
+        )
+    # Gathered K/V are always b l h d
+    k, v = _gather_kv_from_pages(page_table, k_cache, v_cache, page_size, kv_len)
+    return _torch_fallback(
+        q, k, v, mask, causal_offset, scale, q_layout=li, kv_layout=1
+    )
+
+
+def attention(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    mask: torch.Tensor | None = None,
+    causal_offset: int = -1,
+    scale: float = 0.0,
+    layout: str = "bhld",
+) -> torch.Tensor:
+    """Dispatch to decode or prefill attention based on the query length.
+
+    A query length of one is the decode case; longer queries use prefill.
+    The paged-cache decode path cannot be selected here because its page-table
+    arguments are not part of this interface.
+    """
+    li = _parse_layout(layout)
+
+    if q.ndim not in (2, 3, 4) or k.ndim != q.ndim or v.ndim != q.ndim:
+        raise ValueError(
+            "q, k, and v must all have the same rank in {2, 3, 4}, "
+            f"got {q.ndim}D, {k.ndim}D, {v.ndim}D"
+        )
+    if k.shape != v.shape:
+        raise ValueError(
+            f"k and v must have the same shape, got {k.shape} and {v.shape}"
+        )
+
+    original_ndim = q.ndim
+    if original_ndim == 2:
+        # [L, D] -> [1, 1, L, D] or [1, L, 1, D]
+        q = q.unsqueeze(0).unsqueeze(1 if li == 0 else 2)
+        k = k.unsqueeze(0).unsqueeze(1 if li == 0 else 2)
+        v = v.unsqueeze(0).unsqueeze(1 if li == 0 else 2)
+    elif original_ndim == 3:
+        # [B, L, D] -> single-head 4D input.
+        q = q.unsqueeze(1 if li == 0 else 2)
+        k = k.unsqueeze(1 if li == 0 else 2)
+        v = v.unsqueeze(1 if li == 0 else 2)
+
+    q_len = q.size(2 if li == 0 else 1)
+    if q_len == 1:
+        out = attn_decode(q, k, v, mask, causal_offset, scale, layout)
+    else:
+        out = attn_prefill(q, k, v, mask, causal_offset, scale, layout)
+
+    if original_ndim == 2:
+        return out.squeeze(0).squeeze(0 if li == 0 else 1)
+    if original_ndim == 3:
+        return out.squeeze(1 if li == 0 else 2)
+    return out

astrai/factory.py

@@ -0,0 +1,147 @@
+"""Base factory with decorator-based registration and kwarg-filtered instantiation."""
+
+import inspect
+import sys
+from abc import ABC
+from typing import (
+    Callable,
+    Dict,
+    ForwardRef,
+    Generic,
+    List,
+    Optional,
+    Type,
+    TypeVar,
+    Union,
+)
+from typing import get_args as _get_args
+from typing import get_origin as _get_origin
+
+T = TypeVar("T")
+
+
+def _resolve_type(
+    arg: Union[Type, str, ForwardRef], factory_cls: type
+) -> Optional[Type]:
+    """Resolve a generic type-arg (str forward-ref, ForwardRef, or class)."""
+    if not isinstance(arg, (str, ForwardRef)):
+        return arg
+
+    name = arg if isinstance(arg, str) else arg.__forward_arg__
+    if name == factory_cls.__name__:
+        return factory_cls
+
+    mod = sys.modules.get(factory_cls.__module__)
+    if mod is None:
+        return None
+    ns = vars(mod)
+
+    if isinstance(arg, ForwardRef):
+        return arg._evaluate(ns, None, recursive_guard=frozenset())
+
+    return ns.get(name)
+
+
+class BaseFactory(ABC, Generic[T]):
+    """Generic factory with decorator-based component registration.
+
+        class MyFactory(BaseFactory[MyBase]):
+            pass
+
+        @MyFactory.register("custom")
+        class CustomComponent(MyBase):
+            ...
+
+        obj = MyFactory.create("custom", *args, **kwargs)
+
+    ``create()`` filters kwargs to match the component's ``__init__``
+    signature so components don't need ``**kwargs`` just to absorb
+    unrelated parameters.
+    """
+
+    _entries: Dict[str, Type[T]]
+
+    def __init_subclass__(cls, **kwargs):
+        super().__init_subclass__(**kwargs)
+        for orig_base in getattr(cls, "__orig_bases__", ()):
+            if _get_origin(orig_base) is BaseFactory:
+                (arg,) = _get_args(orig_base)
+                cls._entries = {}
+                try:
+                    cls._component_base = _resolve_type(arg, cls)
+                except Exception:
+                    cls._component_base = None
+                return
+
+    @classmethod
+    def register(cls, name: str) -> Callable[[Type[T]], Type[T]]:
+        """Decorator to register a component class.
+
+        Validates that the decorated class inherits from the generic
+        type parameter ``T`` declared on the factory.
+        """
+
+        def decorator(component_cls: Type[T]) -> Type[T]:
+            cls._validate_component(component_cls)
+            if name in cls._entries:
+                raise ValueError(f"Component '{name}' is already registered")
+            cls._entries[name] = component_cls
+            return component_cls
+
+        return decorator
+
+    @classmethod
+    def create(cls, name: str, *args, **kwargs) -> T:
+        """Create a component instance by name, filtering kwargs to match
+        the component's ``__init__`` signature.
+        """
+        entry = cls._entries.get(name)
+        if entry is None:
+            raise ValueError(
+                f"Unknown component: '{name}'. Supported types: {sorted(cls._entries)}"
+            )
+        component_cls = entry
+        sig = inspect.signature(component_cls.__init__)
+        has_var_kwargs = any(
+            p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values()
+        )
+        if not has_var_kwargs:
+            valid = {
+                p.name
+                for p in sig.parameters.values()
+                if p.name != "self" and p.kind != inspect.Parameter.VAR_KEYWORD
+            }
+            kwargs = {k: v for k, v in kwargs.items() if k in valid}
+        return component_cls(*args, **kwargs)
+
+    @classmethod
+    def _validate_component(cls, component_cls: Type[T]):
+        """Validate the decorated class inherits from the factory's base type.
+
+        Override for custom validation beyond ``issubclass``.
+        """
+        base = cls._component_base
+        if base is not None and not issubclass(component_cls, base):
+            raise TypeError(
+                f"{component_cls.__name__} must inherit from {base.__name__}"
+            )
+
+    @classmethod
+    def get_component_class(cls, name: str) -> Type[T]:
+        """Get the registered component class without instantiating it."""
+        entry = cls._entries.get(name)
+        if entry is None:
+            raise ValueError(
+                f"Unknown component: '{name}'. Supported types: {sorted(cls._entries)}"
+            )
+        return entry
+
+    @classmethod
+    def list_registered(cls) -> List[str]:
+        """List all registered component names."""
+        return sorted(cls._entries)
+
+    @classmethod
+    def is_registered(cls, name: str) -> bool:
+        """Check if a component name is registered."""
+        return name in cls._entries

astrai/inference/__init__.py

@@ -0,0 +1,105 @@
+"""Inference module for continuous batching.
+
+Layers:
+  - core/:        Core inference loop (cache, executor, scheduler, task)
+  - api/:         HTTP orchestration (ProtocolHandler, server)
+  - protocols/:   Response builders (OpenAI, Anthropic)
+  - transport/:   SSE transport utilities
+  - engine.py:    Facade (InferenceEngine), Value Object (GenerationRequest)
+  - sample.py:    Strategy pattern (TemperatureStrategy, TopKStrategy, TopPStrategy, FrequencyPenaltyStrategy)
+"""
+
+from astrai.inference.api import (
+    AnthropicMessage,
+    BaseToolParser,
+    ChatCompletionRequest,
+    ChatMessage,
+    FunctionDef,
+    GenContext,
+    MessagesRequest,
+    ProtocolHandler,
+    SimpleJsonToolParser,
+    StopChecker,
+    ToolDef,
+    ToolParserFactory,
+    get_app,
+    run_server,
+)
+from astrai.inference.api.anthropic import AnthropicResponseBuilder
+from astrai.inference.api.openai import OpenAIResponseBuilder
+from astrai.inference.core import (
+    STOP,
+    Allocator,
+    CacheView,
+    ContiguousCache,
+    ContiguousCacheView,
+    Executor,
+    InferenceScheduler,
+    KVCache,
+    PageCache,
+    PageCacheView,
+    PagePool,
+    PrefixCache,
+    Storage,
+    Task,
+    TaskManager,
+    TaskStatus,
+    TaskTable,
+    page_hash,
+)
+from astrai.inference.engine import GenerationRequest, InferenceEngine
+from astrai.inference.sample import (
+    BaseSamplingStrategy,
+    FrequencyPenaltyStrategy,
+    SamplingPipeline,
+    TemperatureStrategy,
+    TopKStrategy,
+    TopPStrategy,
+    sample,
+)
+
+__all__ = [
+    "InferenceEngine",
+    "GenerationRequest",
+    "InferenceScheduler",
+    "Executor",
+    "STOP",
+    "Task",
+    "TaskManager",
+    "TaskStatus",
+    "Allocator",
+    "CacheView",
+    "KVCache",
+    "ContiguousCache",
+    "ContiguousCacheView",
+    "PageCache",
+    "PageCacheView",
+    "PagePool",
+    "PrefixCache",
+    "Storage",
+    "TaskTable",
+    "page_hash",
+    "sample",
+    "BaseSamplingStrategy",
+    "TemperatureStrategy",
+    "TopKStrategy",
+    "TopPStrategy",
+    "FrequencyPenaltyStrategy",
+    "SamplingPipeline",
+    "ProtocolHandler",
+    "StopChecker",
+    "GenContext",
+    "BaseToolParser",
+    "SimpleJsonToolParser",
+    "ToolParserFactory",
+    "OpenAIResponseBuilder",
+    "AnthropicResponseBuilder",
+    "ChatMessage",
+    "ChatCompletionRequest",
+    "FunctionDef",
+    "ToolDef",
+    "AnthropicMessage",
+    "MessagesRequest",
+    "get_app",
+    "run_server",
+]

astrai/inference/api/__init__.py

@@ -0,0 +1,39 @@
+"""Inference API: protocol handler, stop checker, tool parsers, 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,
+    FunctionDef,
+    MessagesRequest,
+    ToolDef,
+    get_app,
+    run_server,
+)
+from astrai.inference.api.tool_parser import (
+    BaseToolParser,
+    SimpleJsonToolParser,
+    ToolParserFactory,
+)
+
+__all__ = [
+    "ProtocolHandler",
+    "StopChecker",
+    "GenContext",
+    "BaseToolParser",
+    "SimpleJsonToolParser",
+    "ToolParserFactory",
+    "AnthropicMessage",
+    "ChatCompletionRequest",
+    "ChatMessage",
+    "FunctionDef",
+    "ToolDef",
+    "MessagesRequest",
+    "get_app",
+    "run_server",
+]

astrai/inference/api/anthropic.py

@@ -0,0 +1,142 @@
+"""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,
+        )
+        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, **kwargs) -> List[str]:
+        return [
+            sse_event(
+                {
+                    "type": "content_block_delta",
+                    "index": 0,
+                    "delta": {"type": "text_delta", "text": token},
+                },
+                event="content_block_delta",
+            )
+        ]
+
+    def format_stream_end(self, ctx: GenContext, stop: StopInfo) -> List[str]:
+        events: List[str] = []
+        if stop.matched:
+            trimmed = stop.body[: stop.body.rfind(stop.matched)]
+            unyielded = trimmed[len(stop.yielded) :]
+            if unyielded:
+                events.append(
+                    sse_event(
+                        {
+                            "type": "content_block_delta",
+                            "index": 0,
+                            "delta": {"type": "text_delta", "text": unyielded},
+                        },
+                        event="content_block_delta",
+                    )
+                )
+        events.append(
+            sse_event(
+                {"type": "content_block_stop", "index": 0},
+                event="content_block_stop",
+            )
+        )
+        events.append(
+            sse_event(
+                {
+                    "type": "message_delta",
+                    "delta": {
+                        "stop_reason": "stop_sequence" if stop.matched else "end_turn",
+                        "stop_sequence": stop.matched,
+                    },
+                    "usage": {"output_tokens": ctx.completion_tokens},
+                },
+                event="message_delta",
+            )
+        )
+        events.append(sse_event({"type": "message_stop"}, event="message_stop"))
+        return events
+
+    def format_response(
+        self, ctx: GenContext, content: str, stop: StopInfo
+    ) -> Dict[str, Any]:
+        if stop.matched:
+            content = content[: content.rfind(stop.matched)]
+        return {
+            "id": ctx.resp_id,
+            "type": "message",
+            "role": "assistant",
+            "model": ctx.model,
+            "content": [{"type": "text", "text": content}],
+            "stop_reason": "stop_sequence" if stop.matched else "end_turn",
+            "stop_sequence": stop.matched,
+            "usage": {
+                "input_tokens": ctx.prompt_tokens,
+                "output_tokens": ctx.completion_tokens,
+            },
+        }

astrai/inference/api/openai.py

@@ -0,0 +1,277 @@
+"""OpenAI chat completion response builder."""
+
+import logging
+import time
+import uuid
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+from pydantic import BaseModel
+
+from astrai.inference.api.protocol import (
+    GenContext,
+    ResponseBuilder,
+    StopInfo,
+    sse_event,
+)
+from astrai.inference.api.tool_parser import BaseToolParser, ToolParserFactory
+from astrai.inference.engine import InferenceEngine
+
+logger = logging.getLogger(__name__)
+
+_UNSUPPORTED_PARAMS = (
+    "n",
+    "presence_penalty",
+    "logit_bias",
+    "user",
+)
+
+
+def _resolve_tool_choice(
+    request: BaseModel,
+) -> Union[str, Dict[str, Any]]:
+    tc = getattr(request, "tool_choice", None)
+    if tc is None:
+        return "auto"
+    if isinstance(tc, str):
+        return tc
+    if isinstance(tc, dict):
+        return tc
+    return "auto"
+
+
+def _resolve_tools(request: BaseModel) -> Optional[List[Dict[str, Any]]]:
+    raw = getattr(request, "tools", None)
+    if not raw:
+        return None
+    if isinstance(raw, list):
+        return [t.model_dump() if hasattr(t, "model_dump") else t for t in raw]
+    return None
+
+
+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]
+        tools = _resolve_tools(request)
+        prompt = engine.tokenizer.apply_chat_template(
+            messages, tokenize=False, tools=tools or []
+        )
+
+        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,
+                )
+
+        self._parser: Optional[BaseToolParser] = None
+        if tools:
+            tool_choice = _resolve_tool_choice(request)
+            self._parser = ToolParserFactory.create(
+                "simple_json", tools=tools, tool_choice=tool_choice
+            )
+        self._content_started = False
+
+        ctx = GenContext(
+            resp_id=self._resp_id,
+            created=int(time.time()),
+            model=self._model,
+        )
+        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, **kwargs) -> List[str]:
+        body = kwargs.get("body", "")
+        if self._parser is not None:
+            return self._format_tool_chunk(body, **kwargs)
+
+        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_tool_chunk(self, body: str, **kwargs) -> List[str]:
+        deltas = self._parser.feed(
+            body,
+            current_token_ids=kwargs.get("current_token_ids"),
+            delta_token_ids=kwargs.get("delta_token_ids"),
+        )
+        events: List[str] = []
+        for d in deltas:
+            if "content" in d:
+                if not self._content_started:
+                    events.append(self._role_chunk())
+                    self._content_started = True
+                events.append(
+                    sse_event(
+                        {
+                            "id": self._resp_id,
+                            "object": "chat.completion.chunk",
+                            "created": 0,
+                            "model": self._model,
+                            "choices": [
+                                {
+                                    "index": 0,
+                                    "delta": {"content": d["content"]},
+                                    "finish_reason": None,
+                                }
+                            ],
+                        }
+                    )
+                )
+            elif "tool_calls" in d:
+                if not self._content_started:
+                    events.append(self._role_chunk())
+                    self._content_started = True
+                events.append(
+                    sse_event(
+                        {
+                            "id": self._resp_id,
+                            "object": "chat.completion.chunk",
+                            "created": 0,
+                            "model": self._model,
+                            "choices": [
+                                {
+                                    "index": 0,
+                                    "delta": {"tool_calls": d["tool_calls"]},
+                                    "finish_reason": None,
+                                }
+                            ],
+                        }
+                    )
+                )
+        return events
+
+    def _role_chunk(self) -> str:
+        return sse_event(
+            {
+                "id": self._resp_id,
+                "object": "chat.completion.chunk",
+                "created": 0,
+                "model": self._model,
+                "choices": [
+                    {
+                        "index": 0,
+                        "delta": {"role": "assistant"},
+                        "finish_reason": None,
+                    }
+                ],
+            }
+        )
+
+    def format_stream_end(self, ctx: GenContext, stop: StopInfo) -> List[str]:
+        finish_reason = "stop"
+        if self._parser is not None and self._parser.has_tool_calls:
+            finish_reason = "tool_calls"
+        return [
+            sse_event(
+                {
+                    "id": self._resp_id,
+                    "object": "chat.completion.chunk",
+                    "created": ctx.created,
+                    "model": self._model,
+                    "choices": [
+                        {"index": 0, "delta": {}, "finish_reason": finish_reason}
+                    ],
+                }
+            ),
+            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]:
+        if self._parser is not None:
+            parsed = self._parser.parse_complete(content)
+            if parsed and parsed.get("tool_calls"):
+                return {
+                    "id": self._resp_id,
+                    "object": "chat.completion",
+                    "created": ctx.created,
+                    "model": self._model,
+                    "choices": [
+                        {
+                            "index": 0,
+                            "message": {
+                                "role": "assistant",
+                                "content": parsed.get("content"),
+                                "tool_calls": parsed["tool_calls"],
+                            },
+                            "finish_reason": "tool_calls",
+                        }
+                    ],
+                    "usage": {
+                        "prompt_tokens": ctx.prompt_tokens,
+                        "completion_tokens": ctx.completion_tokens,
+                        "total_tokens": ctx.prompt_tokens + ctx.completion_tokens,
+                    },
+                }
+
+        return {
+            "id": self._resp_id,
+            "object": "chat.completion",
+            "created": ctx.created,
+            "model": self._model,
+            "choices": [
+                {
+                    "index": 0,
+                    "message": {"role": "assistant", "content": content},
+                    "finish_reason": "stop",
+                }
+            ],
+            "usage": {
+                "prompt_tokens": ctx.prompt_tokens,
+                "completion_tokens": ctx.completion_tokens,
+                "total_tokens": ctx.prompt_tokens + ctx.completion_tokens,
+            },
+        }

astrai/inference/api/protocol.py

@@ -0,0 +1,200 @@
+"""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 = 0
+    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, **kwargs) -> List[str]:
+        """SSE events for a single generated token.
+
+        ``body`` (the full accumulated text so far) is always provided
+        as a keyword argument. Additional keyword arguments such as
+        ``current_token_ids`` and ``delta_token_ids`` may be included
+        for tool parsers that need token-level information.
+        Returns a list of SSE event strings (may be empty).
+        """
+
+    @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,
+            frequency_penalty=getattr(self.request, "frequency_penalty", 0.0),
+        )
+
+        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
+            token_ids: List[int] = []
+            async for token in agen:
+                body += token
+
+                new_ids = self.engine.tokenizer.encode(token)
+                token_ids.extend(new_ids)
+
+                matched = checker.check(body)
+                if matched:
+                    break
+
+                ctx.completion_tokens += 1
+                for event in self.builder.format_chunk(
+                    token,
+                    body=body,
+                    current_token_ids=token_ids,
+                    delta_token_ids=new_ids,
+                ):
+                    yield event
+                yielded += token
+
+            stop = StopInfo(matched=matched, body=body, yielded=yielded)
+            for event in self.builder.format_stream_end(ctx, stop):
+                yield event
+            yield sse_done()
+
+        return StreamingResponse(
+            event_stream(),
+            media_type="text/event-stream",
+            headers={"Cache-Control": "no-cache", "Connection": "keep-alive"},
+        )
+
+    async def _handle_non_stream(
+        self, agen: AsyncGenerator, ctx: GenContext, stop_sequences: List[str]
+    ) -> Dict[str, Any]:
+        checker = StopChecker(stop_sequences)
+        chunks: List[str] = []
+        body = ""
+        matched = None
+
+        async for token in agen:
+            chunks.append(token)
+            body += token
+
+            matched = checker.check(body)
+            if matched:
+                break
+
+            ctx.completion_tokens += 1
+
+        content = "".join(chunks)
+        stop = StopInfo(matched=matched, body=body)
+        return self.builder.format_response(ctx, content, stop)

astrai/inference/api/server.py

@@ -0,0 +1,202 @@
+"""
+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: Optional[str] = None
+    tool_calls: Optional[List[Dict[str, Any]]] = None
+    tool_call_id: Optional[str] = None
+
+
+class FunctionDef(BaseModel):
+    name: str
+    description: Optional[str] = None
+    parameters: Optional[Dict[str, Any]] = None
+
+
+class ToolDef(BaseModel):
+    type: str = "function"
+    function: FunctionDef
+
+
+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
+    tools: Optional[List[ToolDef]] = None
+    tool_choice: Optional[Union[str, Dict[str, Any]]] = "auto"
+
+
+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/api/tool_parser.py

@@ -0,0 +1,344 @@
+"""Tool call parsers for extracting structured tool calls from model output.
+
+Patterned after vLLM's ToolParser abstraction. Each parser knows how to
+detect and incrementally extract tool calls from raw generated text.
+
+Subclasses may optionally consume ``token_ids`` for token-level parsing
+(e.g. Harmony / VLM-style parsers).
+"""
+
+import json
+import re
+import uuid
+from abc import ABC, abstractmethod
+from typing import Dict, List, Optional
+
+from astrai.factory import BaseFactory
+
+
+class BaseToolParser(ABC):
+    """Abstract tool call parser — one instance per request.
+
+    Maintains streaming state internally so that each call to :meth:`feed`
+    can diff against previously emitted content.
+
+    Parameters
+    ----------
+    tools : list of dict, optional
+        Tool definitions from the request.
+    tool_choice : str
+        ``"auto"`` / ``"required"`` / ``"none"`` or a named tool choice
+        dict.
+    """
+
+    def __init__(self, tools: Optional[List[Dict]] = None, tool_choice: str = "auto"):
+        self.tools = tools or []
+        self.tool_choice = tool_choice
+
+    @abstractmethod
+    def feed(
+        self,
+        body: str,
+        current_token_ids: Optional[List[int]] = None,
+        delta_token_ids: Optional[List[int]] = None,
+    ) -> List[Dict]:
+        """Feed the *full* accumulated text each step.
+
+        Returns a list of delta dicts to emit. Each delta is one of:
+
+        - ``{"content": "text"}``      — plain text delta
+        - ``{"tool_calls": [...]}``    — tool-call delta (OpenAI format)
+
+        Returns an empty list when nothing new should be emitted.
+
+        Parameters
+        ----------
+        body : str
+            The complete accumulated generated text so far.
+        current_token_ids : list of int, optional
+            All token IDs decoded into *body* (cumulative).
+        delta_token_ids : list of int, optional
+            Only the token IDs for this chunk.
+        """
+
+    @abstractmethod
+    def parse_complete(self, body: str) -> Optional[Dict]:
+        """Parse the *complete* generated text after generation ends.
+
+        Returns ``None`` when no tool calls were found, otherwise a dict
+        with ``content`` (str or None) and ``tool_calls`` (list of dicts).
+        """
+
+    @property
+    @abstractmethod
+    def has_tool_calls(self) -> bool:
+        """True if the parser detected at least one tool call in the stream."""
+
+
+class ToolParserFactory(BaseFactory["BaseToolParser"]):
+    pass
+
+
+_TOOL_CALL_HEAD_RE = re.compile(r'\{\s*"name"\s*:')
+
+
+def _scan_json(text: str, start: int = 0):
+    """Scan for a complete JSON object starting at *start*.
+
+    Returns ``(end, complete)`` where *end* is one-past the closing
+    brace (or ``len(text)`` if unclosed), and *complete* is a bool.
+    """
+    depth = 0
+    in_string = False
+    escape = False
+    for i in range(start, len(text)):
+        c = text[i]
+        if escape:
+            escape = False
+            continue
+        if c == "\\":
+            escape = True
+            continue
+        if c == '"':
+            in_string = not in_string
+            continue
+        if in_string:
+            continue
+        if c == "{":
+            depth += 1
+        elif c == "}":
+            depth -= 1
+            if depth == 0:
+                return i + 1, True
+    return len(text), False
+
+
+def _parse_tool_call_json(json_str: str, complete: bool):
+    """Extract *name* and *arguments* from a tool-call JSON string.
+
+    Returns ``(name, args, valid)``.
+    """
+    if complete:
+        try:
+            obj = json.loads(json_str)
+        except json.JSONDecodeError:
+            return None, "", False
+        name = obj.get("name")
+        if not isinstance(name, str) or not name:
+            return None, "", False
+        args = obj.get("arguments")
+        if isinstance(args, dict):
+            if not args:
+                args = ""
+            else:
+                args = json.dumps(args, ensure_ascii=False)
+                args = args[1:-1].rstrip()
+        elif isinstance(args, list):
+            args = json.dumps(args, ensure_ascii=False) if args else ""
+        elif isinstance(args, str):
+            pass
+        else:
+            args = str(args) if args is not None else ""
+        return name, args, True
+
+    name_match = re.search(r'"name"\s*:\s*"([^"]*)"', json_str)
+    if not name_match:
+        return None, "", False
+    name = name_match.group(1)
+
+    args_match = re.search(r'"arguments"\s*:\s*(.*)', json_str, re.DOTALL)
+    if not args_match:
+        return name, "", True
+
+    raw = args_match.group(1).rstrip()
+    if raw.startswith("{"):
+        inner = raw[1:].rstrip()
+        if inner.endswith("}"):
+            inner = inner[:-1].rstrip()
+        raw = inner
+    return name, raw, True
+
+
+def _find_tool_calls(text: str, start_pos: int = 0):
+    """Find all complete ``{...}`` tool-call objects in *text*.
+
+    Returns a list of dicts with keys *start*, *end*, *name*, *args*,
+    *complete*.
+    """
+    results = []
+    pos = start_pos
+
+    while True:
+        brace = text.find("{", pos)
+        if brace == -1:
+            break
+
+        end, complete = _scan_json(text, brace)
+        if not complete:
+            break
+
+        json_str = text[brace:end]
+
+        name, args, valid = _parse_tool_call_json(json_str, complete=True)
+        if not valid or name is None:
+            pos = end
+            continue
+
+        results.append(
+            {
+                "start": brace,
+                "end": end,
+                "name": name,
+                "args": args,
+                "complete": True,
+            }
+        )
+        pos = end
+
+    return results
+
+
+def _find_partial_tool_call(text: str, start_pos: int = 0):
+    """Find one incomplete (still-generating) tool-call JSON object."""
+    brace = text.find("{", start_pos)
+    if brace == -1:
+        return None
+
+    json_str = text[brace:]
+    if '"name"' not in json_str:
+        return None
+
+    name, args, valid = _parse_tool_call_json(json_str, complete=False)
+    if not valid or name is None:
+        return None
+
+    return {
+        "start": brace,
+        "name": name,
+        "args": args,
+        "complete": False,
+    }
+
+
+@ToolParserFactory.register("simple_json")
+class SimpleJsonToolParser(BaseToolParser):
+    """Parser for models that output tool calls as plain JSON objects.
+
+    Detects ``{"name": "<func>", "arguments": {...}}`` anywhere in the
+    generated text.  Handles single and (non-overlapping) multiple tool
+    calls.  Text preceding the first tool call is emitted as plain
+    ``content`` deltas.
+    """
+
+    def __init__(self, tools=None, tool_choice="auto"):
+        super().__init__(tools, tool_choice)
+        self._emitted_content_len = 0
+        self._tc_state: List[Dict] = []
+        self._has_tool_calls = False
+
+    # -------------------------------------------------------------- feed
+
+    def feed(
+        self,
+        body: str,
+        current_token_ids: Optional[List[int]] = None,
+        delta_token_ids: Optional[List[int]] = None,
+    ) -> List[Dict]:
+        deltas: List[Dict] = []
+
+        completed = _find_tool_calls(body)
+
+        if not completed:
+            partial = _find_partial_tool_call(body)
+            if not partial:
+                return self._emit_plain_content(body, deltas)
+            all_tcs = [partial]
+        else:
+            all_tcs = completed
+            partial = _find_partial_tool_call(body, completed[-1]["end"])
+            if partial:
+                all_tcs = completed + [partial]
+
+        first_start = all_tcs[0]["start"]
+        if first_start > self._emitted_content_len:
+            content = body[self._emitted_content_len : first_start]
+            self._emitted_content_len = first_start
+            if content:
+                deltas.append({"content": content})
+
+        for i, tc in enumerate(all_tcs):
+            if i >= len(self._tc_state):
+                self._tc_state.append(
+                    {
+                        "id": f"call_{uuid.uuid4().hex[:12]}",
+                        "name_emitted": False,
+                        "args_emitted_len": 0,
+                    }
+                )
+                self._has_tool_calls = True
+            st = self._tc_state[i]
+
+            if not st["name_emitted"]:
+                st["name_emitted"] = True
+                deltas.append(
+                    {
+                        "tool_calls": [
+                            {
+                                "index": i,
+                                "id": st["id"],
+                                "type": "function",
+                                "function": {"name": tc["name"], "arguments": ""},
+                            }
+                        ]
+                    }
+                )
+
+            new_args = tc["args"]
+            if len(new_args) > st["args_emitted_len"]:
+                diff = new_args[st["args_emitted_len"] :]
+                st["args_emitted_len"] = len(new_args)
+                deltas.append(
+                    {
+                        "tool_calls": [
+                            {
+                                "index": i,
+                                "function": {"arguments": diff},
+                            }
+                        ]
+                    }
+                )
+
+        return deltas
+
+    def _emit_plain_content(self, body: str, deltas: List[Dict]) -> List[Dict]:
+        new_content = body[self._emitted_content_len :]
+        if new_content:
+            self._emitted_content_len = len(body)
+            deltas.append({"content": new_content})
+        return deltas
+
+    # -------------------------------------------------------- complete
+
+    def parse_complete(self, body: str) -> Optional[Dict]:
+        completed = _find_tool_calls(body)
+        if not completed:
+            return None
+
+        content = body[: completed[0]["start"]].strip() or None
+        tool_calls = []
+        for i, tc in enumerate(completed):
+            tool_calls.append(
+                {
+                    "id": f"call_{uuid.uuid4().hex[:12]}",
+                    "type": "function",
+                    "function": {
+                        "name": tc["name"],
+                        "arguments": tc["args"],
+                    },
+                }
+            )
+        return {"content": content, "tool_calls": tool_calls}
+
+    @property
+    def has_tool_calls(self) -> bool:
+        return self._has_tool_calls

astrai/inference/core/__init__.py

@@ -0,0 +1,40 @@
+"""Inference core: cache, executor, scheduler, task management."""
+
+from astrai.inference.core.cache import (
+    Allocator,
+    CacheView,
+    ContiguousCache,
+    ContiguousCacheView,
+    KVCache,
+    PageCache,
+    PageCacheView,
+    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",
+    "CacheView",
+    "KVCache",
+    "ContiguousCache",
+    "ContiguousCacheView",
+    "PageCache",
+    "PageCacheView",
+    "PagePool",
+    "PrefixCache",
+    "Storage",
+    "TaskTable",
+    "page_hash",
+    "Executor",
+    "InferenceScheduler",
+    "STOP",
+    "Task",
+    "TaskManager",
+    "TaskStatus",
+]

astrai/inference/core/cache.py

@@ -0,0 +1,525 @@
+import threading
+from abc import ABC, abstractmethod
+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:
+            if idx in self._lru:
+                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 CacheView(ABC):
+    """Abstract view passed to attention layers for KV-cache I/O."""
+
+    @abstractmethod
+    def write(self, layer_id: int, k: Tensor, v: Tensor): ...
+
+    @abstractmethod
+    def gather(self, layer_id: int) -> Tuple[Tensor, Tensor]: ...
+
+
+class KVCache(ABC):
+    """Abstract KV-cache facade for scheduler/executor."""
+
+    @abstractmethod
+    def task_alloc(self, task_id: str, prompt_ids: List[int]) -> bool: ...
+
+    @abstractmethod
+    def task_free(self, task_id: str): ...
+
+    @abstractmethod
+    def task_extend(self, task_id: str, pos: int) -> bool: ...
+
+    @abstractmethod
+    def bind_tasks(
+        self,
+        task_ids: List[str],
+        total_len: int,
+        device: torch.device,
+        write_positions: Optional[Tensor] = None,
+    ) -> CacheView: ...
+
+    def task_cached(self, task_id: str) -> int:
+        return 0
+
+    def task_record_hashes(
+        self, task_id: str, prompt_ids: List[int], start_logical_page: int = 0
+    ): ...
+
+
+class PageCacheView(CacheView):
+    """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 PageCache(KVCache):
+    """Paged KV-cache with prefix sharing."""
+
+    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 bind_tasks(
+        self,
+        task_ids: List[str],
+        total_len: int,
+        device: torch.device,
+        write_positions: Optional[Tensor] = None,
+    ) -> PageCacheView:
+        page_table = self._table.table_tensor(task_ids, device)
+        return PageCacheView(self._storage, page_table, total_len)
+
+
+class ContiguousCacheView(CacheView):
+    """Contiguous KV-cache view for attention layers."""
+
+    def __init__(
+        self,
+        cache: "ContiguousCache",
+        batch_indices: Tensor,
+        total_len: int = 0,
+        write_positions: Optional[Tensor] = None,
+    ):
+        self._cache = cache
+        self._batch_indices = batch_indices
+        self._total_len = total_len
+        self._write_positions = write_positions
+
+    def write(self, layer_id: int, k: Tensor, v: Tensor):
+        seq_len = k.size(1)
+        indices = self._batch_indices
+        if self._write_positions is not None and seq_len == 1:
+            pos = self._write_positions
+            self._cache.k[layer_id, indices, pos] = k.squeeze(1)
+            self._cache.v[layer_id, indices, pos] = v.squeeze(1)
+        else:
+            start_pos = self._total_len - seq_len
+            self._cache.k[layer_id, indices, start_pos : start_pos + seq_len] = k
+            self._cache.v[layer_id, indices, start_pos : start_pos + seq_len] = v
+
+    def gather(self, layer_id: int) -> Tuple[Tensor, Tensor]:
+        max_len = self._total_len
+        indices = self._batch_indices
+        k = self._cache.k[layer_id, indices, :max_len]
+        v = self._cache.v[layer_id, indices, :max_len]
+        return k, v
+
+
+class ContiguousCache(KVCache):
+    """Contiguous per-slot KV cache (default implementation)."""
+
+    def __init__(
+        self,
+        n_layers: int,
+        max_batch_size: int,
+        max_seq_len: int,
+        n_kv_heads: int,
+        head_dim: int,
+        device: torch.device,
+        dtype: torch.dtype,
+    ):
+        self.max_seq_len = max_seq_len
+        self.k = torch.zeros(
+            n_layers,
+            max_batch_size,
+            max_seq_len,
+            n_kv_heads,
+            head_dim,
+            device=device,
+            dtype=dtype,
+        )
+        self.v = torch.zeros(
+            n_layers,
+            max_batch_size,
+            max_seq_len,
+            n_kv_heads,
+            head_dim,
+            device=device,
+            dtype=dtype,
+        )
+        self._slot_len: Dict[int, int] = {}
+        self._task_slot: Dict[str, int] = {}
+        self._free_slots = list(range(max_batch_size))
+        self._device = device
+
+    def task_alloc(self, task_id: str, prompt_ids: List[int]) -> bool:
+        if not self._free_slots:
+            return False
+        slot = self._free_slots.pop(0)
+        self._task_slot[task_id] = slot
+        self._slot_len[slot] = 0
+        return True
+
+    def task_free(self, task_id: str):
+        slot = self._task_slot.pop(task_id, None)
+        if slot is not None:
+            self._slot_len.pop(slot, None)
+            self._free_slots.append(slot)
+
+    def task_extend(self, task_id: str, pos: int) -> bool:
+        return pos < self.max_seq_len
+
+    def task_cached(self, task_id: str) -> int:
+        slot = self._task_slot.get(task_id)
+        if slot is None:
+            return 0
+        return self._slot_len.get(slot, 0)
+
+    def bind_tasks(
+        self,
+        task_ids: List[str],
+        total_len: int,
+        device: torch.device,
+        write_positions: Optional[Tensor] = None,
+    ) -> ContiguousCacheView:
+        slots = [self._task_slot[tid] for tid in task_ids]
+        batch_indices = torch.tensor(slots, dtype=torch.long, device=device)
+        for slot in slots:
+            if total_len > self._slot_len.get(slot, 0):
+                self._slot_len[slot] = total_len
+        return ContiguousCacheView(
+            self, batch_indices, total_len, write_positions=write_positions
+        )

astrai/inference/core/executor.py

@@ -0,0 +1,166 @@
+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,
+        kv_cache: KVCache,
+        device: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        self.model = model
+        self.tokenizer = tokenizer
+        self.kv_cache = kv_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]
+        position_ids = (
+            torch.arange(start_pos, prompt_len, dtype=torch.long, device=self.device)
+            .unsqueeze(0)
+            .expand(batch_sz, -1)
+        )
+        input_mask = position_ids.unsqueeze(-1) >= torch.arange(
+            prompt_len, device=self.device
+        )
+
+        with torch.inference_mode():
+            self.model(
+                input_ids,
+                input_mask=input_mask,
+                position_ids=position_ids,
+                paged_cache=self.kv_cache.bind_tasks(task_ids, prompt_len, self.device),
+            )
+
+    def execute_decode(
+        self, tasks: List[Task], return_logprobs: bool = False
+    ) -> List[int]:
+        """Decode next token for each task.
+
+        Args:
+            return_logprobs: When ``True``, also record (and return)
+                the log-probability of each sampled token under the
+                post-strategy sampling distribution.  The logprob is
+                appended to ``task.output_logprobs`` and the return
+                list becomes ``List[Tuple[int, float]]``.
+
+        Returns:
+            ``List[int]`` of sampled token IDs, or
+            ``List[Tuple[int, float]]`` of ``(token_id, logprob)`` when
+            ``return_logprobs`` is ``True``.
+        """
+        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 = max(t.next_pos for t in tasks) + 1
+        input_mask = position_ids[:, None, None] >= torch.arange(
+            total_len, device=self.device
+        )
+
+        task_ids = [t.task_id for t in tasks]
+
+        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)
+        freq_penalties = torch.tensor(
+            [t.frequency_penalty for t in tasks], device=self.device
+        )
+
+        history_lists = []
+        history_lens = []
+        for t in tasks:
+            window = t.rep_window
+            prompt_part = t.prompt_ids[-window:]
+            ids = prompt_part + t.output_ids
+            history_lists.append(ids)
+            history_lens.append(len(ids))
+
+        max_len = max(history_lens) if history_lens else 0
+        padded_ids = torch.zeros(
+            len(tasks), max_len, dtype=torch.long, device=self.device
+        )
+        padded_mask = torch.zeros(
+            len(tasks), max_len, dtype=torch.bool, device=self.device
+        )
+        for i, h in enumerate(history_lists):
+            L = history_lens[i]
+            padded_ids[i, :L] = torch.as_tensor(h, dtype=torch.long, device=self.device)
+            padded_mask[i, :L] = True
+
+        with torch.inference_mode():
+            outputs = self.model(
+                input_ids.unsqueeze(1),
+                input_mask=input_mask,
+                paged_cache=self.kv_cache.bind_tasks(
+                    task_ids,
+                    total_len,
+                    self.device,
+                    write_positions=position_ids,
+                ),
+                position_ids=position_ids.unsqueeze(1),
+            )
+            logits = outputs["logits"][:, -1, :]
+
+        if return_logprobs:
+            tokens, logprobs = sample(
+                logits,
+                temperature=temperatures,
+                top_k=top_ks,
+                top_p=top_ps,
+                frequency_penalty=freq_penalties,
+                input_ids=padded_ids,
+                input_mask=padded_mask,
+                return_logprobs=True,
+            )
+            tokens_list = tokens.tolist()
+            logprobs_list = logprobs.tolist()
+            for t, lp in zip(tasks, logprobs_list):
+                t.output_logprobs.append(float(lp))
+            return list(zip(tokens_list, logprobs_list))
+
+        return sample(
+            logits,
+            temperature=temperatures,
+            top_k=top_ks,
+            top_p=top_ps,
+            frequency_penalty=freq_penalties,
+            input_ids=padded_ids,
+            input_mask=padded_mask,
+        ).tolist()

astrai/inference/core/scheduler.py

@@ -0,0 +1,311 @@
+import logging
+import threading
+import uuid
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+
+from astrai.inference.core.cache import ContiguousCache, 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:
+    """Continuous batching loop: cleanup -> refill -> prefill -> decode (all groups)."""
+
+    def __init__(
+        self,
+        model: AutoModel,
+        tokenizer: AutoTokenizer,
+        max_batch_size: int = 16,
+        max_seq_len: Optional[int] = None,
+        max_prompt_len: int = 2048,
+        device: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
+        cache: Optional[KVCache] = None,
+    ):
+        config = model.config
+
+        if max_seq_len is not None:
+            self.max_seq_len = max_seq_len
+        elif config.max_position_embeddings is not None:
+            self.max_seq_len = config.max_position_embeddings
+        else:
+            raise ValueError(
+                "max_seq_len must be provided either as argument "
+                "or in model config (config.max_position_embeddings)"
+            )
+        self.device = device or next(model.parameters()).device
+        self.dtype = dtype or next(model.parameters()).dtype
+
+        head_dim = config.hidden_size // config.num_attention_heads
+
+        if cache is not None:
+            self._cache = cache
+        else:
+            self._cache = ContiguousCache(
+                config.num_hidden_layers,
+                max_batch_size,
+                self.max_seq_len,
+                config.num_key_value_heads,
+                head_dim,
+                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,
+            kv_cache=self._cache,
+            device=self.device,
+            dtype=self.dtype,
+        )
+
+        self._stop_event = threading.Event()
+        self._loop_thread: Optional[threading.Thread] = 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._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
+        cache = self._cache
+        try:
+            while not self._stop_event.is_set():
+                finished = self._task_mgr.remove_finished_tasks(stop_ids)
+                for task in finished:
+                    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 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 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),
+                            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 // getattr(
+                            cache, "page_size", 64
+                        )
+                        for t in group:
+                            cache.task_record_hashes(
+                                t.task_id, t.prompt_ids, start_logical_page
+                            )
+
+                decode_tasks = self._task_mgr.get_active_tasks()
+
+                valid: List[Task] = []
+                for t in sorted(decode_tasks, key=lambda t: t.task_id):
+                    if cache.task_extend(t.task_id, t.next_pos):
+                        valid.append(t)
+                    else:
+                        t.status = TaskStatus.ABORTED
+                        self._task_mgr.invoke_callback(t.task_id, 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
+                        new_text = t.decode_new_token(self._task_mgr.tokenizer)
+                        if new_text:
+                            self._task_mgr.invoke_callback(t.task_id, new_text)
+
+                    for t in valid:
+                        if t.is_finished(stop_ids):
+                            remaining = t.flush_remaining(self._task_mgr.tokenizer)
+                            if remaining:
+                                self._task_mgr.invoke_callback(t.task_id, remaining)
+                            self._task_mgr.invoke_callback(t.task_id, STOP)
+
+        except Exception as e:
+            self._stop_event.set()
+            logger.error(f"Scheduler loop crashed: {e}", exc_info=True)
+            for task in self._task_mgr.get_active_tasks():
+                self._task_mgr.invoke_callback(task.task_id, STOP)
+                cache.task_free(task.task_id)
+            for task in self._task_mgr.get_waiting_tasks():
+                self._task_mgr.invoke_callback(task.task_id, STOP)
+            self._task_mgr.clear_queues()
+
+    def start(self):
+        if self._loop_thread is not None and self._loop_thread.is_alive():
+            return
+        self._stop_event.clear()
+        t = threading.Thread(target=self._run_generation_loop, daemon=True)
+        t.start()
+        self._loop_thread = t
+
+    def stop(self):
+        self._stop_event.set()
+        self._task_mgr.wake()
+        if self._loop_thread is not None:
+            self._loop_thread.join(timeout=2.0)
+        self._loop_thread = None
+        for task in self._task_mgr.get_active_tasks():
+            self._task_mgr.invoke_callback(task.task_id, STOP)
+            self._cache.task_free(task.task_id)
+        for task in self._task_mgr.get_waiting_tasks():
+            self._task_mgr.invoke_callback(task.task_id, STOP)
+            self._cache.task_free(task.task_id)
+        self._task_mgr.clear_queues()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+    def run_batch(
+        self,
+        prompt_ids_list: List[List[int]],
+        *,
+        max_tokens: Optional[int] = None,
+        temperature: float = 1.0,
+        top_p: float = 1.0,
+        top_k: int = 50,
+        frequency_penalty: float = 0.0,
+        rep_window: int = 64,
+        return_logprobs: bool = False,
+    ) -> List[List[int]]:
+        """Synchronous batch generation without the scheduler thread.
+
+        Accepts already-tokenized prompts (no string round-trip) and runs
+        prefill + decode to completion on the calling thread.  Designed for
+        RL rollout, where logprobs of the behaviour policy must be collected
+        alongside generated tokens.
+
+        Args:
+            prompt_ids_list: ``B`` prompts, each a list of token IDs.
+            max_tokens: Maximum tokens to generate per prompt.  ``None``
+                uses ``self.max_seq_len - len(prompt_ids)``.
+            temperature/top_p/top_k/frequency_penalty/rep_window: Sampling
+                parameters (uniform across the batch).
+            return_logprobs: If ``True``, return ``(token_ids, logprobs)``
+                tuples per prompt (logprobs aligned 1-to-1 with token_ids).
+
+        Returns:
+            ``List[List[int]]`` of generated token IDs per prompt, or —
+            when ``return_logprobs`` is ``True`` —
+            ``List[Tuple[List[int], List[float]]]``.
+        """
+        stop_ids = self._task_mgr.tokenizer.stop_ids
+        cache = self._cache
+        seq_cap = self.max_seq_len
+
+        tasks: List[Task] = []
+        for ids in prompt_ids_list:
+            if len(ids) >= seq_cap:
+                tasks.append(None)
+                continue
+            t_max = max_tokens
+            if t_max is None:
+                t_max = seq_cap - len(ids)
+            else:
+                t_max = min(t_max, seq_cap - len(ids))
+            task = Task(
+                task_id=f"batch_{uuid.uuid4().hex[:8]}",
+                prompt_ids=list(ids),
+                max_tokens=t_max,
+                temperature=temperature,
+                top_p=top_p,
+                top_k=top_k,
+                frequency_penalty=frequency_penalty,
+                rep_window=rep_window,
+            )
+            if not cache.task_alloc(task.task_id, task.prompt_ids):
+                tasks.append(None)
+                continue
+            task.input_tokens = len(task.prompt_ids)
+            tasks.append(task)
+
+        try:
+            live = [t for t in tasks if t is not None]
+            prefill_groups: Dict[Tuple[int, int], List[Task]] = {}
+            for t in live:
+                key = (len(t.prompt_ids), cache.task_cached(t.task_id))
+                prefill_groups.setdefault(key, []).append(t)
+            for (prompt_len, start_pos), group in prefill_groups.items():
+                self._executor.execute_prefill(group, prompt_len, start_pos)
+
+            while live:
+                valid: List[Task] = []
+                for t in sorted(live, key=lambda x: x.task_id):
+                    if cache.task_extend(t.task_id, t.next_pos):
+                        valid.append(t)
+                    else:
+                        t.status = TaskStatus.ABORTED
+                if not valid:
+                    break
+
+                step_out = self._executor.execute_decode(
+                    valid, return_logprobs=return_logprobs
+                )
+                if return_logprobs:
+                    for t, (ntok, _lp) in zip(valid, step_out):
+                        t.output_ids.append(ntok)
+                        t.output_tokens += 1
+                else:
+                    for t, ntok in zip(valid, step_out):
+                        t.output_ids.append(ntok)
+                        t.output_tokens += 1
+
+                live = [t for t in valid if not t.is_finished(stop_ids)]
+        finally:
+            for t in tasks:
+                if t is not None:
+                    cache.task_free(t.task_id)
+
+        results: List[Any] = []
+        for t in tasks:
+            if t is None:
+                results.append(([], []) if return_logprobs else [])
+            elif return_logprobs:
+                results.append((list(t.output_ids), list(t.output_logprobs)))
+            else:
+                results.append(list(t.output_ids))
+        return results

astrai/inference/core/task.py

@@ -0,0 +1,287 @@
+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 StreamDecoder:
+    """Incremental decoder for byte-level BPE streaming.
+
+    Byte-level BPE may split a single Unicode character (e.g. em-dash,
+    smart quotes) across multiple tokens. Decoding such a token in
+    isolation produces U+FFFD (replacement char). This decoder
+    accumulates token IDs and only emits text once the trailing
+    characters are complete, buffering incomplete multi-byte sequences
+    until the next token arrives.
+    """
+
+    __slots__ = ("_tokenizer", "_ids", "_emitted")
+
+    def __init__(self, tokenizer: AutoTokenizer):
+        self._tokenizer = tokenizer
+        self._ids: List[int] = []
+        self._emitted: str = ""
+
+    def push(self, token_id: int) -> str:
+        """Append a token ID and return newly completed text.
+
+        Returns "" while a multi-byte character is still incomplete.
+        """
+        self._ids.append(token_id)
+        full = self._tokenizer.decode(self._ids, skip_special_tokens=True)
+        if full.endswith("\ufffd"):
+            return ""
+        if len(full) > len(self._emitted):
+            diff = full[len(self._emitted) :]
+            self._emitted = full
+            return diff
+        return ""
+
+
+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,
+        frequency_penalty: float = 0.0,
+        rep_window: int = 64,
+    ):
+        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.frequency_penalty = frequency_penalty
+        self.rep_window = rep_window
+
+        self.status = TaskStatus.PENDING
+        self.output_ids: List[int] = []
+        self.output_logprobs: List[float] = []
+        self.input_tokens: int = 0
+        self.output_tokens: int = 0
+        self.arrival_time = time.time()
+        self.finish_time: Optional[float] = None
+        self._decoder: Optional[StreamDecoder] = None
+
+    def decode_new_token(self, tokenizer: AutoTokenizer) -> str:
+        """Decode the last appended output token, buffering incomplete
+        multi-byte sequences across calls.
+
+        Lazily creates a :class:`StreamDecoder` on first use.
+        """
+        if self._decoder is None:
+            self._decoder = StreamDecoder(tokenizer)
+        return self._decoder.push(self.output_ids[-1])
+
+    def flush_remaining(self, tokenizer: AutoTokenizer) -> str:
+        """Emit any text still buffered in the decoder.
+
+        Called when generation terminates (max_tokens reached, stop
+        sequence, or external removal) to avoid dropping a final
+        incomplete-looking fragment that is actually complete when
+        adjacent to the stop token.
+        """
+        if self._decoder is None or not self.output_ids:
+            return ""
+        full = tokenizer.decode(self.output_ids, skip_special_tokens=True)
+        if len(full) > len(self._decoder._emitted):
+            diff = full[len(self._decoder._emitted) :]
+            self._decoder._emitted = full
+            return diff
+        return ""
+
+    @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._callbacks: Dict[str, Callable[[str], None]] = {}
+
+        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,
+        frequency_penalty: float = 0.0,
+        rep_window: int = 64,
+        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,
+            frequency_penalty=frequency_penalty,
+            rep_window=rep_window,
+        )
+
+        with self._lock:
+            self.waiting_queue.append(task)
+            self._total_tasks += 1
+            if stream_callback:
+                self._callbacks[task_id] = stream_callback
+
+        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]
+            self._callbacks.pop(task_id, None)
+        return removed_active
+
+    def invoke_callback(self, task_id: str, token: str):
+        cb = self._callbacks.get(task_id)
+        if cb:
+            cb(token)
+
+    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()
+            self._callbacks.clear()
+
+    def wake(self):
+        self._task_event.set()

astrai/inference/engine.py

@@ -0,0 +1,348 @@
+"""Unified inference engine for continuous batching."""
+
+import asyncio
+import gc
+import threading
+from typing import Any, AsyncGenerator, Dict, Generator, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+
+from astrai.inference.core.cache import KVCache
+from astrai.inference.core.scheduler import InferenceScheduler
+from astrai.inference.core.task import STOP
+from astrai.tokenize import AutoTokenizer
+
+
+class GenerateResult:
+    """Thread-safe token accumulator for streaming and non-streaming modes."""
+
+    def __init__(self, count: int = 1):
+        self._cond = threading.Condition()
+        self._event = threading.Event()
+        self.tokens: List[Tuple[int, str]] = []
+        self.results: List[str] = [""] * count
+        self._done: List[bool] = [False] * count
+        self._completed = 0
+        self._total = count
+
+    def append(self, token: str, idx: int = 0):
+        with self._cond:
+            self.tokens.append((idx, token))
+            if token is not STOP:
+                self.results[idx] += token
+            else:
+                if not self._done[idx]:
+                    self._done[idx] = True
+                    self._completed += 1
+                    self._cond.notify_all()
+            self._event.set()
+
+    def pop_all(self) -> List[Tuple[int, str]]:
+        with self._cond:
+            out = self.tokens.copy()
+            self.tokens.clear()
+            if not out:
+                self._event.clear()
+            return out
+
+    def wait(self, timeout: Optional[float] = None) -> bool:
+        return self._event.wait(timeout=timeout)
+
+    def wait_completion(self, timeout: float = 300.0):
+        with self._cond:
+            if not self._cond.wait_for(
+                lambda: self._completed >= self._total, timeout=timeout
+            ):
+                raise TimeoutError(
+                    f"Generation timeout after {timeout}s "
+                    f"({self._completed}/{self._total} completed)"
+                )
+
+    def get_results(self) -> List[str]:
+        with self._cond:
+            return self.results.copy()
+
+
+class GenerationRequest:
+    """Request parameters for text generation."""
+
+    def __init__(
+        self,
+        messages: List[Dict[str, str]],
+        top_k: int = 50,
+        top_p: float = 1.0,
+        temperature: float = 1.0,
+        max_tokens: Optional[int] = None,
+        frequency_penalty: float = 0.0,
+        rep_window: int = 64,
+        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 non-negative number")
+        if not (
+            isinstance(frequency_penalty, (int, float))
+            and -2.0 <= frequency_penalty <= 2.0
+        ):
+            raise ValueError("frequency_penalty must be between -2.0 and 2.0")
+        if not (isinstance(rep_window, int) and rep_window > 0):
+            raise ValueError("rep_window must be a positive integer")
+
+        self.messages = messages
+        self.top_k = top_k
+        self.top_p = top_p
+        self.temperature = temperature
+        self.max_tokens = max_tokens
+        self.frequency_penalty = frequency_penalty
+        self.rep_window = rep_window
+        self.stream = stream
+
+
+class InferenceEngine:
+    """Unified inference engine backed by continuous-batching scheduler."""
+
+    def __init__(
+        self,
+        model: nn.Module,
+        tokenizer: AutoTokenizer,
+        max_batch_size: int = 1,
+        max_seq_len: Optional[int] = None,
+        max_prompt_len: int = 2048,
+        page_size: int = 128,
+        cache: Optional[KVCache] = None,
+    ):
+        self.model = model
+        self.tokenizer = tokenizer
+        self.scheduler = InferenceScheduler(
+            model=self.model,
+            tokenizer=self.tokenizer,
+            max_batch_size=max_batch_size,
+            max_seq_len=max_seq_len,
+            max_prompt_len=max_prompt_len,
+            cache=cache,
+        )
+
+        self.scheduler.start()
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.shutdown()
+        return False
+
+    def generate(
+        self,
+        prompt: Union[str, List[str]],
+        stream: bool = False,
+        max_tokens: Optional[int] = None,
+        temperature: float = 1.0,
+        top_p: float = 1.0,
+        top_k: int = 50,
+        frequency_penalty: float = 0.0,
+        rep_window: int = 64,
+    ) -> Union[Generator, str, List[str]]:
+        is_batch = isinstance(prompt, list)
+        prompts = prompt if is_batch else [prompt]
+
+        if stream:
+            return self._generate_streaming(
+                prompts,
+                is_batch,
+                max_tokens,
+                temperature,
+                top_p,
+                top_k,
+                frequency_penalty,
+                rep_window,
+            )
+        else:
+            return self._generate_non_streaming(
+                prompts,
+                is_batch,
+                max_tokens,
+                temperature,
+                top_p,
+                top_k,
+                frequency_penalty,
+                rep_window,
+            )
+
+    def generate_async(
+        self,
+        prompt: str,
+        max_tokens: Optional[int] = None,
+        temperature: float = 1.0,
+        top_p: float = 1.0,
+        top_k: int = 50,
+        frequency_penalty: float = 0.0,
+        rep_window: int = 64,
+    ) -> AsyncGenerator[str, None]:
+        sync_gen = self._generate_streaming(
+            [prompt],
+            False,
+            max_tokens,
+            temperature,
+            top_p,
+            top_k,
+            frequency_penalty,
+            rep_window,
+        )
+
+        async def _agen():
+            loop = asyncio.get_event_loop()
+            while True:
+                token = await loop.run_in_executor(None, self._next_token, sync_gen)
+                if token is None:
+                    break
+                yield token
+
+        return _agen()
+
+    @staticmethod
+    def _next_token(gen: Generator) -> Optional[str]:
+        try:
+            return next(gen)
+        except StopIteration:
+            return None
+
+    def generate_with_request(
+        self, request: GenerationRequest
+    ) -> Union[Generator[str, None, None], str, List[str]]:
+        prompt = self.tokenizer.apply_chat_template(request.messages, tokenize=False)
+        return self.generate(
+            prompt=prompt,
+            stream=request.stream,
+            max_tokens=request.max_tokens,
+            temperature=request.temperature,
+            top_p=request.top_p,
+            top_k=request.top_k,
+            frequency_penalty=request.frequency_penalty,
+            rep_window=request.rep_window,
+        )
+
+    def _submit_tasks(
+        self,
+        prompts: List[str],
+        max_tokens: Optional[int],
+        temperature: float,
+        top_p: float,
+        top_k: int,
+        frequency_penalty: float,
+        rep_window: 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,
+                frequency_penalty=frequency_penalty,
+                rep_window=rep_window,
+                stream_callback=cb,
+            )
+            task_ids.append(task_id)
+        return result, task_ids
+
+    @staticmethod
+    def _make_callback(result: GenerateResult, idx: int):
+        def cb(token):
+            result.append(token, idx)
+
+        return cb
+
+    def _generate_streaming(
+        self,
+        prompts: List[str],
+        is_batch: bool,
+        max_tokens: Optional[int],
+        temperature: float,
+        top_p: float,
+        top_k: int,
+        frequency_penalty: float,
+        rep_window: int,
+    ) -> Generator:
+        result, task_ids = self._submit_tasks(
+            prompts,
+            max_tokens,
+            temperature,
+            top_p,
+            top_k,
+            frequency_penalty,
+            rep_window,
+        )
+        n = len(prompts)
+        remaining = n
+        finished = [False] * n
+
+        def gen():
+            nonlocal remaining
+            try:
+                while remaining > 0:
+                    items = result.pop_all()
+                    for idx, token in items:
+                        if token is STOP:
+                            if not finished[idx]:
+                                finished[idx] = True
+                                remaining -= 1
+                        else:
+                            yield (idx, token) if is_batch else token
+                    if remaining > 0:
+                        result.wait(timeout=0.05)
+            finally:
+                for tid in task_ids:
+                    self.scheduler.remove_task(tid)
+
+        return gen()
+
+    def _generate_non_streaming(
+        self,
+        prompts: List[str],
+        is_batch: bool,
+        max_tokens: Optional[int],
+        temperature: float,
+        top_p: float,
+        top_k: int,
+        frequency_penalty: float,
+        rep_window: int,
+    ) -> Union[str, List[str]]:
+        result, task_ids = self._submit_tasks(
+            prompts,
+            max_tokens,
+            temperature,
+            top_p,
+            top_k,
+            frequency_penalty,
+            rep_window,
+        )
+
+        try:
+            result.wait_completion()
+        except TimeoutError:
+            for tid in task_ids:
+                self.scheduler.remove_task(tid)
+            raise
+
+        for tid in task_ids:
+            self.scheduler.remove_task(tid)
+
+        res = result.get_results()
+        return res if is_batch else res[0]
+
+    def get_stats(self) -> Dict[str, Any]:
+        return self.scheduler.get_stats()
+
+    def shutdown(self):
+        self.scheduler.stop()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        gc.collect()

astrai/inference/sample.py

@@ -0,0 +1,375 @@
+"""Composable sampling strategies for logit transformation.
+
+Implements the Strategy pattern: each sampling technique
+(temperature, top-k, top-p, frequency penalty) 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, Optional, 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"),
+        input_ids: Optional[Tensor] = None,
+        input_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        """Applies the strategy to logits.
+
+        Args:
+            logits: Raw logits tensor (batch, vocab_size).
+            filter_value: Value assigned to filtered-out positions.
+            input_ids: Previously generated token IDs ``[batch, seq_len]``,
+                padded with 0. Used by frequency penalty.
+            input_mask: Boolean mask ``[batch, seq_len]``, True for real
+                tokens, False for padding. Used to exclude padding from
+                penalty computation.
+
+        Returns:
+            Transformed logits tensor.
+        """
+        raise NotImplementedError
+
+
+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: Tensor,
+        filter_value: float = -float("inf"),
+        input_ids: Optional[Tensor] = None,
+        input_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        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: Tensor,
+        filter_value: float = -float("inf"),
+        input_ids: Optional[Tensor] = None,
+        input_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        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: Tensor, top_p: Union[float, Tensor], filter_value: float
+    ) -> Tensor:
+        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: Tensor,
+        filter_value: float = -float("inf"),
+        input_ids: Optional[Tensor] = None,
+        input_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        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 FrequencyPenaltyStrategy(BaseSamplingStrategy):
+    """Penalizes tokens based on how many times they appeared in history.
+
+    Subtracts ``penalty * count(token)`` from each token's logit, where
+    ``count(token)`` is the number of occurrences in the generation history
+    (prompt + output). A penalty of ``0.0`` disables the strategy.
+
+    Unlike repetition penalty (which only checks *presence*), frequency
+    penalty scales linearly with occurrence count: the first use is
+    penalized once, the third use three times. This allows natural
+    repetition of common words while suppressing degenerate loops.
+
+    Reference: OpenAI API ``frequency_penalty`` parameter.
+
+    Args:
+        penalty: Scalar or ``[batch]`` tensor (0.0 disables, range -2.0~2.0).
+    """
+
+    def __init__(self, penalty: Union[float, Tensor] = 0.0):
+        self.penalty = penalty
+
+    def apply(
+        self,
+        logits: Tensor,
+        filter_value: float = -float("inf"),
+        input_ids: Optional[Tensor] = None,
+        input_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        if input_ids is None:
+            return logits
+
+        p = self.penalty
+        if isinstance(p, Tensor):
+            p = p.to(logits.device, non_blocking=True).view(-1, 1)
+            if (p == 0.0).all():
+                return logits
+        elif p == 0.0:
+            return logits
+
+        input_ids = input_ids.to(logits.device, non_blocking=True)
+
+        if input_mask is not None:
+            input_mask = input_mask.to(logits.device, non_blocking=True)
+            masked_ids = input_ids.clone()
+            masked_ids[~input_mask] = -1
+        else:
+            masked_ids = input_ids
+
+        batch_sz, seq_len = masked_ids.shape
+        vocab_size = logits.size(-1)
+
+        if isinstance(p, Tensor):
+            penalty_per_row = p.expand(batch_sz, 1)
+        else:
+            penalty_per_row = torch.full(
+                (batch_sz, 1), float(p), device=logits.device, dtype=logits.dtype
+            )
+
+        counts = torch.zeros(
+            batch_sz, vocab_size, device=logits.device, dtype=logits.dtype
+        )
+        valid_mask = masked_ids >= 0
+        if valid_mask.any():
+            valid_ids = masked_ids[valid_mask]
+            row_indices = (
+                torch.arange(batch_sz, device=logits.device)
+                .unsqueeze(1)
+                .expand_as(masked_ids)[valid_mask]
+            )
+            counts.index_put_(
+                (row_indices, valid_ids),
+                torch.ones_like(valid_ids, dtype=logits.dtype),
+                accumulate=True,
+            )
+
+        return logits - penalty_per_row * counts
+
+
+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: Tensor,
+        filter_value: float = -float("inf"),
+        input_ids: Optional[Tensor] = None,
+        input_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        for strategy in self.strategies:
+            logits = strategy.apply(logits, filter_value, input_ids, input_mask)
+        return logits
+
+    @staticmethod
+    def _is_greedy(temperature: Union[float, Tensor]) -> bool:
+        if isinstance(temperature, Tensor):
+            return temperature.numel() == 1 and temperature.item() == 0
+        return temperature == 0
+
+    @torch.inference_mode()
+    def sample(
+        self,
+        logits: Tensor,
+        filter_value: float = -float("inf"),
+        input_ids: Optional[Tensor] = None,
+        input_mask: Optional[Tensor] = None,
+        return_logprobs: bool = False,
+    ):
+        """Apply strategies then sample (softmax + multinomial).
+
+        Short-circuits to ``argmax`` when temperature is exactly 0
+        (deterministic / greedy decode).
+
+        Args:
+            logits: Raw logits ``[batch, vocab_size]``.
+            input_ids: Previously generated token IDs ``[batch, seq_len]``.
+            input_mask: Boolean mask for ``input_ids`` padding.
+            return_logprobs: If ``True``, return ``(tokens, logprobs)``
+                where ``logprobs[i]`` is the log-probability of
+                ``tokens[i]`` under the (post-strategy) sampling
+                distribution.
+
+        Returns:
+            Sampled token IDs ``[batch]``, or — when ``return_logprobs``
+            is ``True`` — a ``(token_ids, chosen_logprobs)`` tuple.
+        """
+        if self._is_greedy_pipeline():
+            tokens = logits.argmax(dim=-1)
+            if not return_logprobs:
+                return tokens
+            log_probs = torch.log_softmax(logits.float(), dim=-1)
+            chosen = torch.gather(log_probs, -1, tokens.unsqueeze(-1)).squeeze(-1)
+            return tokens, chosen
+
+        transformed = self.apply(logits, filter_value, input_ids, input_mask)
+        log_probs = torch.log_softmax(transformed.float(), dim=-1)
+        tokens = torch.multinomial(
+            torch.softmax(transformed, dim=-1), num_samples=1
+        ).squeeze(-1)
+        if not return_logprobs:
+            return tokens
+        chosen = torch.gather(log_probs, -1, tokens.unsqueeze(-1)).squeeze(-1)
+        return tokens, chosen
+
+    def _is_greedy_pipeline(self) -> bool:
+        """True if the first strategy is greedy temperature (temp=0)."""
+        if not self.strategies:
+            return False
+        first = self.strategies[0]
+        return isinstance(first, TemperatureStrategy) and self._is_greedy(
+            first.temperature
+        )
+
+
+@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,
+    frequency_penalty: Union[float, Tensor] = 0.0,
+    input_ids: Optional[Tensor] = None,
+    input_mask: Optional[Tensor] = None,
+    filter_value: float = -float("inf"),
+    return_logprobs: bool = False,
+):
+    """Apply sampling strategies then sample (softmax + multinomial).
+
+    Shortcut for ``SamplingPipeline(...).sample(logits, return_logprobs=)``.
+
+    When **temperature** is exactly 0 (scalar or single-element tensor)
+    the function short-circuits to ``argmax`` for deterministic decode.
+
+    Args:
+        logits: Raw logits ``[batch, vocab_size]``.
+        frequency_penalty: Penalty per occurrence for repeated tokens
+            (0.0 disables, range -2.0~2.0).
+        input_ids: Previously generated token IDs ``[batch, seq_len]``.
+        input_mask: Boolean mask for ``input_ids`` padding.
+        return_logprobs: If ``True``, also return the log-probability
+            of each sampled token under the (post-strategy) sampling
+            distribution — useful for RL rollout (PPO/GRPO importance
+            ratios).
+
+    Returns:
+        Sampled token IDs ``[batch]``, or — when ``return_logprobs`` is
+        ``True`` — a ``(token_ids, chosen_logprobs)`` tuple where
+        ``chosen_logprobs`` has shape ``[batch]``.
+    """
+    return SamplingPipeline(
+        [
+            TemperatureStrategy(temperature),
+            TopKStrategy(top_k),
+            TopPStrategy(top_p),
+            FrequencyPenaltyStrategy(frequency_penalty),
+        ]
+    ).sample(
+        logits,
+        filter_value=filter_value,
+        input_ids=input_ids,
+        input_mask=input_mask,
+        return_logprobs=return_logprobs,
+    )

astrai/model/__init__.py

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

astrai/model/automodel.py

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

astrai/model/components/__init__.py

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

astrai/model/components/attention.py

@@ -0,0 +1,213 @@
+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 CacheView
+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]):
+    pass
+
+
+@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,
+        n_layers: int = 1,
+    ):
+        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, init_std=0.02 / (2 * n_layers) ** 0.5)
+
+        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[CacheView] = None,
+        is_causal: bool = False,
+    ) -> Tensor:
+        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,
+        n_layers: int = 1,
+    ):
+        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, init_std=0.02 / (2 * n_layers) ** 0.5
+        )
+
+        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[CacheView] = None,
+        is_causal: bool = False,
+    ) -> Tensor:
+        bsz, seq_len, _ = x.size()
+
+        q = self.q_proj(x)
+        q = q.view(bsz, seq_len, self.n_heads, self.head_dim)
+
+        kv_compressed = self.kv_a_proj(x)
+        kv_compressed = self.kv_norm(kv_compressed)
+
+        kv = self.kv_b_proj(kv_compressed)
+        kv = kv.view(bsz, seq_len, self.n_kv_heads, -1)
+
+        k_nope, k_rope, v = torch.split(
+            kv, [self.qk_nope_head_dim, self.qk_rope_head_dim, self.head_dim], dim=-1
+        )
+
+        q_nope, q_rope = (
+            q[..., : self.qk_nope_head_dim],
+            q[..., self.qk_nope_head_dim :],
+        )
+        q_rope = apply_rotary_emb(q_rope, rotary_emb)
+        k_rope = apply_rotary_emb(k_rope, rotary_emb)
+
+        q = torch.cat([q_nope, q_rope], dim=-1)
+        k = torch.cat([k_nope, k_rope], dim=-1)
+
+        if self.use_qk_norm:
+            q = self.q_norm(q)
+            k = self.k_norm(k)
+
+        if paged_cache is not None:
+            paged_cache.write(self.layer_id, k, v)
+            k, v = paged_cache.gather(self.layer_id)
+
+        q = q.permute(0, 2, 1, 3)
+        k = k.permute(0, 2, 1, 3)
+        v = v.permute(0, 2, 1, 3)
+
+        attn_out = F.scaled_dot_product_attention(
+            q, k, v, attn_mask, is_causal=is_causal
+        )
+        attn_out = attn_out.permute(0, 2, 1, 3).contiguous().flatten(2)
+
+        if self.use_gated_attention:
+            attn_out = attn_out * F.sigmoid(self.gate(x))
+
+        out = self.o_proj(attn_out)
+        return out

astrai/model/components/decoder_block.py

@@ -0,0 +1,49 @@
+from dataclasses import asdict
+from typing import Optional
+
+import torch.nn as nn
+from torch import Tensor
+
+from astrai.inference.core.cache import CacheView
+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, config, layer_id: int):
+        super().__init__()
+        cfg = asdict(config)
+        cfg.update(
+            dim=config.hidden_size,
+            dim_ffn=config.intermediate_size,
+            n_layers=config.num_hidden_layers,
+            n_heads=config.num_attention_heads,
+            n_kv_heads=config.num_key_value_heads,
+            norm_eps=config.rms_norm_eps,
+            down_init_std=0.02 / (2 * config.num_hidden_layers) ** 0.5,
+        )
+        self.attention = AttnFactory.create(config.attn_type, **cfg, layer_id=layer_id)
+        self.input_norm = RMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.post_attention_norm = RMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.mlp = FFNFactory.create(config.ffn_type, **cfg)
+
+    def forward(
+        self,
+        x: Tensor,
+        rotary_emb: Tensor,
+        attention_mask: Optional[Tensor] = None,
+        paged_cache: Optional[CacheView] = None,
+        is_causal: bool = False,
+    ) -> Tensor:
+        attn_output = self.attention(
+            self.input_norm(x),
+            rotary_emb,
+            attention_mask,
+            paged_cache,
+            is_causal,
+        )
+        x = attn_output + x
+        x = self.mlp(self.post_attention_norm(x)) + x
+
+        return x

astrai/model/components/embedding.py

@@ -0,0 +1,26 @@
+import math
+
+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, neftune_alpha: float = 0.0):
+        super().__init__()
+        self.weight = nn.Parameter(torch.empty((vocab_size, embedding_dim)))
+        self.neftune_noise_alpha = neftune_alpha
+
+    def set_neftune_alpha(self, alpha: float):
+        self.neftune_noise_alpha = alpha
+
+    def reset_parameters(self):
+        nn.init.normal_(self.weight, mean=0.0, std=0.02)
+
+    def forward(self, x: Tensor) -> Tensor:
+        out = F.embedding(x, self.weight)
+        if self.training and self.neftune_noise_alpha > 0.0:
+            eps = self.neftune_noise_alpha / math.sqrt(out.size(1))
+            out = out + eps * torch.randn_like(out)
+        return out

astrai/model/components/linear.py

@@ -0,0 +1,24 @@
+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, init_std: float = 0.02
+    ):
+        super().__init__()
+        self.weight = nn.Parameter(torch.empty((out_dim, in_dim)))
+        self.bias = nn.Parameter(torch.zeros(out_dim)) if bias else None
+        self.init_std = init_std
+
+    def reset_parameters(self):
+        nn.init.normal_(self.weight, mean=0.0, std=self.init_std)
+        if self.bias is not None:
+            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
+            bound = 1 / (fan_in**0.5)
+            nn.init.uniform_(self.bias, -bound, bound)
+
+    def forward(self, x: Tensor) -> Tensor:
+        return F.linear(x, self.weight, self.bias)

astrai/model/components/lora.py

@@ -0,0 +1,198 @@
+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
+        device = self.weight.device
+        dtype = self.weight.dtype
+        lora_a = torch.randn(r, self.weight.shape[1], device=device, dtype=dtype) / r
+        lora_b = torch.zeros(self.weight.shape[0], r, device=device, dtype=dtype)
+        self.lora_A = nn.Parameter(lora_a)
+        self.lora_B = nn.Parameter(lora_b)
+        self._merged = False
+
+    def forward(self, x):
+        out = F.linear(x, self.weight, self.bias)
+        if not self._merged:
+            out += (F.linear(x, self.lora_A) @ self.lora_B.T) * self.scaling
+        return out
+
+    def merge(self):
+        if self._merged:
+            return
+        self.weight.data += (self.lora_B @ self.lora_A) * self.scaling
+        self._merged = True
+        del self.lora_A
+        del self.lora_B
+
+
+def _collect_lora_info(model: nn.Module) -> dict:
+    names = {}
+    for n, m in model.named_modules():
+        if isinstance(m, Linear):
+            _, _, child = n.rpartition(".")
+            names.setdefault(child, []).append(n)
+    return names
+
+
+def _get_lora_count(model: nn.Module) -> int:
+    return sum(1 for m in model.modules() if isinstance(m, LoRALinear))
+
+
+def inject_lora(
+    model: nn.Module,
+    r: int = 16,
+    alpha: int = 32,
+    target_modules: Optional[Set[str]] = None,
+) -> LoRAConfig:
+    if target_modules is None:
+        target_modules = TARGET_MODULES_ATTN
+
+    available = _collect_lora_info(model)
+    injected = 0
+
+    for name, module in list(model.named_modules()):
+        if not isinstance(module, Linear):
+            continue
+        parent_name, _, child_name = name.rpartition(".")
+        if child_name not in target_modules:
+            continue
+        parent = model.get_submodule(parent_name) if parent_name else model
+        setattr(parent, child_name, LoRALinear(module, r=r, alpha=alpha))
+        injected += 1
+
+    if injected == 0:
+        logger.warning(
+            "No LoRA layers injected. Available Linear child names: %s. "
+            "target_modules: %s. Check model type and target_modules.",
+            sorted(available),
+            sorted(target_modules),
+        )
+    else:
+        logger.info("LoRA injected: %d layers (r=%d, alpha=%d)", injected, r, alpha)
+
+    return LoRAConfig(r=r, alpha=alpha, target_modules=tuple(target_modules))
+
+
+def merge_lora(model: nn.Module):
+    n = 0
+    for module in model.modules():
+        if isinstance(module, LoRALinear):
+            module.merge()
+            n += 1
+    if n == 0:
+        logger.warning("No LoRA layers to merge.")
+    else:
+        logger.info("Merged %d LoRA layers", n)
+
+
+def save_lora(model: nn.Module, save_dir: str, config: LoRAConfig):
+    lora_sd = {
+        k: v
+        for k, v in model.state_dict().items()
+        if k.endswith((".lora_A", ".lora_B"))
+    }
+    if not lora_sd:
+        raise RuntimeError(
+            "No LoRA parameters found in model. "
+            "The model may not have been injected or was already merged."
+        )
+
+    path = Path(save_dir)
+    path.mkdir(parents=True, exist_ok=True)
+    save_safetensors(lora_sd, path / "adapter_model.safetensors")
+    save_json(asdict(config), path / "adapter_config.json")
+    logger.info("LoRA adapter saved to %s (%d keys)", save_dir, len(lora_sd))
+
+
+def load_lora(model: nn.Module, load_dir: str) -> LoRAConfig:
+    path = Path(load_dir)
+    raw = load_json(path / "adapter_config.json")
+    config = LoRAConfig(
+        r=raw["r"], alpha=raw["alpha"], target_modules=tuple(raw["target_modules"])
+    )
+
+    existing = _get_lora_count(model)
+    if existing > 0:
+        logger.warning(
+            "Model already has %d LoRA layers. Skipping injection, "
+            "loading weights onto existing layers only.",
+            existing,
+        )
+    else:
+        inject_lora(
+            model,
+            r=config.r,
+            alpha=config.alpha,
+            target_modules=set(config.target_modules),
+        )
+
+    weights = load_safetensors(path / "adapter_model.safetensors")
+    try:
+        missing, unexpected = model.load_state_dict(weights, strict=False)
+    except RuntimeError as e:
+        msg = str(e)
+        if "size mismatch" in msg:
+            raise RuntimeError(
+                f"LoRA weight shapes do not match the model. "
+                f"The adapter config (r={config.r}) may not match the injected layers. "
+                f"Original error: {msg}"
+            ) from e
+        raise
+
+    injected = _get_lora_count(model)
+    if injected == 0:
+        raise RuntimeError(
+            "No LoRA layers found after loading. "
+            "Inject LoRA before calling load_lora, or check the adapter config."
+        )
+
+    if missing:
+        lora_missing = [k for k in missing if "lora" in k]
+        if lora_missing:
+            raise RuntimeError(
+                f"LoRA weight keys not found in model: {lora_missing}. "
+                f"The adapter config (r={config.r}) may not match the model."
+            )
+        logger.debug("LoRA load: %d missing base-weight keys (expected)", len(missing))
+    if unexpected:
+        logger.warning("LoRA load: %d unexpected keys", len(unexpected))
+
+    logger.info("LoRA adapter loaded from %s", load_dir)
+    return config

astrai/model/components/mlp.py

@@ -0,0 +1,100 @@
+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]):
+    pass
+
+
+@FFNFactory.register("mlp")
+class MLP(nn.Module):
+    def __init__(self, dim: int, dim_ffn: int, down_init_std: float = 0.02):
+        super().__init__()
+        self.up = Linear(dim, dim_ffn)
+        self.gate = Linear(dim, dim_ffn)
+        self.down = Linear(dim_ffn, dim, init_std=down_init_std)
+
+    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",
+        n_layers: int = 1,
+    ):
+        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)
+        moe_scale = 1 / max(n_shared_experts, 1) + 1 / n_activated_experts
+        down_init_std = 0.02 / (2 * n_layers * moe_scale) ** 0.5
+
+        self.shared_experts = nn.ModuleList(
+            [
+                MLP(dim, dim_ffn, down_init_std=down_init_std)
+                for _ in range(n_shared_experts)
+            ]
+        )
+        self.routed_experts = nn.ModuleList(
+            [
+                MLP(dim, dim_ffn, down_init_std=down_init_std)
+                for _ in range(n_routed_experts)
+            ]
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        bsz, seq_len, dim = x.shape
+        x_flat = x.view(-1, dim)
+
+        shared_out = self._shared_forward(x_flat)
+        routed_out = self._routed_forward(x_flat)
+
+        out = (shared_out + routed_out).view(bsz, seq_len, dim)
+        return out
+
+    def _shared_forward(self, x: Tensor) -> Tensor:
+        if self.n_shared_experts == 0:
+            return torch.zeros_like(x)
+        return sum(e(x) for e in self.shared_experts) / self.n_shared_experts
+
+    def _routed_forward(self, x: Tensor) -> Tensor:
+        N, D = x.shape
+        K = self.n_activated_experts
+
+        router_logits = self.router(x)
+        router_probs = torch.softmax(router_logits.float(), dim=-1).to(x.dtype)
+
+        topk_weights, topk_indices = torch.topk(router_probs, K, dim=-1)
+        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+
+        output = torch.zeros(N, D, device=x.device, dtype=x.dtype)
+        for expert_idx in range(self.n_routed_experts):
+            expert_mask = topk_indices == expert_idx
+            token_idx, k_idx = expert_mask.nonzero(as_tuple=True)
+            if token_idx.numel() == 0:
+                continue
+            expert_input = x[token_idx]
+            expert_output = self.routed_experts[expert_idx](expert_input)
+            weights = topk_weights[token_idx, k_idx].unsqueeze(-1)
+            output.index_add_(0, token_idx, expert_output * weights)
+
+        return output

astrai/model/components/norm.py

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

astrai/model/components/rope.py

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

astrai/model/encoder.py

@@ -0,0 +1,97 @@
+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.hidden_size // config.num_attention_heads
+        rope_base = config.rope_theta if config.rope_theta is not None else 10000
+        self.rotary_embedding = RotaryEmbedding(
+            rope_dim,
+            config.max_position_embeddings,
+            rope_base,
+            rope_scaling=config.rope_scaling,
+        )
+        self.embed_tokens = Embedding(
+            config.vocab_size,
+            config.hidden_size,
+            neftune_alpha=config.neftune_alpha,
+        )
+
+        self.layers = nn.ModuleList(
+            [
+                DecoderBlock(config, layer_id)
+                for layer_id in range(config.num_hidden_layers)
+            ]
+        )
+
+        self.norm = RMSNorm(config.hidden_size, config.rms_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(input_mask)
+
+        for layer in self.layers:
+            x = layer(x, rotary_emb, attn_mask)
+
+        hidden_states = self.norm(x)
+
+        if self.pooling_type == "cls":
+            pooled = hidden_states[:, 0]
+        elif self.pooling_type == "last":
+            if input_mask is not None:
+                lengths = input_mask.sum(dim=1) - 1
+                pooled = hidden_states[torch.arange(B, device=x.device), lengths]
+            else:
+                pooled = hidden_states[:, -1]
+        else:
+            if input_mask is not None:
+                mask = input_mask.unsqueeze(-1).to(dtype=hidden_states.dtype)
+                pooled = (hidden_states * mask).sum(dim=1) / mask.sum(dim=1).clamp(
+                    min=1.0
+                )
+            else:
+                pooled = hidden_states.mean(dim=1)
+
+        if self.normalize_embeddings:
+            pooled = torch.nn.functional.normalize(pooled, p=2, dim=-1)
+
+        return pooled

astrai/model/transformer.py

@@ -0,0 +1,122 @@
+from typing import Any, Dict, Mapping, Optional
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+
+from astrai.config.model_config import AutoRegressiveLMConfig
+from astrai.inference.core.cache import CacheView
+from astrai.model.automodel import AutoModel
+from astrai.model.components.decoder_block import DecoderBlock
+from astrai.model.components.embedding import Embedding
+from astrai.model.components.linear import Linear
+from astrai.model.components.norm import RMSNorm
+from astrai.model.components.rope import RotaryEmbedding
+
+
+def process_attention_mask(
+    input_mask: Optional[Tensor],
+) -> Optional[Tensor]:
+    if input_mask is None:
+        return None
+    if input_mask.dim() == 2:
+        return input_mask[:, None, None, :]
+    if input_mask.dim() == 3:
+        return input_mask[:, None, :, :]
+    return input_mask
+
+
+@AutoModel.register("autoregressive_lm")
+class AutoRegressiveLM(AutoModel):
+    """Autoregressive language model with paged KV cache."""
+
+    def __init__(self, config: AutoRegressiveLMConfig):
+        super().__init__(config)
+        self.config = config
+        rope_dim = (
+            config.qk_rope_head_dim
+            if config.attn_type == "mla"
+            else config.hidden_size // config.num_attention_heads
+        )
+        rope_base = config.rope_theta if config.rope_theta is not None else 10000
+        self.rotary_embedding = RotaryEmbedding(
+            rope_dim,
+            config.max_position_embeddings,
+            rope_base,
+            rope_scaling=config.rope_scaling,
+        )
+        self.embed_tokens = Embedding(
+            config.vocab_size,
+            config.hidden_size,
+            neftune_alpha=config.neftune_alpha,
+        )
+
+        self.layers = nn.ModuleList(
+            [
+                DecoderBlock(config, layer_id)
+                for layer_id in range(config.num_hidden_layers)
+            ]
+        )
+
+        self.norm = RMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.lm_head = Linear(config.hidden_size, config.vocab_size)
+
+        if self.config.tie_word_embeddings is True:
+            self.lm_head.weight = self.embed_tokens.weight
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if hasattr(module, "reset_parameters"):
+            module.reset_parameters()
+
+    def load_state_dict(self, state_dict: Mapping[str, Any], strict=True, assign=False):
+        lm_head_key = "lm_head.weight"
+        embed_key = "embed_tokens.weight"
+
+        state_dict = dict(state_dict)
+
+        if self.config.tie_word_embeddings is True:
+            # same tensor for embed and lm_head
+            if embed_key in state_dict:
+                state_dict[lm_head_key] = state_dict[embed_key]
+        else:
+            if lm_head_key not in state_dict and embed_key in state_dict:
+                # clone to avoid sharing gradients
+                state_dict[lm_head_key] = torch.clone(state_dict[embed_key])
+
+        return super().load_state_dict(state_dict, strict, assign)
+
+    def state_dict(self, destination=None, prefix="", keep_vars=False):
+        state_dict = super().state_dict(
+            destination=destination, prefix=prefix, keep_vars=keep_vars
+        )
+
+        if self.config.tie_word_embeddings is True:
+            lm_head_key = prefix + "lm_head.weight"
+            if lm_head_key in state_dict:
+                del state_dict[lm_head_key]
+
+        return state_dict
+
+    def forward(
+        self,
+        input_ids: Tensor,
+        input_mask: Optional[Tensor] = None,
+        paged_cache: Optional[CacheView] = None,
+        position_ids: Optional[Tensor] = None,
+    ) -> Dict[str, Tensor]:
+        assert input_ids.ndim == 2
+
+        x = self.embed_tokens(input_ids)
+        rotary_emb = self.rotary_embedding(x, position_ids)
+        attn_mask = process_attention_mask(input_mask)
+        use_sdpa_causal_mask = attn_mask is None
+
+        for layer in self.layers:
+            x = layer(x, rotary_emb, attn_mask, paged_cache, use_sdpa_causal_mask)
+
+        hidden_states = self.norm(x)
+        logits = self.lm_head(hidden_states)
+
+        return {"logits": logits, "hidden_states": hidden_states}

astrai/parallel/__init__.py

@@ -0,0 +1,40 @@
+from astrai.parallel.executor import (
+    AccumOptimizer,
+    AccumScheduler,
+    BaseExecutor,
+    DDPExecutor,
+    ExecutorFactory,
+    FSDP2Executor,
+    FSDPExecutor,
+    GradientState,
+    NoneExecutor,
+)
+from astrai.parallel.module import ColumnParallelLinear, RowParallelLinear
+from astrai.parallel.setup import (
+    get_current_device,
+    get_rank,
+    get_world_size,
+    only_on_rank,
+    setup_parallel,
+    spawn_parallel_fn,
+)
+
+__all__ = [
+    "get_world_size",
+    "get_rank",
+    "get_current_device",
+    "only_on_rank",
+    "setup_parallel",
+    "spawn_parallel_fn",
+    "RowParallelLinear",
+    "ColumnParallelLinear",
+    "ExecutorFactory",
+    "BaseExecutor",
+    "GradientState",
+    "AccumOptimizer",
+    "AccumScheduler",
+    "NoneExecutor",
+    "DDPExecutor",
+    "FSDPExecutor",
+    "FSDP2Executor",
+]

astrai/parallel/executor.py

@@ -0,0 +1,407 @@
+"""Unified training executor — parallel strategy + gradient accumulation."""
+
+import contextlib
+import logging
+import os
+from contextlib import contextmanager
+from typing import Any, Callable, Optional, Tuple
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from torch.distributed.fsdp import (
+    FSDPModule,
+    FullStateDictConfig,
+    StateDictType,
+    fully_shard,
+)
+from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+from torch.distributed.tensor import DTensor
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.optim import Optimizer
+from torch.optim.lr_scheduler import LRScheduler
+
+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_fn: Callable[[], nn.Module],
+        optimizer_fn: Optional[Callable[[nn.Module], Optimizer]] = None,
+        scheduler_fn: Optional[Callable[[Optimizer], LRScheduler]] = None,
+        before_wrap: Optional[Callable[[nn.Module], nn.Module]] = None,
+    ) -> Tuple[nn.Module, Optional[Optimizer], Optional[LRScheduler]]:
+        model = model_fn()
+        if before_wrap is not None:
+            model = before_wrap(model)
+        model = self._prepare_model(model)
+        optimizer = None
+        scheduler = None
+        if optimizer_fn is not None:
+            optimizer = optimizer_fn(model)
+            if scheduler_fn is not None:
+                scheduler = scheduler_fn(optimizer)
+            optimizer = AccumOptimizer(optimizer, self.gradient_state)
+            if scheduler is not None:
+                scheduler = AccumScheduler(scheduler, self.gradient_state)
+        return model, optimizer, 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()
+
+    @contextmanager
+    def checkpoint_context(self, model: nn.Module):
+        if self.use_distributed:
+            dist.barrier()
+        state_dict = self._gather_state_dict(model)
+        yield state_dict
+        if self.use_distributed:
+            dist.barrier()
+
+    def _gather_state_dict(self, model: nn.Module):
+        state_dict = self.unwrap_model(model)
+        if self.use_distributed and get_rank() != 0:
+            return None
+        return 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
+
+    def clip_grad_norm(self, model: nn.Module, max_norm: float) -> float:
+        total_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
+        if isinstance(total_norm, torch.Tensor):
+            return total_norm.item()
+        return total_norm
+
+
+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 clip_grad_norm(self, model: nn.Module, max_norm: float) -> float:
+        if isinstance(model, FSDP) and self.use_distributed:
+            total_norm = model.clip_grad_norm_(max_norm)
+            if isinstance(total_norm, torch.Tensor):
+                return total_norm.item()
+            return total_norm
+        return super().clip_grad_norm(model, max_norm)
+
+    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=True),
+            ):
+                return model.state_dict()
+
+        return model.state_dict()
+
+
+@ExecutorFactory.register("fsdp2")
+class FSDP2Executor(BaseExecutor):
+    """FSDP2 executor using `torch.distributed.fsdp.fully_shard` (per-module API).
+
+    Wraps each child module individually via ``fully_shard``.
+    Skips the root model because ``ABC + Generic[T]`` in the MRO makes
+    FSDP2's dynamic ``__class__`` assignment fail at the CPython level.
+    Original ``Parameter`` objects are preserved (as DTensors) — no
+    ``FlatParameter``, no ``use_orig_params=True`` hack.
+    """
+
+    def __init__(
+        self,
+        grad_accum_steps: int = 1,
+        mesh: Optional[Any] = None,
+        mp_policy: Optional[Any] = None,
+        reshard_after_forward: bool = True,
+    ):
+        super().__init__(grad_accum_steps=grad_accum_steps)
+        self._mesh = mesh
+        self._mp_policy = mp_policy
+        self._reshard_after_forward = reshard_after_forward
+
+    def _prepare_model(self, model: nn.Module) -> nn.Module:
+        if not self.use_distributed:
+            logger.warning("FSDP2 backend selected but world_size=1, model not wrapped")
+            return model
+
+        kwargs = dict(
+            mesh=self._mesh,
+            mp_policy=self._mp_policy,
+            reshard_after_forward=self._reshard_after_forward,
+        )
+        kwargs = {k: v for k, v in kwargs.items() if v is not None}
+
+        for child in model.children():
+            if isinstance(child, nn.ModuleList):
+                for sub in child:
+                    fully_shard(sub, **kwargs)
+            else:
+                fully_shard(child, **kwargs)
+
+        logger.info(
+            "FSDP2 wrapping applied to %d direct children (root skipped for ABC compat)",
+            len(list(model.children())),
+        )
+        return model
+
+    @contextmanager
+    def _no_sync(self, model: nn.Module):
+        fsdp_modules = [m for m in model.modules() if isinstance(m, FSDPModule)]
+        if fsdp_modules:
+            for m in fsdp_modules:
+                m.set_requires_gradient_sync(False, recurse=True)
+            try:
+                yield
+            finally:
+                for m in fsdp_modules:
+                    m.set_requires_gradient_sync(True, recurse=True)
+        else:
+            yield
+
+    def clip_grad_norm(self, model: nn.Module, max_norm: float) -> float:
+        if self.use_distributed:
+            total_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
+            if isinstance(total_norm, torch.Tensor):
+                return total_norm.item()
+            return total_norm
+        return super().clip_grad_norm(model, max_norm)
+
+    def unwrap_model(self, model: nn.Module):
+        if not self.use_distributed:
+            return model.state_dict()
+
+        if get_rank() != 0:
+            return None
+
+        for module in model.modules():
+            if isinstance(module, FSDPModule):
+                module.unshard()
+
+        state_dict = model.state_dict()
+        result = {
+            k: (v.full_tensor() if isinstance(v, DTensor) else v)
+            for k, v in state_dict.items()
+        }
+
+        for module in model.modules():
+            if isinstance(module, FSDPModule):
+                module.reshard()
+
+        return result

astrai/parallel/module.py

@@ -0,0 +1,115 @@
+from typing import Dict
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+
+
+class ParallelModel(nn.Module):
+    def __init__(self, process_group: dist.ProcessGroup):
+        super().__init__()
+        self.process_group = process_group
+        self.rank = dist.get_rank(self.process_group)
+        self.world_size = dist.get_world_size(self.process_group)
+
+
+class RowParallelLinear(ParallelModel):
+    def __init__(
+        self,
+        process_group: dist.ProcessGroup,
+        in_features: int,
+        out_features: int,
+        bias: bool = True,
+        reduce_results: bool = True,
+    ):
+        super().__init__(process_group)
+
+        self.in_features = in_features
+        self.out_features = out_features
+        self.in_features_per_rank = in_features // self.world_size
+        self.reduce_results = reduce_results
+
+        if in_features % self.world_size != 0:
+            raise ValueError(
+                f"in_features must be divisible by world_size. Got {in_features} and {self.world_size}"
+            )
+
+        self.weight = nn.Parameter(torch.empty(out_features, self.in_features_per_rank))
+        self.bias = nn.Parameter(torch.zeros(out_features)) if bias else None
+
+    def forward(self, input: Tensor) -> Tensor:
+        output = F.linear(input, self.weight)
+
+        if self.reduce_results:
+            dist.all_reduce(output, op=dist.ReduceOp.SUM, group=self.process_group)
+
+        if self.bias is not None:
+            output += self.bias
+
+        return output
+
+    def load_state_dict(self, state_dict: Dict[str, Tensor]):
+        full_weight = state_dict.get("weight")
+        full_bias = state_dict.get("bias")
+
+        start_idx = self.rank * self.in_features_per_rank
+        end_idx = start_idx + self.in_features_per_rank
+        weight_slice = full_weight[:, start_idx:end_idx]
+        self.weight.data.copy_(weight_slice)
+
+        if self.bias is not None:
+            self.bias.data.copy_(full_bias)
+
+
+class ColumnParallelLinear(ParallelModel):
+    def __init__(
+        self,
+        process_group: dist.ProcessGroup,
+        in_features: int,
+        out_features: int,
+        bias: bool = True,
+        gather_results: bool = True,
+    ):
+        super().__init__(process_group)
+
+        self.in_features = in_features
+        self.out_features = out_features
+        self.out_features_per_rank = out_features // self.world_size
+        self.gather_results = gather_results
+
+        if out_features % self.world_size != 0:
+            raise ValueError(
+                f"out_features must be divisible by world_size. Got {out_features} and {self.world_size}"
+            )
+
+        self.weight = nn.Parameter(
+            torch.empty(self.out_features_per_rank, self.in_features)
+        )
+        self.bias = (
+            nn.Parameter(torch.zeros(self.out_features_per_rank)) if bias else None
+        )
+
+    def forward(self, input: Tensor) -> Tensor:
+        output = F.linear(input, self.weight, self.bias)
+
+        if self.gather_results:
+            output_list = [torch.empty_like(output) for _ in range(self.world_size)]
+            dist.all_gather(output_list, output, group=self.process_group)
+            output = torch.cat(output_list, dim=-1)
+
+        return output
+
+    def load_state_dict(self, state_dict: Dict[str, Tensor]):
+        full_weight = state_dict.get("weight")
+        full_bias = state_dict.get("bias")
+
+        start_idx = self.rank * self.out_features_per_rank
+        end_idx = start_idx + self.out_features_per_rank
+        weight_slice = full_weight[start_idx:end_idx, :]
+        self.weight.data.copy_(weight_slice)
+
+        if self.bias is not None:
+            bias_slice = full_bias[start_idx:end_idx]
+            self.bias.data.copy_(bias_slice)

astrai/parallel/setup.py

@@ -0,0 +1,243 @@
+import os
+import socket
+from abc import ABC, abstractmethod
+from contextlib import contextmanager
+from functools import wraps
+from typing import Callable, Optional
+
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+
+def find_free_port() -> str:
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("", 0))
+        return str(s.getsockname()[1])
+
+
+def get_current_device():
+    return os.environ["LOCAL_DEVICE"]
+
+
+def get_world_size() -> int:
+    if dist.is_available() and dist.is_initialized():
+        return dist.get_world_size()
+    else:
+        return 1
+
+
+def get_rank() -> int:
+    if dist.is_available() and dist.is_initialized():
+        return dist.get_rank()
+    else:
+        return 0
+
+
+@contextmanager
+def setup_parallel(
+    rank: int,
+    world_size: int,
+    local_rank: int,
+    backend: str = "nccl",
+    master_addr: str = "localhost",
+    master_port: str = "29500",
+    device_type: str = "cuda",
+):
+
+    if dist.is_available() and dist.is_initialized():
+        yield dist.group.WORLD
+        return
+
+    if world_size <= 1:
+        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)
+
+    os.environ["MASTER_ADDR"] = master_addr
+    os.environ["MASTER_PORT"] = master_port
+    os.environ["LOCAL_RANK"] = str(local_rank)
+    os.environ["WORLD_SIZE"] = str(world_size)
+    os.environ["LOCAL_DEVICE"] = str(device_id)
+
+    pg_kwargs = dict(rank=rank, world_size=world_size, backend=backend)
+    if backend in ("nccl", "ccl"):
+        pg_kwargs["device_id"] = device_id
+
+    dist.init_process_group(**pg_kwargs)
+
+    try:
+        if backend == "nccl" and torch.cuda.is_available():
+            torch.cuda.set_device(device_id)
+        elif backend == "ccl" and hasattr(torch, "xpu") and torch.xpu.is_available():
+            torch.xpu.set_device(device_id)
+
+        yield dist.group.WORLD
+    finally:
+        if dist.is_initialized():
+            dist.destroy_process_group()
+
+
+def only_on_rank(rank, sync=False):
+    """
+    decorator to run a function only on a specific rank.
+    """
+
+    def decorator(func):
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            ret_args = None
+            if get_rank() == rank:
+                ret_args = func(*args, **kwargs)
+
+            if sync and dist.is_available() and dist.is_initialized():
+                dist.barrier()
+
+            return ret_args
+
+        return wrapper
+
+    return decorator
+
+
+def _run_single_rank(
+    rank: int,
+    world_size: int,
+    backend: str,
+    master_addr: str,
+    master_port: str,
+    device_type: str,
+    func: Callable,
+    kwargs: dict,
+):
+    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,
+    ):
+        func(**kwargs)
+
+
+class LaunchStrategy(ABC):
+    """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,
+    backend: str = "nccl",
+    master_addr: str = "localhost",
+    master_port: Optional[str] = None,
+    device_type: str = "cuda",
+    start_method: str = "spawn",
+    **kwargs,
+):
+    if master_port is None:
+        master_port = find_free_port()
+    launcher = _detect_launcher()
+    if launcher in ("torchelastic", "torchrun", "external"):
+        strategy = TorchrunStrategy(
+            world_size, backend, master_addr, master_port, device_type, start_method
+        )
+    else:
+        strategy = LocalStrategy(
+            world_size, backend, master_addr, master_port, device_type, start_method
+        )
+    strategy.launch(func, **kwargs)

astrai/preprocessing/__init__.py

@@ -0,0 +1,40 @@
+from astrai.preprocessing.builder import (
+    BaseMaskBuilder,
+    MaskBuilderFactory,
+    MultiOutputMaskBuilder,
+    SectionedMaskBuilder,
+    SingleOutputMaskBuilder,
+)
+from astrai.preprocessing.packing import (
+    PackingStrategy,
+    PackingStrategyFactory,
+    plan_bfd,
+)
+from astrai.preprocessing.pipeline import Pipeline, filter_by_length
+from astrai.preprocessing.position_id import (
+    PositionIdStrategy,
+    PositionIdStrategyFactory,
+)
+from astrai.preprocessing.transform import TokenizeTransform
+from astrai.preprocessing.writer import (
+    StoreWriter,
+    StoreWriterFactory,
+)
+
+__all__ = [
+    "BaseMaskBuilder",
+    "MaskBuilderFactory",
+    "MultiOutputMaskBuilder",
+    "PackingStrategy",
+    "PackingStrategyFactory",
+    "Pipeline",
+    "PositionIdStrategy",
+    "PositionIdStrategyFactory",
+    "SectionedMaskBuilder",
+    "SingleOutputMaskBuilder",
+    "StoreWriter",
+    "StoreWriterFactory",
+    "TokenizeTransform",
+    "filter_by_length",
+    "plan_bfd",
+]

astrai/preprocessing/builder.py

@@ -0,0 +1,537 @@
+"""Mask building for preprocessing pipeline.
+
+:class:`SectionRenderer` converts section specs into token ids and loss
+masks (template / text / value extraction).  :class:`SingleOutputMaskBuilder`
+handles single-output (SFT / pretrain), :class:`MultiOutputMaskBuilder`
+handles multi-output (DPO / GRPO), and :class:`SectionedMaskBuilder`
+orchestrates both modes as a façade.
+"""
+
+from abc import ABC, abstractmethod
+from typing import Optional
+
+from astrai.factory import BaseFactory
+
+
+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:
+    if action == "$role":
+        return config.mask.get(role, config.mask_default)
+    return action
+
+
+class SectionRenderer:
+    """Render section specs into ``(ids, loss_mask)`` tuples."""
+
+    def process_sections(
+        self,
+        item: dict,
+        sections: list,
+        config,
+        tokenizer,
+        *,
+        is_top_level: bool = False,
+    ):
+        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(
+                    item, field, action, tokenizer, config, all_ids, loss_mask
+                )
+                if not success:
+                    continue
+            else:
+                success = self._append_text(
+                    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 process_sections_batch(
+        self,
+        items: list[dict],
+        sections: list,
+        config,
+        tokenizer,
+        *,
+        is_top_level=False,
+        filter_text=True,
+    ):
+        """Render and tokenize a group of records with batched Rust tokenization."""
+        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
+        )
+        plans: list[list[tuple[str, str, bool]]] = []
+
+        for item in items:
+            plan: list[tuple[str, str, bool]] = []
+            first_section = True
+            for sec in sections:
+                field = sec["field"]
+                action = sec["action"]
+                use_template = sec.get("template", False)
+                add_special = sec.get(
+                    "add_special_tokens", not use_template and first_section
+                )
+
+                if use_template:
+                    messages = item.get(field)
+                    if not isinstance(messages, list) or not messages:
+                        continue
+                    for msg in messages:
+                        role = msg.get("role", "")
+                        rendered = tokenizer.apply_chat_template(
+                            [msg], tokenize=False, add_generation_prompt=False
+                        )
+                        plan.append(
+                            (rendered, _resolve_action(action, role, config), False)
+                        )
+                else:
+                    text = str(item.get(field, ""))
+                    if not text.strip():
+                        continue
+                    if is_text_config and filter_text:
+                        pp = config.preprocessing
+                        if pp.min_chars > 0 and len(text) < pp.min_chars:
+                            continue
+                        if len(text) > pp.max_chars:
+                            continue
+                    plan.append((text, action, add_special))
+
+                first_section = False
+            plans.append(plan)
+
+        encoded: dict[tuple[int, int], list[int]] = {}
+        for add_special in (False, True):
+            refs = [
+                (item_idx, unit_idx, text)
+                for item_idx, plan in enumerate(plans)
+                for unit_idx, (text, _, add) in enumerate(plan)
+                if add == add_special
+            ]
+            if not refs:
+                continue
+            ids_batch = tokenizer.encode(
+                [text for _, _, text in refs], add_special_tokens=add_special
+            )
+            for (item_idx, unit_idx, _), ids in zip(refs, ids_batch):
+                encoded[(item_idx, unit_idx)] = ids
+
+        outputs = []
+        max_len = config.preprocessing.max_seq_len
+        for item_idx, plan in enumerate(plans):
+            all_ids = []
+            loss_mask = []
+            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)
+            for unit_idx, (_, action, _) in enumerate(plan):
+                ids = encoded[(item_idx, unit_idx)]
+                all_ids.extend(ids)
+                loss_mask.extend([1 if action == "train" else 0] * len(ids))
+            all_ids = all_ids[:max_len]
+            loss_mask = loss_mask[: len(all_ids)]
+            if not all_ids or (is_top_level and has_template and len(all_ids) <= 1):
+                outputs.append((None, None))
+            else:
+                outputs.append((all_ids, loss_mask))
+        return outputs
+
+    def process_list_field(self, item: dict, sections: list, config, tokenizer):
+        """Tokenize a list-valued field, preserving per-element boundaries.
+
+        Returns ``(list_of_id_lists, list_of_mask_lists)`` where each
+        inner list corresponds to one element of the source list.  This
+        is critical for GRPO where each response must stay a separate
+        sequence so the strategy can form a ``[G, R]`` tensor.
+        """
+        per_item_ids: list[list[int]] = []
+        per_item_masks: list[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:
+                ids: list[int] = []
+                mask: list[int] = []
+                if use_template:
+                    if isinstance(val, list):
+                        wrapper = {field: val}
+                        self._append_template(
+                            wrapper, field, action, tokenizer, config, ids, mask
+                        )
+                else:
+                    wrapper = {field: str(val)}
+                    self._append_text(
+                        wrapper,
+                        field,
+                        action,
+                        tokenizer,
+                        False,
+                        False,
+                        config,
+                        ids,
+                        mask,
+                    )
+                if ids:
+                    max_len = config.preprocessing.max_seq_len
+                    ids = ids[:max_len]
+                    mask = mask[: len(ids)]
+                    per_item_ids.append(ids)
+                    per_item_masks.append(mask)
+
+        if not per_item_ids:
+            return None, None
+        return per_item_ids, per_item_masks
+
+    def process_list_field_batch(self, items, sections, config, tokenizer):
+        per_item_ids = [[] for _ in items]
+        per_item_masks = [[] for _ in items]
+
+        for sec in sections:
+            wrappers = []
+            owners = []
+            field = sec["field"]
+            for item_idx, item in enumerate(items):
+                values = item.get(field)
+                if not isinstance(values, list):
+                    continue
+                for val in values:
+                    if sec.get("template", False) and not isinstance(val, list):
+                        continue
+                    wrappers.append({field: val if isinstance(val, list) else str(val)})
+                    owners.append(item_idx)
+
+            rendered = self.process_sections_batch(
+                wrappers,
+                [sec],
+                config,
+                tokenizer,
+                is_top_level=False,
+                filter_text=False,
+            )
+            for owner, (ids, mask) in zip(owners, rendered):
+                if ids:
+                    per_item_ids[owner].append(ids)
+                    per_item_masks[owner].append(mask)
+
+        return [
+            (ids, masks) if ids else (None, None)
+            for ids, masks in zip(per_item_ids, per_item_masks)
+        ]
+
+    @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):
+        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 _append_template(
+        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(
+        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
+
+
+class BaseMaskBuilder(ABC):
+    """Convert a JSONL item into token ids and optional loss_mask."""
+
+    @abstractmethod
+    def build(self, item: dict, config, tokenizer) -> Optional[dict]: ...
+
+    def build_batch(self, items: list[dict], config, tokenizer) -> list[Optional[dict]]:
+        return [self.build(item, config, tokenizer) for item in items]
+
+
+class MaskBuilderFactory(BaseFactory["BaseMaskBuilder"]):
+    pass
+
+
+@MaskBuilderFactory.register("single")
+class SingleOutputMaskBuilder(BaseMaskBuilder):
+    """Build a single output sequence with optional loss mask.
+
+    Expects ``config.input.sections`` (list of section specs).
+    """
+
+    def __init__(self, renderer: Optional[SectionRenderer] = None):
+        self.renderer = renderer or SectionRenderer()
+
+    def build(self, item: dict, config, tokenizer) -> Optional[dict]:
+        sections = config.input.sections
+        if not sections:
+            return None
+
+        ids, mask = self.renderer.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_batch(self, items, config, tokenizer):
+        sections = config.input.sections
+        if not sections:
+            return [None] * len(items)
+        rendered = self.renderer.process_sections_batch(
+            items, sections, config, tokenizer, is_top_level=True
+        )
+        results = []
+        for item, (ids, mask) in zip(items, rendered):
+            if ids is None:
+                results.append(None)
+                continue
+            result = {
+                "sequence": ids,
+                "domain": _extract_domain(item, config.output.domain_key),
+            }
+            if not all(m == 1 for m in mask):
+                result["loss_mask"] = mask
+            results.append(result)
+        return results
+
+
+@MaskBuilderFactory.register("multi")
+class MultiOutputMaskBuilder(BaseMaskBuilder):
+    """Build multiple output sequences (DPO / GRPO).
+
+    Expects ``config.input.sources`` (dict of output_key → spec).
+    """
+
+    def __init__(self, renderer: Optional[SectionRenderer] = None):
+        self.renderer = renderer or SectionRenderer()
+
+    def build(self, item: dict, config, tokenizer) -> Optional[dict]:
+        sources_spec = getattr(config.input, "sources", None)
+        if not sources_spec:
+            return None
+
+        result: dict = {}
+        any_output = False
+
+        for output_key, spec in sources_spec.items():
+            sections = spec.get("sections", [])
+            if not sections:
+                continue
+
+            if self.renderer.is_value_section(sections):
+                ids = self.renderer.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.renderer.process_list_field(
+                    item, sections, config, tokenizer
+                )
+                if ids is None:
+                    continue
+                # ids is List[List[int]] — preserve per-response structure
+                result[output_key] = ids
+                if mask is not None:
+                    result[mask_key] = mask
+                any_output = True
+                continue
+
+            ids, mask = self.renderer.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
+
+    def build_batch(self, items, config, tokenizer):
+        sources_spec = getattr(config.input, "sources", None)
+        if not sources_spec:
+            return [None] * len(items)
+
+        results = [{} for _ in items]
+        for output_key, spec in sources_spec.items():
+            sections = spec.get("sections", [])
+            if not sections:
+                continue
+            if self.renderer.is_value_section(sections):
+                for item, result in zip(items, results):
+                    value = self.renderer.extract_raw_value(item, sections)
+                    if value is not None:
+                        result[output_key] = value
+                continue
+
+            mask_key = spec.get("mask_key", f"{output_key}_mask")
+            if spec.get("list_field", False):
+                rendered = self.renderer.process_list_field_batch(
+                    items, sections, config, tokenizer
+                )
+            else:
+                rendered = self.renderer.process_sections_batch(
+                    items, sections, config, tokenizer, is_top_level=True
+                )
+
+            for result, (ids, mask) in zip(results, rendered):
+                if ids is None:
+                    continue
+                result[output_key] = ids
+                if spec.get("list_field", False) or not all(m == 1 for m in mask):
+                    result[mask_key] = mask
+                elif "mask_key" in spec:
+                    result[mask_key] = mask
+
+        return [
+            ({**result, "domain": _extract_domain(item, config.output.domain_key)})
+            if result
+            else None
+            for item, result in zip(items, results)
+        ]
+
+
+@MaskBuilderFactory.register("sectioned")
+class SectionedMaskBuilder(BaseMaskBuilder):
+    """Façade that dispatches to SingleOutputMaskBuilder or MultiOutputMaskBuilder.
+
+    Preserves backward compatibility for existing configs and code that rely
+    on the ``"sectioned"`` factory name.
+    """
+
+    def __init__(self):
+        self._single = SingleOutputMaskBuilder()
+        self._multi = MultiOutputMaskBuilder()
+
+    def build(self, item: dict, config, tokenizer) -> Optional[dict]:
+        sources_spec = getattr(config.input, "sources", None)
+        if sources_spec:
+            return self._multi.build(item, config, tokenizer)
+        return self._single.build(item, config, tokenizer)
+
+    def build_batch(self, items, config, tokenizer):
+        sources_spec = getattr(config.input, "sources", None)
+        if sources_spec:
+            return self._multi.build_batch(items, config, tokenizer)
+        return self._single.build_batch(items, config, tokenizer)

astrai/preprocessing/core.py

@@ -0,0 +1,124 @@
+"""Shared preprocessing kernel used by both :class:`Pipeline` and
+:class:`TokenizeTransform`.
+
+The two entry points previously duplicated ~60 % of their logic:
+record iteration, mask-builder invocation, primary-id extraction,
+per-key accumulation, dtype inference and position-id generation.
+This module factors out the common core as pure functions so that
+the online (``TokenizeTransform``) and offline (``Pipeline``) paths
+stay in lockstep.
+"""
+
+from itertools import chain
+from typing import Dict, Iterator, List, Optional
+
+import torch
+
+from astrai.config.preprocess_config import PipelineConfig
+from astrai.preprocessing.builder import MaskBuilderFactory
+from astrai.preprocessing.position_id import PositionIdStrategyFactory
+from astrai.tokenize import AutoTokenizer
+
+
+def build_preprocessing_components(config: PipelineConfig, tokenizer_path: str):
+    """Load tokenizer, mask builder and position-id strategy together.
+
+    Both ``Pipeline`` and ``TokenizeTransform`` need the same triple;
+    centralising the construction avoids drift (e.g. one path forgetting
+    to create the position-id strategy).
+    """
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
+    mask_builder = MaskBuilderFactory.create("sectioned")
+    position_strategy = PositionIdStrategyFactory.create(
+        config.output.position_ids_mode
+    )
+    return tokenizer, mask_builder, position_strategy
+
+
+def primary_ids(result: dict) -> List[int]:
+    """Return the first flat int-list value in *result*.
+
+    Used for token counting and position-id generation when the
+    primary key name is not known (DPO uses ``chosen``, GRPO uses
+    ``prompts``, SFT uses ``sequence``).
+    """
+    for val in result.values():
+        if isinstance(val, list) and val and isinstance(val[0], int):
+            return val
+    return []
+
+
+def infer_dtype(ids: List) -> torch.dtype:
+    """Float values become float32, everything else int32."""
+    if ids and isinstance(ids[0], float):
+        return torch.float32
+    return torch.int32
+
+
+def iter_raw_records(
+    records: List[dict],
+    mask_builder,
+    config: PipelineConfig,
+    tokenizer,
+) -> Iterator[dict]:
+    """Yield mask-builder output dicts for each record, skipping failures.
+
+    Drops ``domain`` from the result (callers that need it should read
+    it before calling this).  Each yielded dict maps a key
+    (``sequence``, ``chosen``, ``responses``…) to either a flat
+    ``List[int]`` or a nested ``List[List[int]]`` (GRPO responses/masks).
+    """
+    for item in records:
+        result = mask_builder.build(item, config, tokenizer)
+        if result is None:
+            continue
+        result.pop("domain", None)
+        if not primary_ids(result):
+            continue
+        yield result
+
+
+def to_per_record_tensors(
+    raw: Dict[str, list],
+) -> Dict[str, List[torch.Tensor]]:
+    """Convert an accumulated ``{key: [per-record ids]}`` dict to tensors.
+
+    Handles three shapes transparently:
+
+    - ``List[int]`` per record (``sequence``, ``chosen``…) → one tensor per record.
+    - ``List[List[int]]`` per record (GRPO ``responses``/``masks``) → one
+      ``List[Tensor]`` per record (nested), preserving the per-response
+      boundary so downstream code can index responses individually.
+    - ``List[int]`` for the whole shard (pre-packed keys) → single tensor.
+
+    The detection mirrors the previous inline logic in
+    ``Pipeline._flush`` and ``TokenizeTransform.apply``.
+    """
+    tensors: Dict[str, List[torch.Tensor]] = {}
+    for key, ids_list in raw.items():
+        if ids_list and isinstance(ids_list[0], list):
+            tensors[key] = [
+                [torch.tensor(sub, dtype=infer_dtype(sub)) for sub in ids]
+                if ids and isinstance(ids[0], list)
+                else torch.tensor(ids, dtype=infer_dtype(ids))
+                for ids in ids_list
+            ]
+        else:
+            tensors[key] = [
+                torch.tensor(list(chain.from_iterable(ids_list)), dtype=torch.int32)
+            ]
+    return tensors
+
+
+def build_position_ids(
+    sequences: List[List[int]],
+    strategy,
+) -> Optional[List[int]]:
+    """Generate position ids for *sequences* using *strategy*.
+
+    Returns ``None`` when the strategy produces no ids (e.g. ``none``
+    mode), so callers can skip attaching the key instead of storing
+    an empty list.
+    """
+    pos_ids = strategy.generate(sequences)
+    return pos_ids or None

astrai/preprocessing/packing.py

@@ -0,0 +1,176 @@
+"""Sequence packing strategies for shard-level reordering and truncation.
+
+Each strategy receives the accumulated ``{key: [list of token lists]}``
+dict for a shard and returns a reordered / truncated version.  The
+pipeline later flattens the result into contiguous tensors.
+"""
+
+from abc import ABC, abstractmethod
+from typing import Dict, List
+
+from astrai.factory import BaseFactory
+
+
+def _truncate(seq: List[int], max_len: int, mode: str) -> List[int]:
+    if len(seq) <= max_len:
+        return seq
+    if mode == "keep_end":
+        return seq[-max_len:]
+    return seq[:max_len]
+
+
+def plan_bfd(
+    sequences: List[List[int]], max_packed_len: int, truncation_mode: str = "keep_start"
+) -> List[List[int]]:
+    """Best-Fit Decreasing bin packing of *sequences* into bins.
+
+    Returns a list of bins, each bin a list of original indices into
+    *sequences*.  Bin capacities are respected on the *truncated*
+    length of each sequence (so a sequence longer than
+    *max_packed_len* counts at *max_packed_len*).
+
+    Pure index-based so callers can apply the same plan to any
+    aligned key (``loss_mask``, ``position_ids``…).
+    """
+    n = len(sequences)
+    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 = len(_truncate(sequences[orig_idx], max_packed_len, truncation_mode))
+        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)
+
+    return bins
+
+
+class PackingStrategy(ABC):
+    """Reorder and truncate sequences within a shard."""
+
+    @abstractmethod
+    def apply(
+        self,
+        keys: Dict[str, List[List[int]]],
+        max_packed_len: int,
+        truncation_mode: str,
+    ) -> Dict[str, List[List[int]]]:
+        raise NotImplementedError
+
+
+class PackingStrategyFactory(BaseFactory["PackingStrategy"]):
+    pass
+
+
+@PackingStrategyFactory.register("simple")
+class SimplePacking(PackingStrategy):
+    def apply(
+        self,
+        keys: Dict[str, List[List[int]]],
+        max_packed_len: int,
+        truncation_mode: str,
+    ) -> Dict[str, List[List[int]]]:
+        return {
+            k: [_truncate(v, max_packed_len, truncation_mode) for v in vals]
+            for k, vals in keys.items()
+        }
+
+
+@PackingStrategyFactory.register("bfd")
+class BFDPacking(PackingStrategy):
+    """Best-Fit Decreasing bin packing.
+
+    Assigns sequences to bins using a best-fit heuristic (sorted by
+    decreasing length) and concatenates sequences within each bin into
+    a single packed sequence.  Packed sequences are truncated to
+    *max_packed_len* so that each packed bin fits within one context
+    window during training.
+    """
+
+    def apply(
+        self,
+        keys: Dict[str, List[List[int]]],
+        max_packed_len: int,
+        truncation_mode: str,
+    ) -> Dict[str, List[List[int]]]:
+        sequences = keys.get("sequence", [])
+        if not sequences:
+            return keys
+        bins = plan_bfd(sequences, max_packed_len, truncation_mode)
+
+        packed: Dict[str, List[List[int]]] = {}
+        for k, vals in keys.items():
+            packed[k] = [
+                _truncate(
+                    self._concat_bin(vals, bin_indices),
+                    max_packed_len,
+                    truncation_mode,
+                )
+                for bin_indices in bins
+            ]
+        return packed
+
+    @staticmethod
+    def _concat_bin(vals: List[List[int]], indices: List[int]) -> List[int]:
+        result: List[int] = []
+        for i in indices:
+            result.extend(vals[i])
+        return result
+
+
+@PackingStrategyFactory.register("bfd_split")
+class BFDSplitPacking(BFDPacking):
+    """BFD packing with over-length sequences split into chunks.
+
+    Sequences longer than *max_packed_len* are split into consecutive
+    chunks of at most *max_packed_len* tokens instead of being
+    truncated.  Each chunk becomes an independent sequence that enters
+    BFD planning.  All keys (``loss_mask``, ``position_ids``, …) are
+    split in lockstep so per-token alignment is preserved.
+
+    Note: because each chunk is treated as a separate document, the
+    second chunk of a split sequence loses the preceding context.
+    """
+
+    def apply(
+        self,
+        keys: Dict[str, List[List[int]]],
+        max_packed_len: int,
+        truncation_mode: str,
+    ) -> Dict[str, List[List[int]]]:
+        sequences = keys.get("sequence", [])
+        if not sequences:
+            return keys
+        if max_packed_len <= 0:
+            return super().apply(keys, max_packed_len, truncation_mode)
+
+        split_keys = self._split_all(keys, max_packed_len)
+        return super().apply(split_keys, max_packed_len, truncation_mode)
+
+    @staticmethod
+    def _split_all(
+        keys: Dict[str, List[List[int]]], max_packed_len: int
+    ) -> Dict[str, List[List[int]]]:
+        """Split every sequence exceeding *max_packed_len* into chunks,
+        applying the same chunk boundaries to all keys."""
+        sequences = keys["sequence"]
+        chunk_bounds = [list(range(0, len(s), max_packed_len)) for s in sequences]
+        result: Dict[str, List[List[int]]] = {}
+        for key, vals in keys.items():
+            split_vals: List[List[int]] = []
+            for val, starts in zip(vals, chunk_bounds):
+                for start in starts:
+                    split_vals.append(val[start : start + max_packed_len])
+            result[key] = split_vals
+        return result

astrai/preprocessing/pipeline.py

@@ -0,0 +1,277 @@
+"""Config-driven JSONL preprocessing pipeline.
+
+Composes a :class:`BaseMaskBuilder` (selected by ``input.type``) with
+sharding and flush to ``.h5`` / ``.bin`` storage.  Packing, position-id
+generation and storage writing are each delegated to pluggable strategies,
+dispatched by configuration keys.
+
+Record iteration, mask building, primary-id extraction and per-key
+accumulation are shared with :class:`TokenizeTransform` via the
+:mod:`astrai.preprocessing.core` helpers.
+"""
+
+import json
+import logging
+import os
+from collections import defaultdict
+from itertools import chain
+from typing import Dict, List, Optional
+
+import torch
+import tqdm
+
+from astrai.config.preprocess_config import PipelineConfig
+from astrai.preprocessing.core import (
+    build_preprocessing_components,
+    primary_ids,
+)
+from astrai.preprocessing.packing import PackingStrategyFactory
+from astrai.preprocessing.writer import StoreWriterFactory
+
+logger = logging.getLogger(__name__)
+
+_STR_TO_DTYPE: dict[str, torch.dtype] = {
+    "bool": torch.bool,
+    "uint8": torch.uint8,
+    "int8": torch.int8,
+    "int16": torch.int16,
+    "int32": torch.int32,
+    "int64": torch.int64,
+    "float16": torch.float16,
+    "float32": torch.float32,
+    "float64": torch.float64,
+}
+
+
+def filter_by_length(text: str, min_len: int = 50, max_len: int = 2_000_000) -> bool:
+    return min_len <= len(text) <= max_len
+
+
+class Pipeline:
+    """Tokenization pipeline driven by a declarative :class:`PipelineConfig`.
+
+    Usage::
+
+        config = PipelineConfig.from_file("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.tokenizer, self.mask_builder, self._position_id = (
+            build_preprocessing_components(config, tokenizer_path)
+        )
+        self._packer = PackingStrategyFactory.create(
+            config.preprocessing.packing_strategy
+        )
+        self._writer = StoreWriterFactory.create(config.output.storage_format)
+
+    def transform(self, item: dict) -> Optional[dict]:
+        return self.mask_builder.build(item, self.config, self.tokenizer)
+
+    def transform_batch(self, items: list[dict]) -> list[Optional[dict]]:
+        return self.mask_builder.build_batch(items, self.config, self.tokenizer)
+
+    def run(self):
+        domains: dict = defaultdict(lambda: defaultdict(list))
+        total_tokens = 0
+        shard_idx: dict[str, int] = defaultdict(int)
+        count = 0
+
+        pp = self.config.preprocessing
+
+        progress = tqdm.tqdm(desc="Tokenizing", unit="docs", mininterval=0.5)
+        stop = False
+        for items in self._iter_batches(pp.batch_size):
+            progress.update(len(items))
+            try:
+                results = self.transform_batch(items)
+            except Exception:
+                logger.warning(
+                    "Failed to process batch, retrying records individually",
+                    exc_info=True,
+                )
+                results = []
+                for item in items:
+                    try:
+                        results.append(self.transform(item))
+                    except Exception:
+                        logger.warning(
+                            "Failed to process item, skipping", exc_info=True
+                        )
+                        results.append(None)
+
+            for result in results:
+                if pp.max_items and count >= pp.max_items:
+                    stop = True
+                    break
+                if result is None:
+                    continue
+
+                domain = result.pop("domain", "__default__")
+                ids = primary_ids(result)
+                if not ids:
+                    continue
+
+                bucket = domains[domain]
+                self._align_bucket(bucket, result, ids)
+                for key, val in result.items():
+                    bucket[key].append(val)
+
+                count += 1
+                total_tokens += len(ids)
+
+                if total_tokens >= self.config.output.max_tokens_per_shard:
+                    self._flush(domains, shard_idx)
+                    domains.clear()
+                    total_tokens = 0
+            if stop:
+                break
+
+        progress.close()
+
+        if total_tokens > 0:
+            self._flush(domains, shard_idx)
+
+    @staticmethod
+    def _align_bucket(bucket: dict, result: dict, ids: list):
+        """Pad previously-accumulated keys that are missing from *result*."""
+        for key in list(bucket.keys()):
+            if key in result:
+                continue
+            bucket[key].append([0] * len(ids))
+
+    def _iter_items(self):
+        for path in self.paths:
+            with open(path, "r", encoding="utf-8") as f:
+                if path.endswith(".json"):
+                    data = json.load(f)
+                    if isinstance(data, dict):
+                        yield data
+                    elif isinstance(data, list):
+                        yield from data
+                else:
+                    for line in f:
+                        line = line.strip()
+                        if not line:
+                            continue
+                        yield json.loads(line)
+
+    def _iter_batches(self, batch_size: int):
+        batch_size = max(1, batch_size)
+        batch = []
+        for item in self._iter_items():
+            batch.append(item)
+            if len(batch) >= batch_size:
+                yield batch
+                batch = []
+        if batch:
+            yield batch
+
+    def _flush(self, domains, shard_idx):
+        for domain, keys in domains.items():
+            idx = shard_idx[domain]
+
+            pp = self.config.preprocessing
+            original_sequences = keys.get("sequence", [])
+            mode = self.config.output.position_ids_mode
+
+            keys = self._inject_doc_reset_position_ids(keys, mode, original_sequences)
+            keys = self._packer.apply(dict(keys), pp.max_packed_len, pp.truncation_mode)
+            tensors = self._to_tensors(keys)
+            tensors = self._inject_continuous_position_ids(
+                tensors, mode, keys.get("sequence", [])
+            )
+
+            self._writer.save(self.output_dir, domain, idx, 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)"
+            )
+
+    def _inject_doc_reset_position_ids(
+        self,
+        keys: Dict[str, list],
+        mode: str,
+        original_sequences: List[List[int]],
+    ) -> Dict[str, list]:
+        """Attach per-document position_ids before packing (``doc_reset``).
+
+        ``doc_reset`` position ids must enter the packer so that each
+        packed bin concatenates the per-doc ranges in bin order.  The
+        per-record structure ``[range(len(s)) for s in seqs]`` is required
+        by the packer (it concatenates per-record lists per bin); the
+        ``PositionIdStrategy.generate`` flattens, so it cannot be used
+        directly here — it is only consulted for the ``continuous``
+        post-packing path.
+        """
+        if mode != "doc_reset" or not original_sequences:
+            return keys
+        keys["position_ids"] = [list(range(len(s))) for s in original_sequences]
+        return keys
+
+    def _inject_continuous_position_ids(
+        self,
+        tensors: Dict[str, List[torch.Tensor]],
+        mode: str,
+        packed_sequences: List[List[int]],
+    ) -> Dict[str, List[torch.Tensor]]:
+        """Attach a single continuous position_ids tensor after packing.
+
+        ``continuous`` mode spans the whole shard (post-packing), so it
+        cannot participate in bin packing — it is computed from the
+        packed sequences and appended directly to the tensor dict.
+        """
+        if mode != "continuous" or not packed_sequences:
+            return tensors
+        pos_ids = self._position_id.generate(packed_sequences)
+        if pos_ids:
+            tensors["position_ids"] = [torch.tensor(pos_ids, dtype=torch.int32)]
+        return tensors
+
+    def _to_tensors(self, keys: Dict[str, list]) -> Dict[str, List[torch.Tensor]]:
+        """Convert packed per-key id lists to tensors.
+
+        Honours ``config.output.dtype`` overrides per key; falls back to
+        ``int32``.  Handles three shapes (see
+        :func:`astrai.preprocessing.core.to_per_record_tensors` for the
+        equivalent online-path helper):
+        - ``List[int]`` per record → one tensor per record.
+        - ``List[List[int]]`` per record (GRPO responses/masks) → one tensor
+          per record, inner lists flattened.
+        - ``List[int]`` for the whole shard (pre-packed keys) → single tensor.
+        """
+        tensors: Dict[str, List[torch.Tensor]] = {}
+        for key, ids_list in keys.items():
+            dt = _STR_TO_DTYPE.get(
+                self.config.output.dtype.get(key, "int32"), torch.int32
+            )
+            if ids_list and isinstance(ids_list[0], list):
+                tensors[key] = [
+                    torch.tensor(
+                        list(chain.from_iterable(ids))
+                        if ids and isinstance(ids[0], list)
+                        else ids,
+                        dtype=dt,
+                    )
+                    for ids in ids_list
+                ]
+            else:
+                tensors[key] = [
+                    torch.tensor(list(chain.from_iterable(ids_list)), dtype=dt)
+                ]
+        return tensors

astrai/preprocessing/position_id.py

@@ -0,0 +1,46 @@
+"""Position-id generation strategies for packed sequences.
+
+Each strategy takes the list of per-document token sequences after packing
+and returns a flat list of position ids (same total length as all
+sequences combined).  The pipeline wraps the result into a tensor and
+attaches it as ``position_ids``.
+"""
+
+from abc import ABC, abstractmethod
+from typing import List
+
+from astrai.factory import BaseFactory
+
+
+class PositionIdStrategy(ABC):
+    """Generate ``position_ids`` for packed sequences."""
+
+    @abstractmethod
+    def generate(self, sequences: List[List[int]]) -> List[int]:
+        raise NotImplementedError
+
+
+class PositionIdStrategyFactory(BaseFactory["PositionIdStrategy"]):
+    pass
+
+
+@PositionIdStrategyFactory.register("none")
+class NoPositionId(PositionIdStrategy):
+    def generate(self, sequences: List[List[int]]) -> List[int]:
+        return []
+
+
+@PositionIdStrategyFactory.register("doc_reset")
+class DocResetPositionId(PositionIdStrategy):
+    def generate(self, sequences: List[List[int]]) -> List[int]:
+        pos_ids = []
+        for seq in sequences:
+            pos_ids.extend(range(len(seq)))
+        return pos_ids
+
+
+@PositionIdStrategyFactory.register("continuous")
+class ContinuousPositionId(PositionIdStrategy):
+    def generate(self, sequences: List[List[int]]) -> List[int]:
+        total = sum(len(seq) for seq in sequences)
+        return list(range(total))

astrai/preprocessing/transform.py

@@ -0,0 +1,92 @@
+"""Tokenization transform for JSONL record streams.
+
+Bridges the Reader layer (``JsonlStore`` reads raw JSON records) and the
+Dataset layer (expects per-record tensors).  Holds the tokenizer,
+mask-builder and position-id strategy together so that I/O code stays
+free of model dependencies.
+
+The record-processing core (mask building, primary-id extraction,
+per-key tensorisation, position-id generation) is shared with
+:class:`astrai.preprocessing.pipeline.Pipeline` via the
+:mod:`astrai.preprocessing.core` helpers.
+"""
+
+import json
+from pathlib import Path
+from typing import Dict, List
+
+import torch
+
+from astrai.config.preprocess_config import PipelineConfig
+from astrai.preprocessing.core import (
+    build_position_ids,
+    build_preprocessing_components,
+    iter_raw_records,
+    to_per_record_tensors,
+)
+
+
+class TokenizeTransform:
+    """Tokenize raw JSONL record dicts into per-key tensor lists.
+
+    Owns the three preprocessing concerns that were previously inlined in
+    ``JsonlStore``: tokenization, loss-mask construction and position-id
+    generation.  Constructing it loads the tokenizer, so it is intentionally
+    cheap to pass around once built.
+
+    Args:
+        config: Pipeline config describing sections / masks / position mode.
+        tokenizer_path: Path passed to ``AutoTokenizer.from_pretrained``.
+    """
+
+    def __init__(self, config: PipelineConfig, tokenizer_path: str):
+        self.config = config
+        self.tokenizer, self.mask_builder, self.position_strategy = (
+            build_preprocessing_components(config, tokenizer_path)
+        )
+
+    @classmethod
+    def from_config_file(cls, config_path: str) -> "TokenizeTransform":
+        """Build from a ``dataset_config.json`` file path.
+
+        The config file follows :class:`PipelineConfig` schema with an
+        extra ``tokenizer_path`` field.  When omitted, the config's
+        parent directory is used as the tokenizer path.
+        """
+        root = Path(config_path).parent
+        with open(config_path, "r", encoding="utf-8") as f:
+            raw_config = json.load(f)
+        tokenizer_path = raw_config.pop("tokenizer_path", None) or str(root)
+        config = PipelineConfig.from_dict(raw_config)
+        return cls(config, tokenizer_path)
+
+    def apply(self, records: List[dict]) -> Dict[str, list]:
+        """Tokenize a list of raw record dicts.
+
+        Returns a dict mapping key (``sequence``, ``chosen``, ``responses``,
+        …) to a list of per-record tensors (or nested tensor lists for
+        multi-response keys such as GRPO ``responses``).
+        """
+        raw: Dict[str, list] = {}
+        doc_sequences: List[List[int]] = []
+
+        for result in iter_raw_records(
+            records, self.mask_builder, self.config, self.tokenizer
+        ):
+            primary = None
+            for val in result.values():
+                if isinstance(val, list) and val and isinstance(val[0], int):
+                    primary = val
+                    break
+            if primary is not None:
+                doc_sequences.append(primary)
+            for key, ids in result.items():
+                raw.setdefault(key, []).append(ids)
+
+        tensors = to_per_record_tensors(raw)
+
+        pos_ids = build_position_ids(doc_sequences, self.position_strategy)
+        if pos_ids is not None:
+            tensors["position_ids"] = [torch.tensor(pos_ids, dtype=torch.int32)]
+
+        return tensors

astrai/preprocessing/writer.py

@@ -0,0 +1,75 @@
+"""Storage writer strategies for pipeline output.
+
+The :class:`StoreWriter` abstraction decouples the pipeline from the
+concrete storage format (bin / h5).  The pipeline builds a ``{key:
+List[Tensor]}`` dict and delegates the write to the writer selected
+by ``output.storage_format``.
+"""
+
+import logging
+import os
+import shutil
+from abc import ABC, abstractmethod
+from typing import Dict, List
+
+import torch
+
+from astrai.factory import BaseFactory
+from astrai.serialization import save_bin, save_h5
+
+logger = logging.getLogger(__name__)
+
+
+class StoreWriter(ABC):
+    """Write pre-tokenized tensors to disk in a format-specific way."""
+
+    @abstractmethod
+    def save(
+        self,
+        output_dir: str,
+        domain: str,
+        shard_idx: int,
+        tensors: Dict[str, List[torch.Tensor]],
+    ) -> None: ...
+
+
+class StoreWriterFactory(BaseFactory["StoreWriter"]):
+    pass
+
+
+@StoreWriterFactory.register("bin")
+class BinWriter(StoreWriter):
+    def save(self, output_dir, domain, shard_idx, tensors):
+        shard_path = os.path.join(output_dir, domain, f"shard_{shard_idx:04d}")
+        try:
+            save_bin(shard_path, tensors)
+        except Exception:
+            if os.path.exists(shard_path):
+                shutil.rmtree(shard_path, ignore_errors=True)
+            logger.error(
+                "Failed to write shard %s/%s_%04d, cleaned up partial output",
+                domain,
+                "shard",
+                shard_idx,
+                exc_info=True,
+            )
+            raise
+
+
+@StoreWriterFactory.register("h5")
+class H5Writer(StoreWriter):
+    def save(self, output_dir, domain, shard_idx, tensors):
+        chunk_dir = os.path.join(output_dir, domain)
+        file_path = os.path.join(chunk_dir, f"data_{shard_idx:04d}.h5")
+        try:
+            save_h5(chunk_dir, f"data_{shard_idx:04d}", tensors)
+        except Exception:
+            if os.path.exists(file_path):
+                os.remove(file_path)
+            logger.error(
+                "Failed to write shard %s/data_%04d.h5, cleaned up partial output",
+                domain,
+                shard_idx,
+                exc_info=True,
+            )
+            raise

astrai/protocols.py

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

astrai/serialization/__init__.py

@@ -0,0 +1,45 @@
+"""Serialization utilities for models and datasets.
+
+This package re-exports checkpoint helpers and dataset storage helpers so
+that existing imports from ``astrai.serialization`` continue to work.
+"""
+
+from astrai.serialization.checkpoint import (
+    Checkpoint,
+    load_json,
+    load_model_config,
+    load_model_weights,
+    load_safetensors,
+    load_state_dict,
+    load_torch,
+    save_json,
+    save_model,
+    save_safetensors,
+    save_torch,
+)
+from astrai.serialization.dataset import (
+    load_bin,
+    load_bin_offsets,
+    load_h5,
+    save_bin,
+    save_h5,
+)
+
+__all__ = [
+    "Checkpoint",
+    "load_json",
+    "load_model_config",
+    "load_model_weights",
+    "load_safetensors",
+    "load_state_dict",
+    "load_torch",
+    "save_json",
+    "save_model",
+    "save_safetensors",
+    "save_torch",
+    "load_bin",
+    "load_bin_offsets",
+    "load_h5",
+    "save_bin",
+    "save_h5",
+]

astrai/serialization/checkpoint.py

@@ -0,0 +1,201 @@
+"""Model checkpoint serialization helpers."""
+
+import io
+import json
+import time
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Dict, Optional, Union
+
+import safetensors.torch as st
+import torch
+import torch.distributed as dist
+
+from astrai.parallel.setup import get_rank
+
+_META_FILE = "meta.json"
+_CONFIG_FILE = "config.json"
+_WEIGHTS_FILE = "model.safetensors"
+
+
+def save_safetensors(state_dict: dict, path: Union[str, Path]):
+    st.save_file(state_dict, str(path))
+
+
+def load_safetensors(path: Union[str, Path], broadcast: bool = False) -> dict:
+    if not broadcast or not dist.is_initialized():
+        return st.load_file(str(path))
+
+    rank = get_rank()
+    if rank == 0:
+        state_dict = st.load_file(str(path))
+    else:
+        state_dict = {}
+    tmp = [state_dict]
+    dist.broadcast_object_list(tmp, src=0)
+    return tmp[0]
+
+
+def save_json(data: dict, path: Union[str, Path]):
+    with open(str(path), "w") as f:
+        json.dump(data, f, indent=2)
+
+
+def load_json(path: Union[str, Path], broadcast: bool = False) -> dict:
+    if not broadcast or not dist.is_initialized():
+        with open(str(path), "r") as f:
+            return json.load(f)
+
+    rank = get_rank()
+    if rank == 0:
+        with open(str(path), "r") as f:
+            data = json.load(f)
+    else:
+        data = {}
+    tmp = [data]
+    dist.broadcast_object_list(tmp, src=0)
+    return tmp[0]
+
+
+def save_torch(obj: Any, path: Union[str, Path]):
+    torch.save(obj, str(path))
+
+
+def load_torch(path: Union[str, Path], broadcast: bool = False) -> Any:
+    if not broadcast or not dist.is_initialized():
+        return torch.load(str(path), map_location="cpu", weights_only=False)
+
+    path = Path(path)
+    rank = get_rank()
+
+    if rank == 0:
+        with open(path, "rb") as f:
+            raw = f.read()
+        data_tensor = torch.frombuffer(bytearray(raw), dtype=torch.uint8)
+        num_bytes = torch.tensor([len(raw)], dtype=torch.long)
+    else:
+        num_bytes = torch.tensor([0], dtype=torch.long)
+
+    dist.broadcast(num_bytes, src=0)
+
+    if rank != 0:
+        data_tensor = torch.empty(num_bytes.item(), dtype=torch.uint8)
+
+    dist.broadcast(data_tensor, src=0)
+
+    buf = io.BytesIO(data_tensor.numpy().tobytes())
+    return torch.load(buf, map_location="cpu", weights_only=False)
+
+
+def save_model(config: dict, state_dict: dict, save_directory: str):
+    save_path = Path(save_directory)
+    save_path.mkdir(parents=True, exist_ok=True)
+    save_json(config, save_path / _CONFIG_FILE)
+    save_safetensors(state_dict, save_path / _WEIGHTS_FILE)
+
+
+def load_model_config(save_directory: str) -> dict:
+    return load_json(Path(save_directory) / _CONFIG_FILE)
+
+
+def load_model_weights(save_directory: str) -> dict:
+    return load_state_dict(Path(save_directory) / _WEIGHTS_FILE)
+
+
+def load_state_dict(path: Union[str, Path], broadcast: bool = False) -> dict:
+    path = Path(path)
+    if not broadcast or not dist.is_initialized():
+        return load_safetensors(path)
+
+    rank = get_rank()
+    if rank == 0:
+        state_dict = load_safetensors(path)
+        specs = [
+            (k, list(state_dict[k].shape), str(state_dict[k].dtype).split(".")[-1])
+            for k in sorted(state_dict)
+        ]
+    else:
+        state_dict = {}
+        specs = []
+
+    specs_list = [specs]
+    dist.broadcast_object_list(specs_list, src=0)
+    specs = specs_list[0]
+
+    for key, shape, dtype_name in specs:
+        dtype = getattr(torch, dtype_name)
+        if rank != 0:
+            tensor = torch.empty(shape, dtype=dtype, device="cpu")
+        else:
+            tensor = state_dict[key].contiguous().cpu()
+        dist.broadcast(tensor, src=0)
+        if rank != 0:
+            state_dict[key] = tensor
+    return state_dict
+
+
+@dataclass
+class Checkpoint:
+    state_dict: Dict[str, Any] = field(default_factory=dict)
+    epoch: int = 0
+    consumed_samples: int = 0
+    extra: Dict[str, Any] = field(default_factory=dict)
+    meta: Dict[str, Any] = field(default_factory=dict)
+    config: Dict[str, Any] = field(default_factory=dict)
+
+    def save(self, save_dir: str):
+        save_path = Path(save_dir)
+        save_path.mkdir(parents=True, exist_ok=True)
+
+        meta = {
+            "epoch": self.epoch,
+            "consumed_samples": self.consumed_samples,
+            "timestamp": time.strftime("%Y-%m-%dT%H:%M:%S"),
+            **self.meta,
+        }
+        save_json(meta, save_path / _META_FILE)
+        save_json(self.config, save_path / _CONFIG_FILE)
+        save_safetensors(self.state_dict, save_path / _WEIGHTS_FILE)
+        for key, value in self.extra.items():
+            save_torch(value, save_path / f"{key}.pt")
+
+    @classmethod
+    def load(cls, save_dir: str, broadcast: bool = False) -> "Checkpoint":
+        save_path = Path(save_dir)
+
+        meta = load_json(save_path / _META_FILE, broadcast)
+        config = load_json(save_path / _CONFIG_FILE, broadcast)
+        state_dict = load_state_dict(save_path / _WEIGHTS_FILE, broadcast=broadcast)
+
+        extra = {}
+        for f in sorted(save_path.iterdir()):
+            if f.suffix == ".pt":
+                extra[f.stem] = load_torch(f, broadcast=broadcast)
+
+        return cls(
+            state_dict=state_dict,
+            epoch=meta.get("epoch", 0),
+            consumed_samples=meta.get("consumed_samples", 0),
+            extra=extra,
+            meta=meta,
+            config=config,
+        )
+
+    @classmethod
+    def load_any(cls, save_dir: str, broadcast: bool = False) -> Optional["Checkpoint"]:
+        save_path = Path(save_dir)
+        meta_path = save_path / _META_FILE
+        weights_path = save_path / _WEIGHTS_FILE
+
+        if meta_path.exists():
+            return cls.load(save_dir, broadcast=broadcast)
+
+        if weights_path.exists():
+            state_dict = load_state_dict(weights_path, broadcast=broadcast)
+            config = {}
+            config_path = save_path / _CONFIG_FILE
+            if config_path.exists():
+                config = load_json(config_path, broadcast)
+            return cls(state_dict=state_dict, config=config)
+
+        return None

astrai/serialization/dataset.py

@@ -0,0 +1,123 @@
+"""Dataset storage serialization helpers (HDF5 / memory-mapped binary)."""
+
+import json
+import os
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+import h5py
+import numpy as np
+import torch
+from torch import Tensor
+
+
+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)
+    if root_path.is_file() and root_path.suffix in (".h5", ".hdf5"):
+        h5_files = [root_path]
+    else:
+        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]],
+    record_keys: Optional[List[str]] = None,
+):
+    """Save tensors as memory-mapped binary files.
+
+    When *record_keys* is provided, those keys are written with per-record
+    cumulative offsets in ``meta.json`` so that ``MmapStore.fetch_record``
+    can slice individual records from the concatenated binary without
+    cross-record concatenation.  Keys not in *record_keys* (e.g. SEQ
+    ``sequence``) are written as a single contiguous stream without
+    offsets, preserving backward compatibility.
+
+    Nested keys (``List[List[Tensor]]`` such as GRPO ``responses``) are
+    not supported in bin format — use H5 for those.
+    """
+    os.makedirs(file_path, exist_ok=True)
+    record_keys = set(record_keys or [])
+    meta = {}
+    for key, tensors in tensor_group.items():
+        if tensors and isinstance(tensors[0], list):
+            raise ValueError(
+                f"Nested key '{key}' (List[List[Tensor]]) is not supported "
+                f"in bin format. Use H5 or JSONL storage instead."
+            )
+        cat = torch.cat(tensors, dim=0)
+        entry: Dict[str, Any] = {
+            "shape": list(cat.shape),
+            "dtype": str(cat.dtype).split(".")[-1],
+        }
+        if key in record_keys:
+            offsets = [0]
+            for t in tensors:
+                offsets.append(offsets[-1] + t.shape[0])
+            entry["offsets"] = offsets
+        meta[key] = entry
+        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="c",
+            shape=tuple(info["shape"]),
+        )
+        segments[key] = [torch.from_numpy(arr)]
+    return segments
+
+
+def load_bin_offsets(file_path: str) -> Dict[str, List[int]]:
+    """Read per-record cumulative offsets from ``meta.json``.
+
+    Returns an empty dict when no key has offsets (legacy bin files),
+    in which case record-mode access falls back to per-record segment
+    indexing (H5/JSONL layout).
+    """
+    with open(os.path.join(file_path, "meta.json"), "r") as f:
+        meta = json.load(f)
+    offsets: Dict[str, List[int]] = {}
+    for key, info in meta.items():
+        if "offsets" in info:
+            offsets[key] = info["offsets"]
+    return offsets

astrai/tokenize/__init__.py

@@ -0,0 +1,10 @@
+from astrai.tokenize.chat_template import ChatTemplate, MessageType
+from astrai.tokenize.tokenizer import AutoTokenizer, Message, Messages
+
+__all__ = [
+    "AutoTokenizer",
+    "ChatTemplate",
+    "MessageType",
+    "Message",
+    "Messages",
+]

astrai/tokenize/chat_template.py

@@ -0,0 +1,87 @@
+from functools import cached_property
+from typing import Any, Dict, List, Optional
+
+from jinja2 import Template
+
+type MessageType = Dict[str, Any]
+
+
+class ChatTemplate:
+    """A chat template with Jinja2 rendering support.
+
+    Attributes:
+        name: Unique identifier for the template.
+        template_str: Jinja2 template string.
+        description: Optional description.
+        default_variables: Optional dictionary of default variable values.
+        special_tokens: Optional dictionary mapping token names to their string values.
+    """
+
+    def __init__(
+        self,
+        name: str = "",
+        template_str: str = "",
+        description: str = "",
+        default_variables: Optional[Dict[str, Any]] = None,
+        special_tokens: Optional[Dict[str, str]] = None,
+    ):
+        self.name = name
+        self.template_str = template_str
+        self.description = description
+        self.default_variables = default_variables or {}
+        self.special_tokens = special_tokens or {}
+
+    @cached_property
+    def _compiled(self) -> Template:
+        """Lazy-compiled Jinja2 template, cached on first access.
+
+        The compiled :class:`~jinja2.Template` holds a dynamically-generated
+        ``root`` render function whose ``__module__`` is ``None``; under
+        ``pickle`` it falls back to ``__main__`` and breaks ``spawn``-based
+        multiprocessing.  By deferring compilation to first access, the
+        default pickle protocol serialises only ``template_str``; each
+        worker rebuilds the cache on first render.
+        """
+        return Template(self.template_str)
+
+    @classmethod
+    def from_string(
+        cls,
+        template_str: str,
+        description: str = "",
+        default_variables: Optional[Dict[str, Any]] = None,
+        special_tokens: Optional[Dict[str, str]] = None,
+    ) -> "ChatTemplate":
+        """Create a ChatTemplate instance directly from a template string."""
+        return cls(
+            name="",
+            template_str=template_str,
+            description=description,
+            default_variables=default_variables,
+            special_tokens=special_tokens,
+        )
+
+    def render(
+        self,
+        messages: List[MessageType],
+        system_prompt: Optional[str] = None,
+        **extra_variables: Any,
+    ) -> str:
+        """Render the template with given messages and variables.
+
+        Args:
+            messages: List of message dicts with 'role' and 'content'.
+            system_prompt: Optional system prompt string.
+            **extra_variables: Additional variables to pass to the template.
+                These override default_variables and special_tokens.
+
+        Returns:
+            Rendered prompt string.
+        """
+        # Merge default variables, special tokens, and extra variables
+        variables = {**self.default_variables, **self.special_tokens, **extra_variables}
+        variables["messages"] = messages
+        if system_prompt is not None:
+            variables["system_prompt"] = system_prompt
+
+        return self._compiled.render(**variables)

astrai/tokenize/tokenizer.py

@@ -0,0 +1,302 @@
+"""
+Tokenizer module with implementation and auto-loading support.
+"""
+
+import json
+from pathlib import Path
+from typing import Dict, List, Optional, Union
+
+from tokenizers import Tokenizer
+
+from astrai.tokenize.chat_template import ChatTemplate
+
+Message = Dict[str, str]
+"""Single chat message with ``role`` and ``content`` keys."""
+
+Messages = List[Message]
+"""Single conversation — a list of messages."""
+
+
+class AutoTokenizer:
+    """Base tokenizer class with automatic loading support"""
+
+    TOKENIZER_CLASSES = {}  # Registry for auto-loading
+
+    def __init__(
+        self,
+        path: Optional[Union[str, Path]] = None,
+        special_token_map: Optional[Dict[str, str]] = None,
+        chat_template: Optional[str] = None,
+    ):
+        self._tokenizer: Tokenizer = None
+        self._chat_template: Optional[ChatTemplate] = None
+        self._special_token_map: Optional[Dict] = special_token_map or {}
+
+        if chat_template:
+            self.set_chat_template(chat_template)
+
+        if path:
+            self.load(path)
+
+    def load(self, path: Union[str, Path]):
+        """Load tokenizer from directory."""
+        path = Path(path)
+        tokenizer_file = path / "tokenizer.json"
+        config_file = path / "tokenizer_config.json"
+        self._tokenizer = Tokenizer.from_file(str(tokenizer_file))
+
+        if config_file.exists():
+            with open(config_file, "r", encoding="utf-8") as f:
+                config = json.load(f)
+
+            if "special_tokens" in config:
+                self._special_token_map.update(config["special_tokens"])
+
+            # Load chat template from config
+            if "chat_template" in config:
+                self.set_chat_template(config["chat_template"])
+
+    @classmethod
+    def from_pretrained(cls, path: Union[str, Path]) -> "AutoTokenizer":
+        """Load tokenizer from pretrained directory.
+
+        Raises:
+            FileNotFoundError: If tokenizer.json is missing.
+            RuntimeError: If tokenizer failed to initialize.
+        """
+        path = Path(path)
+        tokenizer_file = path / "tokenizer.json"
+        if not tokenizer_file.exists():
+            raise FileNotFoundError(
+                f"Tokenizer file not found: {tokenizer_file}. "
+                "A valid tokenizer.json is required."
+            )
+        instance = cls(path)
+        if instance._tokenizer is None:
+            raise RuntimeError(
+                f"Failed to load tokenizer from {path}. "
+                "The tokenizer.json may be corrupted or incompatible."
+            )
+        return instance
+
+    def save_pretrained(self, save_path: str):
+        """
+        Save tokenizer to pretrained directory.
+
+        Args:
+            save_path: Path to save the tokenizer
+        """
+
+        if self._tokenizer is None:
+            raise RuntimeError(
+                "Tokenizer not initialized. Load or create a tokenizer first."
+            )
+
+        save_path = Path(save_path)
+        save_path.mkdir(parents=True, exist_ok=True)
+
+        # Save tokenizer
+        self._tokenizer.save(str(save_path / "tokenizer.json"))
+
+        # Save tokenizer config
+        config = {}
+        if self._special_token_map is not None:
+            config["special_tokens"] = self._special_token_map
+        if self._chat_template is not None:
+            config["chat_template"] = self._chat_template.template_str
+
+        with open(save_path / "tokenizer_config.json", "w", encoding="utf-8") as f:
+            json.dump(config, f, ensure_ascii=False, indent=2)
+
+    @classmethod
+    def register_tokenizer(cls, name: str, tokenizer_class: type):
+        """
+        Register a new tokenizer class.
+
+        Args:
+            name: Name to register the tokenizer class under
+            tokenizer_class: The tokenizer class to register
+        """
+        cls.TOKENIZER_CLASSES[name] = tokenizer_class
+
+    def encode(
+        self,
+        tokens: Union[str, List[str]],
+        out_ids: bool = True,
+        is_pretokenized: bool = False,
+        add_special_tokens: bool = True,
+    ) -> List:
+        """Encode text to token IDs.
+
+        Accepts both single strings and batches:
+
+        - ``encode("hello")`` → ``[123, 456]``
+        - ``encode(["hello", "world"])`` → ``[[123, 456], [789]]``
+
+        Batches are tokenised in parallel via the Rust backend's
+        ``encode_batch`` (uses all available CPU cores).
+        """
+        if self._tokenizer is None:
+            raise RuntimeError(
+                "Tokenizer not initialized. Load or create a tokenizer first."
+            )
+
+        if isinstance(tokens, str):
+            encoded = self._tokenizer.encode(
+                tokens,
+                is_pretokenized=is_pretokenized,
+                add_special_tokens=add_special_tokens,
+            )
+            return encoded.ids if out_ids else encoded.tokens
+
+        encoded_list = self._tokenizer.encode_batch(
+            tokens,
+            is_pretokenized=is_pretokenized,
+            add_special_tokens=add_special_tokens,
+        )
+        return [encoded.ids if out_ids else encoded.tokens for encoded in encoded_list]
+
+    def decode(self, tokens: List[int], skip_special_tokens: bool = True) -> str:
+        """Decode token IDs to text."""
+        if self._tokenizer is None:
+            raise RuntimeError(
+                "Tokenizer not initialized. Load or create a tokenizer first."
+            )
+
+        return self._tokenizer.decode(tokens, skip_special_tokens=skip_special_tokens)
+
+    def __len__(self) -> int:
+        if self._tokenizer is None:
+            return 0
+        return self._tokenizer.get_vocab_size()
+
+    def __getattr__(self, key: str):
+        """
+        Dynamically intercept special token attribute access.
+        Supports three forms:
+          - tokenizer.bos_token   → returns string
+          - tokenizer.bos_token_id → returns corresponding integer ID
+          - tokenizer.stop_ids → returns list of corresponding integer IDs for all special tokens
+
+        Internal/private attrs are not intercepted: during unpickle
+        ``__dict__`` is empty, so probing ``self._special_token_map``
+        would recurse infinitely.
+        """
+        if key.startswith("_"):
+            raise AttributeError(key)
+
+        # Handle stop_ids - return IDs for all special tokens
+        if key == "stop_ids":
+            stop_ids = []
+
+            if self._tokenizer is None:
+                return stop_ids
+
+            for val in self._special_token_map.values():
+                token_id = self._tokenizer.token_to_id(val)
+                if token_id is not None:
+                    stop_ids.append(token_id)
+
+            return stop_ids
+
+        # Handle _id suffix (e.g., bos_token_id -> bos_token)
+        if key.endswith("_id"):
+            base_attr = key[:-3]  # Remove "_id"
+            token_str = self._special_token_map.get(base_attr)
+            if token_str is None:
+                return None
+            if self._tokenizer is None:
+                raise RuntimeError("Tokenizer not loaded, cannot convert token to id.")
+            return self._tokenizer.token_to_id(token_str)
+
+        # Handle regular string attributes
+        if key in self._special_token_map:
+            return self._special_token_map.get(key)
+
+        # Other attributes trigger default AttributeError
+        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{key}'")
+
+    @property
+    def vocab_size(self) -> int:
+        return len(self)
+
+    def set_chat_template(self, template: Union[str, ChatTemplate]):
+        """
+        Set the chat template for the tokenizer.
+
+        Args:
+            template: Either a template name (str) registered in the global registry,
+                      or a ChatTemplate instance, or a Jinja2 template string.
+
+        Raises:
+            KeyError: If template name is not registered.
+        """
+        if isinstance(template, str):
+            self._chat_template = ChatTemplate.from_string(template)
+        elif isinstance(template, ChatTemplate):
+            self._chat_template = template
+        else:
+            raise ValueError("Invalid template type, must be str or ChatTemplate.")
+
+    def apply_chat_template(
+        self,
+        messages: Union[Messages, List[Messages]],
+        system_prompt: Optional[str] = None,
+        tokenize: bool = True,
+        add_generation_prompt: bool = True,
+        **kwargs,
+    ) -> Union[str, List[int], List[str], List[List[int]]]:
+        """Apply the chat template and optionally tokenize.
+
+        Accepts both single conversations and batches:
+
+        - ``apply_chat_template([msg1, msg2])`` → ``"..."`` or ``[ids]``
+        - ``apply_chat_template([[msg1, msg2], [msg3]])`` → ``["..", ".."]``
+          or ``[[ids], [ids]]``
+
+        Batches render each conversation list and tokenise all at once via
+        :meth:`encode` (``List[str]`` → Rust parallel ``encode_batch``).
+
+        Args:
+            messages: Single conversation (``Messages``) or batch of
+                conversations (``BatchMessages``).
+            system_prompt: Optional system prompt prepended (single mode only).
+            tokenize: Whether to return token IDs (True) or raw string (False).
+            add_generation_prompt: Whether to add the generation prompt.
+            **kwargs: Additional template variables.
+
+        Returns:
+            Single mode: ``str`` or ``List[int]``.
+            Batch mode: ``List[str]`` or ``List[List[int]]``.
+        """
+        if self._chat_template is None:
+            raise RuntimeError(
+                "Chat template not set. Use set_chat_template() to set a template first."
+            )
+
+        is_batch = bool(messages) and isinstance(messages[0], list)
+
+        if is_batch:
+            rendered = [
+                self._chat_template.render(
+                    messages=msgs,
+                    add_generation_prompt=add_generation_prompt,
+                    **kwargs,
+                )
+                for msgs in messages
+            ]
+            if tokenize:
+                return self.encode(rendered)  # List[str] → batch encode
+            return rendered
+
+        # Single conversation
+        if system_prompt:
+            messages = [{"role": "system", "content": system_prompt}] + list(messages)
+        rendered = self._chat_template.render(
+            messages=messages,
+            add_generation_prompt=add_generation_prompt,
+            **kwargs,
+        )
+        if tokenize:
+            return self.encode(rendered)
+        return rendered

astrai/trainer/__init__.py

@@ -0,0 +1,21 @@
+from astrai.trainer.schedule import BaseScheduler, SchedulerFactory
+from astrai.trainer.strategy import BaseStrategy, StrategyFactory
+from astrai.trainer.train_callback import (
+    CallbackFactory,
+    TrainCallback,
+)
+from astrai.trainer.trainer import Trainer
+
+__all__ = [
+    # Main trainer
+    "Trainer",
+    # Strategy factory
+    "StrategyFactory",
+    "BaseStrategy",
+    # Scheduler factory
+    "SchedulerFactory",
+    "BaseScheduler",
+    # Callback factory
+    "TrainCallback",
+    "CallbackFactory",
+]

astrai/trainer/metric_util.py

@@ -0,0 +1,38 @@
+from typing import Dict
+
+import torch
+import torch.nn as nn
+
+
+def grad_norm(model: nn.Module, per_param: bool = False) -> float | Dict[str, float]:
+    grads = [p.grad.detach() for p in model.parameters() if p.grad is not None]
+    if not grads:
+        return 0.0
+
+    total_sq = torch.stack([g.pow(2).sum() for g in grads]).sum()
+    if per_param:
+        norms = {}
+        for name, param in model.named_parameters():
+            if param.grad is not None:
+                norms[name] = param.grad.norm(2).item()
+            else:
+                norms[name] = 0.0
+        norms["total"] = total_sq.sqrt().item()
+        return norms
+    return total_sq.sqrt().item()
+
+
+def ctx_get_loss(ctx):
+    return ctx.loss
+
+
+def ctx_get_lr(ctx):
+    return ctx.optimizer.param_groups[-1]["lr"]
+
+
+def ctx_get_val_loss(ctx):
+    return ctx.val_loss
+
+
+def ctx_get_grad_norm(ctx):
+    return ctx.grad_norm

astrai/trainer/rollout.py

@@ -0,0 +1,345 @@
+"""Online rollout runner for RL training.
+
+Provides:
+- :class:`RawRollout` — generation output container (no reward yet)
+- :class:`RolloutResult` — a :class:`RawRollout` with rewards attached
+- :class:`BaseRewardModel` — pluggable reward interface
+- :class:`RolloutGenerator` — KV-cache-backed generation of grouped
+  responses + decoding (no reward); delegates the generation loop to
+  :class:`~astrai.inference.core.scheduler.InferenceScheduler.run_batch`
+  so rollout and the production inference server share one code path
+- :class:`RolloutRunner` — orchestrates generation + scoring with a
+  step-driven cache; its ``__call__`` returns ``(RolloutResult, is_fresh)``
+  so callers do not need to rely on object identity to detect refreshes.
+"""
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Tuple
+
+import torch
+from torch import Tensor
+
+from astrai.inference.core.scheduler import InferenceScheduler
+
+
+@dataclass(kw_only=True)
+class RawRollout:
+    """Generation output before reward scoring.
+
+    Produced by :class:`RolloutGenerator`; consumed by :class:`RolloutRunner`
+    to assemble a :class:`RolloutResult` once rewards are attached.
+
+    Fields are designed to cover all common RL algorithms:
+    GRPO, PPO, Online DPO, Rejection Sampling, etc.
+
+    Fields:
+        prompts: Tokenized prompts, shape ``[B, P_len]``.
+        responses: Generated response token IDs, shape ``[B, G, R_max]``.
+        response_mask: Boolean mask for real (non-pad) response tokens,
+            shape ``[B, G, R_max]``.
+        logprobs_old: Per-token log-probs under the behaviour policy,
+            shape ``[B, G, R_max]``.
+        prompt_texts: Decoded prompt strings (for reward models that
+            need text).
+        response_texts: Decoded response strings, shape ``[B, G]``
+            (for reward models).
+    """
+
+    prompts: Tensor
+    responses: Tensor
+    response_mask: Tensor
+    logprobs_old: Tensor
+    prompt_texts: List[str] = field(default_factory=list)
+    response_texts: List[List[str]] = field(default_factory=list)
+
+
+@dataclass(kw_only=True)
+class RolloutResult(RawRollout):
+    """A :class:`RawRollout` with reward scoring attached.
+
+    Produced by :class:`RolloutRunner` once the :class:`BaseRewardModel`
+    has scored the decoded responses.
+
+    Fields:
+        rewards: Reward per response, shape ``[B, G]``.
+    """
+
+    rewards: Tensor
+
+
+class BaseRewardModel(ABC):
+    """Pluggable reward model interface.
+
+    Subclasses should implement ``score()`` to return a ``[B, G]`` float
+    tensor of rewards.  Implementations can be:
+    * A loaded reward model (e.g. ArmoRM, Skywork-Reward)
+    * An external API call
+    * A rule-based function (format, length, keyword matching)
+    """
+
+    @abstractmethod
+    def score(self, prompts: List[str], responses: List[List[str]]) -> Tensor:
+        """Score each generated response.
+
+        Args:
+            prompts: Raw prompt strings, length ``B``.
+            responses: Generated response strings, shape ``[B, G]``.
+
+        Returns:
+            Float tensor of shape ``[B, G]``.
+        """
+        ...
+
+
+_PAD = 0
+
+
+class RolloutGenerator:
+    """Pure generation + decoding for a group of responses per prompt.
+
+    Delegates the prefill/decode loop to
+    :meth:`~astrai.inference.core.scheduler.InferenceScheduler.run_batch`,
+    which uses a real KV cache (no O(n²) recompute).  Has no dependency
+    on any reward model; can be reused in isolation for offline
+    generation, qualitative sampling, or eval pipelines.
+    """
+
+    def __init__(
+        self,
+        scheduler: InferenceScheduler,
+        tokenizer,
+        max_tokens: int = 1024,
+        group_size: int = 8,
+        temperature: float = 1.0,
+        top_k: int = 0,
+        top_p: float = 1.0,
+        frequency_penalty: float = 0.0,
+        rep_window: int = 64,
+    ):
+        self.scheduler = scheduler
+        self.tokenizer = tokenizer
+        self.max_tokens = max_tokens
+        self.group_size = group_size
+        self.temperature = temperature
+        self.top_k = top_k
+        self.top_p = top_p
+        self.frequency_penalty = frequency_penalty
+        self.rep_window = rep_window
+
+    @torch.no_grad()
+    def generate(self, batch: Dict) -> RawRollout:
+        """Expand prompts by ``group_size`` and generate one response each.
+
+        Accepted batch formats (per sample, repeated B times):
+
+        - **messages**: ``{"messages": [{"role": "user", "content": "..."}, ...]}``
+        - **instruction + input + output**: ``{"instruction": "...",
+          "input": "...", "output": "..."}`` — mapped to ``system`` /
+          ``user`` / ``assistant`` messages; ``input`` and ``output``
+          are optional and skipped when empty.
+
+        Both are rendered through the tokenizer's chat template with
+        ``add_generation_prompt=True`` so rollout prompts match the
+        format the policy was SFT-trained on.
+        """
+        prompt_texts, flat_prompt_ids = self._prepare_prompts(batch)
+        B = len(prompt_texts)
+        G = self.group_size
+        # Re-expand flat list to G copies per prompt for run_batch.
+        expanded_prompt_ids: List[List[int]] = []
+        for ids in flat_prompt_ids:
+            expanded_prompt_ids.extend([list(ids)] * G)
+
+        results = self.scheduler.run_batch(
+            expanded_prompt_ids,
+            max_tokens=self.max_tokens,
+            temperature=self.temperature,
+            top_k=self.top_k,
+            top_p=self.top_p,
+            frequency_penalty=self.frequency_penalty,
+            rep_window=self.rep_window,
+            return_logprobs=True,
+        )
+
+        # Each element is (token_ids, logprobs); pad to max length.
+        max_len = 0
+        for token_ids, _lp in results:
+            max_len = max(max_len, len(token_ids))
+        max_len = max(max_len, 1)
+
+        device = self.scheduler.device
+        P_len = max(len(ids) for ids in flat_prompt_ids)
+        prompts_tensor = torch.zeros(B, P_len, dtype=torch.long, device=device)
+        for i, ids in enumerate(flat_prompt_ids):
+            prompts_tensor[i, : len(ids)] = torch.tensor(
+                ids, dtype=torch.long, device=device
+            )
+
+        responses = torch.full((B, G, max_len), _PAD, dtype=torch.long, device=device)
+        response_mask = torch.zeros((B, G, max_len), dtype=torch.bool, device=device)
+        logprobs_old = torch.zeros((B, G, max_len), dtype=torch.float, device=device)
+
+        flat_idx = 0
+        response_texts: List[List[str]] = [[] for _ in range(B)]
+        for i in range(B):
+            for g in range(G):
+                token_ids, lps = results[flat_idx]
+                flat_idx += 1
+                n = len(token_ids)
+                if n:
+                    responses[i, g, :n] = torch.tensor(
+                        token_ids, dtype=torch.long, device=device
+                    )
+                    response_mask[i, g, :n] = True
+                    logprobs_old[i, g, :n] = torch.tensor(
+                        lps, dtype=torch.float, device=device
+                    )
+                response_texts[i].append(
+                    self.tokenizer.decode(token_ids, skip_special_tokens=True)
+                )
+
+        return RawRollout(
+            prompts=prompts_tensor,
+            responses=responses,
+            response_mask=response_mask,
+            logprobs_old=logprobs_old,
+            prompt_texts=prompt_texts,
+            response_texts=response_texts,
+        )
+
+    def _prepare_prompts(self, batch: Dict) -> Tuple[List[str], List[List[int]]]:
+        """Render batch prompts to ``(texts, token_id_lists)``.
+
+        Returns two parallel lists of length B (number of prompts in
+        the batch).  Dispatches by batch keys:
+
+        - ``"messages"``: treated as a pre-built message list per sample.
+        - ``"instruction"`` (optionally ``"input"`` and ``"output"``): mapped
+          to ``system`` / ``user`` / ``assistant`` messages respectively.
+
+        Both paths go through the tokenizer's chat template with
+        ``add_generation_prompt=True``.
+        """
+        if "messages" in batch:
+            messages_list = batch["messages"]
+        elif "instruction" in batch:
+            instructions = batch["instruction"]
+            B = len(instructions)
+            inputs = batch.get("input") or [""] * B
+            outputs = batch.get("output") or [""] * B
+            messages_list = [
+                self._instruction_to_messages(i, u, o)
+                for i, u, o in zip(instructions, inputs, outputs)
+            ]
+        else:
+            raise ValueError(
+                "Rollout batch must contain either 'messages' or "
+                "'instruction' (optionally 'input'/'output'); got keys: "
+                f"{list(batch.keys())}"
+            )
+
+        prompt_texts: List[str] = []
+        flat_prompt_ids: List[List[int]] = []
+        for messages in messages_list:
+            text = self.tokenizer.apply_chat_template(
+                messages, tokenize=False, add_generation_prompt=True
+            )
+            ids = self.tokenizer.apply_chat_template(
+                messages, tokenize=True, add_generation_prompt=True
+            )
+            prompt_texts.append(text)
+            flat_prompt_ids.append(list(ids))
+        return prompt_texts, flat_prompt_ids
+
+    @staticmethod
+    def _instruction_to_messages(
+        instruction: str, inp: str = "", output: str = ""
+    ) -> List[Dict[str, str]]:
+        """Map instruction/input/output to chat messages.
+
+        Role mapping follows the convention used throughout the
+        preprocessing pipeline: ``instruction`` → system, ``input`` →
+        user, ``output`` → assistant.  Empty fields are skipped so a
+        bare instruction produces a ``[system]`` list and the chat
+        template's ``add_generation_prompt`` adds the assistant header
+        for sampling.
+        """
+        messages: List[Dict[str, str]] = []
+        if instruction:
+            messages.append({"role": "system", "content": instruction})
+        if inp:
+            messages.append({"role": "user", "content": inp})
+        if output:
+            messages.append({"role": "assistant", "content": output})
+        return messages
+
+
+class RolloutRunner:
+    """Produces :class:`RolloutResult` from a prompt batch.
+
+    Composes a :class:`RolloutGenerator` (generation + decoding) with a
+    :class:`BaseRewardModel` (scoring).  Maintains an internal cache so
+    the same batch prompt can be replayed for multiple gradient steps.
+    A new rollout is triggered every ``rollout_interval`` calls to
+    :meth:`step` (or after :meth:`clear_cache`).
+
+    The ``__call__`` contract returns a ``(RolloutResult, is_fresh)``
+    tuple — callers must use the boolean to detect a refreshed rollout
+    rather than relying on object identity.
+
+    Usage::
+
+        generator = RolloutGenerator(policy, tokenizer, pipeline, ...)
+        runner = RolloutRunner(generator, reward_model, rollout_interval=512)
+        result, is_fresh = runner(prompt_batch)
+        if is_fresh:
+            ...  # e.g. sync behaviour policy
+    """
+
+    def __init__(
+        self,
+        generator: RolloutGenerator,
+        reward_model: BaseRewardModel,
+        rollout_interval: int = 512,
+    ):
+        self.generator = generator
+        self.reward_model = reward_model
+        self.rollout_interval = rollout_interval
+
+        self._cache: Optional[RolloutResult] = None
+        self._steps_since_rollout: int = 0
+
+    def step(self):
+        """Advance the internal counter (call once per optimizer step)."""
+        self._steps_since_rollout += 1
+
+    def clear_cache(self):
+        """Force next call to re-run rollout."""
+        self._cache = None
+
+    def _score(self, raw: RawRollout) -> RolloutResult:
+        rewards = self.reward_model.score(raw.prompt_texts, raw.response_texts)
+        device = raw.prompts.device
+        return RolloutResult(
+            prompts=raw.prompts,
+            responses=raw.responses,
+            response_mask=raw.response_mask,
+            rewards=rewards.to(device=device),
+            logprobs_old=raw.logprobs_old,
+            prompt_texts=raw.prompt_texts,
+            response_texts=raw.response_texts,
+        )
+
+    def __call__(self, batch: Dict[str, Tensor]) -> Tuple[RolloutResult, bool]:
+        """Return ``(cached or fresh) RolloutResult`` plus an ``is_fresh`` flag.
+
+        Triggers a new rollout when ``_steps_since_rollout >= rollout_interval``
+        or when the cache is empty.
+        """
+        if self._cache is None or self._steps_since_rollout >= self.rollout_interval:
+            raw = self.generator.generate(batch)
+            self._cache = self._score(raw)
+            self._steps_since_rollout = 0
+            return self._cache, True
+        return self._cache, False

astrai/trainer/schedule.py

@@ -0,0 +1,235 @@
+"""Learning rate scheduler implementations with factory pattern."""
+
+import math
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List
+
+from torch.optim.lr_scheduler import LRScheduler
+
+from astrai.factory import BaseFactory
+
+
+class BaseScheduler(LRScheduler, ABC):
+    """Base scheduler class for all other schedulers."""
+
+    def __init__(self, optimizer, last_epoch: int = -1):
+        super().__init__(optimizer, last_epoch)
+
+    @abstractmethod
+    def get_lr(self) -> List[float]:
+        """Calculate the current learning rate."""
+        raise NotImplementedError
+
+    def state_dict(self) -> Dict[str, Any]:
+        return super().state_dict()
+
+    def load_state_dict(self, state_dict: Dict[str, Any]):
+        super().load_state_dict(state_dict)
+
+
+class SchedulerFactory(BaseFactory["BaseScheduler"]):
+    """Factory class for creating learning rate schedulers.
+
+    Supports decorator-based registration for extensible scheduler types.
+
+    Example usage:
+        @SchedulerFactory.register("custom")
+        class CustomScheduler(BaseScheduler):
+            ...
+
+        scheduler = SchedulerFactory.create("custom", optimizer, **kwargs)
+    """
+
+
+# ----------- Scheduler implementations -----------
+
+
+@SchedulerFactory.register("cosine")
+class CosineScheduler(BaseScheduler):
+    """Cosine decay scheduler with warmup, implemented as PyTorch LRScheduler."""
+
+    def __init__(
+        self,
+        optimizer,
+        warmup_steps: int,
+        lr_decay_steps: int,
+        min_rate: float = 0.01,
+        last_epoch: int = -1,
+    ):
+        self.warmup_steps = warmup_steps
+        self.lr_decay_steps = lr_decay_steps
+        self.min_rate = min_rate
+        self.total_steps = warmup_steps + lr_decay_steps
+        super().__init__(optimizer, last_epoch)
+
+    def get_lr(self) -> List[float]:
+        # warmup
+        if self.last_epoch < self.warmup_steps:
+            warmup_factor = max(
+                self.min_rate, self.last_epoch / max(self.warmup_steps, 1)
+            )
+            return [base_lr * warmup_factor for base_lr in self.base_lrs]
+
+        # cosine decay
+        decay_progress = (self.last_epoch - self.warmup_steps) / max(
+            self.lr_decay_steps, 1
+        )
+        decay_progress = min(decay_progress, 1.0)
+        cosine_decay = 0.5 * (1.0 + math.cos(math.pi * decay_progress))
+        decay_factor = max(self.min_rate, cosine_decay)
+        return [base_lr * decay_factor for base_lr in self.base_lrs]
+
+    def state_dict(self):
+        state = super().state_dict()
+        state.update(
+            {
+                "warmup_steps": self.warmup_steps,
+                "lr_decay_steps": self.lr_decay_steps,
+                "min_rate": self.min_rate,
+                "total_steps": self.total_steps,
+            }
+        )
+        return state
+
+    def load_state_dict(self, state_dict):
+        self.warmup_steps = state_dict.pop("warmup_steps")
+        self.lr_decay_steps = state_dict.pop("lr_decay_steps")
+        self.min_rate = state_dict.pop("min_rate")
+        self.total_steps = state_dict.pop("total_steps")
+        super().load_state_dict(state_dict)
+
+
+@SchedulerFactory.register("sgdr")
+class SGDRScheduler(BaseScheduler):
+    """SGDR (Stochastic Gradient Descent with Warm Restarts) scheduler."""
+
+    def __init__(
+        self,
+        optimizer,
+        warmup_steps: int,
+        cycle_length: int,
+        min_rate: float = 0.01,
+        t_mult: int = 2,
+        last_epoch: int = -1,
+    ):
+        self.warmup_steps = warmup_steps
+        self.cycle_length = cycle_length
+        self.min_rate = min_rate
+        self.t_mult = t_mult
+
+        super().__init__(optimizer, last_epoch)
+
+    def get_lr(self):
+        # warmup
+        if self.last_epoch < self.warmup_steps:
+            warmup_factor = max(
+                self.min_rate, self.last_epoch / max(self.warmup_steps, 1)
+            )
+            return [base_lr * warmup_factor for base_lr in self.base_lrs]
+
+        # SGDR
+        steps_since_warmup = self.last_epoch - self.warmup_steps
+
+        # 1. Calculate current cycle and position within cycle
+        current_cycle_length = self.cycle_length
+        total_cycles_length = 0
+        cycle_num = 0
+
+        while total_cycles_length + current_cycle_length <= steps_since_warmup:
+            total_cycles_length += current_cycle_length
+            current_cycle_length *= self.t_mult
+            cycle_num += 1
+
+        steps_in_cycle = steps_since_warmup - total_cycles_length
+
+        # 2. Cosine annealing within the current cycle
+        cosine_factor = 0.5 * (
+            1 + math.cos(math.pi * steps_in_cycle / current_cycle_length)
+        )
+        learning_rate_factor = self.min_rate + (1 - self.min_rate) * cosine_factor
+
+        return [base_lr * learning_rate_factor for base_lr in self.base_lrs]
+
+    def state_dict(self):
+        """Returns the state of the scheduler as a dict."""
+        state = super().state_dict()
+        state.update(
+            {
+                "warmup_steps": self.warmup_steps,
+                "cycle_length": self.cycle_length,
+                "min_rate": self.min_rate,
+                "t_mult": self.t_mult,
+            }
+        )
+        return state
+
+    def load_state_dict(self, state_dict):
+        """Loads the scheduler's state."""
+        self.warmup_steps = state_dict.pop("warmup_steps")
+        self.cycle_length = state_dict.pop("cycle_length")
+        self.min_rate = state_dict.pop("min_rate")
+        self.t_mult = state_dict.pop("t_mult")
+        super().load_state_dict(state_dict)
+
+
+@SchedulerFactory.register("wsd")
+class WSDScheduler(BaseScheduler):
+    """WSD (Warmup-Stable-Decay) scheduler with sqrt cooldown.
+
+    warmup_steps:  linear warmup from min_rate to 1.0
+    stable_steps:   constant at base_lr
+    decay_steps:    sqrt decay from base_lr to min_rate
+    min_rate:       minimum lr as fraction of base_lr (default 0.0)
+    """
+
+    def __init__(
+        self,
+        optimizer,
+        warmup_steps: int,
+        stable_steps: int,
+        decay_steps: int,
+        min_rate: float = 0.01,
+        last_epoch: int = -1,
+    ):
+        self.warmup_steps = warmup_steps
+        self.stable_steps = stable_steps
+        self.decay_steps = decay_steps
+        self.min_rate = min_rate
+        self.total_steps = warmup_steps + stable_steps + decay_steps
+        super().__init__(optimizer, last_epoch)
+
+    def get_lr(self) -> List[float]:
+        if self.last_epoch < self.warmup_steps:
+            factor = max(self.min_rate, self.last_epoch / max(self.warmup_steps, 1))
+            return [base_lr * factor for base_lr in self.base_lrs]
+
+        offset = self.last_epoch - self.warmup_steps
+
+        if offset < self.stable_steps:
+            return list(self.base_lrs)
+
+        decay_ratio = (offset - self.stable_steps) / max(self.decay_steps, 1)
+        decay_ratio = min(decay_ratio, 1.0)
+        factor = (1.0 - self.min_rate) * (1.0 - decay_ratio) ** 2 + self.min_rate
+        return [base_lr * factor for base_lr in self.base_lrs]
+
+    def state_dict(self):
+        state = super().state_dict()
+        state.update(
+            {
+                "warmup_steps": self.warmup_steps,
+                "stable_steps": self.stable_steps,
+                "decay_steps": self.decay_steps,
+                "min_rate": self.min_rate,
+                "total_steps": self.total_steps,
+            }
+        )
+        return state
+
+    def load_state_dict(self, state_dict):
+        self.warmup_steps = state_dict.pop("warmup_steps")
+        self.stable_steps = state_dict.pop("stable_steps")
+        self.decay_steps = state_dict.pop("decay_steps")
+        self.min_rate = state_dict.pop("min_rate")
+        self.total_steps = state_dict.pop("total_steps")
+        super().load_state_dict(state_dict)

astrai/trainer/strategy.py

@@ -0,0 +1,502 @@
+"""Training strategy implementations with factory pattern."""
+
+from abc import ABC, abstractmethod
+from typing import Callable, Dict, Optional, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+
+from astrai.factory import BaseFactory
+from astrai.trainer.rollout import RolloutResult
+
+
+def create_ref_model(
+    model_fn: Callable[[], nn.Module], state_dict: Dict[str, Tensor]
+) -> nn.Module:
+    """Create a frozen reference model from model_fn + full state dict."""
+    ref_model = model_fn()
+    ref_model.load_state_dict(state_dict)
+    ref_model.requires_grad_(False)
+    ref_model.eval()
+    return ref_model
+
+
+def move_to_device(batch: Dict[str, Tensor], device: str) -> Dict[str, Tensor]:
+    """Move batch tensors to specified device with non-blocking transfer."""
+    return {key: value.to(device, non_blocking=True) for key, value in batch.items()}
+
+
+def get_logprobs(
+    model: nn.Module,
+    input_ids: Tensor,
+    attn_mask: Tensor,
+    loss_mask: Tensor,
+    reduction: str,
+) -> Tensor:
+    """Compute token-wise log probabilities from model outputs.
+
+    Args:
+        model: The language model
+        input_ids: Input token IDs of shape [batch_size, seq_len]
+        attn_mask: Attention mask passed to the model (may include causal).
+        loss_mask: Per-token mask for loss reduction.
+        reduction: How to reduce over sequence dimension ("mean", "sum", "none")
+
+    Returns:
+        Log probabilities with reduction applied over sequence dimension
+    """
+    allowed_reductions = ["mean", "sum", "none"]
+    if reduction not in allowed_reductions:
+        raise ValueError(
+            f"reduction must be one of {allowed_reductions}, got '{reduction}'"
+        )
+
+    shifted_input_ids = input_ids[:, 1:]
+    shifted_loss_mask = loss_mask[:, 1:]
+
+    logits = model(
+        input_ids[:, :-1],
+        attn_mask[:, :, :-1, :-1] if attn_mask.dim() == 4 else attn_mask[:, :-1],
+    )["logits"]
+    log_probs = torch.log_softmax(logits.float(), dim=-1)
+
+    token_logprobs = torch.gather(
+        log_probs, dim=-1, index=shifted_input_ids.unsqueeze(-1)
+    ).squeeze(-1)
+
+    if reduction == "mean":
+        return (token_logprobs * shifted_loss_mask).sum(dim=-1) / shifted_loss_mask.sum(
+            dim=-1
+        ).clamp(min=1.0)
+    elif reduction == "sum":
+        return (token_logprobs * shifted_loss_mask).sum(dim=-1)
+    else:
+        return token_logprobs * shifted_loss_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.
+
+    When a :class:`~astrai.trainer.rollout.RolloutRunner` is injected via
+    :meth:`set_rollout_runner`, the strategy transparently switches to
+    online mode: each ``__call__`` produces a :class:`RolloutResult`,
+    converts it to a training batch via :meth:`prepare_from_rollout`, and
+    then computes the loss.  Without a runner the strategy runs in
+    offline mode and consumes the batch directly.
+    """
+
+    def __init__(
+        self,
+        model: Union[nn.Module, Callable[..., Dict[str, Tensor]]],
+        device: str,
+        **kwargs,
+    ):
+        self.model = model
+        self.device = device
+        self.executor = kwargs.pop("executor", None)
+        self.extra_kwargs = kwargs
+        self._rollout_runner = None
+
+    @abstractmethod
+    def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
+        """Compute loss for the given batch.
+
+        Args:
+            batch: Dictionary containing batch tensors
+
+        Returns:
+            Computed loss tensor
+        """
+        raise NotImplementedError
+
+    def supports_online(self) -> bool:
+        """Whether this strategy can operate with a rollout runner.
+
+        Base implementation returns ``False``; strategies that implement
+        :meth:`prepare_from_rollout` should override to return ``True``.
+        """
+        return False
+
+    def set_rollout_runner(self, runner):
+        """Inject a :class:`RolloutRunner` to enable online rollout mode."""
+        self._rollout_runner = runner
+
+    def prepare_from_rollout(self, result: RolloutResult) -> Dict[str, Tensor]:
+        """Map a :class:`RolloutResult` to the batch layout expected by
+        :meth:`compute_loss`.
+
+        Strategies that return ``True`` from :meth:`supports_online` must
+        override this.  Default raises :class:`NotImplementedError`.
+        """
+        raise NotImplementedError(
+            f"{type(self).__name__} does not support online rollout"
+        )
+
+    def _on_rollout_refresh(self):
+        """Hook fired when a fresh rollout result is produced.
+
+        Override to refresh stale state (e.g. syncing the behaviour
+        policy).  Default is a no-op.
+        """
+        pass
+
+    def __call__(self, batch: Dict[str, Tensor]) -> Tensor:
+        """Run offline or online forward depending on runner injection."""
+        if self._rollout_runner is None:
+            return self.compute_loss(batch)
+
+        result, is_fresh = self._rollout_runner(batch)
+        if is_fresh:
+            self._on_rollout_refresh()
+            if self.executor and self.executor.sync_gradients:
+                self._rollout_runner.step()
+
+        train_batch = self.prepare_from_rollout(result)
+        return self.compute_loss(train_batch)
+
+
+class StrategyFactory(BaseFactory["BaseStrategy"]):
+    """Factory class for creating training strategy instances.
+
+    Supports decorator-based registration for extensible strategy types.
+    All default strategies (seq, sft, dpo, grpo) are automatically registered.
+
+    Example usage:
+        @StrategyFactory.register("custom")
+        class CustomStrategy(BaseStrategy):
+            ...
+
+        strategy = StrategyFactory.create("custom", model, device)
+    """
+
+
+# ============== Strategy Classes ==============
+# All strategies are registered at class definition time using the decorator
+
+
+@StrategyFactory.register("seq")
+class SEQStrategy(BaseStrategy):
+    """Standard next-token prediction training strategy.
+
+    Computes cross-entropy loss for next token prediction.
+    """
+
+    def __init__(
+        self,
+        model: Union[nn.Module, Callable[..., Dict[str, Tensor]]],
+        device: str,
+        label_smoothing: float = 0.0,
+        **kwargs,
+    ):
+        super().__init__(model, device, **kwargs)
+        self.label_smoothing = label_smoothing
+
+    def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
+        batch = move_to_device(batch, self.device)
+        input_ids, target_ids = batch["input_ids"], batch["target_ids"]
+        logits = self.model(input_ids=input_ids)["logits"]
+
+        loss = F.cross_entropy(
+            input=logits.flatten(0, 1).float(),
+            target=target_ids.flatten(),
+            label_smoothing=self.label_smoothing,
+        )
+
+        return loss
+
+
+@StrategyFactory.register("sft")
+class SFTStrategy(BaseStrategy):
+    """Supervised Fine-tuning strategy with loss masking.
+
+    Applies cross-entropy loss only to tokens where loss_mask is True.
+    """
+
+    def __init__(
+        self,
+        model: Union[nn.Module, Callable[..., Dict[str, Tensor]]],
+        device: str,
+        label_smoothing: float = 0.0,
+        **kwargs,
+    ):
+        super().__init__(model, device, **kwargs)
+        self.label_smoothing = label_smoothing
+
+    def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
+        batch = move_to_device(batch, self.device)
+        input_ids, target_ids, position_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)
+        target_ids = target_ids.masked_fill(~loss_mask, 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(),
+            target=target_ids.flatten(),
+            ignore_index=ignore_index,
+            label_smoothing=self.label_smoothing,
+        )
+
+        return loss
+
+
+@StrategyFactory.register("dpo")
+class DPOStrategy(BaseStrategy):
+    """Direct Preference Optimization strategy.
+
+    Implements the DPO loss from the paper "Direct Preference Optimization".
+    Uses a reference model to compute KL divergence penalty.
+    """
+
+    def __init__(
+        self,
+        model: nn.Module,
+        device: str,
+        ref_model: nn.Module,
+        beta: float = 0.1,
+        reduction: str = "sum",
+        **kwargs,
+    ):
+        super().__init__(model, device, **kwargs)
+        self.ref_model = ref_model
+        self.beta = beta
+        self.reduction = reduction
+
+    def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
+        batch = move_to_device(batch, self.device)
+        chosen_ids, rejected_ids = batch["chosen"], batch["rejected"]
+        chosen_mask, rejected_mask = batch["chosen_mask"], batch["rejected_mask"]
+
+        concat_ids = torch.cat([chosen_ids, rejected_ids], dim=0)
+        concat_loss_mask = torch.cat([chosen_mask, rejected_mask], dim=0)
+
+        # Build full attention mask: key-padding + causal
+        key_pad = concat_ids.bool()[:, None, None, :]  # [B*2, 1, 1, S]
+        S = key_pad.shape[-1]
+        causal = torch.tril(
+            torch.ones(S, S, dtype=torch.bool, device=concat_ids.device)
+        )[None, None, :, :]  # [1, 1, S, S]
+        full_mask = key_pad & causal  # [B*2, 1, S, S] — composed
+
+        log_pi = get_logprobs(
+            self.model,
+            concat_ids,
+            full_mask,
+            concat_loss_mask,
+            self.reduction,
+        )
+
+        with torch.no_grad():
+            log_ref = get_logprobs(
+                self.ref_model,
+                concat_ids,
+                full_mask,
+                concat_loss_mask,
+                self.reduction,
+            )
+
+        log_pi_chosen = log_pi[: chosen_ids.shape[0]]
+        log_pi_rejected = log_pi[chosen_ids.shape[0] :]
+        log_ref_chosen = log_ref[: chosen_ids.shape[0]]
+        log_ref_rejected = log_ref[chosen_ids.shape[0] :]
+
+        pi_log_ratio = log_pi_chosen - log_pi_rejected
+        ref_log_ratio = log_ref_chosen - log_ref_rejected
+
+        ratio_diff = pi_log_ratio - ref_log_ratio
+        dpo_loss = -F.logsigmoid(self.beta * ratio_diff).mean()
+
+        return dpo_loss
+
+    def supports_online(self) -> bool:
+        return True
+
+    def prepare_from_rollout(self, result: RolloutResult) -> Dict[str, Tensor]:
+        """Pick best/worst response per prompt by reward as chosen/rejected."""
+        rewards = result.rewards
+        responses = result.responses
+        masks = result.response_mask
+        best = rewards.argmax(dim=-1)
+        worst = rewards.argmin(dim=-1)
+        B = responses.shape[0]
+        idx = torch.arange(B, device=responses.device)
+        chosen = responses[idx, best]
+        chosen_mask = masks[idx, best].float()
+        rejected = responses[idx, worst]
+        rejected_mask = masks[idx, worst].float()
+        return {
+            "chosen": chosen,
+            "chosen_mask": chosen_mask,
+            "rejected": rejected,
+            "rejected_mask": rejected_mask,
+        }
+
+
+@StrategyFactory.register("grpo")
+class GRPOStrategy(BaseStrategy):
+    """Group Relative Policy Optimization strategy.
+
+    Implements GRPO following DeepSeek-R1 with token-level PPO clipping.
+    Advantages are group-normalized from scalar per-response rewards and
+    broadcast across all response tokens.  The loss is computed **only on
+    response tokens** — prompt tokens are masked out.
+
+    Three model roles are distinguished:
+
+    * **Policy** ``self.model`` — the model being trained.
+    * **Old policy** ``self.old_model`` — the behaviour policy that generated
+      the responses.  Used for the importance sampling ratio
+      ``ρ = π_θ / π_old``.  Synced externally after each data-generation round.
+    * **Reference model** ``self.ref_model`` — a frozen copy of the initial
+      policy (typically the SFT checkpoint) used **only** for the KL
+      regularisation term.  It is never updated during training.
+    """
+
+    def __init__(
+        self,
+        model: nn.Module,
+        device: str,
+        old_model: nn.Module,
+        ref_model: nn.Module,
+        clip_eps: float = 0.2,
+        kl_coef: float = 0.01,
+        group_size: int = 4,
+        **kwargs,
+    ):
+        super().__init__(model, device, **kwargs)
+        self.old_model = old_model
+        self.ref_model = ref_model
+        self.clip_eps = clip_eps
+        self.kl_coef = kl_coef
+        self.group_size = group_size
+
+    def sync_old_model(self):
+        """Copy current policy weights to old model."""
+        self.old_model.load_state_dict(self.executor.unwrap_model(self.model))
+
+    def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
+        batch = move_to_device(batch, self.device)
+        prompts = batch["prompts"]
+        responses = batch["responses"]
+        masks = batch["masks"]
+        rewards = batch["rewards"]
+
+        batch_size, group_size, response_len = responses.shape
+        responses_flat = responses.view(-1, response_len)
+        masks_flat = masks.view(-1, response_len)
+        prompt_expanded = prompts.unsqueeze(1).repeat(1, group_size, 1).flatten(0, 1)
+        prompt_len = prompt_expanded.size(1)
+
+        full_sequences = torch.cat([prompt_expanded, responses_flat], dim=-1)
+        # Prompt tokens are masked out (0) so logprobs are computed only for
+        # response tokens.  get_logprobs shifts the mask by one position, so
+        # the first response token's logprob (predicted from the last prompt
+        # token) is correctly included.
+        full_masks = torch.cat(
+            [torch.zeros_like(prompt_expanded, dtype=torch.bool), masks_flat], dim=-1
+        )
+
+        # Build full attention mask: key-padding + causal
+        key_pad = full_sequences.bool()[:, None, None, :]
+        S = key_pad.shape[-1]
+        causal = torch.tril(
+            torch.ones(S, S, dtype=torch.bool, device=full_sequences.device)
+        )[None, None, :, :]
+        attn_mask = key_pad & causal
+
+        # get_logprobs returns [B*G, S-1] (S = prompt_len + response_len).
+        # Response token logprobs occupy the last ``response_len`` positions
+        # (the first response token is predicted from the last prompt token).
+        token_log_probs_policy = get_logprobs(
+            self.model, full_sequences, attn_mask, full_masks, "none"
+        )[:, prompt_len - 1 :]
+        with torch.no_grad():
+            token_log_probs_old = get_logprobs(
+                self.old_model, full_sequences, attn_mask, full_masks, "none"
+            )[:, prompt_len - 1 :]
+            token_log_probs_ref = get_logprobs(
+                self.ref_model, full_sequences, attn_mask, full_masks, "none"
+            )[:, prompt_len - 1 :]
+
+        # Reshape to [B, G, response_len]
+        token_log_probs_policy = token_log_probs_policy.view(batch_size, group_size, -1)
+        token_log_probs_old = token_log_probs_old.view(batch_size, group_size, -1)
+        token_log_probs_ref = token_log_probs_ref.view(batch_size, group_size, -1)
+        token_masks = masks_flat.view(batch_size, group_size, -1).float()
+
+        # Group-normalized advantages from scalar per-response rewards.
+        eps = 1e-8
+        mean = rewards.mean(dim=-1, keepdim=True)
+        std = rewards.std(dim=-1, keepdim=True, unbiased=False)
+        advantages = (rewards - mean) / (std + eps)
+        # Broadcast scalar advantage to every response token: [B, G, 1]
+        advantages = advantages.unsqueeze(-1)
+
+        # Token-level ratio (π_θ / π_old) and PPO clipping.
+        log_ratio = token_log_probs_policy - token_log_probs_old
+        ratio = torch.exp(log_ratio)
+
+        surr1 = ratio * advantages
+        surr2 = torch.clamp(ratio, 1 - self.clip_eps, 1 + self.clip_eps) * advantages
+        per_token_policy_loss = -torch.min(surr1, surr2)
+        token_count = token_masks.sum().clamp(min=1.0)
+        policy_loss = (per_token_policy_loss * token_masks).sum() / token_count
+
+        # KL penalty to frozen reference model with k1 estimator (non-negative):
+        # k1 = π_ref / π_θ - log(π_ref / π_θ) - 1, where π_ref / π_θ = exp(log_ref - log_policy).
+        log_ref_ratio = token_log_probs_ref - token_log_probs_policy
+        r = torch.exp(log_ref_ratio)
+        kl_per_token = r - torch.log(r + eps) - 1.0
+        kl_penalty = self.kl_coef * (kl_per_token * token_masks).sum() / token_count
+
+        total_loss = policy_loss + kl_penalty
+
+        return total_loss
+
+    def supports_online(self) -> bool:
+        return True
+
+    def prepare_from_rollout(self, result: RolloutResult) -> Dict[str, Tensor]:
+        return {
+            "prompts": result.prompts,
+            "responses": result.responses,
+            "masks": result.response_mask,
+            "rewards": result.rewards,
+        }
+
+    def _on_rollout_refresh(self):
+        """Sync the behaviour policy whenever a fresh rollout arrives."""
+        self.sync_old_model()
+
+
+# Factory aliases: online variants use the same strategy class; the
+# ``RolloutRunner`` is injected by ``TrainContextBuilder`` to enable
+# online mode, so no separate subclass is needed.
+StrategyFactory._entries["online_grpo"] = GRPOStrategy
+StrategyFactory._entries["online_dpo"] = DPOStrategy

astrai/trainer/train_callback.py

@@ -0,0 +1,344 @@
+import json
+import logging
+import os
+import sys
+import time
+from pathlib import Path
+from typing import IO, Callable, List, Optional, Protocol, runtime_checkable
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from torch.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
+from astrai.serialization import Checkpoint
+from astrai.trainer.metric_util import (
+    ctx_get_grad_norm,
+    ctx_get_loss,
+    ctx_get_lr,
+    ctx_get_val_loss,
+)
+from astrai.trainer.train_context import TrainContext
+
+logger = logging.getLogger(__name__)
+
+
+@runtime_checkable
+class TrainCallback(Protocol):
+    """
+    Callback interface for trainer.
+    """
+
+    def on_train_begin(self, context: TrainContext):
+        """Called at the beginning of training."""
+
+    def on_train_end(self, context: TrainContext):
+        """Called at the end of training."""
+
+    def on_epoch_begin(self, context: TrainContext):
+        """Called at the beginning of each epoch."""
+
+    def on_epoch_end(self, context: TrainContext):
+        """Called at the end of each epoch."""
+
+    def on_batch_begin(self, context: TrainContext):
+        """Called at the beginning of each batch."""
+
+    def on_batch_end(self, context: TrainContext):
+        """Called at the end of each batch."""
+
+    def on_optimizer_step(self, context: TrainContext):
+        """Called on every optimizer step (sync step only)."""
+
+    def on_error(self, context: TrainContext):
+        """Called when an error occurs during training."""
+
+
+class CallbackFactory(BaseFactory[TrainCallback]):
+    """Factory for registering and creating training callbacks.
+
+    Example:
+        @CallbackFactory.register("my_callback")
+        class MyCallback(TrainCallback):
+            ...
+
+        callback = CallbackFactory.create("my_callback", **kwargs)
+    """
+
+
+@CallbackFactory.register("gradient_clipping")
+class GradientClippingCallback(TrainCallback):
+    """
+    Gradient clipping callback for trainer.
+    """
+
+    def __init__(self, max_grad_norm: float):
+        self.max_grad_norm = max_grad_norm
+
+    def on_optimizer_step(self, context: TrainContext):
+        context.grad_norm = context.executor.clip_grad_norm(
+            context.model, self.max_grad_norm
+        )
+
+
+@CallbackFactory.register("gradient_checkpointing")
+class GradientCheckpointingCallback(TrainCallback):
+    """
+    Activation checkpointing callback — trades compute for memory
+    by recomputing specified module activations during the backward pass.
+
+    Args:
+        modules: Module types to apply checkpointing to.
+    """
+
+    def __init__(self, modules: Optional[List[type]] = None):
+        self.modules = tuple(modules) if modules else ()
+
+    def _enable(self, module: nn.Module):
+        if self.modules and isinstance(module, self.modules):
+            fn = module.forward
+            module._original_forward = fn
+            module.forward = lambda *a, **kw: torch_checkpoint(
+                fn, *a, use_reentrant=False, **kw
+            )
+
+    @staticmethod
+    def _disable(module: nn.Module):
+        if hasattr(module, "_original_forward"):
+            module.forward = module._original_forward
+            del module._original_forward
+
+    def on_train_begin(self, context: TrainContext):
+        context.model.apply(self._enable)
+        logger.info("Gradient checkpointing enabled")
+
+    def on_train_end(self, context: TrainContext):
+        context.model.apply(self._disable)
+
+
+@CallbackFactory.register("checkpoint")
+class CheckpointCallback(TrainCallback):
+    """
+    Checkpoint callback for trainer.
+    """
+
+    extra_keys = ("optimizer", "scheduler")
+
+    def __init__(
+        self,
+        save_dir: str,
+        interval: int,
+        weight_only: bool = False,
+        save_extra_fn: Optional[Callable[["TrainContext"], dict]] = None,
+    ):
+        self.save_dir = save_dir
+        self.interval = interval
+        self.weight_only = weight_only
+        self.save_extra_fn = save_extra_fn or CheckpointCallback.save_extra
+        self.last_ckpt_step = None
+
+    def on_train_begin(self, context: TrainContext):
+        self.last_ckpt_step = context.optimizer_step
+
+    def _save_checkpoint(self, context: TrainContext):
+        self.last_ckpt_step = context.optimizer_step
+
+        with context.executor.checkpoint_context(context.model) as state_dict:
+            if state_dict is not None:
+                save_path = os.path.join(
+                    self.save_dir,
+                    f"epoch_{context.epoch}_step_{context.optimizer_step}",
+                )
+                extra = self.save_extra_fn(context)
+                meta = context.config.to_dict()
+                context.checkpoint = Checkpoint(
+                    state_dict=state_dict,
+                    epoch=context.epoch,
+                    consumed_samples=context.consumed_samples,
+                    config=context.model_config,
+                    extra=extra,
+                    meta=meta,
+                )
+                context.checkpoint.save(save_path)
+
+    def on_batch_end(self, context: TrainContext):
+        if context.optimizer_step - self.last_ckpt_step >= self.interval:
+            self._save_checkpoint(context)
+
+    def on_train_end(self, context: TrainContext):
+        if context.optimizer_step != self.last_ckpt_step:
+            self._save_checkpoint(context)
+
+    def on_error(self, context: TrainContext):
+        self._save_checkpoint(context)
+
+    @staticmethod
+    def save_extra(context: TrainContext) -> dict:
+        extra = {}
+        for name in CheckpointCallback.extra_keys:
+            obj = getattr(context, name, None)
+            if obj:
+                extra[name] = obj.state_dict()
+        return extra
+
+
+@CallbackFactory.register("progress_bar")
+class ProgressBarCallback(TrainCallback):
+    """
+    Progress bar callback for trainer.
+    """
+
+    def __init__(
+        self, num_epoch: int, log_interval: int = 100, file: Optional[IO[str]] = None
+    ):
+        self.num_epoch = num_epoch
+        self.log_interval = log_interval
+        self.file = file
+        self.progress_bar: tqdm = None
+
+    @only_on_rank(0)
+    def on_epoch_begin(self, context: TrainContext):
+        total_steps = len(context.dataloader) // context.executor.grad_accum_steps
+        self.progress_bar = tqdm(
+            total=total_steps,
+            desc=f"Epoch {context.epoch + 1}/{self.num_epoch}",
+            dynamic_ncols=True,
+            file=self.file or sys.stdout,
+        )
+
+    @only_on_rank(0)
+    def on_optimizer_step(self, context: TrainContext):
+        postfix = {
+            "step": f"{context.optimizer_step:d}",
+            "loss": f"{context.loss:.4f}",
+            "lr": f"{context.optimizer.param_groups[-1]['lr']:.2e}",
+        }
+        if context.grad_norm is not None:
+            postfix["grad_norm"] = f"{context.grad_norm:.2f}"
+        if context.val_loss is not None:
+            postfix["val_loss"] = f"{context.val_loss:.4f}"
+        self.progress_bar.set_postfix(postfix)
+        self.progress_bar.update(1)
+
+    @only_on_rank(0)
+    def on_epoch_end(self, context: TrainContext):
+        _ = context
+        if self.progress_bar:
+            self.progress_bar.close()
+
+
+@CallbackFactory.register("metric")
+class MetricCallback(TrainCallback):
+    def __init__(
+        self,
+        log_dir: str,
+        save_interval: int,
+        metrics: List[str] = None,
+        val_step: int = 0,
+    ):
+        self.last_log_flush_step = None
+        self.save_interval = save_interval
+        self.metrics = metrics or ["loss", "lr"]
+        self.val_step = val_step
+        self._next_val_step = 0
+
+        self.log_dir = Path(log_dir) if log_dir else Path.cwd() / "logs"
+        self.log_dir.mkdir(parents=True, exist_ok=True)
+
+        self.log_cache = []
+
+        self._metric_funcs = {
+            "loss": ctx_get_loss,
+            "lr": ctx_get_lr,
+            "val_loss": ctx_get_val_loss,
+            "grad_norm": ctx_get_grad_norm,
+        }
+
+    def _metrics(self, context: TrainContext, names):
+        return {
+            m: self._metric_funcs[m](context)
+            for m in names
+            if self._metric_funcs[m](context) is not None
+        }
+
+    @only_on_rank(0)
+    def _append(self, event_type: str, context: TrainContext, **extra):
+        entry = {
+            "type": event_type,
+            "timestamp": time.strftime("%Y-%m-%dT%H:%M:%S"),
+            "epoch": context.epoch,
+            "step": context.optimizer_step,
+            "consumed_samples": context.consumed_samples,
+            **extra,
+        }
+        self.log_cache.append(entry)
+
+    def _run_validation(self, context: TrainContext) -> float:
+        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
+
+        if context.world_size > 1 and dist.is_initialized():
+            stats = torch.tensor(
+                [total_loss, float(num_batches)], device=get_current_device()
+            )
+            dist.all_reduce(stats, op=dist.ReduceOp.SUM)
+            avg_loss = (stats[0] / stats[1]).item()
+        else:
+            avg_loss = total_loss / max(num_batches, 1)
+
+        context.model.train()
+        return avg_loss
+
+    def on_train_begin(self, context: TrainContext):
+        self.last_log_flush_step = context.optimizer_step
+
+    @only_on_rank(0)
+    def _flush(self, epoch, step):
+        log_file = self.log_dir / f"epoch_{epoch}_step_{step}_metric.jsonl"
+        log_file.parent.mkdir(parents=True, exist_ok=True)
+        with open(log_file, "w") as f:
+            for log in self.log_cache:
+                f.write(json.dumps(log) + "\n")
+
+    def on_optimizer_step(self, context):
+        if (
+            context.val_dataloader is not None
+            and self.val_step > 0
+            and context.optimizer_step >= self._next_val_step
+        ):
+            context.val_loss = self._run_validation(context)
+            self._next_val_step = context.optimizer_step + self.val_step
+            self._append("validation", context, val_loss=context.val_loss)
+
+        step_metrics = [m for m in self.metrics if m != "val_loss"]
+        self._append("step", context, **self._metrics(context, step_metrics))
+
+        if context.optimizer_step - self.last_log_flush_step >= self.save_interval:
+            self._flush(context.epoch, context.optimizer_step)
+            self.last_log_flush_step = context.optimizer_step
+
+    def on_epoch_end(self, context):
+        self._append("epoch", context)
+
+    def on_train_end(self, context):
+        if (
+            self.last_log_flush_step is None
+            or context.optimizer_step != self.last_log_flush_step
+        ):
+            self._flush(context.epoch, context.optimizer_step)
+            self.last_log_flush_step = context.optimizer_step
+
+    def on_error(self, context):
+        self._flush(context.epoch, context.optimizer_step)

astrai/trainer/train_context.py

@@ -0,0 +1,269 @@
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Dict, Optional, Self
+
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader, random_split
+
+from astrai.config.train_config import TrainConfig
+from astrai.dataset import RDSampler
+from astrai.inference.core.scheduler import InferenceScheduler
+from astrai.model.components.lora import inject_lora
+from astrai.parallel.executor import BaseExecutor, ExecutorFactory
+from astrai.parallel.setup import get_current_device, get_rank, get_world_size
+from astrai.protocols import OptimizerProtocol, SchedulerProtocol
+from astrai.serialization import Checkpoint, load_json
+from astrai.tokenize import AutoTokenizer
+from astrai.trainer.rollout import RolloutGenerator, RolloutRunner
+from astrai.trainer.strategy import BaseStrategy, StrategyFactory, create_ref_model
+
+
+@dataclass
+class TrainContext:
+    model: nn.Module = field(default=None)
+    strategy: BaseStrategy = field(default=None)
+    dataloader: DataLoader = field(default=None)
+    optimizer: OptimizerProtocol = field(default=None)
+    scheduler: SchedulerProtocol = field(default=None)
+    checkpoint: Checkpoint = field(default=None)
+    config: TrainConfig = field(default=None)
+    model_config: dict = field(default_factory=dict)
+    executor: BaseExecutor = field(default=None)
+    epoch: int = field(default=0)
+    consumed_samples: int = field(default=0)
+    loss: float = field(default=0.0)
+    grad_norm: Optional[float] = field(default=None)
+    val_dataloader: Optional[DataLoader] = field(default=None)
+    val_loss: Optional[float] = field(default=None)
+
+    world_size: int = field(default=1)
+    rank: int = field(default=0)
+    kwargs: Dict[str, Any] = field(default_factory=dict)
+
+    @property
+    def optimizer_step(self) -> int:
+        return self.consumed_samples // (
+            self.config.batch_per_device
+            * self.world_size
+            * self.config.grad_accum_steps
+        )
+
+
+class TrainContextBuilder:
+    def __init__(
+        self,
+        config: TrainConfig,
+    ):
+        self.config = config
+        self._param_path: Optional[str] = None
+        self._resume: bool = False
+
+    def with_param_path(self, param_path: Optional[str], resume: bool = False) -> Self:
+        self._param_path = param_path
+        self._resume = resume
+        return self
+
+    def build(self) -> TrainContext:
+        cfg = self.config
+        device = get_current_device()
+
+        executor = ExecutorFactory.create(
+            cfg.parallel_mode,
+            grad_accum_steps=cfg.grad_accum_steps,
+            **cfg.executor_kwargs,
+        )
+
+        model_config = {}
+        if self._param_path:
+            config_path = Path(self._param_path) / "config.json"
+            if config_path.exists():
+                model_config = load_json(config_path)
+
+        preloaded_state_dict = None
+        preloaded_epoch = cfg.start_epoch
+        preloaded_consumed = cfg.start_samples * get_world_size()
+        preloaded_checkpoint = None
+        if self._param_path:
+            checkpoint = Checkpoint.load_any(self._param_path)
+            if checkpoint is not None:
+                preloaded_state_dict = checkpoint.state_dict
+                if checkpoint.config:
+                    model_config = checkpoint.config
+                if self._resume:
+                    preloaded_epoch = checkpoint.epoch or cfg.start_epoch
+                    if checkpoint.consumed_samples > 0:
+                        per_step = (
+                            cfg.batch_per_device
+                            * get_world_size()
+                            * cfg.grad_accum_steps
+                        )
+                        preloaded_consumed = (
+                            checkpoint.consumed_samples // per_step
+                        ) * per_step
+                    else:
+                        preloaded_consumed = cfg.start_samples * get_world_size()
+                    preloaded_checkpoint = checkpoint
+
+        if not model_config and hasattr(cfg.model_fn(), "config"):
+            model_config = cfg.model_fn().config.to_dict()
+
+        def _before_wrap(m):
+            m = m.to(device=device)
+            if cfg.lora is not None:
+                inject_lora(
+                    m,
+                    r=cfg.lora.r,
+                    alpha=cfg.lora.alpha,
+                    target_modules=set(cfg.lora.target_modules),
+                )
+            if preloaded_state_dict is not None:
+                m.load_state_dict(preloaded_state_dict, strict=False)
+            return m
+
+        context = TrainContext(
+            world_size=get_world_size(),
+            rank=get_rank(),
+            config=cfg,
+            model_config=model_config,
+            executor=executor,
+            epoch=preloaded_epoch,
+            consumed_samples=preloaded_consumed,
+            checkpoint=preloaded_checkpoint,
+        )
+
+        context.model, context.optimizer, context.scheduler = executor.prepare(
+            cfg.model_fn,
+            cfg.optimizer_fn,
+            cfg.scheduler_fn,
+            before_wrap=_before_wrap,
+        )
+
+        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.consumed_samples // context.world_size
+        sampler = RDSampler(
+            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,
+            collate_fn=cfg.collate_fn,
+        )
+
+        if val_dataset is not None:
+            val_sampler = RDSampler(
+                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,
+                collate_fn=cfg.collate_fn,
+            )
+
+        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])
+
+        strategy_kwargs = dict(cfg.extra_kwargs)
+
+        needs_ref = cfg.strategy in (
+            "dpo",
+            "grpo",
+            "online_grpo",
+            "online_dpo",
+        )
+        needs_old = cfg.strategy in ("grpo", "online_grpo")
+
+        if needs_ref:
+            ref_model = create_ref_model(
+                cfg.model_fn, executor.unwrap_model(context.model)
+            ).to(device=device)
+            strategy_kwargs["ref_model"] = ref_model
+
+        old_model = None
+        if needs_old:
+            old_model = create_ref_model(
+                cfg.model_fn, executor.unwrap_model(context.model)
+            ).to(device=device)
+            strategy_kwargs["old_model"] = old_model
+
+        context.strategy = StrategyFactory.create(
+            cfg.strategy,
+            model=context.model,
+            device=device,
+            executor=executor,
+            **strategy_kwargs,
+        )
+
+        # Enable online rollout when the train_type is an ``online_*`` variant.
+        is_online = cfg.strategy.startswith("online_")
+        if is_online:
+            if not context.strategy.supports_online():
+                raise ValueError(
+                    f"Strategy '{cfg.strategy}' does not support online rollout"
+                )
+            if cfg.reward_model_fn is None:
+                raise ValueError("reward_model_fn is required for online RL strategies")
+
+            tokenizer = AutoTokenizer.from_pretrained(self._param_path)
+            reward_model = cfg.reward_model_fn()
+
+            group_size = strategy_kwargs.get("group_size", 1)
+            rollout_batch_size = group_size * max(1, cfg.batch_per_device)
+            max_seq_len = getattr(context.model.config, "max_position_embeddings", None)
+
+            scheduler = InferenceScheduler(
+                model=context.model,
+                tokenizer=tokenizer,
+                max_batch_size=rollout_batch_size,
+                max_seq_len=max_seq_len,
+                max_prompt_len=max_seq_len or 4096,
+            )
+
+            generator = RolloutGenerator(
+                scheduler=scheduler,
+                tokenizer=tokenizer,
+                max_tokens=cfg.rollout_max_tokens,
+                group_size=group_size,
+                temperature=cfg.rollout_temperature,
+                top_k=cfg.rollout_top_k,
+                top_p=cfg.rollout_top_p,
+            )
+            runner = RolloutRunner(
+                generator=generator,
+                reward_model=reward_model,
+                rollout_interval=cfg.rollout_interval,
+            )
+            context.strategy.set_rollout_runner(runner)
+
+        return context

astrai/trainer/trainer.py

@@ -0,0 +1,112 @@
+import logging
+from typing import List, Optional
+
+from astrai.config import TrainConfig
+from astrai.parallel.setup import spawn_parallel_fn
+from astrai.trainer.train_callback import (
+    CallbackFactory,
+    TrainCallback,
+)
+from astrai.trainer.train_context import TrainContext, TrainContextBuilder
+
+logger = logging.getLogger(__name__)
+
+
+class Trainer:
+    def __init__(
+        self, train_config: TrainConfig, callbacks: Optional[List[TrainCallback]] = None
+    ):
+        self.train_config = train_config
+        default_callbacks = self._get_default_callbacks()
+        self.callbacks = (
+            default_callbacks + callbacks if callbacks else default_callbacks
+        )
+
+    def _get_default_callbacks(self) -> List[TrainCallback]:
+        cfg = self.train_config
+        callbacks = [
+            CallbackFactory.create(
+                "gradient_checkpointing",
+                modules=cfg.gradient_checkpointing_modules,
+            ),
+            CallbackFactory.create(
+                "checkpoint",
+                cfg.ckpt_dir,
+                cfg.ckpt_interval,
+            ),
+            CallbackFactory.create(
+                "metric",
+                log_dir=cfg.log_dir,
+                save_interval=cfg.ckpt_interval,
+                metrics=cfg.metrics,
+                val_step=cfg.val_step,
+            ),
+            CallbackFactory.create("progress_bar", cfg.n_epoch),
+            CallbackFactory.create("gradient_clipping", cfg.max_grad_norm),
+        ]
+        return callbacks
+
+    def _call_callbacks(self, method_name: str, context: TrainContext):
+        for callback in self.callbacks:
+            method = getattr(callback, method_name, None)
+            if method:
+                method(context)
+
+    def _trainer_loop(self, param_path: Optional[str] = None, resume: bool = False):
+        context = (
+            TrainContextBuilder(self.train_config)
+            .with_param_path(param_path, resume=resume)
+            .build()
+        )
+        executor = context.executor
+        self._call_callbacks("on_train_begin", context)
+
+        try:
+            context.model.train()
+
+            for epoch in range(context.epoch, context.config.n_epoch):
+                context.epoch = epoch
+                self._call_callbacks("on_epoch_begin", context)
+
+                for batch in context.dataloader:
+                    with executor.accumulate(context.model):
+                        self._call_callbacks("on_batch_begin", context)
+                        loss = context.strategy(batch)
+                        context.loss = loss.item()
+                        stand_loss = loss / executor.grad_accum_steps
+                        executor.backward(stand_loss)
+                        context.consumed_samples += (
+                            context.config.batch_per_device * context.world_size
+                        )
+                        self._call_callbacks("on_batch_end", context)
+
+                        if executor.sync_gradients:
+                            self._call_callbacks("on_optimizer_step", context)
+                            context.optimizer.step()
+                            context.optimizer.zero_grad()
+
+                            if context.scheduler:
+                                context.scheduler.step()
+
+                self._call_callbacks("on_epoch_end", context)
+
+        except Exception as e:
+            logger.error("Training failed: %s", str(e), exc_info=True)
+            self._call_callbacks("on_error", context)
+            raise
+        finally:
+            self._call_callbacks("on_train_end", context)
+
+    def train(self, param_path: Optional[str] = None, resume: bool = False):
+        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,
+            param_path=param_path,
+            resume=resume,
+        )

benchmark.py

@@ -1,198 +0,0 @@
-import torch
-from typing import Dict, Any
-from dataclasses import dataclass
-from khaosz.model.transformer import ModelConfig, Transformer
-
-
-@dataclass
-class BenchmarkResult:
-    total_tokens: int
-    total_time: float
-    tokens_per_second: float
-    metadata: Dict[str, Any]
-
-
-class GenerationBenchmark:
-    def __init__(
-        self,
-        config: ModelConfig,
-        device: str = "cuda",
-        dtype: torch.dtype = torch.float16
-    ):
-        self.config = config
-        self.device = device
-        self.dtype = dtype
-        self.model = Transformer(config).to(device=device, dtype=dtype)
-        self.model.eval()
-    
-    def _initialize_kv_cache(self, batch_size: int) -> list:
-        """初始化KV缓存"""
-        config = self.config
-        shape = (batch_size, config.n_layer, config.m_len, config.n_kvhead, config.n_dim // config.n_head)
-        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(
-            low=0,
-            high=self.config.vocab_size,
-            size=(batch_size, prompt_length),
-            device=self.device,
-            dtype=torch.long
-        )
-        
-        gen_ids = torch.randint(
-            low=0,
-            high=self.config.vocab_size,
-            size=(batch_size, total_length - prompt_length),
-            device=self.device,
-            dtype=torch.long
-        )
-        
-        return prompt_ids, gen_ids
-    
-    @torch.inference_mode()
-    def run_prefill_benchmark(
-        self,
-        batch_size: int = 1,
-        prompt_length: int = 512,
-        num_trials: int = 10,
-    ) -> BenchmarkResult:
-        
-        for _ in range(3):
-            prompt_ids, _ = self._prepare_inputs(batch_size, prompt_length, prompt_length)
-            _ = self.model(prompt_ids)
-        
-        torch.cuda.synchronize()
-        
-        total_time = 0.0
-        total_tokens = batch_size * prompt_length * num_trials
-        
-        for trial in range(num_trials):
-            prompt_ids, _ = self._prepare_inputs(batch_size, prompt_length, prompt_length)
-            start_event = torch.cuda.Event(enable_timing=True)
-            end_event = torch.cuda.Event(enable_timing=True)
-            
-            start_event.record()
-            _ = self.model(prompt_ids)
-            end_event.record()
-            torch.cuda.synchronize()
-            
-            trial_time = start_event.elapsed_time(end_event) / 1000
-            total_time += trial_time
-            
-            print(f"Trial {trial + 1}/{num_trials}: {prompt_length} tokens in {trial_time:.3f}s "
-                  f"({prompt_length / trial_time:.1f} tokens/s)")
-        
-        return BenchmarkResult(
-            total_tokens=total_tokens,
-            total_time=total_time,
-            tokens_per_second=total_tokens / total_time,
-            metadata={
-                "benchmark_type": "prefill",
-                "batch_size": batch_size,
-                "prompt_length": prompt_length,
-                "dtype": self.dtype,
-                "device": self.device,
-            }
-        )
-    
-    @torch.inference_mode()
-    def run_decoding_benchmark(
-        self,
-        batch_size: int = 1,
-        prompt_length: int = 512,
-        gen_length: int = 128,
-        num_trials: int = 5,
-    ) -> BenchmarkResult:
-        
-        total_time = 0.0
-        total_tokens = batch_size * gen_length * num_trials
-        
-        for trial in range(num_trials):
-            
-            prompt_ids, gen_ids = self._prepare_inputs(batch_size, prompt_length, prompt_length + gen_length)
-            kv_cache = self._initialize_kv_cache(batch_size)
-            _ = self.model(prompt_ids, persistent_key_values=kv_cache, start_pos=0)
-            
-            torch.cuda.synchronize()
-
-            start_event = torch.cuda.Event(enable_timing=True)
-            end_event = torch.cuda.Event(enable_timing=True)
-            
-            start_event.record()
-            
-            current_pos = prompt_length
-            for i in range(gen_length):
-                input_token = gen_ids[:, i:i+1]
-                _ = self.model(input_token, persistent_key_values=kv_cache, start_pos=current_pos)
-                current_pos += 1
-            
-            end_event.record()
-            torch.cuda.synchronize()
-            
-            trial_time = start_event.elapsed_time(end_event) / 1000
-            total_time += trial_time
-            
-            print(f"Trial {trial + 1}/{num_trials}: {gen_length} tokens in {trial_time:.3f}s "
-                  f"({gen_length / trial_time:.1f} tokens/s)")
-
-        
-        return BenchmarkResult(
-            total_tokens=total_tokens,
-            total_time=total_time,
-            tokens_per_second=total_tokens / total_time,
-            metadata={
-                "benchmark_type": "decoding",
-                "batch_size": batch_size,
-                "prompt_length": prompt_length,
-                "gen_length": gen_length,
-                "dtype": self.dtype,
-                "device": self.device,
-            }
-        )
-
-
-def print_benchmark_result(result: BenchmarkResult):
-    """打印基准测试结果"""
-    benchmark_type = result.metadata["benchmark_type"]
-    
-    print(f"\n{' ' + benchmark_type.upper().replace('_', ' ') + ' Benchmark ':-^80}")
-    print(f"Total Tokens Processed: {result.total_tokens:,}")
-    print(f"Time Consumed: {result.total_time:.3f}s")
-    print(f"Throughput: {result.tokens_per_second:,.1f} tokens/s")
-    
-    if benchmark_type == "prefill":
-        print(f"Batch Size: {result.metadata['batch_size']} | Prompt Length: {result.metadata['prompt_length']}")
-    elif benchmark_type == "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 = ModelConfig(
-        vocab_size=10000,
-        n_dim=1536,
-        n_head=24,
-        n_kvhead=4,
-        d_ffn=6912,
-        m_len=2048,
-        n_layer=24,
-        norm_eps=1e-5,
-    )
-    
-    benchmark = GenerationBenchmark(config)
-    
-    print("=" * 80)
-    print("Running Transformer Generation Benchmark")
-    print("=" * 80)
-    
-    prefill_result = benchmark.run_prefill_benchmark(batch_size=4, prompt_length=512, num_trials=5)
-    print_benchmark_result(prefill_result)
-    
-    gen_result = benchmark.run_decoding_benchmark(batch_size=4, prompt_length=512, gen_length=128, num_trials=5)
-    print_benchmark_result(gen_result)
-    

csrc/__init__.py

@@ -0,0 +1,2 @@
+# Source directory for CUDA kernels — build-time only.
+# Compiled .so files live in astrAI/_ext/.

csrc/build.py

@@ -0,0 +1,48 @@
+from pathlib import Path
+
+
+def _arch_flags() -> list[str]:
+    import torch
+
+    if torch.cuda.is_available():
+        cap = torch.cuda.get_device_capability()
+    else:
+        cap = (8, 0)
+    ver = f"{cap[0]}{cap[1]}"
+    flags = [f"-gencode=arch=compute_{ver},code=sm_{ver}"]
+    # tensor-core mma path (mma.sync.m16n8k16.bf16) requires sm_80+; decide the
+    # kernel dispatch at build time via this define rather than at runtime.
+    if cap[0] < 8:
+        flags.append("-DASTRAI_NO_MMA")
+    return flags
+
+
+_kernels_dir = Path("csrc/kernels")
+REGISTRY: dict[str, dict] = {}
+
+CXX_FLAGS = ["-O3", "-funroll-loops"]
+NVCC_FLAGS = [
+    "-O3",
+    "--expt-relaxed-constexpr",
+    "--use_fast_math",
+    "--ptxas-options=-O3,-v",
+    "--extra-device-vectorization",
+    "--threads=8",
+]
+
+
+def register(name: str, sources: list[str] | None = None, **kwargs):
+    if sources is None:
+        sources = [str(_kernels_dir / f"{name}.cu")]
+    REGISTRY[name] = {
+        "sources": sources,
+        "cxx_flags": [*CXX_FLAGS],
+        "nvcc_flags": [*NVCC_FLAGS, *_arch_flags()],
+        "extra_link_args": kwargs.pop("extra_link_args", []),
+        **kwargs,
+    }
+
+
+register("attn_decode")
+register("attn_prefill")
+register("attn_paged_decode")

csrc/kernels/attn_common.h

@@ -0,0 +1,68 @@
+#pragma once
+
+
+template<typename T, typename AT = float>
+struct AttentionParams {
+    int batch;
+    int q_head;
+    int kv_head;
+    int q_len;
+    int kv_len;
+    int head_dim;
+    int use_mask;
+    int causal_offset;   // -1 = non-causal; >=0 = absolute position of first Q token
+    int num_splits;
+    float scale;
+
+    // Q strides (element offsets for each dim — layout-agnostic)
+    int q_stride_b, q_stride_h, q_stride_l, q_stride_d;
+    // KV strides (K and V share the same layout — only base pointers differ)
+    int kv_stride_b, kv_stride_h, kv_stride_l, kv_stride_d;
+
+    // Mask: 2D [batch, kv_len] (mask_q_stride=0) or 3D [batch, q_len, kv_len]
+    int mask_b_stride;   // = kv_len (both 2D and 3D)
+    int mask_q_stride;   // 2D: 0 (all q rows share); 3D: kv_len
+
+    const T* __restrict__ q;
+    const T* __restrict__ k;
+    const T* __restrict__ v;
+    const bool* __restrict__ mask;
+
+    T* __restrict__ o;
+    AT* __restrict__ o_part;
+    AT* __restrict__ ml_part;
+};
+
+template<typename T, typename AT = float>
+struct PagedAttentionParams {
+    int batch;
+    int q_head;
+    int kv_head;
+    int q_len;
+    int kv_len;
+    int head_dim;
+    int use_mask;
+    int causal_offset;
+    float scale;
+
+    int num_splits;
+    int page_size;
+    int max_pages;
+
+    // Q strides (layout-agnostic)
+    int q_stride_b, q_stride_h, q_stride_l, q_stride_d;
+
+    // Mask strides (2D or 3D)
+    int mask_b_stride;
+    int mask_q_stride;
+
+    const T* __restrict__ q;
+    const T* __restrict__ k_cache;
+    const T* __restrict__ v_cache;
+    const bool* __restrict__ mask;
+    const int64_t* __restrict__ page_table;
+
+    T* __restrict__ o;
+    AT* __restrict__ o_part;
+    AT* __restrict__ ml_part;
+};