feat: fp8 backward reuses pre-quantized operands

- g/x/w may each be bf16 or pre-quantized fp8 matching fmt; a pre-quantized operand skips its quantize kernel
- snapshot sx/sw/sg before the ring finalize overwrites the aliased scale slot so the gemm dequantizes with the quantize scale
- forward carries its scale to backward so gradients reuse the forward's scale
- grad_input/grad_weight forced bf16; a pre-quantized g dequantizes before the bias-sum
- regression test: two delayed steps with a changing amax must not leak the scale ratio

.dockerignore

@@ -0,0 +1,11 @@
+# Ignore everything
+*
+
+# Allow necessary files
+!astrai/
+!scripts/
+!docs/
+!csrc/
+!setup.py
+!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 +1,103 @@
+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, ${{ matrix.cuda_tag }})
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - cuda_tag: "cu128"
+            cuda_ver: "12.8.0"
+          - cuda_tag: "cu130"
+            cuda_ver: "13.0.0"
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+
+      - name: Install torch (${{ matrix.cuda_tag }})
+        run: |
+          pip install torch --index-url https://download.pytorch.org/whl/${{ matrix.cuda_tag }}
+
+      - name: Setup CUDA (${{ matrix.cuda_ver }})
+        uses: Jimver/cuda-toolkit@v0.2.35
+        with:
+          cuda: "${{ matrix.cuda_ver }}"
+
+      - name: Build wheel (with CUDA kernels)
+        run: |
+          CSRC_KERNELS=true pip wheel . --no-deps --no-build-isolation -w dist/
+          for f in dist/*.whl; do
+            mv "$f" "dist/$(basename "$f" .whl)+${{ matrix.cuda_tag }}.whl"
+          done
+
+      - uses: actions/upload-artifact@v4
+        with:
+          name: cuda-wheel-linux-${{ matrix.cuda_tag }}
+          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 wheels (all variants)
+        uses: actions/download-artifact@v4
+        with:
+          pattern: cuda-wheel-linux-*
+          merge-multiple: true
+          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[@]}" -ge 1
+
+      - 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 +1,37 @@
-# cache
-__pycache__
-.pytest_cache
+# Ignore everything
+*
 
-# params
-params/*
+# Allow directories to be traversed
+!*/
 
-# vscode file
-.vscode
+# Allow specific file types and root files
+!astrai/**/*.py
+!scripts/**/*.py
+!tests/**/*.py
+!csrc/**/*.py
+!csrc/CMakeLists.txt
 
-# build file
-build
-*.egg-info
+!csrc/**/*.cu
+!csrc/**/*.h
+!csrc/**/*.cuh
+
+!scripts/**/*.sh
+
+# Allow GitHub files
+!/.github/**
+
+# Allow root files
+!/.gitattributes
+!/.dockerignore
+!/Dockerfile
+!/docker-compose.yml
+!/docs/**
+!/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 +1,103 @@
+# 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 .
+```
+
+### 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 the system temp directory
+> (`$TMPDIR` on Linux/macOS, `%TEMP%` on Windows). Clean them manually if needed.
+
+### 4. (Optional) Full pre-commit check script
+
+If you have `bash` available (Git Bash on Windows works too):
+
+```bash
+bash scripts/pre_commit.sh
+```
+
+The script installs development dependencies by default, then runs the format
+check, import sort check, and tests. If dependencies are already installed, use:
+
+```bash
+bash scripts/pre_commit.sh --skip-deps
+```
+
+## Commit Style
+
+```
+type: 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 check script fails | Dependency install, tests, or lint failed | Fix the failing step; use `--skip-deps` only when dependencies are already installed |
+| 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 [Apache-2.0 License](LICENSE).
+
+---
+
+Questions? Ask in [GitHub Discussions](https://github.com/ViperEkura/AstrAI/discussions) or open an issue.

Dockerfile

@@ -0,0 +1,70 @@
+# AstrAI Dockerfile - Multi-stage Build (Optimized)
+#
+# CUDA version selection:
+#   docker build -t astrai .
+#   docker build -t astrai --build-arg CUDA_TAG=cu128 .
+#   docker build -t astrai --build-arg CUDA_TAG=cu130 .
+# Default: cu128
+
+# Build stage - use base image with minimal build tools
+FROM ubuntu:24.04 AS builder
+
+ARG CUDA_TAG=cu128
+
+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:$PATH"
+
+# Copy source code and install (deps read from pyproject.toml)
+COPY astrai/ ./astrai/
+COPY csrc/ ./csrc/
+COPY setup.py .
+COPY pyproject.toml .
+RUN pip install --no-cache-dir --upgrade pip \
+    && pip install --no-cache-dir . \
+    --extra-index-url "https://download.pytorch.org/whl/${CUDA_TAG}"
+
+# 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:$PATH"
+
+# Copy application code
+COPY astrai/ ./astrai/
+COPY scripts/ ./scripts/
+COPY docs/ ./docs/
+COPY pyproject.toml .
+COPY README.md .
+
+# Create non-root user matching the host uid/gid (passed via build args)
+ARG USER_UID=1000
+ARG USER_GID=1000
+RUN groupadd -g "${USER_GID}" astrai \
+    && useradd -m -u "${USER_UID}" -g astrai astrai \
+    && chown -R astrai:astrai /app
+ENV HOME=/home/astrai
+USER astrai
+
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1

README.md

@@ -1,333 +1,271 @@
-![image-20250306182014120](/assets/images/project_logo_clipped.png)
-
-<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 align="center">
   
-  <div>
-    <a href="#english" style="text-decoration: none; margin: 0 10px; color: blue;">English</a> | 
-    <a href="#chinese" style="text-decoration: none; margin: 0 10px; color: blue;">中文</a>
-  </div>
-
-  <h1 style="margin: 20px 0 0 0; font-size: 2.5em; font-weight: bold;">KHAOSZ </h1>
+  <img src="docs/images/logo.png" width="auto" alt="Logo">
+  <p>
+    <strong>A lightweight Transformer training & inference framework</strong>
+  </p>
 </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-Apache--2.0-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="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**
-2. Run `scripts/download.py` to download parameters
+## 📖 Table of Contents
 
-**Demo Video:** [bilibili](https://www.bilibili.com/video/BV1z5RPYHEkd)
+- [Overview](#overview)
+- [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
-- **🌐 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.
+### Overview
 
-### 📌 Training Guide
+AstrAI is an end-to-end Transformer framework for building, training, evaluating, and serving models. It provides a compact PyTorch codebase for the complete model lifecycle, from declarative data preprocessing and distributed training to continuous-batching inference and OpenAI/Anthropic-compatible APIs.
 
-To train this Transformer model, follow these steps:
+| Area | Capabilities |
+|---|---|
+| **Models** | Autoregressive language models and embedding models with GQA, MLA, MoE, RoPE, and extensible attention/FFN components |
+| **Training** | Pre-training (`seq`), supervised fine-tuning (`sft`), DPO, and GRPO with gradient accumulation, checkpointing, DDP, and FSDP |
+| **Data** | Declarative JSON preprocessing, configurable masking and packing, binary/JSONL storage, and streaming datasets |
+| **Inference** | Continuous batching, paged KV cache, radix prefix caching, streaming generation, and Torch/CUDA/FlashAttention backends |
+| **Serving** | FastAPI server with OpenAI and Anthropic chat completion protocols, including SSE streaming and tool calls |
+| **Evaluation** | Perplexity, MMLU, HumanEval, IFEval, IFD, ROUGE, and weight-analysis evaluation tools |
+| **Extensibility** | Factory and registry architecture for models, datasets, training strategies, callbacks, kernels, and protocol components |
 
-**(1). Prepare Dataset:**
+### Getting Started
 
-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).
+End-to-end walkthrough in 5 steps:
 
-**(2). Install Dependencies:**
+**1. Install**
+
+AstrAI requires Python 3.12+ and pins PyTorch exactly to `2.11.0`. Training, `scripts/tools/generate.py`, generation evaluations, and the generation demos require CUDA; CPU support is limited to components with an explicit CPU device path, such as the HTTP server and direct-scoring evaluations.
 
 ```bash
-pip install -r requirements.txt
-pip install .
+git clone https://github.com/ViperEkura/AstrAI.git
+cd AstrAI
+pip install -e .                                          # kernels auto-build when nvcc + CUDA are detected
+# CSRC_KERNELS=false pip install -e .                     # skip kernels (pure PyTorch)
+# CSRC_KERNELS=true pip install -e . --no-build-isolation  # force the fused CUDA kernel build
+# pip install -e ".[dev]"                                  # dev dependencies (pytest, ruff)
 ```
 
-**(3). Run Training Script:**
+**2. Download model**
 
 ```bash
-python train.py \
---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
+python scripts/demo/download.py    # downloads 1B checkpoint to params/
 ```
 
-**Parameters Explanation:**
-- `--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
+**3. Preprocess data**
 
-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
-
-**(1). Chatting with the Model:**
-
-Open `chat.py` or use streaming/non-streaming interfaces:
-
-**Streaming Output:**
-```python
-import torch
-from khaosz import Khaosz
-
-model_dir = "your_model_parameter_dir"
-model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
-history = []
-
-while True:
-    query = input(">> ")
-    if query == "!exit":
-        break
-    
-    response_size = 0
-    for response, history in model.stream_generate(
-        query=query, 
-        history=history,
-        temperature=0.85,
-        top_p=0.95,
-        top_k=50
-    ):
-        print(response[response_size:], end="")
-        response_size = len(response)       
+```json
+{
+    "version": 1,
+    "input": {"sections": [{"field": "text", "action": "train"}]},
+    "preprocessing": {"max_seq_len": 2048},
+    "output": {"storage_format": "bin"}
+}
 ```
 
-**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
-pip install .
+python scripts/tools/preprocess.py data/*.jsonl -o output/ -c pretrain.json
 ```
 
-#### **(3). 运行训练脚本：**
+**4. Train**
 
 ```bash
-python train.py \
---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 
+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 &
 ```
 
-**参数说明：**
-- `--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
+**5. Serve & query**
 
-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
 
-
-
-### 👉 使用指南
-
-#### **(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)       
+# Terminal 2: query
+curl http://localhost:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{"messages":[{"role":"user","content":"Hello"}],"max_tokens":512}'
 ```
 
-**非流式输出：**
-```python
-import torch
-from khaosz import Khaosz
+### Demo
 
-model_dir = "your_model_parameter_dir"
-model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
-history = []
+Check out the demos in the `scripts/demo/` folder:
 
-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)
+```bash
+# Download model weights (required before running demos)
+python scripts/demo/download.py                      # model → params/
+
+# Single-turn interactive streaming prompt loop (no conversation history)
+python scripts/demo/stream_chat.py
+# Type your message after >>, type !exit to quit
+
+# Batch generation (5 hardcoded prompts, non-streaming)
+python scripts/demo/generate_batch.py
+
+# Single-prompt autoregressive streaming
+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
-import torch
-from khaosz import Khaosz
+Watch a video walkthrough on [bilibili](https://www.bilibili.com/video/BV1fuLB6yEj6).
 
-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)
+See [Documentation](#documentation) for full references beyond the examples above.
+
+#### Text Generation
+
+Batch generation from a JSONL file:
+
+```bash
+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：**
-- 在嵌入层（embedding）与最终线性层之间进行权重绑定（weight tying），这是小型模型中常见的节省参数量的做法
-- 嵌入层优化：若不进行权重绑定，一个包含 10,000 个词的词汇表将消耗约 1.02 亿（0.1B）参数
+# Run inference server
+docker run --gpus all -p 8000:8000 astrai:latest \
+  python -m scripts.tools.server --port 8000 --device cuda
 
-**局限性：**
-- 由于参数规模较小，可能在处理复杂语言现象时表现受限
-- 在特定领域的数据集上容易出现过拟合
-- 多语言能力有限
+# Run with volume mount for data
+docker run --gpus all -v /path/to/data:/data -it astrai:latest
 
-**优势：**
-- 可在低配置硬件上高效运行
-- 相较于大型模型，训练时间更短
+# Docker Compose (GPU, default)
+docker compose up -d
 
-**训练流程：**  
-该模型已完成预训练（pre-training）+ 监督微调（SFT, Supervised Fine-Tuning）+ 直接偏好优化（DPO, Direct Preference Optimization）的全流程。所有相关的训练代码均已包含在代码库中。
+# Docker Compose CPU server profile (CUDA-only generation scripts/demos are unavailable)
+docker compose --profile cpu up -d
+
+# YAML-driven serving (see serve.yaml; up/run/down/logs/status...)
+bash scripts/serve.sh up
+```
+
+> **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](docs/guides/inference.md) for SSE streaming format, error codes, and stats endpoint.
+
+### Documentation
+
+| Document | Description |
+|----------|-------------|
+| [Get Started](./docs/get-started.md) | Installation and quickstart |
+| [CLI Reference](./docs/guides/params.md) | Parameters for all CLI tools (train, server, generate, preprocess) |
+| [Preprocessing](./docs/guides/preprocessing.md) | Declarative JSON-driven data preprocessing |
+| [Training](./docs/guides/training.md) | Training loop, strategies & formulas |
+| [Inference](./docs/guides/inference.md) | KVCache, continuous batching, sampling & HTTP API |
+| [Evaluation](./docs/guides/evaluation.md) | HumanEval, MMLU, PPL, ROUGE, IFD, IFEval |
+| [Distributed](./docs/guides/distributed.md) | Multi-GPU DDP / FSDP training |
+| [Architecture](./docs/developer/architecture.md) | System architecture, class diagram & design patterns |
+| [Data Flow](./docs/developer/dataflow.md) | Data pipeline, storage backends & dataset architecture |
+| [Internals](./docs/developer/internals.md) | Training internals: loss formulas, callback lifecycle, KV cache |
+| [CUDA Kernels](./docs/developer/cuda_kernels.md) | Custom CUDA attention kernels & benchmarks |
+| [Docker Serving](./docs/developer/docker-serving.md) | YAML-driven containerized serving (`serve.yaml`, `serve.sh`) |
+| [Docker Training](./docs/developer/docker-training.md) | YAML-driven containerized training (`train.yaml`, `train.sh`) |
+
+### 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 [Apache-2.0 License](LICENSE).
+
+---
+
+<div align="center">
+  <em>A lightweight Transformer framework designed for both high performance and ease of use.</em>
+</div>

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

astrai/__init__.py

@@ -0,0 +1,95 @@
+__version__ = "1.3.13"
+__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 InferenceEngine, get_app, run_server, sample
+from astrai.inference.network import ProtocolHandler
+from astrai.inference.runtime.sample import SamplingPipeline
+from astrai.logging import setup_logging
+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",
+    "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",
+    "setup_logging",
+    "spawn_parallel_fn",
+]
+
+setup_logging()

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 +1,38 @@
+import json
+from dataclasses import asdict
+from pathlib import Path
+from typing import Any, Dict, Self, Union
+
+from pydantic import ConfigDict
+from pydantic.dataclasses import dataclass
+
+
+@dataclass(config=ConfigDict(use_attribute_docstrings=True))
+class BaseConfig:
+    def to_dict(self) -> Dict[str, Any]:
+        result = {}
+        for k, v in asdict(self).items():
+            if isinstance(v, tuple):
+                v = list(v)
+            try:
+                json.dumps(v)
+                result[k] = v
+            except (TypeError, ValueError):
+                # Skip non-serializable runtime objects (e.g. model_fn, dataset).
+                # TrainConfig mixes hyperparams with callables/datasets; only the
+                # JSON-serializable subset is written to checkpoint meta.
+                pass
+        return result
+
+    @classmethod
+    def from_dict(cls, d: Dict[str, Any]) -> Self:
+        return cls(**d)
+
+    @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 +1,178 @@
+from typing import Any, Dict, Optional
+
+from pydantic import field_validator
+from pydantic.dataclasses import dataclass
+
+from astrai.config.base import BaseConfig
+from astrai.factory import BaseFactory
+
+_ATTN_TYPES = frozenset({"gqa", "mla"})
+_FFN_TYPES = frozenset({"mlp", "moe"})
+
+
+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.
+
+    Args:
+        model_type (Optional[str]): Model type identifier for AutoModel dispatch. Defaults to None.
+        neftune_alpha (float): NEFTune noise alpha, 0=disabled, typical: 5.0. Defaults to 0.0.
+    """
+
+    model_type: Optional[str] = None
+    neftune_alpha: float = 0.0
+
+
+@dataclass
+@ConfigFactory.register("autoregressive_lm")
+class AutoRegressiveLMConfig(BaseModelConfig):
+    """Configuration for autoregressive language model.
+
+    Args:
+        model_type (Optional[str]): Model type identifier for AutoModel dispatch. Defaults to None.
+        neftune_alpha (float): NEFTune noise alpha, 0=disabled, typical: 5.0. Defaults to 0.0.
+        vocab_size (Optional[int]): Vocabulary size. Defaults to None.
+        hidden_size (Optional[int]): Hidden dimension size. Defaults to None.
+        num_hidden_layers (Optional[int]): Number of transformer layers. Defaults to None.
+        rms_norm_eps (Optional[float]): Epsilon for RMSNorm. Defaults to None.
+        intermediate_size (Optional[int]): Intermediate size in FFN. Defaults to None.
+        tie_word_embeddings (Optional[bool]): Whether to tie embedding and lm_head weights. Defaults to None.
+        max_position_embeddings (Optional[int]): Maximum sequence length the model was trained with. Defaults to None.
+        rope_theta (Optional[float]): Base frequency for RoPE. Defaults to None.
+        rope_scaling (Optional[dict]): RoPE scaling config, e.g. {"type": "linear", "factor": 4.0}. Defaults to None.
+        attn_type (str): Attention type: 'gqa' or 'mla'. Defaults to "gqa".
+        num_attention_heads (Optional[int]): Number of query attention heads. Defaults to None.
+        num_key_value_heads (Optional[int]): Number of key/value heads for GQA. Defaults to None.
+        use_qk_norm (Optional[bool]): Whether to apply RMSNorm to Q/K. Defaults to None.
+        use_gated_attention (Optional[bool]): Whether to use gated attention. Defaults to None.
+        kv_lora_rank (Optional[int]): KV compression rank, MLA only. Defaults to None.
+        qk_nope_head_dim (Optional[int]): Non-RoPE head dimension, MLA only. Defaults to None.
+        qk_rope_head_dim (Optional[int]): RoPE head dimension, MLA only. Defaults to None.
+        ffn_type (str): FFN type: 'mlp' or 'moe'. Defaults to "mlp".
+        n_routed_experts (Optional[int]): Number of routed experts, MoE only. Defaults to None.
+        n_shared_experts (Optional[int]): Number of shared experts, MoE only. Defaults to None.
+        n_activated_experts (Optional[int]): Number of activated experts per token, MoE only. Defaults to None.
+        topk_method (Optional[str]): Top-k routing method, MoE only. Defaults to None.
+        moe_intermediate_size (Optional[int]): Expert hidden dim, defaults to intermediate_size if None. MoE only.
+        shared_expert_intermediate_size (Optional[int]): Shared expert hidden dim, defaults to intermediate_size if None. MoE only.
+        norm_topk_prob (bool): Normalize top-k routing probabilities. Defaults to True.
+        decoder_sparse_step (int): Frequency of MoE layers, 1=every layer. Defaults to 1.
+        mlp_only_layers (Optional[list[int]]): Layer indices using dense MLP instead of MoE. Defaults to None.
+    """
+
+    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
+    moe_intermediate_size: Optional[int] = None
+    shared_expert_intermediate_size: Optional[int] = None
+    norm_topk_prob: bool = True
+    decoder_sparse_step: int = 1
+    mlp_only_layers: Optional[list[int]] = None
+    moe_aux_loss_coef: float = 0.01
+
+    @field_validator("attn_type")
+    def _validate_attn_type(cls, v: str) -> str:
+        if v not in _ATTN_TYPES:
+            raise ValueError(
+                f"attn_type must be one of {sorted(_ATTN_TYPES)}, got {v!r}"
+            )
+        return v
+
+    @field_validator("ffn_type")
+    def _validate_ffn_type(cls, v: str) -> str:
+        if v not in _FFN_TYPES:
+            raise ValueError(f"ffn_type must be one of {sorted(_FFN_TYPES)}, got {v!r}")
+        return v
+
+    @field_validator("decoder_sparse_step")
+    def _validate_decoder_sparse_step(cls, v: int) -> int:
+        if v < 1:
+            raise ValueError(f"decoder_sparse_step must be at least 1, got {v}")
+        return v
+
+
+@dataclass
+@ConfigFactory.register("embedding")
+class EncoderConfig(BaseModelConfig):
+    """Configuration for embedding encoder model.
+
+    Args:
+        model_type (Optional[str]): Model type identifier for AutoModel dispatch. Defaults to None.
+        neftune_alpha (float): NEFTune noise alpha, 0=disabled, typical: 5.0. Defaults to 0.0.
+        vocab_size (Optional[int]): Vocabulary size. Defaults to None.
+        hidden_size (Optional[int]): Hidden dimension size. Defaults to None.
+        num_hidden_layers (Optional[int]): Number of transformer layers. Defaults to None.
+        rms_norm_eps (Optional[float]): Epsilon for RMSNorm. Defaults to None.
+        intermediate_size (Optional[int]): Intermediate size in FFN. Defaults to None.
+        max_position_embeddings (Optional[int]): Maximum sequence length the model was trained with. Defaults to None.
+        rope_theta (Optional[float]): Base frequency for RoPE. Defaults to None.
+        rope_scaling (Optional[dict]): RoPE scaling config, e.g. {"type": "linear", "factor": 4.0}. Defaults to None.
+        attn_type (str): Attention type: 'gqa' or 'mla'. Defaults to "gqa".
+        num_attention_heads (Optional[int]): Number of query attention heads. Defaults to None.
+        num_key_value_heads (Optional[int]): Number of key/value heads for GQA. Defaults to None.
+        use_qk_norm (Optional[bool]): Whether to apply RMSNorm to Q/K. Defaults to None.
+        use_gated_attention (Optional[bool]): Whether to use gated attention. Defaults to None.
+        ffn_type (str): FFN type: 'mlp' or 'moe'. Defaults to "mlp".
+        pooling_type (Optional[str]): Pooling strategy for embedding, e.g. 'mean', 'cls'. Defaults to None.
+        normalize_embeddings (Optional[bool]): Whether to L2-normalize output embeddings. Defaults to None.
+    """
+
+    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
+
+    @field_validator("attn_type")
+    def _validate_attn_type(cls, v: str) -> str:
+        if v not in _ATTN_TYPES:
+            raise ValueError(
+                f"attn_type must be one of {sorted(_ATTN_TYPES)}, got {v!r}"
+            )
+        return v
+
+    @field_validator("ffn_type")
+    def _validate_ffn_type(cls, v: str) -> str:
+        if v not in _FFN_TYPES:
+            raise ValueError(f"ffn_type must be one of {sorted(_FFN_TYPES)}, got {v!r}")
+        return v

astrai/config/preprocess_config.py

@@ -0,0 +1,152 @@
+"""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 field
+from typing import Dict, List, Optional
+
+from pydantic import field_validator
+from pydantic.dataclasses import dataclass
+
+from astrai.config.base import BaseConfig
+
+_PACKING_STRATEGIES = frozenset({"simple", "bfd", "bfd_split"})
+_TRUNCATION_MODES = frozenset({"keep_start", "keep_end"})
+_STORAGE_FORMATS = frozenset({"bin", "jsonl"})
+_POSITION_IDS_MODES = frozenset({"none", "doc_reset", "continuous"})
+
+
+@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", ...}]},
+        }}}
+
+    Args:
+        sections (Optional[List[Dict]]): Section list for single-output mode. Defaults to None.
+        sources (Optional[Dict[str, Dict]]): Source map for multi-output mode, DPO/GRPO. Defaults to None.
+    """
+
+    sections: Optional[List[Dict]] = None
+    sources: Optional[Dict[str, Dict]] = None
+
+
+@dataclass
+class ProcessingConfig(BaseConfig):
+    """Processing configuration for tokenization and packing.
+
+    Args:
+        max_seq_len (int): Maximum sequence length. Defaults to 2048.
+        min_chars (int): Minimum number of characters to keep. Defaults to 50.
+        max_chars (int): Maximum number of characters to keep. Defaults to 2_000_000.
+        max_items (Optional[int]): Maximum number of items to process, None=unlimited. Defaults to None.
+        batch_size (int): Number of records tokenized together. Defaults to 256.
+        packing_strategy (str): How to pack sequences: 'simple', 'bfd', or 'bfd_split'. Defaults to "simple".
+        max_packed_len (int): Maximum length of a packed bin. Defaults to 8192.
+        truncation_mode (str): How to truncate over-length sequences: 'keep_start' or 'keep_end'. Defaults to "keep_start".
+    """
+
+    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"
+
+    @field_validator("packing_strategy")
+    def _validate_packing_strategy(cls, v: str) -> str:
+        if v not in _PACKING_STRATEGIES:
+            raise ValueError(
+                f"packing_strategy must be one of {sorted(_PACKING_STRATEGIES)}, got {v!r}"
+            )
+        return v
+
+    @field_validator("truncation_mode")
+    def _validate_truncation_mode(cls, v: str) -> str:
+        if v not in _TRUNCATION_MODES:
+            raise ValueError(
+                f"truncation_mode must be one of {sorted(_TRUNCATION_MODES)}, got {v!r}"
+            )
+        return v
+
+    @field_validator("max_seq_len", "batch_size", "max_packed_len")
+    def _validate_positive_int(cls, v: int) -> int:
+        if v <= 0:
+            raise ValueError(f"must be positive, got {v}")
+        return v
+
+    @field_validator("min_chars")
+    def _validate_non_negative(cls, v: int) -> int:
+        if v < 0:
+            raise ValueError(f"min_chars must be non-negative, got {v}")
+        return v
+
+
+@dataclass
+class OutputConfig(BaseConfig):
+    """Output configuration for storage.
+
+    Args:
+        domain_key (Optional[str]): Domain key for the output store. Defaults to None.
+        storage_format (str): Storage format: 'bin' or 'jsonl'. Defaults to "bin".
+        max_tokens_per_shard (int): Maximum tokens per shard before splitting. Defaults to 100_000_000.
+        dtype (Dict[str, str]): Per-key dtype overrides, e.g. {"input_ids": "int32"}. Defaults to {}.
+        position_ids_mode (str): Position ids mode: 'none', 'doc_reset', or 'continuous'. Defaults to "doc_reset".
+    """
+
+    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"
+
+    @field_validator("storage_format")
+    def _validate_storage_format(cls, v: str) -> str:
+        if v not in _STORAGE_FORMATS:
+            raise ValueError(
+                f"storage_format must be one of {sorted(_STORAGE_FORMATS)}, got {v!r}"
+            )
+        return v
+
+    @field_validator("position_ids_mode")
+    def _validate_position_ids_mode(cls, v: str) -> str:
+        if v not in _POSITION_IDS_MODES:
+            raise ValueError(
+                f"position_ids_mode must be one of {sorted(_POSITION_IDS_MODES)}, got {v!r}"
+            )
+        return v
+
+
+@dataclass
+class PipelineConfig(BaseConfig):
+    """Top-level preprocessing pipeline config.
+
+    Args:
+        version (int): Config schema version. Defaults to 1.
+        input (InputConfig): Input mapping config.
+        mask (Dict[str, str]): Per-field mask labels, e.g. {"system": "mask", "assistant": "train"}. Defaults to {}.
+        mask_default (str): Default mask label for unlisted fields. Defaults to "mask".
+        preprocessing (ProcessingConfig): Processing config.
+        output (OutputConfig): Output config.
+    """
+
+    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 +1,220 @@
+from dataclasses import field
+from typing import Any, Callable, Dict, List, Optional
+
+import torch.nn as nn
+from pydantic import ConfigDict, field_validator, model_validator
+from pydantic.dataclasses import dataclass
+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
+
+TRAIN_TYPES = frozenset({"seq", "sft", "dpo", "grpo", "online_grpo", "online_dpo"})
+PARALLEL_MODES = frozenset({"none", "ddp", "fsdp"})
+BACKENDS = frozenset({"nccl", "gloo"})
+START_METHODS = frozenset({"spawn", "fork", "forkserver"})
+_COMPILE_MODES = frozenset({"default", "reduce-overhead", "max-autotune"})
+
+
+@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
+class TrainConfig(BaseConfig):
+    """Training configuration.
+
+    Combines hyperparameters with runtime objects (model_fn, dataset, etc.).
+    Only JSON-serializable fields are written to checkpoint meta via to_dict().
+
+    Args:
+        model_fn (Callable[[], nn.Module]): Model factory for training.
+        strategy (str): Training strategy (seq, sft, dpo, grpo, online_*).
+        dataset (Dataset): Dataset for training.
+        optimizer_fn (Callable[[nn.Module], Optimizer]): Optimizer factory for training.
+        optimizer_name (Optional[str]): Serializable built-in optimizer identifier. Defaults to None.
+        optimizer_hyperparameters (Dict[str, Any]): Serializable optimizer settings. Defaults to {}.
+        scheduler_fn (Callable[[Optimizer], LRScheduler]): Scheduler factory for training.
+        n_epoch (int): Number of epochs for training. Defaults to 1.
+        batch_per_device (int): Batch size per device. Defaults to 4.
+        grad_accum_steps (int): Number of iterations between optimizer steps. Defaults to 1.
+        max_grad_norm (Optional[float]): Maximum gradient norm. None disables clipping. Defaults to 1.0.
+        gradient_checkpointing_modules (List[type]): Module types to enable activation checkpointing for. Defaults to [].
+        compile_mode (Optional[str]): torch.compile mode: 'default', 'reduce-overhead', 'max-autotune', or None. Defaults to None.
+        start_epoch (int): Start epoch for training. Defaults to 0.
+        start_samples (int): Start samples count (per rank). Superseded by checkpoint consumed_samples. Defaults to 0.
+        ckpt_dir (str): Checkpoint directory. Defaults to "./checkpoint".
+        ckpt_interval (int): Number of optimizer steps between checkpoints. Defaults to 5000.
+        lora (Optional[LoRAConfig]): LoRA config. None means full fine-tuning. Defaults to None.
+        metrics (List[str]): Metrics to record during training. Defaults to ["loss", "lr", "grad_norm"].
+        random_seed (int): Random seed. Defaults to 3407.
+        num_workers (int): Number of workers for dataloader. Defaults to 0.
+        prefetch_factor (Optional[int]): Prefetch factor for dataloader. Defaults to None.
+        persistent_workers (bool): Keep DataLoader workers alive between epochs. Defaults to False.
+        pin_memory (bool): Pin memory for dataloader. Defaults to False.
+        collate_fn (Optional[Callable[[List[Any]], Any]]): Collate function for dataloader (e.g. dpo_collate_fn). Defaults to None.
+        nprocs (int): Number of processes for distributed training. Defaults to 1.
+        backend (str): Distributed training backend. Defaults to "nccl".
+        master_addr (str): Master address for distributed training. Defaults to "localhost".
+        master_port (str): Master port for distributed training. Defaults to "29500".
+        parallel_mode (str): Parallel strategy: none, ddp, fsdp. Defaults to "none".
+        start_method (str): Multiprocessing start method: spawn/fork/forkserver. Defaults to "spawn".
+        device_type (str): Device type for distributed training. Defaults to "cuda".
+        val_dataset (Optional[Dataset]): Dataset for validation. Defaults to None.
+        val_split (Optional[float]): Ratio to split from training dataset for validation, e.g. 0.05. Defaults to None.
+        val_step (int): Number of optimizer steps between validation runs. Defaults to 1000.
+        neftune_alpha (float): NEFTune noise alpha, 0=disabled, typical: 5.0. Defaults to 0.0.
+        moe_aux_loss_coef (float): Weight applied to the MoE load-balancing loss. Defaults to 0.01.
+        rollout_interval (int): Number of optimizer steps between online rollouts. Defaults to 512.
+        rollout_temperature (float): Sampling temperature for online rollout. Defaults to 0.7.
+        rollout_top_k (int): Top-k filtering for online rollout, 0=disable. Defaults to 0.
+        rollout_top_p (float): Top-p (nucleus) filtering for online rollout. Defaults to 0.9.
+        rollout_max_tokens (int): Maximum generated tokens per response in rollout. Defaults to 1024.
+        reward_model_fn (Optional[Callable]): Factory for reward model, required for online RL strategies. Defaults to None.
+        executor_kwargs (Dict[str, Any]): Extra kwargs passed to ExecutorFactory.create(). Defaults to {}.
+        strategy_kwargs (Dict[str, Any]): Extra strategy arguments. Defaults to {}.
+    """
+
+    model_fn: Callable[[], nn.Module]
+    strategy: str
+    dataset: Dataset
+    optimizer_fn: Callable[[nn.Module], Optimizer]
+    scheduler_fn: Callable[[Optimizer], LRScheduler]
+    optimizer_name: Optional[str] = None
+    optimizer_hyperparameters: Dict[str, Any] = field(default_factory=dict)
+    n_epoch: int = 1
+    batch_per_device: int = 4
+    grad_accum_steps: int = 1
+    max_grad_norm: Optional[float] = 1.0
+    gradient_checkpointing_modules: List[type] = field(default_factory=list)
+    compile_mode: Optional[str] = None
+
+    start_epoch: int = 0
+    start_samples: int = 0
+    ckpt_dir: str = "./checkpoint"
+    ckpt_interval: int = 5000
+
+    lora: Optional[LoRAConfig] = None
+
+    metrics: List[str] = field(default_factory=lambda: ["loss", "lr", "grad_norm"])
+
+    random_seed: int = 3407
+    num_workers: int = 0
+    prefetch_factor: Optional[int] = None
+    persistent_workers: bool = False
+    pin_memory: bool = False
+    collate_fn: Optional[Callable[[List[Any]], Any]] = None
+
+    nprocs: int = 1
+    backend: str = "nccl"
+    master_addr: str = "localhost"
+    master_port: str = "29500"
+    parallel_mode: str = "none"
+    start_method: str = "spawn"
+
+    device_type: str = "cuda"
+    val_dataset: Optional[Dataset] = None
+    val_split: Optional[float] = None
+    val_step: int = 1000
+    neftune_alpha: float = 0.0
+    moe_aux_loss_coef: float = 0.01
+
+    rollout_interval: int = 512
+    rollout_temperature: float = 0.7
+    rollout_top_k: int = 0
+    rollout_top_p: float = 0.9
+    rollout_max_tokens: int = 1024
+    reward_model_fn: Optional[Callable] = None
+
+    executor_kwargs: Dict[str, Any] = field(default_factory=dict)
+    strategy_kwargs: Dict[str, Any] = field(default_factory=dict)
+
+    @field_validator("strategy")
+    def _validate_strategy(cls, v: str) -> str:
+        if v not in TRAIN_TYPES:
+            raise ValueError(
+                f"strategy must be one of {sorted(TRAIN_TYPES)}, got {v!r}"
+            )
+        return v
+
+    @field_validator("parallel_mode")
+    def _validate_parallel_mode(cls, v: str) -> str:
+        if v not in PARALLEL_MODES:
+            raise ValueError(
+                f"parallel_mode must be one of {sorted(PARALLEL_MODES)}, got {v!r}"
+            )
+        return v
+
+    @field_validator("backend")
+    def _validate_backend(cls, v: str) -> str:
+        if v not in BACKENDS:
+            raise ValueError(f"backend must be one of {sorted(BACKENDS)}, got {v!r}")
+        return v
+
+    @field_validator("start_method")
+    def _validate_start_method(cls, v: str) -> str:
+        if v not in START_METHODS:
+            raise ValueError(
+                f"start_method must be one of {sorted(START_METHODS)}, got {v!r}"
+            )
+        return v
+
+    @field_validator("compile_mode")
+    def _validate_compile_mode(cls, v: Optional[str]) -> Optional[str]:
+        if v is not None and v not in _COMPILE_MODES:
+            raise ValueError(
+                f"compile_mode must be one of {sorted(_COMPILE_MODES)} or None, got {v!r}"
+            )
+        return v
+
+    @field_validator(
+        "n_epoch",
+        "batch_per_device",
+        "grad_accum_steps",
+        "ckpt_interval",
+        "val_step",
+        "rollout_interval",
+        "rollout_max_tokens",
+    )
+    def _validate_positive_int(cls, v: int) -> int:
+        if v <= 0:
+            raise ValueError(f"must be positive, got {v}")
+        return v
+
+    @field_validator("rollout_temperature")
+    def _validate_positive_float(cls, v: float) -> float:
+        if v <= 0:
+            raise ValueError(f"must be positive, got {v}")
+        return v
+
+    @field_validator("rollout_top_p")
+    def _validate_top_p(cls, v: float) -> float:
+        if not 0 < v <= 1:
+            raise ValueError(f"rollout_top_p must be in (0, 1], got {v}")
+        return v
+
+    @field_validator(
+        "rollout_top_k", "num_workers", "neftune_alpha", "moe_aux_loss_coef"
+    )
+    def _validate_non_negative(cls, v):
+        if v < 0:
+            raise ValueError(f"must be non-negative, got {v}")
+        return v
+
+    @field_validator("max_grad_norm")
+    def _validate_max_grad_norm(cls, v: Optional[float]) -> Optional[float]:
+        if v is not None and v <= 0:
+            raise ValueError(f"max_grad_norm must be positive or None, got {v}")
+        return v
+
+    @field_validator("val_split")
+    def _validate_val_split(cls, v: Optional[float]) -> Optional[float]:
+        if v is not None and not 0 < v < 1:
+            raise ValueError(f"val_split must be in (0, 1) or None, got {v}")
+        return v
+
+    @model_validator(mode="after")
+    def _validate_online_strategy(self) -> "TrainConfig":
+        if self.strategy.startswith("online_") and self.reward_model_fn is None:
+            raise ValueError(
+                f"reward_model_fn is required for online RL strategy {self.strategy!r}"
+            )
+        return self

astrai/dataset/__init__.py

@@ -0,0 +1,37 @@
+from astrai.dataset.dataset import (
+    BaseDataset,
+    DatasetFactory,
+    dpo_collate_fn,
+    grpo_collate_fn,
+)
+from astrai.dataset.sampler import RDSampler
+from astrai.dataset.storage import (
+    JsonlStore,
+    MmapStore,
+    Recordable,
+    Store,
+    StoreFactory,
+    Streamable,
+    detect_format,
+)
+from astrai.serialization import (
+    load_bin,
+    save_bin,
+)
+
+__all__ = [
+    "BaseDataset",
+    "DatasetFactory",
+    "dpo_collate_fn",
+    "grpo_collate_fn",
+    "Store",
+    "Streamable",
+    "Recordable",
+    "StoreFactory",
+    "MmapStore",
+    "JsonlStore",
+    "detect_format",
+    "save_bin",
+    "load_bin",
+    "RDSampler",
+]

astrai/dataset/dataset.py

@@ -0,0 +1,535 @@
+"""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 pathlib import Path
+from typing import Callable, Dict, List, Optional
+
+import torch
+from torch import Tensor
+from torch.utils.data import Dataset
+
+from astrai.config.preprocess_config import PipelineConfig
+from astrai.dataset.storage import (
+    Store,
+    StoreFactory,
+    detect_format,
+)
+from astrai.factory import BaseFactory
+from astrai.preprocessing.transform import TokenizeTransform
+from astrai.tokenize import AutoTokenizer
+
+_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 _build_jsonl_transform(
+    path: str, tokenizer_path: Optional[str] = None
+) -> Optional["TokenizeTransform"]:
+    """Auto-build a TokenizeTransform for JSONL eager loading.
+
+    Reads ``dataset_config.json`` from the data dir if present, or
+    falls back to the built-in chatml SFT config when *tokenizer_path*
+    is provided.
+    """
+    root = Path(path)
+    config_path = root / "dataset_config.json" if root.is_dir() else None
+    if config_path is not None and config_path.exists():
+        return TokenizeTransform.from_config_file(str(config_path))
+    if tokenizer_path:
+        config = PipelineConfig.from_dict(_DEFAULT_MESSAGES_CONFIG)
+        return TokenizeTransform(config, tokenizer_path)
+    return None
+
+
+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], left-padded
+      - prompt_mask: [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)
+    prompt_mask = torch.zeros(B, P_max, dtype=torch.bool)
+    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"]
+        prompt_mask[i, -p_len:] = True
+        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,
+        "prompt_mask": prompt_mask,
+        "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 ("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)
+        elif storage_type == "jsonl":
+            transform = _build_jsonl_transform(load_path, tokenizer_path)
+            if transform is None:
+                raise FileNotFoundError(
+                    "JSONL dataset config not found. Expected "
+                    "dataset_config.json alongside *.jsonl files, pass "
+                    "tokenizer_path= for the built-in messages config, or "
+                    "use processor= for lazy on-the-fly tokenisation."
+                )
+            store.load(load_path, transform=transform, **kwargs)
+        else:
+            store.load(load_path, **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 (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** (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 __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 +1,601 @@
+"""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)
+
+    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 (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.factory import BaseFactory
+from astrai.serialization import (
+    load_bin,
+    load_bin_offsets,
+)
+
+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 == ".jsonl":
+            return "jsonl"
+        raise ValueError(f"Unsupported file format: {suffix}")
+
+    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 (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._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 (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 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 (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("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.
+    """
+
+    segments_are_records = True
+
+    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:
+            raise ValueError(
+                "JsonlStore eager mode requires transform=. "
+                "Use DatasetFactory.load() which auto-constructs it."
+            )
+
+        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 +1,55 @@
+"""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)
+    - ``AttentionBackend`` — ABC for attention computation strategies
+    - ``TorchNativeBackend`` — default SDPA backend with KV cache I/O
+    - ``CudaBackend`` — CUDA kernel backend with paged decode + prefill
+
+Layout convention: all q/k/v are ``[batch, seq_len, n_heads, head_dim]``
+(blhd). Scale is always ``1/sqrt(head_dim)``.
+
+Each wrapper calls its compiled CUDA kernel directly. Fallback to torch
+SDPA is handled by the attention backend, not the wrapper functions.
+"""
+
+from astrai.extension.backend import (
+    ATTN_BACKEND,
+    AttentionBackend,
+    AttentionBackendFactory,
+    CudaBackend,
+    FlashAttnBackend,
+    TorchNativeBackend,
+    apply_rotary_emb,
+    attention,
+    attn_backend,
+    get_backend,
+)
+from astrai.extension.loader import KERNEL_NAMES, is_available
+from astrai.extension.ops import (
+    TensorLayout,
+    attn_decode,
+    attn_paged_decode,
+    attn_prefill,
+)
+
+__all__ = [
+    "ATTN_BACKEND",
+    "AttentionBackend",
+    "AttentionBackendFactory",
+    "CudaBackend",
+    "TorchNativeBackend",
+    "FlashAttnBackend",
+    "TensorLayout",
+    "attention",
+    "attn_backend",
+    "get_backend",
+    "attn_decode",
+    "attn_paged_decode",
+    "attn_prefill",
+    "is_available",
+    "KERNEL_NAMES",
+    "apply_rotary_emb",
+]

astrai/extension/backend/__init__.py

@@ -0,0 +1,27 @@
+"""Backend selection, fallbacks, and execution policies."""
+
+from astrai.extension.backend.attention import (
+    ATTN_BACKEND,
+    AttentionBackend,
+    AttentionBackendFactory,
+    CudaBackend,
+    FlashAttnBackend,
+    TorchNativeBackend,
+    attention,
+    attn_backend,
+    get_backend,
+)
+from astrai.extension.backend.rotary import apply_rotary_emb
+
+__all__ = [
+    "ATTN_BACKEND",
+    "AttentionBackend",
+    "AttentionBackendFactory",
+    "CudaBackend",
+    "FlashAttnBackend",
+    "TorchNativeBackend",
+    "apply_rotary_emb",
+    "attention",
+    "attn_backend",
+    "get_backend",
+]

astrai/extension/backend/attention.py

@@ -0,0 +1,818 @@
+"""Attention backend abstraction with context-manager switching.
+
+The backend encapsulates KV cache I/O and attention computation. The
+attention module (GQA/MLA) keeps projections, rotary, QK-norm, gating,
+and output projection; the backend handles everything from "write K/V
+to cache" through "SDPA output".
+
+Usage — mirroring ``torch.nn.attention.sdpa_kernel``:
+
+    from astrai.extension import attn_backend, ATTN_BACKEND
+
+    with attn_backend(ATTN_BACKEND.TORCH_NATIVE):
+        engine.generate("hello")
+
+    # or with an instance:
+    with attn_backend(TorchNativeBackend()):
+        ...
+
+    # or the shorthand (instance is itself a context manager):
+    with TorchNativeBackend():
+        ...
+
+Thread-safe via ``contextvars`` — each scheduler thread gets its own
+active backend. Backend resolution follows a strict precedence:
+
+1. explicit ``attn_backend(...)`` context (wins over everything),
+2. the process-wide ``ASTR_BACKEND`` environment override,
+3. an implicit default picked from the available backends
+   (cuda > flash > torch).
+
+Capability is polymorphic: every backend declares ``available()``
+(machine-level) and ``supports_call(...)`` (per-call), so adding a new
+backend requires no changes to the resolution logic. Training calls
+(``fwd=None``, no KV cache) resolve through the same priority list: the
+CUDA cache kernels cannot run without a cache, so they fall back to
+flash (when it can handle the call — mask-free/causal only) and finally
+to the reference ``TorchNativeBackend``.
+
+Layout convention: all q/k/v are ``[batch, seq_len, n_heads, head_dim]``
+(blhd). The backend returns ``[batch, seq_len, n_heads * head_dim]``.
+"""
+
+import contextvars
+import enum
+import functools
+import logging
+import os
+import threading
+from abc import ABC, abstractmethod
+from contextlib import contextmanager
+from typing import TYPE_CHECKING, Dict, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+
+from astrai.extension.loader import is_available
+from astrai.extension.ops.attention import (
+    attn_paged_decode,
+    attn_paged_prefill,
+)
+from astrai.factory import BaseFactory
+
+try:
+    import flash_attn as _flash_attn
+except Exception:
+    _flash_attn = None
+
+if TYPE_CHECKING:
+    from astrai.inference.cache import KVCache
+
+logger = logging.getLogger(__name__)
+
+
+_default_backend_lock = threading.Lock()
+_env_backend_name: Optional[str] = None
+_env_backend: Optional["AttentionBackend"] = None
+_current_backend: contextvars.ContextVar[Optional["AttentionBackend"]] = (
+    contextvars.ContextVar("attn_backend", default=None)
+)
+
+# Backends are stateless — one canonical instance per class, created lazily
+# and reused everywhere (resolution, fallback, context managers).
+_singletons: Dict[type, "AttentionBackend"] = {}
+
+
+@functools.lru_cache(maxsize=1)
+def flash_attn_available() -> bool:
+    if not torch.cuda.is_available():
+        return False
+    fa = _flash_attn
+    if fa is None:
+        return False
+
+    try:
+        major = int(fa.__version__.split(".")[0])
+        cc = torch.cuda.get_device_capability()
+        cc_num = cc[0] * 10 + cc[1]
+    except Exception:
+        major, cc_num = 0, 0
+    if (major >= 3 and cc_num < 90) or (major < 3 and 0 < cc_num < 70):
+        return False
+
+    try:
+        if not hasattr(fa, "flash_attn_func"):
+            return False
+        x = torch.zeros(1, 1, 1, 64, device="cuda", dtype=torch.bfloat16)
+        out = fa.flash_attn_func(x, x, x, causal=True)
+        return bool(torch.isfinite(out).all().item())
+    except Exception:
+        return False
+
+
+class ATTN_BACKEND(enum.Enum):
+    """Backend selector enum, mirroring ``torch.nn.attention.SDPBackend``."""
+
+    TORCH_NATIVE = "torch_native"
+    CUDA = "cuda"
+    FLASH = "flash"
+
+
+def _instance(backend_cls: type) -> "AttentionBackend":
+    """Return the canonical singleton instance for a backend class.
+
+    Backends hold no per-instance state, so a single cached instance is
+    safe and avoids per-call allocation on the attention hot path.
+    """
+    backend = _singletons.get(backend_cls)
+    if backend is None:
+        backend = backend_cls()
+        _singletons[backend_cls] = backend
+    return backend
+
+
+@functools.lru_cache(maxsize=1)
+def _priority_backends() -> Tuple["AttentionBackend", ...]:
+    """Available backends in priority order: cuda -> flash -> torch.
+
+    Computed once (machine availability cannot change at runtime) and
+    cached forever; the tuple always ends with ``TorchNativeBackend``,
+    which is unconditionally available.
+    """
+    return tuple(
+        _instance(cls)
+        for cls in (CudaBackend, FlashAttnBackend, TorchNativeBackend)
+        if cls.available()
+    )
+
+
+def _resolve_default_backend() -> "AttentionBackend":
+    """Pick the highest-priority available backend (cuda -> flash -> torch).
+
+    Resolved lazily on first use and cached via ``_priority_backends``.
+    Per-call capability fallback happens in ``attention()``, so the
+    default is safe for training and fp32 models.
+    """
+    return _priority_backends()[0]
+
+
+def _environment_backend() -> Optional["AttentionBackend"]:
+    """Resolve the process-wide ``ASTR_BACKEND`` override, if configured."""
+    global _env_backend, _env_backend_name
+    name = os.environ.get("ASTR_BACKEND", "").strip().lower()
+    if not name:
+        return None
+    if name != _env_backend_name:
+        with _default_backend_lock:
+            if name != _env_backend_name:
+                try:
+                    _env_backend = _resolve_backend(name)
+                except (ValueError, RuntimeError):
+                    _env_backend = None
+                    logger.warning(
+                        "ASTR_BACKEND=%r is not a registered attention backend; "
+                        "falling back to default resolution",
+                        name,
+                    )
+                _env_backend_name = name
+    return _env_backend
+
+
+def _resolve_backend(
+    backend: Optional[Union[str, ATTN_BACKEND, "AttentionBackend", type]] = None,
+) -> "AttentionBackend":
+    """Resolve a backend configuration to its canonical instance.
+
+    Accepts a registered name, ``ATTN_BACKEND`` enum value, backend class,
+    or instance.  Names/classes resolve to the shared singleton; a caller
+    may still pass its own instance to opt out of sharing.
+    """
+    if backend is not None:
+        if isinstance(backend, ATTN_BACKEND):
+            return _instance(AttentionBackendFactory.get_component_class(backend.value))
+        if isinstance(backend, str):
+            return _instance(AttentionBackendFactory.get_component_class(backend))
+        if isinstance(backend, type) and issubclass(backend, AttentionBackend):
+            return _instance(backend)
+        if isinstance(backend, AttentionBackend):
+            return backend
+        raise TypeError(
+            f"expected a registered name, ATTN_BACKEND, AttentionBackend type, "
+            f"or instance, got {type(backend).__name__}"
+        )
+    return _resolve_default_backend()
+
+
+def get_backend(
+    use_default: bool = True,
+) -> Optional["AttentionBackend"]:
+    """Resolve the active backend: explicit context > env > default.
+
+    An ``attn_backend(...)`` context is the caller's explicit choice and
+    always wins.  ``ASTR_BACKEND`` is a process-wide override consulted
+    only when no context is set.  Pass ``use_default=False`` at request
+    submission to retain only an environment override or the caller's
+    :func:`attn_backend` value.
+    """
+    context_backend = _current_backend.get()
+    if context_backend is not None:
+        return context_backend
+    env_backend = _environment_backend()
+    if env_backend is not None:
+        return env_backend
+    return _resolve_default_backend() if use_default else None
+
+
+@contextmanager
+def attn_backend(backend: Union[str, ATTN_BACKEND, "AttentionBackend", type]):
+    """Context manager to select an attention backend.
+
+    Mirrors ``torch.nn.attention.sdpa_kernel``. Accepts an
+    registered name, ``ATTN_BACKEND`` enum value, backend class, or instance.
+
+    Examples::
+
+        with attn_backend(ATTN_BACKEND.TORCH_NATIVE):
+            ...
+        with attn_backend(TorchNativeBackend):
+            ...
+        with attn_backend(TorchNativeBackend()):
+            ...
+    """
+    instance = _resolve_backend(backend)
+    token = _current_backend.set(instance)
+    try:
+        yield instance
+    finally:
+        _current_backend.reset(token)
+
+
+def repeat_kv(x: Tensor, n_rep: int) -> Tensor:
+    """Expand KV heads to match Q heads for GQA."""
+    if n_rep == 1:
+        return x
+    n_heads, head_dim = x.shape[-2:]
+    return (
+        x.unsqueeze(-2)
+        .expand(*x.shape[:-2], n_heads, n_rep, head_dim)
+        .reshape(*x.shape[:-2], n_heads * n_rep, head_dim)
+    )
+
+
+def attention(
+    q: Tensor,
+    k: Tensor,
+    v: Tensor,
+    kv_cache: Optional["KVCache"] = None,
+    layer_id: int = 0,
+    attn_mask: Optional[Tensor] = None,
+    is_causal: bool = False,
+    fwd: Optional[str] = None,
+    backend: Optional[Union[str, ATTN_BACKEND, "AttentionBackend", type]] = None,
+) -> Tensor:
+    """Functional attention entry point — mirrors ``F.scaled_dot_product_attention``.
+
+    Delegates to the active backend.  ``backend`` (optional) is an explicit
+    escape hatch; when omitted the backend is resolved as
+    explicit context > ``ASTR_BACKEND`` env > default (cuda > flash > torch).
+    Handles KV cache I/O, GQA head expansion, and causal masking so the
+    caller only needs to provide projected q/k/v.
+
+    Training calls (``fwd=None``, ``kv_cache=None``) resolve through the
+    same capability chain — the CUDA cache kernels cannot run without a
+    cache, so they fall back to flash (mask-free/causal calls only) and
+    finally to torch SDPA.  An explicitly-selected backend that cannot
+    handle the call raises — an implicit one falls back down the priority
+    list to the first capable backend.
+
+    Args:
+        q: [batch, q_len, n_heads, head_dim] (blhd)
+        k: [batch, q_len, n_kv_heads, head_dim] (blhd)
+        v: [batch, q_len, n_kv_heads, head_dim] (blhd)
+        kv_cache: cache dataclass, or None for training (no cache).
+        layer_id: transformer layer index for buffer access.
+        attn_mask: pre-built attention mask (SDPA-compatible).
+        is_causal: whether to apply causal masking.
+        fwd: "prefill" / "decode" for inference, None for training.
+        backend: optional explicit backend (name, enum, class, or instance).
+
+    Returns:
+        [batch, q_len, n_heads * head_dim]
+    """
+    if backend is not None:
+        selected = _resolve_backend(backend)
+        explicit = True
+    else:
+        context_backend = _current_backend.get()
+        explicit = context_backend is not None
+        # Resolve through the same chain as inference: explicit context >
+        # ASTR_BACKEND env > default.  Training calls (fwd=None, no cache)
+        # land on the CUDA backend and fall back by capability below —
+        # flash when it can handle the call, else torch SDPA.
+        selected = get_backend()
+        assert selected is not None
+
+    if not selected.supports_call(q, kv_cache, attn_mask, is_causal, fwd):
+        if explicit:
+            raise RuntimeError(
+                f"Explicitly-set backend {type(selected).__name__} cannot "
+                f"handle this attention call (shape={q.shape}, "
+                f"dtype={q.dtype}, kv_cache={'none' if kv_cache is None else 'present'}, "
+                f"attn_mask={'none' if attn_mask is None else 'present'}). "
+                f"Remove the attn_backend() context or switch to a compatible backend."
+            )
+        selected = next(
+            (
+                candidate
+                for candidate in _priority_backends()
+                if candidate.supports_call(q, kv_cache, attn_mask, is_causal, fwd)
+            ),
+            _instance(TorchNativeBackend),
+        )
+    return selected.forward(q, k, v, kv_cache, layer_id, attn_mask, is_causal, fwd)
+
+
+class AttentionBackend(ABC):
+    """Abstract base for attention computation strategies.
+
+    Subclasses implement ``fwd_decode`` (q_len == 1, with cache) and
+    ``fwd_prefill`` (q_len > 1, with or without cache). The public
+    ``forward`` method dispatches based on q_len.
+
+    Capability contract — every backend declares:
+
+    * ``available()`` — machine-level: can this backend exist here
+      (kernel ``.so`` loaded, flash-attn present, GPU available)?
+      Used once to build the default priority list.
+    * ``supports_call(q, kv_cache, attn_mask, is_causal, fwd)`` — can this
+      backend run this *specific* call (shape/dtype/cache/mask)?  Used by
+      ``attention()`` for the per-call fallback.  Resolution logic never
+      checks concrete backend types, so adding a backend requires no
+      changes outside its own class.
+
+    Three equivalent ways to activate a backend::
+
+        with attn_backend(ATTN_BACKEND.TORCH_NATIVE):  # enum
+            ...
+        with attn_backend(TorchNativeBackend):          # class
+            ...
+        with TorchNativeBackend():                       # instance
+            ...
+    """
+
+    def __enter__(self) -> "AttentionBackend":
+        self._token = _current_backend.set(self)
+        return self
+
+    def __exit__(self, *exc) -> None:
+        _current_backend.reset(self._token)
+
+    @classmethod
+    @abstractmethod
+    def available(cls) -> bool:
+        """Return True if this backend can run on the current machine.
+
+        Checks static availability only (compiled kernels, optional
+        packages, GPU presence) — not call-specific constraints.
+        """
+
+    @abstractmethod
+    def supports_call(
+        self,
+        q: Tensor,
+        kv_cache: Optional["KVCache"],
+        attn_mask: Optional[Tensor],
+        is_causal: bool,
+        fwd: Optional[str],
+    ) -> bool:
+        """Return True if this backend can run this specific attention call.
+
+        Called on the canonical singleton instance (or a caller-provided
+        one); must be side-effect free.
+        """
+
+    def forward(
+        self,
+        q: Tensor,
+        k: Tensor,
+        v: Tensor,
+        kv_cache: Optional["KVCache"],
+        layer_id: int,
+        attn_mask: Optional[Tensor] = None,
+        is_causal: bool = False,
+        fwd: Optional[str] = None,
+    ) -> Tensor:
+        """Dispatch to decode or extend based on q_len.
+
+        Args:
+            q: [batch, q_len, n_heads, head_dim]
+            k: [batch, q_len, n_kv_heads, head_dim]
+            v: [batch, q_len, n_kv_heads, head_dim]
+            kv_cache: cache dataclass, or None for training (no cache).
+            layer_id: transformer layer index for buffer access.
+            attn_mask: pre-built attention mask compatible with SDPA.
+            is_causal: whether to apply causal masking.
+
+        Returns:
+            [batch, q_len, n_heads * head_dim]
+        """
+        if fwd == "decode":
+            return self.fwd_decode(q, k, v, kv_cache, layer_id, attn_mask, is_causal)
+        if fwd == "prefill" or fwd is None:
+            return self.fwd_prefill(q, k, v, kv_cache, layer_id, attn_mask, is_causal)
+        raise ValueError(f"unsupported attention forward mode: {fwd}")
+
+    @abstractmethod
+    def fwd_decode(
+        self,
+        q: Tensor,
+        k: Tensor,
+        v: Tensor,
+        kv_cache: Optional["KVCache"],
+        layer_id: int,
+        attn_mask: Optional[Tensor] = None,
+        is_causal: bool = False,
+    ) -> Tensor:
+        """Single-token decode with KV cache."""
+
+    @abstractmethod
+    def fwd_prefill(
+        self,
+        q: Tensor,
+        k: Tensor,
+        v: Tensor,
+        kv_cache: Optional["KVCache"],
+        layer_id: int,
+        attn_mask: Optional[Tensor] = None,
+        is_causal: bool = False,
+    ) -> Tensor:
+        """Multi-token prefill or training forward."""
+
+    @staticmethod
+    def supports_graph() -> bool:
+        """Return True if this backend supports CUDA-graph capture.
+
+        Override in subclasses that can run under ``torch.cuda.graph``.
+
+        Called on the *active* backend instance (or its class) — a cheap
+        boolean check with no side-effects.
+        """
+        return False
+
+
+class AttentionBackendFactory(BaseFactory[AttentionBackend]):
+    """Factory for registered attention backends."""
+
+
+@AttentionBackendFactory.register(ATTN_BACKEND.TORCH_NATIVE.value)
+class TorchNativeBackend(AttentionBackend):
+    """Reference backend using torch SDPA with indirect KV cache indexing.
+
+    Writes new K/V into the cache buffers, gathers the full sequence K/V
+    via ``req_to_token`` indirect indexing, then calls
+    ``F.scaled_dot_product_attention``.
+
+    For training (``kv_cache is None``), skips cache I/O entirely and
+    runs SDPA directly on the projected q/k/v.
+    """
+
+    @classmethod
+    def available(cls) -> bool:
+        return True
+
+    def supports_call(
+        self,
+        q: Tensor,
+        kv_cache: Optional["KVCache"],
+        attn_mask: Optional[Tensor],
+        is_causal: bool,
+        fwd: Optional[str],
+    ) -> bool:
+        return True
+
+    def fwd_decode(
+        self,
+        q: Tensor,
+        k: Tensor,
+        v: Tensor,
+        kv_cache: Optional["KVCache"],
+        layer_id: int,
+        attn_mask: Optional[Tensor] = None,
+        is_causal: bool = False,
+    ) -> Tensor:
+        return self._forward(q, k, v, kv_cache, layer_id, attn_mask, is_causal)
+
+    def fwd_prefill(
+        self,
+        q: Tensor,
+        k: Tensor,
+        v: Tensor,
+        kv_cache: Optional["KVCache"],
+        layer_id: int,
+        attn_mask: Optional[Tensor] = None,
+        is_causal: bool = False,
+    ) -> Tensor:
+        return self._forward(q, k, v, kv_cache, layer_id, attn_mask, is_causal)
+
+    def _forward(
+        self,
+        q: Tensor,
+        k: Tensor,
+        v: Tensor,
+        kv_cache: Optional["KVCache"],
+        layer_id: int,
+        attn_mask: Optional[Tensor] = None,
+        is_causal: bool = False,
+    ) -> Tensor:
+        if q.ndim == 4:
+            n_rep = q.size(2) // k.size(2)
+            if n_rep > 1:
+                k = repeat_kv(k, n_rep)
+                v = repeat_kv(v, n_rep)
+            return (
+                F.scaled_dot_product_attention(
+                    q.permute(0, 2, 1, 3),
+                    k.permute(0, 2, 1, 3),
+                    v.permute(0, 2, 1, 3),
+                    attn_mask,
+                    is_causal=is_causal,
+                )
+                .permute(0, 2, 1, 3)
+                .contiguous()
+            )
+
+        if kv_cache is None or kv_cache.qo_indptr is None:
+            raise ValueError("packed attention requires KV cache metadata")
+        kv_cache.k_buffer[layer_id, kv_cache.out_cache_loc] = k
+        kv_cache.v_buffer[layer_id, kv_cache.out_cache_loc] = v
+        outputs = []
+        n_rep = q.size(1) // k.size(1)
+        for i in range(kv_cache.req_pool_indices.numel()):
+            q_start = int(kv_cache.qo_indptr[i])
+            q_end = int(kv_cache.qo_indptr[i + 1])
+            indices = kv_cache.req_to_token[
+                kv_cache.req_pool_indices[i], : kv_cache.seq_lens[i]
+            ]
+            k_i = kv_cache.k_buffer[layer_id, indices]
+            v_i = kv_cache.v_buffer[layer_id, indices]
+            if n_rep > 1:
+                k_i = repeat_kv(k_i, n_rep)
+                v_i = repeat_kv(v_i, n_rep)
+            q_len = q_end - q_start
+            kv_len = k_i.size(0)
+            q_pos = torch.arange(kv_len - q_len, kv_len, device=q.device)
+            causal_mask = q_pos[:, None] >= torch.arange(kv_len, device=q.device)
+            out = F.scaled_dot_product_attention(
+                q[q_start:q_end].transpose(0, 1).unsqueeze(0),
+                k_i.transpose(0, 1).unsqueeze(0),
+                v_i.transpose(0, 1).unsqueeze(0),
+                attn_mask=causal_mask,
+            )
+            outputs.append(out.squeeze(0).transpose(0, 1))
+        return torch.cat(outputs)
+
+
+@AttentionBackendFactory.register(ATTN_BACKEND.CUDA.value)
+class CudaBackend(AttentionBackend):
+    """CUDA kernel backend with direct KV cache access.
+
+    Decode path: writes K/V to the flat pool, then calls
+    ``attn_paged_decode`` with req_to_token + kv_indptr.
+
+    Prefill path: writes K/V to the flat pool, then calls
+    ``attn_paged_prefill`` with ragged-batch support via qo_indptr +
+    kv_indptr.
+
+    ``kv_cache is None`` (training) raises — the per-call fallback to
+    torch SDPA for training / fp32 / unsupported head_dim happens in the
+    ``attention()`` entry point.
+
+    Raises ``RuntimeError`` if the required kernel is not available.
+    """
+
+    # Head dims supported by the CUDA kernels (single source of truth).
+    HEAD_DIMS = (32, 64, 128, 256)
+
+    @classmethod
+    def available(cls) -> bool:
+        return (
+            torch.cuda.is_available()
+            and is_available("attn_paged_decode")
+            and is_available("attn_paged_prefill")
+        )
+
+    def supports_call(
+        self,
+        q: Tensor,
+        kv_cache: Optional["KVCache"],
+        attn_mask: Optional[Tensor],
+        is_causal: bool,
+        fwd: Optional[str],
+    ) -> bool:
+        # The CUDA kernels are bf16-only, support head_dim in
+        # HEAD_DIMS, and need a KV cache (decode/prefill); everything
+        # else falls back down the priority list to torch.
+        return (
+            fwd in ("prefill", "decode")
+            and kv_cache is not None
+            and q.ndim == 3
+            and q.dtype == torch.bfloat16
+            and q.size(-1) in self.HEAD_DIMS
+            and is_available(f"attn_paged_{fwd}")
+        )
+
+    @staticmethod
+    def supports_graph() -> bool:
+        return True
+
+    def fwd_decode(
+        self,
+        q: Tensor,
+        k: Tensor,
+        v: Tensor,
+        kv_cache: Optional["KVCache"],
+        layer_id: int,
+        attn_mask: Optional[Tensor] = None,
+        is_causal: bool = False,
+    ) -> Tensor:
+        if kv_cache is None:
+            raise RuntimeError("CudaBackend does not support training (kv_cache=None)")
+
+        kv_indptr = kv_cache.kv_indptr
+
+        out = attn_paged_decode(
+            q,
+            kv_cache.k_buffer[layer_id],
+            kv_cache.v_buffer[layer_id],
+            kv_cache.req_to_token,
+            kv_cache.req_pool_indices,
+            kv_indptr,
+            new_k=k,
+            new_v=v,
+            is_causal=True,
+            o_part_buf=kv_cache.decode_o_part,
+            ml_part_buf=kv_cache.decode_ml_part,
+            out_buf=kv_cache.decode_out,
+        )
+        return out
+
+    def fwd_prefill(
+        self,
+        q: Tensor,
+        k: Tensor,
+        v: Tensor,
+        kv_cache: Optional["KVCache"],
+        layer_id: int,
+        attn_mask: Optional[Tensor] = None,
+        is_causal: bool = False,
+    ) -> Tensor:
+        if kv_cache is None:
+            raise RuntimeError("CudaBackend does not support training (kv_cache=None)")
+
+        loc = kv_cache.out_cache_loc
+        kv_cache.k_buffer[layer_id, loc] = k
+        kv_cache.v_buffer[layer_id, loc] = v
+
+        out = attn_paged_prefill(
+            q,
+            kv_cache.k_buffer[layer_id],
+            kv_cache.v_buffer[layer_id],
+            kv_cache.req_to_token,
+            kv_cache.req_pool_indices,
+            kv_cache.kv_indptr,
+            kv_cache.qo_indptr,
+            kv_cache.q_tile_to_batch,
+            kv_cache.q_tile_to_index,
+            attn_mask,
+            is_causal=is_causal,
+        )
+        return out
+
+
+@AttentionBackendFactory.register(ATTN_BACKEND.FLASH.value)
+class FlashAttnBackend(AttentionBackend):
+    """FlashAttention backend via the optional ``flash-attn`` package.
+
+    Decode (q_len=1, contiguous cache): uses ``flash_attn_with_kvcache``,
+    which reads K/V directly from the flat pool via cache_batch_idx +
+    cache_seqlens — no materialized KV gather.
+
+    Prefill / non-contiguous decode: falls back to KV gather +
+    ``flash_attn_func``.
+    """
+
+    @classmethod
+    def available(cls) -> bool:
+        return flash_attn_available()
+
+    def supports_call(
+        self,
+        q: Tensor,
+        kv_cache: Optional["KVCache"],
+        attn_mask: Optional[Tensor],
+        is_causal: bool,
+        fwd: Optional[str],
+    ) -> bool:
+        if not self.available():
+            return False
+        if q.dtype not in (torch.float16, torch.bfloat16):
+            return False
+        if fwd is not None:
+            return q.ndim == 3 and hasattr(_flash_attn, "flash_attn_varlen_func")
+        # Dense (training) path: flash_attn_func cannot apply a custom
+        # mask, so only mask-free calls are supported — ``is_causal`` is
+        # a flag, not a mask.  Masked training (SFT/DPO/GRPO) must fall
+        # back to TorchNativeBackend instead of silently ignoring the mask.
+        return attn_mask is None
+
+    def fwd_decode(
+        self,
+        q: Tensor,
+        k: Tensor,
+        v: Tensor,
+        kv_cache: Optional["KVCache"],
+        layer_id: int,
+        attn_mask: Optional[Tensor] = None,
+        is_causal: bool = False,
+    ) -> Tensor:
+        return self._forward_packed(q, k, v, kv_cache, layer_id)
+
+    def fwd_prefill(
+        self,
+        q: Tensor,
+        k: Tensor,
+        v: Tensor,
+        kv_cache: Optional["KVCache"],
+        layer_id: int,
+        attn_mask: Optional[Tensor] = None,
+        is_causal: bool = False,
+    ) -> Tensor:
+        if q.ndim == 3:
+            return self._forward_packed(q, k, v, kv_cache, layer_id)
+        return self._forward_dense(q, k, v, attn_mask, is_causal)
+
+    def _forward_dense(
+        self,
+        q: Tensor,
+        k: Tensor,
+        v: Tensor,
+        attn_mask: Optional[Tensor] = None,
+        is_causal: bool = False,
+    ) -> Tensor:
+        n_rep = q.size(2) // k.size(2)
+        if n_rep > 1:
+            k = repeat_kv(k, n_rep)
+            v = repeat_kv(v, n_rep)
+
+        if attn_mask is not None:
+            raise ValueError(
+                "FlashAttnBackend cannot handle a custom attention mask; "
+                "use a causal mask or select TorchNativeBackend."
+            )
+        fa = _flash_attn
+        if fa is None:
+            raise RuntimeError(
+                "FlashAttnBackend requires the optional 'flash-attn' package. "
+                "Install with `pip install flash-attn`."
+            )
+        out = fa.flash_attn_func(
+            q.contiguous(),
+            k.contiguous(),
+            v.contiguous(),
+            causal=is_causal,
+        )
+        return out.contiguous()
+
+    def _forward_packed(
+        self,
+        q: Tensor,
+        k: Tensor,
+        v: Tensor,
+        kv_cache: "KVCache",
+        layer_id: int,
+    ) -> Tensor:
+        fa = _flash_attn
+        if fa is None or not hasattr(fa, "flash_attn_varlen_func"):
+            raise RuntimeError("packed inference requires flash_attn_varlen_func")
+        kv_cache.k_buffer[layer_id, kv_cache.out_cache_loc] = k
+        kv_cache.v_buffer[layer_id, kv_cache.out_cache_loc] = v
+        page_table = kv_cache.req_to_token[
+            kv_cache.req_pool_indices, : kv_cache.max_len
+        ]
+        positions = torch.arange(kv_cache.max_len, device=q.device)
+        indices = page_table[positions.unsqueeze(0) < kv_cache.seq_lens.unsqueeze(1)]
+        k_flat = kv_cache.k_buffer[layer_id, indices].contiguous()
+        v_flat = kv_cache.v_buffer[layer_id, indices].contiguous()
+        out = fa.flash_attn_varlen_func(
+            q.contiguous(),
+            k_flat,
+            v_flat,
+            kv_cache.qo_indptr,
+            kv_cache.kv_indptr,
+            int((kv_cache.qo_indptr[1:] - kv_cache.qo_indptr[:-1]).max()),
+            int(kv_cache.seq_lens.max()),
+            dropout_p=0.0,
+            causal=True,
+        )
+        return out

astrai/extension/backend/rotary.py

@@ -0,0 +1,53 @@
+"""Rotary embedding with auto-dispatch to CUDA kernel.
+
+Single entry point ``apply_rotary_emb(x, freqs_cis)`` — uses the fused
+CUDA kernel when available, falls back to torch complex multiply otherwise.
+
+Layout: x is [batch, seq_len, n_heads, head_dim] (bf16).
+freqs_cis is [batch, seq_len, dim/2, 2] (f32) — [cos, sin] pairs.
+"""
+
+import torch
+from torch import Tensor
+
+from astrai.extension.loader import is_available
+from astrai.extension.ops.rotary import rotary_emb as _cuda_rotary
+
+_cache = {"available": None}
+
+
+def _cuda_available() -> bool:
+    if _cache["available"] is None:
+        _cache["available"] = is_available("rotary_emb")
+    return _cache["available"]
+
+
+def _torch_apply(x: Tensor, freqs_cis: Tensor) -> Tensor:
+    cos, sin = freqs_cis[..., 0], freqs_cis[..., 1]
+    dtype = x.dtype
+    x_ = x.float().reshape(*x.shape[:-1], -1, 2)
+    x_complex = torch.view_as_complex(x_)
+    freqs_cis_complex = torch.complex(cos, sin).unsqueeze(-2)
+    x_rotated = x_complex * freqs_cis_complex
+    x_out = torch.view_as_real(x_rotated).flatten(-2)
+    return x_out.to(dtype)
+
+
+def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor:
+    """Apply rotary embedding to x.
+
+    Args:
+        x: [batch, seq_len, n_heads, head_dim] (bf16)
+        freqs_cis: [batch, seq_len, dim/2, 2] (f32) — [cos, sin] pairs
+
+    Returns:
+        [batch, seq_len, n_heads, head_dim] (bf16)
+    """
+    if (
+        _cuda_available()
+        and not torch.is_grad_enabled()
+        and x.is_cuda
+        and x.dtype == torch.bfloat16
+    ):
+        return _cuda_rotary(x, freqs_cis)
+    return _torch_apply(x, freqs_cis)

astrai/extension/fp8.py

@@ -0,0 +1,494 @@
+"""FP8 training: scaling recipes, per-tensor state, and aten::linear dispatch.
+
+Layered (see ``ops/fp8.py`` for the CUDA interface adapter):
+1. ``ops.fp8`` — the only module touching the pybind.
+2. This module (strategy layer): scaling *recipes* (TE-style delayed scaling
+   or dynamic current-amax scaling), per-tensor scales + amax history, and the
+   ``fp8_autocast`` context manager (like ``torch.autocast``).
+3. aten::linear integration: registers the CUDA + AutogradCUDA impls.
+
+Usage::
+
+    from astrai.extension.fp8 import fp8_autocast
+    with fp8_autocast(enabled=True, fp8_format="hybrid"):
+        logits = model(input_ids)
+    loss.backward()  # fp8 backward runs anywhere; fwd captured state on the node
+
+Format defaults follow the ecosystem consensus: E4M3 forward / E5M2 backward
+("hybrid"); every operand's scale is a quantization step derived from its amax
+history by the active recipe.
+
+The context mirrors ``torch.autocast`` (``autocast_mode.py``): the active
+``(enabled, recipe, fp8_format)`` triple is thread-local (a ``contextvars``
+``ContextVar``, absent outside any region), and the manager is class-based and
+reentrant with nested ``enabled=False`` disabling dispatch inside it. The module
+targets *training*: every step quantizes x/w/g fresh (no weight-cast cache — the
+optimizer bumps the weight version each step, so a torch-style cached_cast would
+miss anyway), and the per-operand scales come from the delayed/dynamic recipe.
+"""
+
+import functools
+from contextvars import ContextVar, Token
+from dataclasses import dataclass
+from enum import Enum
+from typing import Dict, List, Optional
+
+import torch
+from torch.library import Library
+
+from astrai.extension.ops.fp8 import (
+    linear_backward_fp8,
+    linear_forward_fp8,
+)
+
+# Max representable value per FP8 format (E4M3: 448, E5M2: 57344).
+FP8_MAX = {"e4m3": 448.0, "e5m2": 57344.0}
+
+
+class FP8Format(str, Enum):
+    """Per-direction FP8 format. HYBRID = E4M3 forward / E5M2 backward."""
+
+    E4M3 = "e4m3"
+    E5M2 = "e5m2"
+    HYBRID = "hybrid"
+
+    def fwd(self) -> str:
+        return "e4m3" if self is FP8Format.HYBRID else self.value
+
+    def bwd(self) -> str:
+        return "e5m2" if self is FP8Format.HYBRID else self.value
+
+
+class FP8Recipe:
+    """Scale-from-amax policy: ``scale = (amax / FP8_MAX[fmt]) / 2^margin``.
+
+    ``scale_from_history`` receives the operand's amax tensor (a ring window for
+    delayed scaling, the current amax for dynamic scaling) and returns the
+    quantization step. Subclasses set ``history_len`` / ``margin``.
+    """
+
+    history_len: int = 16
+    margin: int = 0
+
+    def scale_from_history(self, amax: torch.Tensor, fmt: str) -> torch.Tensor:
+        peak = amax.max()
+        return ((peak / FP8_MAX[fmt]) / (2**self.margin)).clamp_min(1e-12)
+
+
+@dataclass
+class DelayedScaling(FP8Recipe):
+    """TE-style delayed scaling: max over the amax history window (amax from
+    *previous* steps; the window trades responsiveness against stability)."""
+
+    history_len: int = 16
+    margin: int = 0
+
+
+@dataclass
+class DynamicScaling(FP8Recipe):
+    """Current-amax scaling (torchao DYNAMIC): measure, then quantize. No
+    history — the scale is derived from the same-step amax, at an extra pass."""
+
+    history_len: int = 1
+    margin: int = 0
+
+
+class _ScaleRing:
+    """One operand's delayed-scaling state: a float32 buffer
+    ``[hist[n] | scale | counter]`` (views). The quantize kernel's last-finishing
+    block records the measured amax into ``hist[idx]``, reduces the window and
+    publishes the next scale entirely on device — the Python-side write/max/write
+    chain is gone. The counter slot stays int32-zero (float bits) between
+    launches; ``idx`` advances host-side each step.
+    """
+
+    __slots__ = ("recipe", "state", "hist", "scale", "idx", "initialized")
+
+    def __init__(self, device: torch.device, recipe: FP8Recipe):
+        self.recipe = recipe
+        n = recipe.history_len
+        self.state = torch.zeros(n + 2, device=device, dtype=torch.float32)
+        self.hist = self.state[:n]
+        self.scale = self.state[n : n + 1]
+        self.idx = 0
+        self.initialized = False
+
+    def advance(self) -> None:
+        """Rotate to the next history slot after an in-kernel finalize."""
+        self.idx = (self.idx + 1) % self.hist.numel()
+
+    def seed(self, t: torch.Tensor, fmt: str) -> None:
+        amax = t.abs().amax().to(torch.float32).clamp_min(1e-12)
+        self.hist.fill_(amax)
+        self.scale.copy_(self.recipe.scale_from_history(self.hist, fmt))
+        self.initialized = True
+
+
+class FP8TensorMeta:
+    """Per-weight delayed-scaling state: one ring per operand role (``w``/``x``/
+    ``g``). Fused kernels record amax while quantizing, so the scale used at step
+    N reflects amax from steps < N. DynamicScaling never allocates a meta — it
+    measures the current amax inline.
+    """
+
+    __slots__ = ("w", "x", "g")
+
+    def __init__(self, device: torch.device, recipe: FP8Recipe):
+        self.w = _ScaleRing(device, recipe)
+        self.x = _ScaleRing(device, recipe)
+        self.g = _ScaleRing(device, recipe)
+
+
+@dataclass(frozen=True)
+class _ActiveConfig:
+    """The immutable (enabled, recipe, format) triple of one open region."""
+
+    enabled: bool
+    recipe: FP8Recipe
+    fp8_format: FP8Format
+
+
+# Thread-local active configuration (torch's autocast TLS analog): set by
+# fp8_autocast on __enter__, absent outside any region. Autograd engine
+# threads run backwards with their own empty context — fine, since backward
+# only reads state captured on ctx at forward time.
+_active_config: ContextVar[Optional[_ActiveConfig]] = ContextVar(
+    "astrai_fp8_active_config", default=None
+)
+
+
+class FP8State:
+    """Global fp8 training state: per-tensor metas + out-of-region defaults.
+
+    The active ``(enabled, recipe, fp8_format)`` triple is a ``ContextVar`` set
+    by ``fp8_autocast``. The properties below read that active config when a
+    region is open and the global defaults otherwise; the setters (and
+    ``fp8_linear_enable``) write the global defaults — the persistent switch
+    applying outside any region. The metas registry is shared across threads
+    (GIL-protected); fp8 backward runs on autograd engine threads and only
+    touches metas captured on ``ctx`` at forward time.
+    """
+
+    def __init__(self):
+        self.default_enabled = False
+        self.default_recipe: FP8Recipe = DelayedScaling()
+        self.default_format: FP8Format = FP8Format.HYBRID
+        self._metas: Dict[tuple, FP8TensorMeta] = {}
+
+    # Active-config views (region config if open, else the defaults).
+    @property
+    def enabled(self) -> bool:
+        cfg = _active_config.get()
+        return cfg.enabled if cfg is not None else self.default_enabled
+
+    @property
+    def recipe(self) -> FP8Recipe:
+        cfg = _active_config.get()
+        return cfg.recipe if cfg is not None else self.default_recipe
+
+    @property
+    def fp8_format(self) -> FP8Format:
+        cfg = _active_config.get()
+        return cfg.fp8_format if cfg is not None else self.default_format
+
+    # Persistent (out-of-region) defaults.
+    @enabled.setter
+    def enabled(self, value: bool) -> None:
+        self.default_enabled = bool(value)
+
+    @recipe.setter
+    def recipe(self, value: FP8Recipe) -> None:
+        self.default_recipe = value
+
+    @fp8_format.setter
+    def fp8_format(self, value: FP8Format) -> None:
+        self.default_format = FP8Format(value)
+
+    def get_weight_meta(self, w: torch.Tensor) -> FP8TensorMeta:
+        key = (w.data_ptr(), w.shape, w.dtype)
+        meta = self._metas.get(key)
+        if meta is None:
+            meta = FP8TensorMeta(w.device, self.recipe)
+            self._metas[key] = meta
+        return meta
+
+    def reset(self) -> None:
+        self.default_enabled = False
+        self._metas.clear()
+
+
+# Process-wide singleton; per-thread/per-region state lives in _active_config.
+_state = FP8State()
+
+
+def fp8_state() -> FP8State:
+    return _state
+
+
+def _active() -> Optional[_ActiveConfig]:
+    """The active config when fp8 dispatch is on, else ``None`` (fast guard).
+
+    A region config wins (honoring nested ``enabled=False`` regions); with no
+    region open this falls back to the persistent global switch
+    (``fp8_linear_enable``), so that flag still routes aten::linear to fp8.
+    """
+    cfg = _active_config.get()
+    if cfg is not None:
+        return cfg if cfg.enabled else None
+    if _state.default_enabled:
+        return _ActiveConfig(True, _state.default_recipe, _state.default_format)
+    return None
+
+
+def _current_config() -> _ActiveConfig:
+    """Like ``_active()`` but always returns a config (disabled regions and
+    out-of-region direct calls resolve to the global defaults)."""
+    cfg = _active_config.get()
+    if cfg is not None:
+        return cfg
+    return _ActiveConfig(
+        _state.default_enabled, _state.default_recipe, _state.default_format
+    )
+
+
+class fp8_autocast:
+    """Autocast-style context: fp8 linear dispatch on this thread.
+
+    Mirrors ``torch.autocast`` — a class-based, reentrant, nestable context
+    over thread-local state::
+
+        with fp8_autocast(enabled=True, fp8_format="hybrid"):
+            logits = model(input_ids)   # aten::linear -> fp8 path
+        loss.backward()  # fp8 backward; state was captured at forward time
+
+    Nesting follows torch: each ``__enter__`` pushes the new active config, each
+    ``__exit__`` restores the previous one, and a nested ``enabled=False`` region
+    simply disables dispatch inside it. The instance doubles as a decorator.
+    """
+
+    def __init__(
+        self,
+        enabled: bool = True,
+        update_interval: int = 16,
+        recipe: Optional[FP8Recipe] = None,
+        fp8_format: str = "hybrid",
+        margin: int = 0,
+    ):
+        if recipe is None:
+            recipe = DelayedScaling(history_len=update_interval, margin=margin)
+        self._config = _ActiveConfig(bool(enabled), recipe, FP8Format(fp8_format))
+        self._tokens: List[Token] = []
+
+    def __enter__(self) -> "fp8_autocast":
+        self._tokens.append(_active_config.set(self._config))
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb) -> bool:
+        token = self._tokens.pop()
+        _active_config.reset(token)
+        return False
+
+    def __call__(self, func):
+        @functools.wraps(func)
+        def decorate(*args, **kwargs):
+            with self:
+                return func(*args, **kwargs)
+
+        return decorate
+
+
+# ---------------------------------------------------------------------------
+# Strategy-level forward / backward (called from the aten::linear impl)
+# ---------------------------------------------------------------------------
+
+
+def _dynamic_scale(t: torch.Tensor, recipe: FP8Recipe, fmt: str) -> torch.Tensor:
+    amax = t.abs().amax().to(torch.float32).clamp_min(1e-12)
+    return recipe.scale_from_history(amax, fmt)
+
+
+_zero_bias: Dict[Optional[int], torch.Tensor] = {}
+
+
+def _empty_bias(x: torch.Tensor) -> torch.Tensor:
+    """Per-device cached 0-element bf16 bias (the binding only checks numel —
+    never mutated), saving a CUDA allocation per bias-less linear."""
+    key = x.device.index
+    t = _zero_bias.get(key)
+    if t is None:
+        t = torch.empty(0, device=x.device, dtype=torch.bfloat16)
+        _zero_bias[key] = t
+    return t
+
+
+def fp8_linear_forward(
+    x: torch.Tensor, w: torch.Tensor, bias=None, cfg: Optional[_ActiveConfig] = None
+):
+    """Scaled fp8 linear forward (called from the aten::linear impl).
+
+    Pure FP8 path for both recipes: quantize x/w with the active scales, run the
+    pre-quantized GEMM. Delayed scaling finalizes the rings inside the quantize
+    kernels (amax folded into the window, next scale published on device);
+    dynamic scaling measures the current amax itself. Training quantizes the
+    weight every step (the optimizer bumps its version, so there is no cast
+    cache, matching ``cached_cast``-less behavior).
+    """
+    state = fp8_state()
+    if cfg is None:
+        cfg = _current_config()
+    fmt = cfg.fp8_format.fwd()
+    margin = cfg.recipe.margin
+    if bias is None:
+        bias = _empty_bias(x)
+    if isinstance(cfg.recipe, DynamicScaling):  # measure-then-quantize, no state
+        sx = _dynamic_scale(x.reshape(-1, w.size(1)), cfg.recipe, fmt)
+        sw = _dynamic_scale(w, cfg.recipe, fmt)
+        out, *_ = linear_forward_fp8(x, w, bias, sx, sw, fmt)
+        return out, sx, sw
+
+    meta = state.get_weight_meta(w)
+    if not meta.w.initialized:
+        meta.w.seed(w, fmt)
+    if not meta.x.initialized:
+        meta.x.seed(x, fmt)
+    # The quantize kernels finalize each ring in-kernel and overwrite the ring's
+    # scale slot, which ALIASES meta.*.scale (a view into the state buffer).
+    # Snapshot the scales first so the GEMM dequantizes with the SAME scale the
+    # operands were quantized with, and so the backward can reuse this step's
+    # scale (gradient consistency with the forward). The ring finalize still
+    # publishes the next step's scale into the original slot.
+    if w.dtype is not torch.bfloat16:  # static pre-quantized weight
+        w_arg, sw_arg, w_ring = w, meta.w.scale, None
+    else:
+        w_arg, sw_arg, w_ring = w, meta.w.scale, meta.w.state
+    sx = meta.x.scale.clone()
+    sw = sw_arg.clone()
+    out, _x8, _w8, _ax, _aw = linear_forward_fp8(
+        x,
+        w_arg,
+        bias,
+        sx,
+        sw,
+        fmt,
+        None,
+        meta.x.state,
+        meta.x.idx,
+        margin,
+        w_ring,
+        meta.w.idx,
+        margin,
+    )
+    meta.x.advance()
+    if w_ring is not None:
+        meta.w.advance()
+    return out, sx, sw
+
+
+class _LinearFp8(torch.autograd.Function):
+    """The fp8 linear forward/backward pair (standard Function style).
+
+    The forward runs inside ``fp8_autocast`` and captures the active
+    fmt/recipe/meta on ``ctx``; the backward reads only that captured state, so
+    ``loss.backward()`` may run after the context exits. The gradient is
+    quantized once (E5M2 in hybrid) and both dX/dW GEMMs share it; the output
+    masks come from ``needs_input_grad``.
+    """
+
+    @staticmethod
+    def forward(ctx, x, w, bias):
+        cfg = _current_config()
+        out, sx, sw = fp8_linear_forward(x, w, bias, cfg)
+        ctx.save_for_backward(x, w, sx, sw)
+        ctx.fmt_bwd = cfg.fp8_format.bwd()
+        ctx.recipe = cfg.recipe
+        ctx.is_dynamic = isinstance(cfg.recipe, DynamicScaling)
+        ctx.meta = None if ctx.is_dynamic else _state.get_weight_meta(w)
+        return out
+
+    @staticmethod
+    @torch.autograd.function.once_differentiable
+    def backward(ctx, g):
+        x, w, _sx_fwd, _sw_fwd = ctx.saved_tensors
+        fmt = ctx.fmt_bwd
+        # Per-recipe scale/ring selection; both branches share one call below.
+        if ctx.is_dynamic:
+            sg = _dynamic_scale(g, ctx.recipe, fmt)
+            sw = _dynamic_scale(w, ctx.recipe, fmt)
+            sx = _dynamic_scale(x, ctx.recipe, fmt)
+            ring, idx = None, 0
+        else:
+            meta = ctx.meta
+            if not meta.g.initialized:
+                meta.g.seed(g, fmt)
+            # Snapshot the g scale before its ring finalize overwrites the slot
+            # (same aliasing as the forward); reuse the forward's w/x scales so
+            # the backward quantizes with the scale the forward actually used.
+            sg = meta.g.scale.clone()
+            ring, idx = meta.g.state, meta.g.idx
+            sw, sx = _sw_fwd, _sx_fwd
+        grad_x, grad_w, grad_b, _amax_g = linear_backward_fp8(
+            g,
+            x,
+            w,
+            list(ctx.needs_input_grad),
+            sg,
+            sw,
+            sx,
+            fmt,
+            ring,
+            idx,
+            ctx.recipe.margin,
+        )
+        if not ctx.is_dynamic:
+            meta.g.advance()  # the g quantize kernel finalized the ring in-kernel
+        return grad_x, grad_w, grad_b if ctx.needs_input_grad[2] else None
+
+
+# ---------------------------------------------------------------------------
+# aten::linear integration
+# ---------------------------------------------------------------------------
+
+
+def fp8_linear_enable(enabled: bool = True) -> None:
+    """Toggle fp8 dispatch for aten::linear globally (the out-of-region default;
+    ``fp8_autocast`` regions override it thread-locally)."""
+    fp8_state().default_enabled = enabled
+
+
+def fp8_linear_enabled() -> bool:
+    """Whether fp8 dispatch is active right now (region config or global)."""
+    return _active() is not None
+
+
+def _fp8_supported(x: torch.Tensor, w: torch.Tensor) -> bool:
+    """Shape guard for the fp8 path. Unlike a strict 16-alignment requirement,
+    the kernels handle unaligned M/N via boundary checks (slower but correct) —
+    so no whole-call bf16 fallback for small decode batches. Only the K-dimension
+    contraction must match and the weight must be 2D."""
+    return x.dim() >= 2 and w.dim() == 2 and x.size(-1) == w.size(1)
+
+
+def _linear_cuda_impl(x: torch.Tensor, w: torch.Tensor, bias=None):
+    if (
+        _active() is not None
+        and x.dtype is torch.bfloat16
+        and w.dtype is torch.bfloat16
+        and _fp8_supported(x, w)
+    ):
+        return _LinearFp8.apply(x, w, bias)
+    return torch.ops.aten.linear.default.redispatch(
+        torch._C.DispatchKeySet(torch._C.DispatchKey.CompositeImplicitAutograd),
+        x,
+        w,
+        bias,
+    )
+
+
+_lib = Library("aten", "IMPL", "CUDA")
+_lib.impl("linear", _linear_cuda_impl)
+# Also replace torch's generated linear autograd formula (which would call
+# aten::linear_backward after the fp8_autocast region exits). The fp8 backward
+# is owned by _LinearFp8 with state captured at forward time, so loss.backward()
+# works wherever it is called; the CUDA registration still covers inference_mode.
+_lib_autograd = Library("aten", "IMPL", "AutogradCUDA")
+_lib_autograd.impl("linear", _linear_cuda_impl)

astrai/extension/lib/__init__.py

@@ -0,0 +1 @@
+"""Compiled CUDA kernel modules (``*.so``) live here, kept separate from Python source."""

astrai/extension/loader.py

@@ -0,0 +1,83 @@
+"""Dynamic discovery and loading of compiled CUDA kernel modules.
+
+Each kernel is built by the CMake build in ``csrc/CMakeLists.txt`` into a
+``.so`` placed in ``astrai/extension/lib/`` — the module name equals the
+``.so`` name equals the pybind name (e.g. ``attn_decode``, defined via
+``TORCH_EXTENSION_NAME``). ``KERNEL_NAMES`` is discovered automatically from
+the ``.so`` files present, so adding a kernel to the CMake ``KERNELS``
+registry needs no change here.
+
+Loading is **lazy and centralized**: module names are discovered eagerly
+(cheap glob), but each ``.so`` is imported on first use via the single
+``get_module`` accessor, then cached. The wrapper modules (``ops/*.py``) never
+touch the internals or keep their own caches — they call ``get_module(name)``
+(or ``is_available(name)`` when a torch fallback is acceptable). A kernel that
+failed to build (or is running on a CPU-only machine) is ``None`` in the cache,
+so ``is_available`` returns ``False`` and ``get_module`` raises a clear error.
+"""
+
+import glob
+import importlib
+import logging
+import os
+
+logger = logging.getLogger(__name__)
+
+_LIB_DIR = os.path.join(os.path.dirname(__file__), "lib")
+
+
+def _discover_kernel_names() -> list[str]:
+    """Return the module names of the compiled kernel ``.so`` files in lib/."""
+    names: list[str] = []
+    for path in glob.glob(os.path.join(_LIB_DIR, "*.so")):
+        # strip the "<soabi>.so" suffix, e.g. attn_decode.cpython-312-...so
+        names.append(os.path.basename(path).split(".", 1)[0])
+    return sorted(names)
+
+
+KERNEL_NAMES = _discover_kernel_names()
+
+_available: dict[str, bool] = {}
+_modules: dict[str, object] = {}
+
+
+def _try_load(name: str) -> object:
+    """Import and cache the ``name`` kernel module (lazy, one attempt).
+
+    Returns the module, or ``None`` if it is unavailable. Cached so each
+    ``.so`` is imported at most once per process.
+    """
+    if name not in _modules:
+        try:
+            _modules[name] = importlib.import_module(
+                f".lib.{name}", package=__package__
+            )
+            _available[name] = True
+        except ImportError:
+            logger.warning("kernel '%s' failed to import; marking unavailable", name)
+            _modules[name] = None
+            _available[name] = False
+    return _modules[name]
+
+
+def is_available(name: str) -> bool:
+    """Return ``True`` if the compiled kernel ``name`` could be loaded."""
+    if name not in _available:
+        _try_load(name)
+    return _available.get(name, False)
+
+
+def get_module(name: str) -> object:
+    """Return the loaded kernel module for ``name``, importing it on first use.
+
+    Raises ``RuntimeError`` if the kernel is unavailable (not built, or failed
+    to import) — callers that can tolerate a torch fallback should check
+    ``is_available(name)`` first instead.
+    """
+    mod = _try_load(name)
+    if mod is None:
+        raise RuntimeError(
+            f"CUDA kernel '{name}' is not available. "
+            f"Build with CSRC_KERNELS=true (or use the torch-native fallback)."
+        )
+    return mod

astrai/extension/ops/__init__.py

@@ -0,0 +1,19 @@
+"""Stateless wrappers around compiled extension kernels."""
+
+from astrai.extension.ops.attention import (
+    TensorLayout,
+    attn_decode,
+    attn_paged_decode,
+    attn_paged_prefill,
+    attn_prefill,
+)
+from astrai.extension.ops.rotary import rotary_emb
+
+__all__ = [
+    "TensorLayout",
+    "attn_decode",
+    "attn_paged_decode",
+    "attn_paged_prefill",
+    "attn_prefill",
+    "rotary_emb",
+]

astrai/extension/ops/attention.py

@@ -0,0 +1,192 @@
+"""Attention kernel wrapper functions - one entry point per compiled kernel.
+
+Each wrapper calls its CUDA kernel directly. If the kernel is not
+available, raises ``RuntimeError``. Fallback to torch SDPA is the
+responsibility of the attention backend, not this module.
+
+Layout convention: all q/k/v are ``[batch, seq_len, n_heads, head_dim]``
+(blhd). Scale is always ``1/sqrt(head_dim)``.
+
+Interface (all functions):
+    is_causal: True = causal mask; False = non-causal
+    mask:      2D [batch, kv_len] or 3D [batch, q_len, kv_len] (bool, True=keep)
+"""
+
+import enum
+from typing import Optional
+
+import torch
+
+from astrai.extension.loader import get_module
+
+
+class TensorLayout(enum.IntEnum):
+    """Q/K/V tensor layout, mirrors the C++ ``TensorLayout`` enum in ``attn_common.h``.
+
+    Kernels internally operate on BHLD; BLHD inputs are transposed at entry.
+    """
+
+    BHLD = 0  # [batch, n_heads, seq_len, head_dim]
+    BLHD = 1  # [batch, seq_len, n_heads, head_dim]
+
+
+def attn_decode(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    mask: Optional[torch.Tensor] = None,
+    is_causal: bool = False,
+) -> torch.Tensor:
+    """GQA decode attention (q_len == 1).
+
+    Args:
+        q: [batch, 1, n_heads, head_dim] (blhd, bf16)
+        k: [batch, kv_len, n_kv_heads, head_dim] (blhd, bf16)
+        v: [batch, kv_len, n_kv_heads, head_dim] (blhd, bf16)
+        mask: 2D [batch, kv_len] or 3D [batch, 1, kv_len] (bool, True=keep)
+        is_causal: apply causal mask
+
+    Returns:
+        [batch, 1, n_heads, head_dim] (blhd, bf16)
+    """
+    mod = get_module("attn_decode")
+    causal_offset = (k.size(1) - 1) if is_causal else -1
+    return mod.attn_decode(
+        q, k, v, mask=mask, causal_offset=causal_offset, layout=TensorLayout.BLHD
+    )
+
+
+def attn_prefill(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    mask: Optional[torch.Tensor] = None,
+    is_causal: bool = False,
+) -> torch.Tensor:
+    """GQA prefill attention (q_len > 1).
+
+    Args:
+        q: [batch, q_len, n_heads, head_dim] (blhd, bf16)
+        k: [batch, kv_len, n_kv_heads, head_dim] (blhd, bf16)
+        v: [batch, kv_len, n_kv_heads, head_dim] (blhd, bf16)
+        mask: 2D [batch, kv_len] or 3D [batch, q_len, kv_len] (bool, True=keep)
+        is_causal: apply causal mask
+
+    Returns:
+        [batch, q_len, n_heads, head_dim] (blhd, bf16)
+    """
+    mod = get_module("attn_prefill")
+    causal_offset = (k.size(1) - q.size(1)) if is_causal else -1
+    return mod.attn_prefill(
+        q, k, v, mask=mask, causal_offset=causal_offset, layout=TensorLayout.BLHD
+    )
+
+
+def attn_paged_decode(
+    q: torch.Tensor,
+    k_cache: torch.Tensor,
+    v_cache: torch.Tensor,
+    req_to_token: torch.Tensor,
+    req_pool_indices: torch.Tensor,
+    kv_indptr: torch.Tensor,
+    new_k: Optional[torch.Tensor] = None,
+    new_v: Optional[torch.Tensor] = None,
+    mask: Optional[torch.Tensor] = None,
+    is_causal: bool = False,
+    o_part_buf: Optional[torch.Tensor] = None,
+    ml_part_buf: Optional[torch.Tensor] = None,
+    out_buf: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+    """SGLang-style paged decode (q_len == 1, flat KV pool).
+
+    Reads K/V directly from a flat pool [size, kv_head, head_dim] via
+    req_to_token indirect indexing.  Each request has its own seq_len
+    (from kv_indptr), eliminating padding waste.
+
+    Args:
+        q: [batch, n_heads, head_dim] (bf16, 3D — no seq dim)
+        k_cache: [pool_size, n_kv_heads, head_dim] (bf16, flat)
+        v_cache: same as k_cache
+        req_to_token: [num_reqs, max_context_len] (int32) — token -> slot
+        req_pool_indices: [batch] (int32) — rows into req_to_token
+        kv_indptr: [batch+1] (int32) — prefix sum of per-request seq_lens
+        new_k: current-token K to append, [batch, n_kv_heads, head_dim]
+        new_v: current-token V to append, same shape as new_k
+        mask: 2D [batch, max_context_len] (bool, True=keep) or None
+        is_causal: apply causal mask
+        o_part_buf: pre-allocated split-KV o partial buffer (workflow bypass)
+        ml_part_buf: pre-allocated split-KV m/l buffer (workflow bypass)
+        out_buf: pre-allocated output buffer [batch, n_heads, head_dim] (graph-safe)
+
+    Returns:
+        [batch, n_heads, head_dim] (bf16, 3D)
+    """
+    mod = get_module("attn_paged_decode")
+    causal_offset = 0 if is_causal else -1
+    return mod.attn_paged_decode(
+        q,
+        k_cache,
+        v_cache,
+        req_to_token,
+        req_pool_indices,
+        kv_indptr,
+        new_k=new_k,
+        new_v=new_v,
+        mask=mask,
+        causal_offset=causal_offset,
+        o_part_buf=o_part_buf,
+        ml_part_buf=ml_part_buf,
+        out_buf=out_buf,
+    )
+
+
+def attn_paged_prefill(
+    q: torch.Tensor,
+    k_cache: torch.Tensor,
+    v_cache: torch.Tensor,
+    req_to_token: torch.Tensor,
+    req_pool_indices: torch.Tensor,
+    kv_indptr: torch.Tensor,
+    qo_indptr: torch.Tensor,
+    q_tile_to_batch: torch.Tensor,
+    q_tile_to_index: torch.Tensor,
+    mask: Optional[torch.Tensor] = None,
+    is_causal: bool = False,
+) -> torch.Tensor:
+    """SGLang-style paged prefill (ragged batch, flat KV pool).
+
+    Reads K/V directly from a flat pool [size, kv_head, head_dim] via
+    req_to_token.  Supports ragged batches: each request has its own
+    q_len and kv_len, addressed via qo_indptr and kv_indptr.
+
+    Args:
+        q: [total_q, n_heads, head_dim] (bf16, 3D — flattened across requests)
+        k_cache: [pool_size, n_kv_heads, head_dim] (bf16, flat)
+        v_cache: same as k_cache
+        req_to_token: [num_reqs, max_context_len] (int32)
+        req_pool_indices: [batch] (int32)
+        kv_indptr: [batch+1] (int32) — prefix sum of per-request kv_lens
+        qo_indptr: [batch+1] (int32) — prefix sum of per-request q_lens
+        q_tile_to_batch: [num_q_tiles] (int32) — request index per Q tile
+        q_tile_to_index: [num_q_tiles] (int32) — local Q tile index per request
+        mask: 4D [batch, 1, q_len, kv_len] (bool, True=keep) or None
+        is_causal: apply causal mask
+
+    Returns:
+        [total_q, n_heads, head_dim] (bf16, 3D)
+    """
+    mod = get_module("attn_paged_prefill")
+    causal_offset = 0 if is_causal else -1
+    return mod.attn_paged_prefill(
+        q,
+        k_cache,
+        v_cache,
+        req_to_token,
+        req_pool_indices,
+        kv_indptr,
+        qo_indptr,
+        q_tile_to_batch,
+        q_tile_to_index,
+        mask,
+        causal_offset=causal_offset,
+    )

astrai/extension/ops/fp8.py

@@ -0,0 +1,245 @@
+"""FP8 CUDA kernel interface adapter (the only module touching the pybind).
+
+Isolates the ``fp8_ops`` CUDA extension behind stable Python primitives:
+
+- ``quantize_bf16(x, scale, fmt) -> (x8, amax)`` — BF16 → FP8 with fused amax
+- ``mm_fp8(a8, b8, sa, sb) -> out`` — pre-quantized FP8 GEMM (BF16 output)
+- ``linear_forward_fp8(x, w, bias, sx, sw) -> (out, x8, w8, amax_x, amax_w)``
+- ``linear_backward_fp8(g, x, w, masks, sg, sw, sx, fmt) -> (gx, gw, gb, amax_g)``
+
+Scale semantics: scales are *quantization steps* — the value divided out when
+quantizing (``x8 = x / scale``). Every primitive computes its own inverse
+internally; callers never pass ``scale_inv``. ``amax`` values are *returned*,
+never passed as output arguments. ``fmt`` is ``"e4m3"`` or ``"e5m2"``.
+
+Policy (scales, amax history, delayed scaling, autocast) lives in ``fp8.py``;
+this module is stateless.
+"""
+
+from typing import List, Optional, Tuple
+
+import torch
+from torch.library import custom_op
+
+from astrai.extension.loader import get_module
+
+# fmt string -> kernel int (0 = E4M3, 1 = E5M2)
+_FMT_TO_INT = {"e4m3": 0, "e5m2": 1}
+
+
+def _fmt_int(fmt: str) -> int:
+    try:
+        return _FMT_TO_INT[fmt]
+    except KeyError:
+        raise ValueError(f"unsupported fp8 format {fmt!r} (expected 'e4m3' or 'e5m2')")
+
+
+def _fmt_dtype(fmt: str) -> torch.dtype:
+    return torch.float8_e5m2 if _fmt_int(fmt) else torch.float8_e4m3fn
+
+
+@custom_op("custom::fp8_quantize", mutates_args=())
+def fp8_quantize(
+    x: torch.Tensor, scale: torch.Tensor, fmt: int
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """BF16 -> FP8 quantize with fused amax; returns ``(x8, amax)``."""
+
+
+@fp8_quantize.register_fake
+def _fp8_quantize_fake(x, scale, fmt):
+    dtype = torch.float8_e5m2 if fmt else torch.float8_e4m3fn
+    return (
+        torch.empty(x.shape, device=x.device, dtype=dtype),
+        torch.empty(1, device=x.device, dtype=torch.float32),
+    )
+
+
+@fp8_quantize.register_kernel("cuda")
+def _fp8_quantize_cuda(x, scale, fmt):
+    if x.dtype != torch.bfloat16:
+        raise TypeError(f"fp8 quantize requires bf16 input, got {x.dtype}")
+    return get_module("fp8_ops").quantize_bf16(x, scale, int(fmt))
+
+
+@fp8_quantize.register_kernel("cpu")
+def _fp8_quantize_cpu(x, scale, fmt):
+    x8 = (x.float() / scale).to(_fmt_dtype("e5m2" if fmt else "e4m3"))
+    amax = x.abs().amax().float().reshape(1).clamp_min(1e-12)
+    return x8, amax
+
+
+@custom_op("custom::fp8_gemm", mutates_args=())
+def fp8_gemm(
+    a: torch.Tensor,
+    b: torch.Tensor,
+    sa: torch.Tensor,
+    sb: torch.Tensor,
+    out_dtype: int = 0,
+    out_scale: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+    """FP8 GEMM: ``a @ b * (sa * sb)`` with FP32 accumulation.
+
+    ``out_dtype``: 0 = BF16 (default), 1 = FP8 E4M3 (requires ``out_scale``,
+    the quantization step for the output — mirrors ``torch._scaled_mm``).
+    """
+
+
+@fp8_gemm.register_fake
+def _fp8_gemm_fake(a, b, sa, sb, out_dtype=0, out_scale=None):
+    dtype = torch.float8_e4m3fn if out_dtype else torch.bfloat16
+    return torch.empty((a.size(0), b.size(1)), device=a.device, dtype=dtype)
+
+
+@fp8_gemm.register_kernel("cuda")
+def _fp8_gemm_cuda(a, b, sa, sb, out_dtype=0, out_scale=None):
+    if a.dtype != b.dtype or a.dtype not in (torch.float8_e4m3fn, torch.float8_e5m2):
+        raise TypeError(
+            f"fp8 GEMM requires matching fp8 inputs, got {a.dtype}/{b.dtype}"
+        )
+    return get_module("fp8_ops").mm_fp8(a, b, sa, sb, int(out_dtype), out_scale)
+
+
+@fp8_gemm.register_kernel("cpu")
+def _fp8_gemm_cpu(a, b, sa, sb, out_dtype=0, out_scale=None):
+    acc = a.float() @ b.float() * sa * sb
+    if out_dtype:
+        os_ = 1.0 if out_scale is None else out_scale
+        return (acc * os_).to(torch.float8_e4m3fn)
+    return acc.to(torch.bfloat16)
+
+
+def quantize_bf16(
+    x: torch.Tensor, scale: torch.Tensor, fmt: str = "e4m3"
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """BF16 -> FP8 quantize with fused amax; returns ``(x8, amax)``.
+
+    ``scale`` is the quantization step (device scalar); ``fmt`` selects
+    E4M3 or E5M2. ``amax`` is a fresh 1-element float32 tensor — the caller
+    never clears it.
+    """
+    return fp8_quantize(x, scale, _fmt_int(fmt))
+
+
+def mm_fp8(
+    a: torch.Tensor,
+    b: torch.Tensor,
+    sa: torch.Tensor,
+    sb: torch.Tensor,
+    out_dtype: str = "bf16",
+    out_scale: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+    """Pre-quantized FP8 GEMM: ``a @ b * (sa * sb)``.
+
+    ``a``/``b`` must be FP8 tensors of the same format (E4M3 or E5M2);
+    ``sa``/``sb`` are their quantization steps. ``out_dtype`` is ``"bf16"``
+    (default) or ``"e4m3"`` — FP8 output for layer-to-layer pipelines, which
+    requires ``out_scale`` (the output quantization step).
+    """
+    if out_dtype not in ("bf16", "e4m3"):
+        raise ValueError(
+            f"unsupported out_dtype {out_dtype!r} (expected 'bf16' or 'e4m3')"
+        )
+    return fp8_gemm(a, b, sa, sb, int(out_dtype == "e4m3"), out_scale)
+
+
+def linear_forward_fp8(
+    x: torch.Tensor,
+    w: torch.Tensor,
+    bias: Optional[torch.Tensor],
+    sx: torch.Tensor,
+    sw: torch.Tensor,
+    fmt: str = "e4m3",
+    bias_scale: Optional[torch.Tensor] = None,
+    x_ring: Optional[torch.Tensor] = None,
+    x_ring_idx: int = 0,
+    x_ring_margin: int = 0,
+    w_ring: Optional[torch.Tensor] = None,
+    w_ring_idx: int = 0,
+    w_ring_margin: int = 0,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    """Pure FP8 linear forward: quantize x/w to ``fmt``, pre-quantized GEMM.
+
+    Returns ``(out, x8, w8, amax_x, amax_w)`` — the quantized operands are
+    handed back so the policy layer can cache the weight quantization while
+    the weight tensor is unchanged (torch autocast's cached_cast analog).
+    ``x8`` is ``[M, K]`` and ``w8`` is ``[N, K]`` (the passed-in ``w`` itself
+    on the pre-quantized path). ``bias`` may be ``None``. For static fp8
+    inference, ``w`` and ``bias`` may arrive pre-quantized to ``fmt``
+    (produced by :func:`quantize_bf16` with their scales as ``sw`` /
+    ``bias_scale``); a pre-quantized ``bias`` requires ``bias_scale``, and
+    its ``amax_w`` comes back 0. The bias is fused into the GEMM epilogue.
+    ``x_ring`` / ``w_ring`` (delayed scaling) are ``[hist | scale | counter]``
+    float32 buffers the quantize kernels finalize in-kernel: the measured
+    amax lands in ``hist[idx]`` and the next step's scale is published on
+    device, replacing the eager hist/max/scale update chain.
+    """
+    fmt8 = _fmt_dtype(fmt)
+    if x.dtype != torch.bfloat16 or w.dtype not in (torch.bfloat16, fmt8):
+        raise TypeError(
+            f"fp8 forward requires bf16 x and bf16-or-{fmt} w, got {x.dtype}/{w.dtype}"
+        )
+    if bias is None:
+        bias = torch.empty(0, device=x.device, dtype=x.dtype)
+    return get_module("fp8_ops").linear_forward_fp8(
+        x,
+        w,
+        bias,
+        sx,
+        sw,
+        _fmt_int(fmt),
+        bias_scale,
+        x_ring,
+        x_ring_idx,
+        x_ring_margin,
+        w_ring,
+        w_ring_idx,
+        w_ring_margin,
+    )
+
+
+def linear_backward_fp8(
+    g: torch.Tensor,
+    x: torch.Tensor,
+    w: torch.Tensor,
+    masks: List[bool],
+    sg: torch.Tensor,
+    sw: torch.Tensor,
+    sx: torch.Tensor,
+    fmt: str = "e5m2",
+    g_ring: Optional[torch.Tensor] = None,
+    g_ring_idx: int = 0,
+    g_ring_margin: int = 0,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    """FP8 linear backward; returns ``(grad_input, grad_weight, grad_bias, amax_g)``.
+
+    ``g``/``x``/``w`` may each be bf16 (quantized to ``fmt`` here) or already
+    pre-quantized fp8 matching ``fmt`` — a pre-quantized operand skips its
+    quantize kernel and is read directly by the GEMM (the ``cached_cast``
+    analog for the backward, symmetric with :func:`linear_forward_fp8`'s
+    pre-quantized weight path). ``fmt`` defaults to E5M2 (larger dynamic range
+    for gradients); the two GEMMs run as FP8 tensor-core products sharing a
+    single gradient quantization. ``g_ring`` (delayed scaling) is a
+    ``[hist | scale | counter]`` buffer the g quantize kernel finalizes
+    in-kernel (see :func:`linear_forward_fp8`); a pre-quantized ``g`` does not
+    finalize it and reports ``amax_g = 0``.
+    """
+    f8 = _fmt_dtype(fmt)
+    for name, t in (("g", g), ("x", x), ("w", w)):
+        if t.dtype not in (torch.bfloat16, f8):
+            raise TypeError(
+                f"fp8 backward requires bf16 or pre-quantized {fmt} inputs, "
+                f"got {name}={t.dtype}"
+            )
+    return get_module("fp8_ops").linear_backward_fp8(
+        g,
+        x,
+        w,
+        list(masks),
+        sg,
+        sw,
+        sx,
+        _fmt_int(fmt),
+        g_ring,
+        g_ring_idx,
+        g_ring_margin,
+    )

astrai/extension/ops/rotary.py

@@ -0,0 +1,31 @@
+"""Rotary embedding CUDA kernel wrapper.
+
+Calls the compiled CUDA kernel directly. If the kernel is not available,
+raises ``RuntimeError``. Fallback to torch complex multiply is the
+responsibility of ``astrai.extension.backend.rotary.apply_rotary_emb``.
+
+Layout: x is packed [tokens, n_heads, head_dim] or dense
+[batch, seq_len, n_heads, head_dim]. ``freqs_cis`` has matching token axes.
+"""
+
+import torch
+
+from astrai.extension.loader import get_module
+
+
+def rotary_emb(x: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
+    """Fused rotary embedding kernel.
+
+    Args:
+        x: packed 3D or dense 4D bf16 tensor.
+        freqs_cis: matching token axes followed by [head_dim/2, 2].
+
+    Returns:
+        Tensor with the same shape as ``x``.
+    """
+    mod = get_module("rotary_emb")
+    if not x.is_contiguous():
+        x = x.contiguous()
+    if not freqs_cis.is_contiguous():
+        freqs_cis = freqs_cis.contiguous()
+    return mod.rotary_emb(x, freqs_cis)

astrai/factory.py

@@ -0,0 +1,153 @@
+"""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,
+    get_args,
+    get_origin,
+)
+
+T = TypeVar("T")
+
+
+def _resolve_base_type(
+    arg: Union[Type, str, ForwardRef], factory_cls: type
+) -> Optional[Type]:
+    """Resolve the generic type-arg T to a concrete class.
+
+    - Concrete class (``BaseFactory[MyBase]``): returned directly.
+    - Forward reference (``BaseFactory["MyBase"]``): ``Base["X"]``
+      produces a ``ForwardRef("X")`` at class-creation time.  We
+      extract the name and evaluate it in the factory module's
+      global namespace — the same mechanism ``typing.get_type_hints``
+      uses internally.
+    """
+    if isinstance(arg, type):
+        return arg
+
+    if isinstance(arg, str):
+        name = arg
+    elif isinstance(arg, ForwardRef):
+        name = arg.__forward_arg__
+    else:
+        return None
+
+    mod = sys.modules.get(factory_cls.__module__)
+    if mod is None:
+        return None
+    try:
+        return eval(name, vars(mod))  # noqa: S307
+    except NameError:
+        return None
+
+
+def _validate_component(component_cls: Type, base: Optional[Type]) -> None:
+    """Validate that *component_cls* inherits from *base*.
+
+    No-op when *base* is ``None`` (e.g. forward-ref resolution failed).
+    """
+    if base is not None and not issubclass(component_cls, base):
+        raise TypeError(f"{component_cls.__name__} must inherit from {base.__name__}")
+
+
+class BaseFactory(ABC, Generic[T]):
+    """Generic factory with decorator-based registration.
+
+    Create a factory by subclassing with the desired base type::
+
+        class MyFactory(BaseFactory[MyBase]):
+            pass
+
+    Register components with the ``register`` decorator::
+
+        @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 = {}
+                cls._component_base = _resolve_base_type(arg, cls)
+                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]:
+            _validate_component(component_cls, cls._component_base)
+            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.
+        """
+        component_cls = cls._entries.get(name)
+        if component_cls is None:
+            raise ValueError(
+                f"Unknown component: '{name}'. Supported types: {sorted(cls._entries)}"
+            )
+        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 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 +1,33 @@
+"""Inference module for continuous batching.
+
+Subpackages:
+  - cache/:     KV cache (buffers, strategies, pool)
+  - runtime/:   Execution + sampling (executor, CUDA graph, sampling strategies)
+  - task/:      Request lifecycle + performance metrics
+  - network/:   HTTP protocol handling (server, protocol, OpenAI/Anthropic builders)
+
+Modules:
+  - scheduler.py:  Continuous batching loop
+  - workspace.py:  Pre-allocated GPU buffers
+  - engine.py:     Facade (InferenceEngine)
+"""
+
+from astrai.inference.engine import InferenceEngine
+from astrai.inference.network import get_app, run_server
+from astrai.inference.runtime.executor import Executor
+from astrai.inference.runtime.sample import sample
+from astrai.inference.scheduler import InferenceScheduler
+from astrai.inference.task import STOP, Task, TaskManager, TaskStatus
+
+__all__ = [
+    "InferenceEngine",
+    "InferenceScheduler",
+    "Executor",
+    "STOP",
+    "Task",
+    "TaskManager",
+    "TaskStatus",
+    "sample",
+    "get_app",
+    "run_server",
+]

astrai/inference/cache/__init__.py

@@ -0,0 +1,27 @@
+"""KV cache subsystem: buffers, strategies, pool management."""
+
+from astrai.inference.cache.buffer import KVCache, KVStorage, ReqToTokenPool
+from astrai.inference.cache.pool import PagePool, TaskCacheManager, page_hash
+from astrai.inference.cache.strategy import (
+    AllocationStrategy,
+    Allocator,
+    ContiguousStrategy,
+    PagedStrategy,
+    RadixCache,
+    TaskCacheState,
+)
+
+__all__ = [
+    "KVCache",
+    "KVStorage",
+    "ReqToTokenPool",
+    "Allocator",
+    "RadixCache",
+    "TaskCacheState",
+    "AllocationStrategy",
+    "ContiguousStrategy",
+    "PagedStrategy",
+    "PagePool",
+    "TaskCacheManager",
+    "page_hash",
+]

astrai/inference/cache/buffer.py

@@ -0,0 +1,96 @@
+"""Physical KV cache buffers.
+
+Layer 1 — ``KVStorage``:   flat token-level K/V GPU buffers [n_layers, size, n_kv_heads, head_dim]
+Layer 2 — ``ReqToTokenPool``: index table [req_idx, pos] → physical token slot
+Layer 3 — ``KVCache``:       pure dataclass passed to the model for direct buffer access
+
+These classes have no knowledge of tasks, allocation policies, or scheduling.
+They are the "dumb" physical storage layer.
+"""
+
+import threading
+from dataclasses import dataclass
+from typing import List, Optional
+
+import torch
+from torch import Tensor
+
+
+class ReqToTokenPool:
+    """Maps [req_idx, pos] → physical token slot in KV storage.
+
+    Each row is one request; each column is a sequence position.  The value
+    at [req_idx, pos] is the flat index into the KV storage buffers.
+    """
+
+    def __init__(self, size: int, max_context_len: int, device: torch.device):
+        self.size = size
+        self.max_context_len = max_context_len
+        self.req_to_token = torch.zeros(
+            (size, max_context_len), dtype=torch.int32, device=device
+        )
+        self.free_slots = list(range(size))
+        self._lock = threading.Lock()
+
+    def alloc(self, num_reqs: int) -> Optional[List[int]]:
+        with self._lock:
+            if num_reqs > len(self.free_slots):
+                return None
+            slots = self.free_slots[:num_reqs]
+            self.free_slots = self.free_slots[num_reqs:]
+            return slots
+
+    def free(self, req_indices: List[int]):
+        with self._lock:
+            self.free_slots.extend(req_indices)
+
+    def write(self, indices, values):
+        self.req_to_token[indices] = values
+
+
+class KVStorage:
+    """Token-level KV cache storage.
+
+    Buffers: ``[n_layers, size, n_kv_heads, head_dim]``.  Each token occupies
+    one slot indexed by ``ReqToTokenPool``.
+    """
+
+    def __init__(
+        self,
+        size: int,
+        n_layers: int,
+        n_kv_heads: int,
+        head_dim: int,
+        device: torch.device,
+        dtype: torch.dtype,
+    ):
+        self.size = size
+        self.k_buffer = torch.empty(
+            (n_layers, size, n_kv_heads, head_dim), device=device, dtype=dtype
+        )
+        self.v_buffer = torch.empty(
+            (n_layers, size, n_kv_heads, head_dim), device=device, dtype=dtype
+        )
+
+
+@dataclass
+class KVCache:
+    """Pure data struct passed to model for KV cache I/O.
+
+    The attention layer does raw buffer indexing — no methods, no abstraction.
+    """
+
+    k_buffer: Tensor
+    v_buffer: Tensor
+    req_to_token: Tensor
+    req_pool_indices: Tensor
+    seq_lens: Tensor
+    out_cache_loc: Tensor
+    max_len: int = 0
+    kv_indptr: Optional[Tensor] = None
+    qo_indptr: Optional[Tensor] = None
+    q_tile_to_batch: Optional[Tensor] = None
+    q_tile_to_index: Optional[Tensor] = None
+    decode_o_part: Optional[Tensor] = None
+    decode_ml_part: Optional[Tensor] = None
+    decode_out: Optional[Tensor] = None

astrai/inference/cache/pool.py

@@ -0,0 +1,382 @@
+"""KV cache orchestration: PagePool + TaskCacheManager.
+
+PagePool owns the physical buffers (``KVStorage`` + ``ReqToTokenPool``)
+and wires them to an allocation strategy.  It assembles the ``KVCache``
+dataclass passed to the model forward.
+
+TaskCacheManager owns the ``task_id`` → ``TaskCacheState`` mapping and
+delegates physical slot allocation to the strategy, and KV bind to the pool.
+
+See ``cache_buffer.py`` for the raw buffer primitives and ``cache_strategy.py``
+for the allocation policies.
+"""
+
+from dataclasses import dataclass
+from typing import Dict, List, Optional
+
+import torch
+
+from astrai.inference.cache.buffer import KVCache, KVStorage, ReqToTokenPool
+from astrai.inference.cache.strategy import (
+    AllocationStrategy,
+    Allocator,
+    ContiguousStrategy,
+    PagedStrategy,
+    RadixCache,
+    TaskCacheState,
+)
+from astrai.inference.workspace import Q_TILE_ROWS, InferenceWorkspace
+
+# Re-export everything so existing ``from astrai.inference.cache import ...``
+# continues to work unchanged after the file split.
+__all__ = [
+    "KVCache",
+    "KVStorage",
+    "ReqToTokenPool",
+    "Allocator",
+    "RadixCache",
+    "AllocationStrategy",
+    "ContiguousStrategy",
+    "PagedStrategy",
+    "PagePool",
+    "TaskCacheManager",
+    "TaskCacheState",
+    "page_hash",
+]
+
+# ---- helpers ----
+
+
+def page_hash(
+    token_ids: List[int], page_idx: int, page_size: int, parent_hash: int = 0
+) -> int:
+    start = page_idx * page_size
+    end = min(start + page_size, len(token_ids))
+    h = parent_hash
+    for i in range(start, end):
+        h = (h * 31 + token_ids[i]) & 0xFFFFFFFFFFFFFFFF
+    return h
+
+
+def _is_steady_increment(
+    prev_sig: Optional[tuple],
+    prev_vals: Optional[List[int]],
+    cur_sig: tuple,
+    cur_vals: List[int],
+) -> bool:
+    return (
+        prev_sig is not None
+        and prev_vals is not None
+        and prev_sig == cur_sig
+        and len(prev_vals) == len(cur_vals)
+        and all(c == p + 1 for c, p in zip(cur_vals, prev_vals))
+    )
+
+
+# ---- task-scoped bind state ----
+@dataclass
+class _BindState:
+    """Cached bind metadata for steady-state decode increment detection."""
+
+    sig: tuple
+    seq_lens: List[int]
+
+
+# ---- pool + manager ----
+
+
+class PagePool:
+    """Physical KV cache: buffers + req-to-token table + allocation strategy + bind.
+
+    Does not know about tasks — task lifecycle is managed by
+    :class:`TaskCacheManager`, which holds a reference to this pool.
+    """
+
+    def __init__(
+        self,
+        n_layers: int,
+        n_kv_heads: int,
+        head_dim: int,
+        max_batch_size: int,
+        max_seq_len: int,
+        device: torch.device,
+        dtype: torch.dtype,
+        page_size: int = 1,
+        n_tokens: Optional[int] = None,
+    ):
+        self.page_size = page_size
+        self.max_batch_size = max_batch_size
+        self.max_seq_len = max_seq_len
+        self.device = device
+        self.dtype = dtype
+        self.n_layers = n_layers
+        self.n_kv_heads = n_kv_heads
+        self.head_dim = head_dim
+
+        self.contiguous = n_tokens is None
+        self.n_tokens = max_batch_size * max_seq_len if self.contiguous else n_tokens
+        if self.n_tokens > torch.iinfo(torch.int32).max:
+            raise ValueError("KV cache token count exceeds the int32 slot index limit")
+
+        self._storage = KVStorage(
+            self.n_tokens, n_layers, n_kv_heads, head_dim, device, dtype
+        )
+        self._req_pool = ReqToTokenPool(max_batch_size, max_seq_len, device)
+
+        if self.contiguous:
+            for i in range(max_batch_size):
+                self._req_pool.req_to_token[i] = torch.arange(
+                    i * max_seq_len,
+                    (i + 1) * max_seq_len,
+                    dtype=torch.int32,
+                    device=device,
+                )
+            self._strategy: AllocationStrategy = ContiguousStrategy()
+        else:
+            n_pages = self.n_tokens // page_size
+            alloc = Allocator(n_pages)
+            prefix = RadixCache(page_size) if page_size > 1 else None
+            if prefix is not None:
+                alloc.on_evict = prefix.evict
+            self._strategy = PagedStrategy(
+                alloc, prefix, page_size, self._req_pool, device
+            )
+
+    @property
+    def strategy(self) -> AllocationStrategy:
+        return self._strategy
+
+    @property
+    def req_pool(self) -> ReqToTokenPool:
+        return self._req_pool
+
+    def bind_tasks(
+        self,
+        req_indices: List[int],
+        seq_lens: List[int],
+        workspace: InferenceWorkspace,
+        device: Optional[torch.device] = None,
+        start_pos: Optional[int] = None,
+        incremental: bool = False,
+    ) -> KVCache:
+        """Assemble the ``KVCache`` metadata for a batch of tasks.
+
+        Args:
+            req_indices: request slot indices (from ``ReqToTokenPool``).
+            seq_lens:    current sequence length per task.
+            workspace:   pre-allocated fixed-shape buffers (CUDA-graph safe).
+            start_pos:   if set, produce **prefill** cache (full q_len range).
+                         If ``None``, produce **decode** cache (last position).
+            incremental: if ``True``, reuse workspace state from previous step
+                         by incrementing counters in-place (decode hot path).
+
+        Returns:
+            ``KVCache`` dataclass with the correct output shapes for the
+            attention backend (prefill: ``[B, q_len]``, decode: ``[B, 1]``).
+        """
+        if device is None:
+            device = workspace.device
+        b = len(req_indices)
+
+        rpi_buf = workspace.req_pool_indices
+        sl_buf = workspace.seq_lens
+        kvp_buf = workspace.kv_indptr
+        inc_buf = workspace.inc
+        ocl_buf = workspace.out_cache_loc
+
+        if incremental:
+            sl_buf[:b] += 1
+            kvp_buf[: b + 1] += inc_buf[: b + 1]
+        else:
+            rpi_buf[:b].copy_(
+                torch.tensor(req_indices, dtype=torch.int32, device=device)
+            )
+            sl_buf[:b].copy_(torch.tensor(seq_lens, dtype=torch.long, device=device))
+            kvp_buf[: b + 1].zero_()
+            kvp_buf[1 : b + 1] = sl_buf[:b].cumsum(0).to(torch.int32)
+
+        req_pool_indices = rpi_buf[:b]
+        seq_lens_t = sl_buf[:b]
+        kv_indptr = kvp_buf[: b + 1]
+
+        if start_pos is not None:
+            # Packed prefill concatenates each request's query tokens.
+            q_lens = [seq_len - start_pos for seq_len in seq_lens]
+            if any(q_len <= 0 for q_len in q_lens):
+                raise ValueError("prefill sequence lengths must exceed start_pos")
+            out_cache_loc = torch.cat(
+                [
+                    self._req_pool.req_to_token[
+                        req_pool_indices[i], start_pos : seq_lens[i]
+                    ]
+                    for i in range(b)
+                ]
+            )
+            workspace.qo_indptr[: b + 1].zero_()
+            workspace.qo_indptr[1 : b + 1].copy_(
+                torch.tensor(q_lens, dtype=torch.int32, device=device).cumsum(0)
+            )
+            qo_indptr = workspace.qo_indptr[: b + 1]
+            tile_batches = []
+            tile_indices = []
+            for batch, q_len in enumerate(q_lens):
+                n_tiles = (q_len + Q_TILE_ROWS - 1) // Q_TILE_ROWS
+                tile_batches.extend([batch] * n_tiles)
+                tile_indices.extend(range(n_tiles))
+            n_tiles = len(tile_batches)
+            workspace.q_tile_to_batch[:n_tiles].copy_(
+                torch.tensor(tile_batches, dtype=torch.int32, device=device)
+            )
+            workspace.q_tile_to_index[:n_tiles].copy_(
+                torch.tensor(tile_indices, dtype=torch.int32, device=device)
+            )
+            q_tile_to_batch = workspace.q_tile_to_batch[:n_tiles]
+            q_tile_to_index = workspace.q_tile_to_index[:n_tiles]
+            decode_o_part = decode_ml_part = decode_out = None
+        else:
+            # ---- decode: out_cache_loc is a single column (last position) ----
+            write_pos = seq_lens_t - 1
+            loc = self._req_pool.req_to_token[req_pool_indices, write_pos].unsqueeze(-1)
+            ocl_buf[:b].copy_(loc)
+            out_cache_loc = ocl_buf[:b].reshape(-1)
+            workspace.qo_indptr[: b + 1].copy_(inc_buf[: b + 1])
+            qo_indptr = workspace.qo_indptr[: b + 1]
+            q_tile_to_batch = q_tile_to_index = None
+            decode_o_part = getattr(workspace, "decode_o_part", None)
+            decode_ml_part = getattr(workspace, "decode_ml_part", None)
+            decode_out = getattr(workspace, "decode_out", None)
+
+        return KVCache(
+            k_buffer=self._storage.k_buffer,
+            v_buffer=self._storage.v_buffer,
+            req_to_token=self._req_pool.req_to_token,
+            req_pool_indices=req_pool_indices,
+            seq_lens=seq_lens_t,
+            out_cache_loc=out_cache_loc,
+            max_len=max(seq_lens),
+            kv_indptr=kv_indptr,
+            qo_indptr=qo_indptr,
+            q_tile_to_batch=q_tile_to_batch,
+            q_tile_to_index=q_tile_to_index,
+            decode_o_part=decode_o_part,
+            decode_ml_part=decode_ml_part,
+            decode_out=decode_out,
+        )
+
+
+class TaskCacheManager:
+    """Task ↔ KV slot lifecycle manager.
+
+    Sole owner of ``task_id → TaskCacheState``.  Delegates physical slot
+    allocation to the strategy (via ``pool.strategy``) and KV bind to
+    ``pool.bind_tasks()``.
+
+    Usage::
+
+        pool = PagePool(...)
+        mgr = TaskCacheManager(pool)
+        mgr.task_alloc("req_1", [101, 202, 303])
+        ...
+        kv = mgr.bind(["req_1"], workspace)
+    """
+
+    def __init__(self, pool: PagePool):
+        self._pool = pool
+        self._strategy = pool.strategy
+        self._req_pool = pool.req_pool
+        self._max_seq_len = pool.max_seq_len
+        self._states: Dict[str, TaskCacheState] = {}
+        self._bind_state: Optional[_BindState] = None
+        self._bind_was_steady = False
+
+    # -- public task lifecycle --
+
+    def task_alloc(self, task_id: str, prompt_ids: List[int]) -> bool:
+        self._bind_state = None
+        req_slots = self._req_pool.alloc(1)
+        if req_slots is None:
+            return False
+        state = TaskCacheState(req_idx=req_slots[0])
+        self._states[task_id] = state
+        if not self._strategy.alloc(state, prompt_ids):
+            self._rollback(state, task_id)
+            return False
+        self._strategy.write_indices(state, prompt_ids)
+        state.length = len(prompt_ids)
+        return True
+
+    def task_free(self, task_id: str):
+        self._bind_state = None
+        state = self._states.pop(task_id, None)
+        if state is None:
+            return
+        self._strategy.free(state)
+        self._req_pool.free([state.req_idx])
+
+    def task_extend(self, task_id: str, pos: int) -> bool:
+        state = self._states.get(task_id)
+        if state is None or pos >= self._max_seq_len:
+            return False
+        if not self._strategy.extend(state, pos):
+            return False
+        state.length = pos + 1
+        return True
+
+    def task_cached(self, task_id: str) -> int:
+        state = self._states.get(task_id)
+        return state.cached if state is not None else 0
+
+    def task_record_hashes(
+        self, task_id: str, prompt_ids: List[int], start_logical_page: int = 0
+    ):
+        state = self._states.get(task_id)
+        if state is not None:
+            self._strategy.record_hashes(state, prompt_ids, start_logical_page)
+
+    @staticmethod
+    def task_cacheable_ids(task_id: str, prompt_ids: List[int], output_ids: List[int]):
+        return list(prompt_ids) + list(output_ids[:-1])
+
+    # -- bind (assemble KVCache for the model forward) --
+
+    def bind(
+        self,
+        task_ids: List[str],
+        workspace: InferenceWorkspace,
+        device: Optional[torch.device] = None,
+        start_pos: Optional[int] = None,
+    ) -> KVCache:
+        """Build ``KVCache`` for an ordered list of task IDs."""
+        states = [self._states[tid] for tid in task_ids]
+        req_indices = [s.req_idx for s in states]
+        seq_lens = [s.length for s in states]
+        sig = tuple(req_indices)
+
+        prev = self._bind_state
+        incremental = (
+            start_pos is None
+            and prev is not None
+            and _is_steady_increment(prev.sig, prev.seq_lens, sig, seq_lens)
+        )
+        self._bind_state = _BindState(sig, list(seq_lens))
+        self._bind_was_steady = incremental
+
+        return self._pool.bind_tasks(
+            req_indices,
+            seq_lens,
+            workspace,
+            device=device,
+            start_pos=start_pos,
+            incremental=incremental,
+        )
+
+    @property
+    def bind_was_steady(self) -> bool:
+        return self._bind_was_steady
+
+    # -- internals --
+
+    def _rollback(self, state: TaskCacheState, task_id: str):
+        self._strategy.free(state)
+        self._req_pool.free([state.req_idx])
+        self._states.pop(task_id, None)

astrai/inference/cache/strategy.py

@@ -0,0 +1,318 @@
+"""KV cache allocation layer.
+
+Encapsulates the physical slot allocation policy, isolated from GPU buffers
+and task lifecycle management.
+
+- ``TaskCacheState``: data contract between strategy and manager (per-task slot state)
+- ``Allocator``:       bitmask-based page allocator with LRU eviction
+- ``RadixCache``:      page-granular prefix index (exact token match)
+- ``AllocationStrategy``: ABC for physical slot allocation
+- ``ContiguousStrategy``: statically partitioned, no dynamic allocation
+- ``PagedStrategy``:    dynamic paged allocation from a shared pool
+"""
+
+import threading
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from typing import Callable, Dict, List, Optional, OrderedDict
+
+from astrai.inference.cache.buffer import ReqToTokenPool
+
+# ---- data contract: per-task slot state ----
+
+
+@dataclass
+class TaskCacheState:
+    """Per-task cache allocation state.
+
+    Co-locates all task-owned cache metadata so the alloc/free/extend
+    lifecycle is atomic.  Owned by ``TaskCacheManager``, consumed by
+    every ``AllocationStrategy`` method.
+    """
+
+    req_idx: int
+    length: int = 0
+    cached: int = 0
+    pages: List[int] = field(default_factory=list)
+
+
+# ---- allocation primitives ----
+
+
+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 RadixNode:
+    """A page-aligned edge in the CPU-side prefix radix trie."""
+
+    __slots__ = ("parent", "children", "page_idx", "tokens", "lock_ref")
+
+    def __init__(self, parent=None, tokens=(), page_idx=None):
+        self.parent = parent
+        self.children: Dict[tuple, "RadixNode"] = {}
+        self.page_idx = page_idx
+        self.tokens = tuple(tokens)
+        self.lock_ref = 0
+
+
+class RadixCache:
+    """Page-granular radix prefix index with exact token matching."""
+
+    def __init__(self, page_size: int):
+        self._page_size = page_size
+        self._root = RadixNode()
+        self._page_to_node: Dict[int, RadixNode] = {}
+        self._lock = threading.Lock()
+
+    def evict(self, idx: int):
+        with self._lock:
+            node = self._page_to_node.pop(idx, None)
+            if node is None:
+                return
+            node.page_idx = None
+            parent = node.parent
+            if parent is not None:
+                parent.children.pop(node.tokens, None)
+
+    def has_page(self, idx: int) -> bool:
+        with self._lock:
+            return idx in self._page_to_node
+
+    def lookup(self, token_ids: List[int]) -> List[int]:
+        with self._lock:
+            full_pages = len(token_ids) // self._page_size
+            hits: List[int] = []
+            node = self._root
+            for i in range(full_pages):
+                start = i * self._page_size
+                page_tokens = tuple(token_ids[start : start + self._page_size])
+                child = node.children.get(page_tokens)
+                if child is None or child.page_idx is None:
+                    break
+                hits.append(child.page_idx)
+                node = child
+            return hits
+
+    def record(self, page_idx: int, token_ids: List[int], logical_page_idx: int):
+        with self._lock:
+            full_pages = len(token_ids) // self._page_size
+            if logical_page_idx >= full_pages:
+                return
+            old = self._page_to_node.pop(page_idx, None)
+            if old is not None and old.parent is not None:
+                old.parent.children.pop(old.tokens, None)
+
+            node = self._root
+            for i in range(logical_page_idx + 1):
+                start = i * self._page_size
+                page_tokens = tuple(token_ids[start : start + self._page_size])
+                child = node.children.get(page_tokens)
+                if child is None:
+                    child = RadixNode(node, page_tokens)
+                    node.children[page_tokens] = child
+                node = child
+            if node.page_idx is not None and node.page_idx != page_idx:
+                replaced = node.page_idx
+                self._page_to_node.pop(replaced, None)
+            node.page_idx = page_idx
+            self._page_to_node[page_idx] = node
+
+    def release(self, pages: List[int]) -> None:
+        with self._lock:
+            for page_idx in pages:
+                node = self._page_to_node.get(page_idx)
+                if node is not None and node.lock_ref:
+                    node.lock_ref -= 1
+
+
+class AllocationStrategy(ABC):
+    """Physical slot allocation policy.
+
+    Subclasses implement the actual allocation semantics.  This ABC declares
+    the contract; there are no default implementations.
+    """
+
+    @abstractmethod
+    def alloc(self, state: TaskCacheState, prompt_ids: List[int]) -> bool: ...
+
+    @abstractmethod
+    def free(self, state: TaskCacheState) -> None: ...
+
+    @abstractmethod
+    def extend(self, state: TaskCacheState, pos: int) -> bool: ...
+
+    @abstractmethod
+    def write_indices(self, state: TaskCacheState, prompt_ids: List[int]) -> None: ...
+
+    @abstractmethod
+    def record_hashes(
+        self,
+        state: TaskCacheState,
+        prompt_ids: List[int],
+        start: int,
+    ) -> None: ...
+
+
+class ContiguousStrategy(AllocationStrategy):
+    """Static contiguous allocation: slots are pre-assigned at pool init.
+
+    No dynamic allocation or prefix caching.  All operations are no-ops
+    because ``ReqToTokenPool`` is pre-filled with contiguous ranges.
+    """
+
+    def alloc(self, state: TaskCacheState, prompt_ids: List[int]) -> bool:
+        return True
+
+    def free(self, state: TaskCacheState) -> None:
+        pass
+
+    def extend(self, state: TaskCacheState, pos: int) -> bool:
+        return True
+
+    def write_indices(self, state: TaskCacheState, prompt_ids: List[int]) -> None:
+        pass
+
+    def record_hashes(
+        self,
+        state: TaskCacheState,
+        prompt_ids: List[int],
+        start: int,
+    ) -> None:
+        pass
+
+
+class PagedStrategy(AllocationStrategy):
+    """Dynamic paged allocation from a shared bitmask pool.
+
+    ``page_size`` is a parameter, not a separate strategy: at ``page_size=1``
+    each allocated page *is* one token slot (``page * 1 + 0``), and prefix
+    caching is simply disabled (``prefix=None``).  The unified page formula
+    ``pages[page_idx] * page_size + offset`` holds for both.
+    """
+
+    def __init__(
+        self,
+        alloc: Allocator,
+        prefix: Optional[RadixCache],
+        page_size: int,
+        req_pool: ReqToTokenPool,
+        device,
+    ):
+        self._alloc = alloc
+        self._prefix = prefix
+        self._page_size = page_size
+        self._req_pool = req_pool
+        self._device = device
+
+    def alloc(self, state: TaskCacheState, prompt_ids: List[int]) -> bool:
+        if self._prefix is not None:
+            hits = self._prefix.lookup(prompt_ids)
+            state.cached = len(hits) * self._page_size
+            for p in hits:
+                self._alloc.inc_ref(p)
+            state.pages = list(hits)
+
+        remaining = len(prompt_ids) - state.cached
+        if remaining <= 0:
+            return True
+        n_new = (remaining + self._page_size - 1) // self._page_size
+        for _ in range(n_new):
+            p = self._alloc.alloc()
+            if p < 0:
+                return False
+            state.pages.append(p)
+        return True
+
+    def free(self, state: TaskCacheState) -> None:
+        if self._prefix is not None:
+            for p in state.pages:
+                keep = self._prefix.has_page(p)
+                self._alloc.free(p, keep_cached=keep)
+                if not keep:
+                    self._prefix.evict(p)
+        else:
+            for p in state.pages:
+                self._alloc.free(p)
+
+    def extend(self, state: TaskCacheState, pos: int) -> bool:
+        page_idx = pos // self._page_size
+        if page_idx >= len(state.pages):
+            p = self._alloc.alloc()
+            if p < 0:
+                return False
+            state.pages.append(p)
+        offset = pos % self._page_size
+        self._req_pool.req_to_token[state.req_idx, pos] = (
+            state.pages[page_idx] * self._page_size + offset
+        )
+        return True
+
+    def write_indices(self, state: TaskCacheState, prompt_ids: List[int]) -> None:
+        total = len(prompt_ids)
+        for pos in range(total):
+            page_idx = pos // self._page_size
+            offset = pos % self._page_size
+            if page_idx < len(state.pages):
+                self._req_pool.req_to_token[state.req_idx, pos] = (
+                    state.pages[page_idx] * self._page_size + offset
+                )
+
+    def record_hashes(
+        self,
+        state: TaskCacheState,
+        prompt_ids: List[int],
+        start: int,
+    ) -> None:
+        if self._prefix is None:
+            return
+        full = len(prompt_ids) // self._page_size
+        for i in range(start, min(full, len(state.pages))):
+            self._prefix.record(state.pages[i], prompt_ids, i)

astrai/inference/engine.py

@@ -0,0 +1,242 @@
+"""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.extension import ATTN_BACKEND, AttentionBackend, get_backend
+from astrai.inference.cache import PagePool
+from astrai.inference.scheduler import InferenceScheduler
+from astrai.inference.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 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,
+        cache: Optional[PagePool] = None,
+        enable_cuda_graph: bool = True,
+        backend: Optional[Union[str, ATTN_BACKEND, AttentionBackend, type]] = 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,
+            cache=cache,
+            enable_cuda_graph=enable_cuda_graph,
+            backend=backend,
+        )
+
+        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 max_tokens is not None and max_tokens <= 0:
+            if stream:
+                return iter(())
+            results = [""] * len(prompts)
+            return results if is_batch else results[0]
+
+        return self._generate(
+            prompts,
+            is_batch,
+            stream,
+            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(
+            [prompt],
+            False,
+            True,
+            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, next, sync_gen, None)
+                if token is None:
+                    break
+                yield token
+
+        return _agen()
+
+    def _generate(
+        self,
+        prompts: List[str],
+        is_batch: bool,
+        stream: bool,
+        max_tokens: Optional[int],
+        temperature: float,
+        top_p: float,
+        top_k: int,
+        frequency_penalty: float,
+        rep_window: int,
+    ) -> Union[Generator, str, List[str]]:
+        n = len(prompts)
+        request_backend = get_backend(use_default=False)
+        result = GenerateResult(count=n)
+        task_ids = [
+            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,
+                backend=request_backend,
+                stream_callback=lambda token, idx=i: result.append(token, idx),
+            )
+            for i, p in enumerate(prompts)
+        ]
+
+        if not stream:
+            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]
+
+        remaining = n
+        finished = [False] * n
+
+        def gen():
+            nonlocal remaining
+            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)
+
+        return gen()
+
+    def get_stats(self) -> Dict[str, Any]:
+        return self.scheduler.get_stats()
+
+    @property
+    def backend_name(self) -> str:
+        return self.scheduler.backend_name
+
+    @property
+    def cuda_graph_enabled(self) -> bool:
+        return self.scheduler.cuda_graph_enabled
+
+    def shutdown(self):
+        self.scheduler.stop()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        gc.collect()

astrai/inference/metrics.py

@@ -0,0 +1,201 @@
+"""Unified per-task perf/stats: timing records, context-manager scopes, aggregate reporting."""
+
+import time
+from collections import deque
+from contextlib import contextmanager
+from dataclasses import dataclass
+from typing import Any, Deque, Dict, Generator, List, Literal, Optional
+
+
+@dataclass
+class TaskTiming:
+    """Timestamp snapshots and computed metrics for one generation task.
+
+    Created by :class:`MetricsCollector` at task-registration time;
+    updated via ``record`` / ``mark_finished``.
+    """
+
+    task_id: str
+    arrival_time: float
+    prefill_start_time: Optional[float] = None
+    first_token_time: Optional[float] = None
+    finish_time: Optional[float] = None
+    input_tokens: int = 0
+    output_tokens: int = 0
+    _decode_steps: int = 0
+    _decode_total_s: float = 0.0
+
+    # derived metrics
+
+    @property
+    def queue_wait_ms(self) -> Optional[float]:
+        if self.prefill_start_time is not None:
+            return (self.prefill_start_time - self.arrival_time) * 1000
+        return None
+
+    @property
+    def ttft_ms(self) -> Optional[float]:
+        if self.first_token_time is not None:
+            return (self.first_token_time - self.arrival_time) * 1000
+        return None
+
+    @property
+    def prefill_tps(self) -> Optional[float]:
+        if self.prefill_start_time is not None and self.first_token_time is not None:
+            d = self.first_token_time - self.prefill_start_time
+            if d > 0 and self.input_tokens > 0:
+                return self.input_tokens / d
+        return None
+
+    @property
+    def decode_tps(self) -> Optional[float]:
+        if self.first_token_time is not None and self.finish_time is not None:
+            d = self.finish_time - self.first_token_time
+            dt = self.output_tokens - 1
+            if dt > 0 and d > 0:
+                return dt / d
+        return None
+
+    @property
+    def decode_avg_ms(self) -> Optional[float]:
+        if self._decode_steps > 0 and self._decode_total_s > 0:
+            return (self._decode_total_s / self._decode_steps) * 1000
+        return None
+
+    @property
+    def e2e_latency_ms(self) -> Optional[float]:
+        if self.finish_time is not None:
+            return (self.finish_time - self.arrival_time) * 1000
+        return None
+
+    @property
+    def total_tps(self) -> Optional[float]:
+        if self.finish_time is not None:
+            total = self.input_tokens + self.output_tokens
+            d = self.finish_time - self.arrival_time
+            if total > 0 and d > 0:
+                return total / d
+        return None
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "task_id": self.task_id,
+            "input_tokens": self.input_tokens,
+            "output_tokens": self.output_tokens,
+            "queue_wait_ms": (
+                round(self.queue_wait_ms, 2) if self.queue_wait_ms is not None else None
+            ),
+            "ttft_ms": (round(self.ttft_ms, 2) if self.ttft_ms is not None else None),
+            "prefill_tps": (
+                round(self.prefill_tps, 2) if self.prefill_tps is not None else None
+            ),
+            "decode_tps": (
+                round(self.decode_tps, 2) if self.decode_tps is not None else None
+            ),
+            "decode_avg_ms": (
+                round(self.decode_avg_ms, 2) if self.decode_avg_ms is not None else None
+            ),
+            "total_tps": (
+                round(self.total_tps, 2) if self.total_tps is not None else None
+            ),
+            "e2e_latency_ms": (
+                round(self.e2e_latency_ms, 2)
+                if self.e2e_latency_ms is not None
+                else None
+            ),
+        }
+
+
+class MetricsCollector:
+    """Single-owner perf/stats hub for all generation tasks.
+
+    Usage::
+
+        metrics = MetricsCollector()
+        metrics.register(task_id, arrival_time)
+
+        with metrics.record(task_ids, "prefill"):
+            run_prefill(...)
+
+        metrics.mark_finished(task_id, input_tokens, output_tokens)
+
+        stats = metrics.get_stats()
+    """
+
+    def __init__(self, max_recent: int = 128):
+        self._timings: Dict[str, TaskTiming] = {}
+        self._completed: Deque[TaskTiming] = deque(maxlen=max_recent)
+
+        self._ttft_ms_sum = 0.0
+        self._ttft_ms_count = 0
+        self._decode_tps_sum = 0.0
+        self._decode_tps_count = 0
+        self._e2e_ms_sum = 0.0
+        self._e2e_ms_count = 0
+
+    def register(self, task_id: str):
+        """Create a timing record for a newly-created task."""
+        self._timings[task_id] = TaskTiming(task_id=task_id, arrival_time=time.time())
+
+    def mark_finished(self, task_id: str, input_tokens: int, output_tokens: int):
+        """Close timing for a finished/aborted task and move it to completed."""
+        timing = self._timings.pop(task_id, None)
+        if timing is None:
+            return
+        timing.finish_time = time.time()
+        timing.input_tokens = input_tokens
+        timing.output_tokens = output_tokens
+        self._completed.append(timing)
+        self._accumulate(timing)
+
+    # timing scopes
+
+    @contextmanager
+    def record(
+        self, task_ids: List[str], phase: Literal["prefill", "decode"]
+    ) -> Generator[None, None, None]:
+        tic = time.time()
+        yield
+        toc = time.time()
+        dt = toc - tic
+        for tid in task_ids:
+            t = self._timings.get(tid)
+            if t is None:
+                continue
+            if phase == "prefill":
+                t.prefill_start_time = tic
+                t.first_token_time = toc
+            elif phase == "decode":
+                t._decode_steps += 1
+                t._decode_total_s += dt
+
+    # aggregate stats
+
+    def get_stats(self) -> Dict[str, Any]:
+        stats: Dict[str, Any] = {}
+        if self._ttft_ms_count > 0:
+            stats["avg_ttft_ms"] = round(self._ttft_ms_sum / self._ttft_ms_count, 2)
+        if self._decode_tps_count > 0:
+            stats["avg_decode_tps"] = round(
+                self._decode_tps_sum / self._decode_tps_count, 2
+            )
+        if self._e2e_ms_count > 0:
+            stats["avg_e2e_latency_ms"] = round(
+                self._e2e_ms_sum / self._e2e_ms_count, 2
+            )
+        if self._completed:
+            stats["recent_tasks"] = [t.to_dict() for t in self._completed]
+        return stats
+
+    # internal
+
+    def _accumulate(self, t: TaskTiming):
+        if t.ttft_ms is not None:
+            self._ttft_ms_sum += t.ttft_ms
+            self._ttft_ms_count += 1
+        if t.decode_tps is not None:
+            self._decode_tps_sum += t.decode_tps
+            self._decode_tps_count += 1
+        if t.e2e_latency_ms is not None:
+            self._e2e_ms_sum += t.e2e_latency_ms
+            self._e2e_ms_count += 1

astrai/inference/network/__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.network.app import (
+    AnthropicMessage,
+    ChatCompletionRequest,
+    ChatMessage,
+    FunctionDef,
+    MessagesRequest,
+    ToolDef,
+    get_app,
+    run_server,
+)
+from astrai.inference.network.protocol import GenContext, ProtocolHandler, StopChecker
+from astrai.inference.network.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/network/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.engine import InferenceEngine
+from astrai.inference.network.protocol import (
+    GenContext,
+    ResponseBuilder,
+    StopInfo,
+    sse_event,
+)
+
+
+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/network/app.py

@@ -0,0 +1,206 @@
+"""
+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.engine import InferenceEngine
+from astrai.inference.network.anthropic import AnthropicResponseBuilder
+from astrai.inference.network.openai import OpenAIResponseBuilder
+from astrai.inference.network.protocol import ProtocolHandler
+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,
+    max_seq_len: Optional[int] = None,
+) -> 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,
+        max_seq_len=max_seq_len,
+    )
+    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,
+    max_seq_len: Optional[int] = None,
+):
+    app = get_app()
+    app.state.server_config = {
+        "device": device,
+        "dtype": dtype,
+        "param_path": param_path,
+        "max_batch_size": max_batch_size,
+        "max_seq_len": max_seq_len,
+    }
+    uvicorn.run(
+        app,
+        host=host,
+        port=port,
+        reload=reload,
+    )

astrai/inference/network/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.engine import InferenceEngine
+from astrai.inference.network.protocol import (
+    GenContext,
+    ResponseBuilder,
+    StopInfo,
+    sse_event,
+)
+from astrai.inference.network.tool_parser import BaseToolParser, ToolParserFactory
+
+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/network/protocol.py

@@ -0,0 +1,197 @@
+"""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)
+        body = ""
+        matched = None
+
+        async for token in agen:
+            body += token
+
+            matched = checker.check(body)
+            if matched:
+                break
+
+            ctx.completion_tokens += 1
+
+        stop = StopInfo(matched=matched, body=body)
+        return self.builder.format_response(ctx, body, stop)

astrai/inference/network/tool_parser.py

@@ -0,0 +1,339 @@
+"""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.
+
+    Args:
+        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.
+
+        Args:
+            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/runtime/__init__.py

@@ -0,0 +1,25 @@
+"""Execution primitives: forward passes, CUDA graphs, and sampling."""
+
+from astrai.inference.runtime.executor import Executor
+from astrai.inference.runtime.graph import CudaGraphContext
+from astrai.inference.runtime.sample import (
+    BaseSamplingStrategy,
+    FrequencyPenaltyStrategy,
+    SamplingPipeline,
+    TemperatureStrategy,
+    TopKStrategy,
+    TopPStrategy,
+    sample,
+)
+
+__all__ = [
+    "Executor",
+    "CudaGraphContext",
+    "BaseSamplingStrategy",
+    "FrequencyPenaltyStrategy",
+    "SamplingPipeline",
+    "TemperatureStrategy",
+    "TopKStrategy",
+    "TopPStrategy",
+    "sample",
+]

astrai/inference/runtime/executor.py

@@ -0,0 +1,421 @@
+import logging
+import time
+from contextlib import contextmanager
+from dataclasses import dataclass
+from typing import List, Optional
+
+import torch
+from torch import Tensor
+
+from astrai.extension.backend.attention import (
+    CudaBackend,
+    get_backend,
+)
+from astrai.inference.cache import PagePool, TaskCacheManager
+from astrai.inference.runtime.graph import CudaGraphContext
+from astrai.inference.runtime.sample import sample
+from astrai.inference.task import Task
+from astrai.inference.workspace import InferenceWorkspace
+from astrai.model.automodel import AutoModel
+
+logger = logging.getLogger(__name__)
+
+
+@contextmanager
+def timed(label: str, log: Optional[logging.Logger] = None):
+    """GPU-precise timer via CUDA events; falls back to perf_counter on CPU."""
+    log = log or logger
+    if not log.isEnabledFor(logging.DEBUG):
+        yield
+        return
+    use_cuda = torch.cuda.is_available()
+    if use_cuda:
+        start = torch.cuda.Event(enable_timing=True)
+        end = torch.cuda.Event(enable_timing=True)
+        start.record()
+    else:
+        tic = time.perf_counter()
+    yield
+    if use_cuda:
+        end.record()
+        torch.cuda.synchronize()
+        elapsed_ms = start.elapsed_time(end)
+    else:
+        elapsed_ms = (time.perf_counter() - tic) * 1000
+    log.debug("%s %.2fms", label, elapsed_ms)
+
+
+@dataclass
+class SamplingBatchInfo:
+    """Per-batch sampling parameters, cached across decode steps.
+
+    Sampling params are constant for a given ordered task set, so they are
+    built once (pinned-memory async H2D) and reused until the task set
+    changes.  ``top_ks`` is int32 to match the native consumers.
+    """
+
+    temperatures: Tensor  # float32 [B]
+    top_ks: Tensor  # int32  [B]
+    top_ps: Tensor  # float32 [B]
+    freq_penalties: Tensor  # float32 [B]
+    has_freq: bool  # any frequency_penalty != 0 (avoids per-step GPU .any())
+
+
+@dataclass
+class DecodeSteadyState:
+    """Cached decode metadata for the steady-state case.
+
+    When the same ordered task set decodes one token per step, sampling
+    params and task signature are reused; only positions advance by 1.
+    """
+
+    task_sig: tuple
+    positions: list[int]
+    sampling_info: SamplingBatchInfo
+
+
+def _build_sampling_batch_info(tasks: List[Task], device) -> SamplingBatchInfo:
+    pin = str(device).startswith("cuda")
+    freq_penalties = torch.tensor(
+        [t.frequency_penalty for t in tasks], dtype=torch.float32, pin_memory=pin
+    ).to(device, non_blocking=True)
+    return SamplingBatchInfo(
+        temperatures=torch.tensor(
+            [t.temperature for t in tasks], dtype=torch.float32, pin_memory=pin
+        ).to(device, non_blocking=True),
+        top_ks=torch.tensor(
+            [t.top_k for t in tasks], dtype=torch.int32, pin_memory=pin
+        ).to(device, non_blocking=True),
+        top_ps=torch.tensor(
+            [t.top_p for t in tasks], dtype=torch.float32, pin_memory=pin
+        ).to(device, non_blocking=True),
+        freq_penalties=freq_penalties,
+        has_freq=bool((freq_penalties != 0).any()),
+    )
+
+
+def _warmup_cuda_graphs(
+    model: AutoModel,
+    pool: PagePool,
+    task_cache: TaskCacheManager,
+    ws: InferenceWorkspace,
+    gctx: CudaGraphContext,
+    max_batch_size: int,
+    prompt_len: int = 1,
+    device: Optional[str] = None,
+):
+    dev = device or next(model.parameters()).device
+
+    # Prefill warmup: cuBLAS auto-tunes for the actual prompt-length tensor
+    # shapes on first call (F.linear is the dominant cost).  This also warms
+    # up the CUDA context (driver init) and compiles the graph-capture trace
+    # that follows.  Custom .so kernels do NOT need this — they are pre-built.
+    warmup_len = 64
+    tid = "_warmup_prefill"
+    if task_cache.task_alloc(tid, list(range(warmup_len))):
+        with (
+            torch.inference_mode(),
+            timed("warmup prefill", logger),
+        ):
+            kv = task_cache.bind([tid], ws, start_pos=0)
+            ids_in = torch.arange(warmup_len, device=dev)
+            pos_in = ids_in
+            model(
+                ids_in,
+                kv_cache=kv,
+                position_ids=pos_in,
+                fwd="prefill",
+            )
+        task_cache.task_free(tid)
+
+    batch_sizes = [1]
+    n = 2
+    while n <= max_batch_size:
+        batch_sizes.append(n)
+        n *= 2
+    if max_batch_size not in batch_sizes:
+        batch_sizes.append(max_batch_size)
+
+    for b in batch_sizes:
+        task_ids = [f"_warmup_decode_{b}_{i}" for i in range(b)]
+        prompt_tokens = [list(range(prompt_len)) for _ in range(b)]
+        alloc_ok = True
+        for tid, pt in zip(task_ids, prompt_tokens):
+            if not task_cache.task_alloc(tid, pt):
+                alloc_ok = False
+                break
+        if not alloc_ok:
+            for tid in task_ids:
+                task_cache.task_free(tid)
+            continue
+
+        with (
+            torch.inference_mode(),
+            timed(f"warmup decode b={b}", logger),
+        ):
+            for step in range(2):
+                seq_pos = step
+                ws.position_ids[:b] = seq_pos
+                for tid in task_ids:
+                    task_cache.task_extend(tid, seq_pos)
+                kv = task_cache.bind(task_ids, ws)
+                ids_buf = ws.fill_input_ids([step] * b)
+                gctx.forward(
+                    model,
+                    key=(b,),
+                    input_ids=ids_buf,
+                    kv_cache=kv,
+                    position_ids=ws.position_ids[:b],
+                    fwd="decode",
+                )
+
+        for tid in task_ids:
+            task_cache.task_free(tid)
+        torch.cuda.synchronize()
+
+
+class Executor:
+    """Model forward passes for prefill and decode phases."""
+
+    def __init__(
+        self,
+        model: AutoModel,
+        kv_cache: PagePool,
+        task_cache: TaskCacheManager,
+        device: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
+        enable_cuda_graph: bool = True,
+    ):
+        self.model = model
+        self.kv_cache = kv_cache
+        self.task_cache = task_cache
+        self.device = device or next(model.parameters()).device
+        self.dtype = dtype or next(model.parameters()).dtype
+
+        # Per-step decode cache for the steady-state case (same ordered
+        # task set decodes one token per step).  Sampling params stay
+        # constant; only positions advance.
+        self._decode_cache: Optional[DecodeSteadyState] = None
+
+        # Pre-allocated fixed-shape buffers for the decode hot path
+        # (input_ids, decode mask, KV bind metadata).  Eagerly sized at init
+        # so the workspace is CUDA-graph-capture friendly — no allocation
+        # during capture.
+        config = model.config
+        max_q_heads = config.num_attention_heads
+        head_dim = config.hidden_size // config.num_attention_heads
+        backend = get_backend()
+        self._graph_supported = backend.supports_graph() and (
+            CudaBackend.available() and head_dim in CudaBackend.HEAD_DIMS
+        )
+        self._workspace = InferenceWorkspace(
+            max_batch_size=kv_cache.max_batch_size,
+            max_seq_len=kv_cache.max_seq_len,
+            max_q_heads=max_q_heads,
+            head_dim=head_dim,
+            device=self.device,
+            dtype=self.dtype,
+        )
+
+        # CUDA-graph capture: one graph per (batch_size,) key.
+        # Enabled at init-time via _warmup_cuda_graphs for CudaBackend
+        # on supported head_dims; left disabled otherwise.
+        self._graph_ctx = CudaGraphContext()
+        if enable_cuda_graph:
+            self._try_enable_cuda_graph()
+
+    def _try_enable_cuda_graph(self):
+        if not self._graph_supported:
+            return
+
+        self._graph_ctx.set_enabled(True)
+        _warmup_cuda_graphs(
+            self.model,
+            self.kv_cache,
+            self.task_cache,
+            self._workspace,
+            self._graph_ctx,
+            max_batch_size=self.kv_cache.max_batch_size,
+            device=self.device,
+        )
+
+    @property
+    def cuda_graph_enabled(self) -> bool:
+        return self._graph_ctx.enabled and self._graph_supported
+
+    def _sample_logits(
+        self,
+        logits: Tensor,
+        tasks: List[Task],
+        return_logprobs: bool = False,
+        info: Optional[SamplingBatchInfo] = None,
+    ):
+        info = info or _build_sampling_batch_info(tasks, self.device)
+        if info.has_freq:
+            history_lists = [
+                t.prompt_ids[-t.rep_window :] + t.output_ids for t in tasks
+            ]
+            history_lens = [len(ids) for ids in history_lists]
+            max_len = max(history_lens, default=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, ids in enumerate(history_lists):
+                length = len(ids)
+                padded_ids[i, :length] = torch.as_tensor(
+                    ids, dtype=torch.long, device=self.device
+                )
+                padded_mask[i, :length] = True
+        else:
+            padded_ids = None
+            padded_mask = None
+
+        result = sample(
+            logits,
+            temperature=info.temperatures,
+            top_k=info.top_ks,
+            top_p=info.top_ps,
+            frequency_penalty=info.freq_penalties,
+            input_ids=padded_ids,
+            input_mask=padded_mask,
+            return_logprobs=return_logprobs,
+        )
+        if not return_logprobs:
+            return result.tolist()
+
+        tokens, logprobs = result
+        tokens_list = tokens.tolist()
+        logprobs_list = logprobs.tolist()
+        for task, logprob in zip(tasks, logprobs_list):
+            task.output_logprobs.append(float(logprob))
+        return list(zip(tokens_list, logprobs_list))
+
+    def execute_prefill(
+        self,
+        tasks: List[Task],
+        prompt_len: int,
+        start_pos: int = 0,
+        return_logprobs: bool = False,
+    ):
+        if start_pos >= prompt_len:
+            return []
+
+        tasks = sorted(tasks, key=lambda t: t.task_id)
+        batch_sz = len(tasks)
+
+        input_ids = torch.tensor(
+            [token for t in tasks for token in t.prompt_ids[start_pos:prompt_len]],
+            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
+        ).repeat(batch_sz)
+
+        with (
+            torch.inference_mode(),
+            timed(f"execute_prefill b={batch_sz} prompt_len={prompt_len}", logger),
+        ):
+            outputs = self.model(
+                input_ids,
+                position_ids=position_ids,
+                kv_cache=self.task_cache.bind(
+                    task_ids,
+                    self._workspace,
+                    start_pos=start_pos,
+                ),
+                fwd="prefill",
+            )
+            q_len = prompt_len - start_pos
+            logits = outputs["logits"][
+                torch.arange(1, batch_sz + 1, device=self.device) * q_len - 1
+            ]
+
+        return tasks, self._sample_logits(logits, tasks, return_logprobs)
+
+    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 []
+
+        b = len(tasks)
+        ws = self._workspace
+
+        # ---- pre-replay: update input buffers in-place ----
+
+        input_ids = ws.fill_input_ids(
+            [t.output_ids[-1] if t.output_ids else t.prompt_ids[-1] for t in tasks]
+        )
+
+        task_ids = [t.task_id for t in tasks]
+        cur_positions = [t.next_pos for t in tasks]
+
+        kv_cache = self.task_cache.bind(task_ids, ws)
+
+        task_sig = tuple(task_ids)
+        reuse_decode_state = (
+            self.task_cache.bind_was_steady
+            and self._decode_cache is not None
+            and self._decode_cache.task_sig == task_sig
+        )
+        if reuse_decode_state:
+            info = self._decode_cache.sampling_info
+            ws.position_ids[:b] += 1
+        else:
+            info = _build_sampling_batch_info(tasks, self.device)
+            ws.position_ids[:b].copy_(
+                torch.tensor(cur_positions, dtype=torch.long, device=self.device)
+            )
+        self._decode_cache = DecodeSteadyState(task_sig, cur_positions, info)
+
+        # ---- forward (graph replay or live run + capture) ----
+
+        use_graph = (
+            self._graph_ctx.enabled
+            and self._graph_supported
+            and get_backend().supports_graph()
+        )
+        key = (b,)
+        with (
+            torch.inference_mode(),
+            timed(f"execute_decode forward b={b}", logger),
+        ):
+            if use_graph:
+                outputs = self._graph_ctx.forward(
+                    self.model,
+                    key=key,
+                    input_ids=input_ids,
+                    kv_cache=kv_cache,
+                    position_ids=ws.position_ids[:b],
+                    fwd="decode",
+                )
+            else:
+                outputs = self.model(
+                    input_ids,
+                    kv_cache=kv_cache,
+                    position_ids=ws.position_ids[:b],
+                    fwd="decode",
+                )
+            logits = outputs["logits"]
+
+        return self._sample_logits(logits, tasks, return_logprobs, info=info)

astrai/inference/runtime/graph.py

@@ -0,0 +1,103 @@
+"""CUDA-graph capture for the decode model-forward step.
+
+Mirrors SGLang's cuda-graph manager: one graph per batch size.  The graph
+pair.  The graph captures ``model.forward()`` with workspace-backed inputs
+(all at fixed addresses).  Before each replay the caller updates the input
+buffer content in-place so the graph sees fresh data at the same tensor
+addresses.
+
+Only the model forward is captured — sampling runs outside the graph
+(via ``torch.multinomial`` which consumes a mutable RNG state).
+"""
+
+import torch
+from torch import Tensor
+
+
+class CudaGraphContext:
+    """CUDA-graph capture/replay for decode steps.
+
+    Parameters:
+        enabled: When ``False``, ``forward()`` always runs the live model
+            forward without capture/replay (graphs are cleared).  Toggle at
+            runtime via the ``set_enabled()`` method.
+
+    Usage::
+
+        gctx = CudaGraphContext()
+        with torch.inference_mode():
+            outputs = gctx.forward(
+                model,
+                key=(batch_size,),
+                input_ids=workspace.input_ids[:b].unsqueeze(1),
+                input_mask=input_mask,
+                kv_cache=kv_cache,
+                position_ids=workspace.position_ids[:b].unsqueeze(1),
+            )
+
+    The first call at a given key runs *without* capture (warmup).  The
+    second call captures the graph.  Subsequent calls replay the captured
+    graph.  A ``torch.cuda.synchronize()`` before capture drains in-flight
+    work so the graph trace is clean.
+    """
+
+    def __init__(self, enabled: bool = False):
+        self._enabled = enabled
+        self._graphs: dict[tuple, torch.cuda.CUDAGraph] = {}
+        self._outputs: dict[tuple, dict[str, Tensor]] = {}
+        self._warmed: set[tuple] = set()
+
+    @property
+    def enabled(self) -> bool:
+        return self._enabled
+
+    def set_enabled(self, flag: bool):
+        """Enable or disable CUDA-graph capture at runtime.
+
+        Disabling clears all captured graphs (frees GPU memory) and warmup
+        state.  Re-enabling after disable starts fresh — graphs are
+        re-captured on the next warmup cycle.
+        """
+        if flag == self._enabled:
+            return
+        self._enabled = flag
+        if not flag:
+            self._graphs.clear()
+            self._outputs.clear()
+            self._warmed.clear()
+
+    def forward(self, model, *, key, **kwargs) -> dict[str, Tensor]:
+        """Run ``model(**kwargs)`` via graph replay or live forward.
+
+        Args:
+            model: callable, e.g. ``self.model.forward``.
+            key: ``(batch_size,)`` — the dispatch key (one graph per batch size).
+            **kwargs: arguments forwarded to ``model``.  All tensor arguments
+                must reside at stable addresses (workspace buffers).
+
+        Returns:
+            The dict produced by ``model(**kwargs)``, e.g.
+            ``{"logits": ..., "h0": ...}``.
+        """
+        if not self._enabled:
+            self._outputs[key] = model(**kwargs)
+            return self._outputs[key]
+
+        if key in self._graphs:
+            self._graphs[key].replay()
+        elif key in self._warmed:
+            cap_output = model(**kwargs)
+            torch.cuda.synchronize()
+            graph = torch.cuda.CUDAGraph()
+            with torch.cuda.graph(graph):
+                self._outputs[key] = model(**kwargs)
+            self._graphs[key] = graph
+            self._warmed.discard(key)
+            return cap_output
+        else:
+            self._warmed.add(key)
+            self._outputs[key] = model(**kwargs)
+        return self._outputs[key]
+
+    def has_graph(self, key: tuple) -> bool:
+        return key in self._graphs

astrai/inference/runtime/sample.py

@@ -0,0 +1,386 @@
+"""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 bool((temperature == 0).all())
+        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)
+        tokens = torch.multinomial(
+            torch.softmax(transformed, dim=-1), num_samples=1
+        ).squeeze(-1)
+        if not return_logprobs:
+            return tokens
+        log_probs = torch.log_softmax(transformed.float(), dim=-1)
+        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.
+
+    When **frequency_penalty** is 0 (the common decode case), the entire
+    frequency penalty computation — including the O(batch * vocab) count
+    tensor allocation — is skipped.
+
+    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]``.
+    """
+    has_freq = (
+        (isinstance(frequency_penalty, Tensor) and (frequency_penalty != 0).any())
+        if isinstance(frequency_penalty, Tensor)
+        else frequency_penalty != 0
+    )
+
+    strategies: List[BaseSamplingStrategy] = [
+        TemperatureStrategy(temperature),
+        TopKStrategy(top_k),
+        TopPStrategy(top_p),
+    ]
+    if has_freq:
+        strategies.append(FrequencyPenaltyStrategy(frequency_penalty))
+
+    return SamplingPipeline(strategies).sample(
+        logits,
+        filter_value=filter_value,
+        input_ids=input_ids,
+        input_mask=input_mask,
+        return_logprobs=return_logprobs,
+    )

astrai/inference/scheduler.py

@@ -0,0 +1,400 @@
+import logging
+import threading
+import uuid
+from contextlib import nullcontext
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+
+from astrai.extension import (
+    ATTN_BACKEND,
+    AttentionBackend,
+    attn_backend,
+    get_backend,
+)
+from astrai.inference.cache import PagePool, TaskCacheManager
+from astrai.inference.metrics import MetricsCollector
+from astrai.inference.runtime.executor import Executor
+from astrai.inference.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,
+        device: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
+        cache: Optional[PagePool] = None,
+        enable_cuda_graph: bool = True,
+        backend: Optional[Union[str, ATTN_BACKEND, AttentionBackend, type]] = 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 = PagePool(
+                n_layers=config.num_hidden_layers,
+                n_kv_heads=config.num_key_value_heads,
+                head_dim=head_dim,
+                max_batch_size=max_batch_size,
+                max_seq_len=self.max_seq_len,
+                device=self.device,
+                dtype=self.dtype,
+            )
+
+        self._metrics = MetricsCollector()
+
+        self._task_cache = TaskCacheManager(self._cache)
+
+        self._task_mgr = TaskManager(
+            tokenizer=tokenizer,
+            max_batch_size=max_batch_size,
+            max_seq_len=self.max_seq_len,
+            metrics=self._metrics,
+        )
+
+        if backend is None:
+            self._backend = None
+            active_backend = get_backend()
+        else:
+            active_backend = backend
+        with attn_backend(active_backend):
+            if backend is not None:
+                self._backend = get_backend()
+            self._backend_name = type(get_backend()).__name__
+            self._executor = Executor(
+                model=model,
+                kv_cache=self._cache,
+                task_cache=self._task_cache,
+                device=self.device,
+                dtype=self.dtype,
+                enable_cuda_graph=enable_cuda_graph,
+            )
+
+        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._task_cache.task_free(task.task_id)
+
+    def get_stats(self) -> Dict[str, Any]:
+        return self._task_mgr.get_stats()
+
+    @property
+    def backend_name(self) -> str:
+        return self._backend_name
+
+    @property
+    def cuda_graph_enabled(self) -> bool:
+        return self._executor.cuda_graph_enabled
+
+    def _backend_context(self):
+        if self._backend is None:
+            return nullcontext()
+        return attn_backend(self._backend)
+
+    @staticmethod
+    def _task_backend_groups(tasks: List[Task]):
+        groups = {}
+        for task in tasks:
+            groups.setdefault(task.backend, (task.backend, []))[1].append(task)
+        return groups.values()
+
+    def _step(
+        self, tasks: List[Task], return_logprobs: bool = False
+    ) -> Tuple[List[Task], List[Task]]:
+        """Advance every active task by one token (prefill + decode).
+
+        Single shared primitive for both the continuous-batching loop and
+        the synchronous ``run_batch`` path, so the two cannot drift.
+
+        Tasks must already be allocated in the KV cache. Tasks without output
+        are prefilled first and sample their first token from the final prompt
+        position. Tasks with output extend the cache by one position and decode
+        from their latest generated token.
+
+        Args:
+            tasks: Active tasks to advance by one token.
+            return_logprobs: Forwarded to ``execute_decode``; per-token
+                logprobs are recorded on each task's ``output_logprobs``.
+
+        Returns:
+            ``(decoded, aborted)``: tasks that produced a new token (its ID
+            already appended to ``output_ids``) and tasks that hit the
+            sequence cap and were marked ``ABORTED``.
+        """
+        to_prefill = [t for t in tasks if not t.prefill_done and t.prompt_ids]
+        prefilled_ids = set()
+        produced: List[Task] = []
+        if to_prefill:
+            for t in to_prefill:
+                t.input_tokens = len(t.prompt_ids)
+
+            groups: Dict[Tuple[int, int, Optional[AttentionBackend]], List[Task]] = {}
+            for t in to_prefill:
+                start_pos = min(
+                    self._task_cache.task_cached(t.task_id), len(t.prompt_ids) - 1
+                )
+                groups.setdefault((len(t.prompt_ids), start_pos, t.backend), []).append(
+                    t
+                )
+
+            for (prompt_len, start_pos, _), group in groups.items():
+                backend = group[0].backend
+                backend_context = (
+                    attn_backend(backend) if backend is not None else nullcontext()
+                )
+                with (
+                    backend_context,
+                    self._metrics.record([t.task_id for t in group], "prefill"),
+                ):
+                    prefilled, step_out = self._executor.execute_prefill(
+                        group, prompt_len, start_pos, return_logprobs=return_logprobs
+                    )
+
+                for t, out in zip(prefilled, step_out):
+                    t.output_ids.append(out[0] if return_logprobs else out)
+                    t.output_tokens += 1
+                    t.mark_prefill_done()
+                    prefilled_ids.add(t.task_id)
+                    produced.append(t)
+
+                start_logical_page = start_pos // self._cache.page_size
+                for t in group:
+                    self._task_cache.task_record_hashes(
+                        t.task_id, t.prompt_ids, start_logical_page
+                    )
+
+        decoded: List[Task] = []
+        aborted: List[Task] = []
+        for t in tasks:
+            if t.task_id in prefilled_ids:
+                continue
+            if self._task_cache.task_extend(t.task_id, t.next_pos):
+                decoded.append(t)
+            else:
+                t.status = TaskStatus.ABORTED
+                aborted.append(t)
+
+        for backend, group in self._task_backend_groups(decoded):
+            backend_context = (
+                attn_backend(backend) if backend is not None else nullcontext()
+            )
+            with (
+                backend_context,
+                self._metrics.record([t.task_id for t in group], "decode"),
+            ):
+                step_out = self._executor.execute_decode(
+                    group, return_logprobs=return_logprobs
+                )
+            for t, out in zip(group, step_out):
+                t.output_ids.append(out[0] if return_logprobs else out)
+                t.output_tokens += 1
+                t.advance_kv()
+                produced.append(t)
+
+        return produced, aborted
+
+    def _run_generation_loop(self):
+        stop_ids = self._task_mgr.tokenizer.stop_ids
+        try:
+            with self._backend_context():
+                while not self._stop_event.is_set():
+                    finished = self._task_mgr.remove_finished_tasks(stop_ids)
+                    for task in finished:
+                        if task.status == TaskStatus.FINISHED:
+                            self._task_cache.task_record_hashes(
+                                task.task_id,
+                                self._task_cache.task_cacheable_ids(
+                                    task.task_id, task.prompt_ids, task.output_ids
+                                ),
+                            )
+                        self._task_cache.task_free(task.task_id)
+
+                    active = self._task_mgr.get_active_tasks()
+                    available = self._task_mgr.max_batch_size - len(active)
+                    if available > 0:
+                        candidates = self._task_mgr.pull_candidates(available)
+                        failed = []
+                        for task in candidates:
+                            if self._task_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
+
+                    active = self._task_mgr.get_active_tasks()
+
+                    decoded, aborted = self._step(active)
+
+                    for t in aborted:
+                        self._task_mgr.invoke_callback(t.task_id, STOP)
+
+                    for t in decoded:
+                        new_text = t.decode_new_token(self._task_mgr.tokenizer)
+                        if new_text:
+                            self._task_mgr.invoke_callback(t.task_id, new_text)
+                        if t.is_finished(stop_ids):
+                            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)
+            self._abort_and_clear(free_waiting=False)
+
+    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
+        self._abort_and_clear(free_waiting=True)
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+    def _abort_and_clear(self, free_waiting: bool):
+        """Invoke STOP callbacks, release cache slots, and clear task queues."""
+        for task in self._task_mgr.get_active_tasks():
+            self._task_mgr.invoke_callback(task.task_id, STOP)
+            self._task_cache.task_free(task.task_id)
+        for task in self._task_mgr.get_waiting_tasks():
+            self._task_mgr.invoke_callback(task.task_id, STOP)
+            if free_waiting:
+                self._task_cache.task_free(task.task_id)
+        self._task_mgr.clear_queues()
+
+    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
+        seq_cap = self.max_seq_len
+        request_backend = get_backend(use_default=False)
+
+        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))
+            if t_max <= 0:
+                tasks.append(None)
+                continue
+            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,
+                backend=request_backend,
+            )
+            if not self._task_cache.task_alloc(task.task_id, task.prompt_ids):
+                tasks.append(None)
+                continue
+            task.input_tokens = len(task.prompt_ids)
+            self._metrics.register(task.task_id)
+            tasks.append(task)
+
+        try:
+            live = [t for t in tasks if t is not None]
+
+            with self._backend_context():
+                while live:
+                    decoded, _ = self._step(live, return_logprobs=return_logprobs)
+                    live = [t for t in decoded if not t.is_finished(stop_ids)]
+        finally:
+            for t in tasks:
+                if t is not None:
+                    self._metrics.mark_finished(
+                        t.task_id, t.input_tokens, t.output_tokens
+                    )
+                    self._task_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/task.py

@@ -0,0 +1,290 @@
+import threading
+import time
+import uuid
+from collections import deque
+from enum import Enum
+from typing import TYPE_CHECKING, Any, Callable, Deque, Dict, List, Optional
+
+from tokenizers.decoders import DecodeStream
+
+from astrai.inference.metrics import MetricsCollector
+from astrai.tokenize.tokenizer import AutoTokenizer
+
+if TYPE_CHECKING:
+    from astrai.extension import AttentionBackend
+
+STOP = object()
+
+
+class StreamDecoder:
+    """Incremental decoder backed by the tokenizers library's DecodeStream.
+
+    Delegates to the Rust-native streaming decoder which maintains an
+    O(1) bounded token buffer internally (via prefix drain), avoiding
+    the O(n²) cost of re-decoding the full history on each step.
+
+    Multi-byte UTF-8 sequences split across token boundaries are
+    buffered until complete; ``push`` returns "" while the trailing
+    sequence is still incomplete.
+    """
+
+    __slots__ = ("_stream", "_tok")
+
+    def __init__(self, tokenizer: AutoTokenizer):
+        self._tok = tokenizer._tokenizer
+        self._stream = DecodeStream(skip_special_tokens=True)
+
+    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.
+        """
+        chunk = self._stream.step(self._tok, token_id)
+        return chunk or ""
+
+
+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,
+        backend: Optional["AttentionBackend"] = None,
+    ):
+        self.task_id = task_id
+        self.prompt_ids = prompt_ids
+        self.max_tokens = max_tokens
+        self.temperature = temperature
+        self.top_p = top_p
+        self.top_k = top_k
+        self.frequency_penalty = frequency_penalty
+        self.rep_window = rep_window
+        self.backend = backend
+
+        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._kv_len: int = 0
+        self._decoder: Optional[StreamDecoder] = None
+
+    def mark_prefill_done(self):
+        """Prompt KV is materialized by prefill; first output sampled but
+        not yet written to KV."""
+        self._kv_len = self.input_tokens
+
+    def advance_kv(self):
+        """One more position written to KV (after a decode forward)."""
+        self._kv_len += 1
+
+    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])
+
+    @property
+    def next_pos(self) -> int:
+        """KV position where the next decode step will write."""
+        return self._kv_len
+
+    @property
+    def prefill_done(self) -> bool:
+        """True when all prompt KV entries are materialized."""
+        return self._kv_len >= self.input_tokens > 0
+
+    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,
+        metrics: Optional["MetricsCollector"] = None,
+    ):
+        self.tokenizer = tokenizer
+        self.max_batch_size = max_batch_size
+        self.max_seq_len = max_seq_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
+
+        self._metrics = metrics
+
+    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,
+        backend: Optional["AttentionBackend"] = None,
+        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_seq_len:
+            prompt_ids = prompt_ids[-self.max_seq_len :]
+
+        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,
+            backend=backend,
+        )
+
+        with self._lock:
+            self.waiting_queue.append(task)
+            self._total_tasks += 1
+            if stream_callback:
+                self._callbacks[task_id] = stream_callback
+
+        if self._metrics is not None:
+            self._metrics.register(task_id)
+
+        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]:
+        stats: Dict[str, Any] = {
+            "total_tasks": self._total_tasks,
+            "total_tokens": self._total_tokens,
+            "active_tasks": len(self.active_tasks),
+            "waiting_queue": len(self.waiting_queue),
+        }
+        if self._metrics is not None:
+            stats.update(self._metrics.get_stats())
+        return stats
+
+    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:
+                    finished.append(task)
+                elif task.is_finished(stop_ids):
+                    task.status = TaskStatus.FINISHED
+                    finished.append(task)
+                    self._total_tokens += task.output_tokens
+
+            if self._metrics is not None:
+                for task in finished:
+                    self._metrics.mark_finished(
+                        task.task_id, task.input_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/workspace.py

@@ -0,0 +1,152 @@
+"""Pre-allocated buffers for the inference decode hot path.
+
+Mirrors FlashInfer / SGLang's global workspace pattern: all per-step tensors
+are allocated eagerly at init (nothing is lazy), so the decode step
+reads/writes fixed-address tensors with zero ``torch.empty`` calls during
+the hot loop — a prerequisite for CUDA-graph capture.
+"""
+
+import torch
+from torch import Tensor
+
+_MAX_SPLITS = 32
+Q_TILE_ROWS = 64
+
+
+class InferenceWorkspace:
+    """Reusable fixed-shape per-step buffers for decode.
+
+    Families of buffers, all sized to ``max_batch_size`` / ``max_seq_len``
+    and sliced via views each step:
+
+    - ``decode_mask``: a ``[B, 1, total_len]`` validity mask, the RHS
+      ``arange`` pre-computed so only a single ``torch.ge(out=)`` runs per
+      step.
+    - ``input_ids``: per-step token IDs filled from host (pinned, double-
+      buffered so an in-flight async H2D copy never races the next fill).
+    - KV-cache bind metadata (``req_pool_indices``, ``seq_lens``,
+      ``kv_indptr``, ``inc``, ``out_cache_loc``), written by
+      ``PagePool.bind_tasks`` when the Executor passes this workspace.
+    - ``decode_o_part`` / ``decode_ml_part``: split-KV partial result buffers
+      (mirrors FlashInfer's workspace).  One global alloc, reused by every
+      decode step across all layers.  Sliced views are passed to the CUDA
+      attention kernel so its internal ``torch.empty`` hot-path alloc goes
+      through a stable address (CUDA-graph capturable).
+
+    No re-allocation while the server's bounds are respected.
+    """
+
+    def __init__(
+        self,
+        max_batch_size: int,
+        max_seq_len: int,
+        max_q_heads: int,
+        head_dim: int,
+        device: torch.device,
+        dtype: torch.dtype,
+    ):
+        self.max_batch_size = max_batch_size
+        self.max_seq_len = max_seq_len
+        self.max_q_heads = max_q_heads
+        self.head_dim = head_dim
+        self.device = device
+        self.dtype = dtype
+
+        # ``position_ids[:, None, None] >= arange`` RHS, reused every step.
+        self.arange = torch.arange(max_seq_len, device=device)
+        # Decode validity mask: [max_batch, 1, max_seq_len] bool.
+        self.input_mask = torch.empty(
+            (max_batch_size, 1, max_seq_len), dtype=torch.bool, device=device
+        )
+
+        # Per-step token IDs.  Values come from host Python lists every
+        # step, so the device buffer is pre-allocated (stable address for
+        # CUDA-graph capture) and filled via a host staging buffer.  A
+        # double buffer keeps a copy in flight from being overwritten by
+        # the next fill.
+        self.input_ids = torch.empty((max_batch_size,), dtype=torch.long, device=device)
+        self._pin = [
+            torch.empty((max_batch_size,), dtype=torch.long),
+            torch.empty((max_batch_size,), dtype=torch.long),
+        ]
+        self._pin_idx = 0
+
+        # KV-cache bind metadata (fixed shape, written by ``PagePool.bind_tasks``
+        # when the Executor passes this workspace).  Stable addresses make the
+        # decode forward CUDA-graph capturable.
+        self.req_pool_indices = torch.empty(
+            (max_batch_size,), dtype=torch.int32, device=device
+        )
+        self.seq_lens = torch.empty((max_batch_size,), dtype=torch.long, device=device)
+        self.kv_indptr = torch.empty(
+            (max_batch_size + 1,), dtype=torch.int32, device=device
+        )
+        self.qo_indptr = torch.empty(
+            (max_batch_size + 1,), dtype=torch.int32, device=device
+        )
+        max_q_tiles = max_batch_size * ((max_seq_len + Q_TILE_ROWS - 1) // Q_TILE_ROWS)
+        self.q_tile_to_batch = torch.empty(
+            (max_q_tiles,), dtype=torch.int32, device=device
+        )
+        self.q_tile_to_index = torch.empty(
+            (max_q_tiles,), dtype=torch.int32, device=device
+        )
+        self.inc = torch.arange(max_batch_size + 1, dtype=torch.int32, device=device)
+        self.out_cache_loc = torch.empty(
+            (max_batch_size, 1), dtype=torch.int32, device=device
+        )
+
+        # Per-step position IDs (must be at a fixed address for CUDA-graph capture).
+        self.position_ids = torch.empty(
+            (max_batch_size,), dtype=torch.long, device=device
+        )
+
+        # Split-KV partial-result buffers for decode (persistent, one global
+        # alloc per process — mirrors FlashInfer's workspace pattern).
+        # Shape: [max_batch_size, max_q_heads, _MAX_SPLITS, head_dim] (o_part)
+        #        [max_batch_size, max_q_heads, _MAX_SPLITS, 2]     (ml_part)
+        self.decode_o_part = torch.empty(
+            (max_batch_size, max_q_heads, _MAX_SPLITS, head_dim),
+            dtype=torch.float32,
+            device=device,
+        )
+        self.decode_ml_part = torch.empty(
+            (max_batch_size, max_q_heads, _MAX_SPLITS, 2),
+            dtype=torch.float32,
+            device=device,
+        )
+
+        # Decode output buffer (graph-safe pre-alloc).  Shape matches the
+        # decode kernel's output: [batch, q_head, head_dim].
+        self.decode_out = torch.empty(
+            (max_batch_size, max_q_heads, head_dim),
+            dtype=dtype,
+            device=device,
+        )
+
+    def fill_input_ids(self, ids: "list[int]") -> Tensor:
+        """Write ``ids`` into the device buffer and return ``[B]``.
+
+        Host values are staged through the double buffer and copied into the
+        stable device buffer (``copy_`` without pinning is synchronous, so
+        the alternating buffers guard against an in-flight transfer).
+        """
+        b = len(ids)
+        pin = self._pin[self._pin_idx]
+        self._pin_idx ^= 1
+        for i, v in enumerate(ids):
+            pin[i] = v
+        self.input_ids[:b].copy_(pin[:b])
+        return self.input_ids[:b]
+
+    def decode_mask(self, position_ids: Tensor, total_len: int) -> Tensor:
+        """Return the ``[B, 1, total_len]`` validity mask for this step.
+
+        Written into the pre-allocated buffer via ``torch.ge(out=)`` — no
+        new tensor is allocated.  ``position_ids`` is the current step's
+        ``[B]`` positions; ``total_len`` must not exceed ``max_seq_len``.
+        """
+        b = position_ids.size(0)
+        out = self.input_mask[:b, :, :total_len]
+        torch.ge(position_ids[:, None, None], self.arange[:total_len], out=out)
+        return out

astrai/logging.py

@@ -0,0 +1,35 @@
+import logging
+import os
+
+
+class _DistributedContextFilter(logging.Filter):
+    def filter(self, record: logging.LogRecord) -> bool:
+        record.rank = os.environ.get("RANK", "0")
+        record.world_size = os.environ.get("WORLD_SIZE", "1")
+        return True
+
+
+def setup_logging(level: str = "INFO"):
+    """Attach a StreamHandler to the ``astrai`` logger (idempotent).
+
+    Call once per process at the top of CLI scripts.
+    Set ``ASTR_LOG_LEVEL`` env var to override the default level.
+
+    Level names: ``DEBUG``, ``INFO``, ``WARNING``, ``ERROR``, ``CRITICAL``.
+    ``DEBUG`` enables per-step prefill/decode timing logs
+    (:func:`astrai.inference.runtime.executor.timed`).
+    """
+    logger = logging.getLogger("astrai")
+    if logger.handlers:
+        return
+    level_name = os.environ.get("ASTR_LOG_LEVEL", level).upper()
+    logger.setLevel(getattr(logging, level_name, logging.INFO))
+    handler = logging.StreamHandler()
+    handler.addFilter(_DistributedContextFilter())
+    handler.setFormatter(
+        logging.Formatter(
+            "%(asctime)s | %(levelname)-8s | rank=%(rank)2s/%(world_size)-2s | %(name)-32s | %(message)s",
+            datefmt="%Y-%m-%d %H:%M:%S",
+        )
+    )
+    logger.addHandler(handler)

astrai/model/__init__.py

@@ -0,0 +1,35 @@
+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, DeepSeekMoE
+from astrai.model.components.norm import RMSNorm
+from astrai.model.encoder import EmbeddingEncoder
+from astrai.model.transformer import AutoRegressiveLM
+
+__all__ = [
+    # Modules
+    "Linear",
+    "RMSNorm",
+    "MLP",
+    "DeepSeekMoE",
+    "GQA",
+    "DecoderBlock",
+    # Models
+    "AutoRegressiveLM",
+    "EmbeddingEncoder",
+    "AutoModel",
+    # LoRA
+    "LoRAConfig",
+    "inject_lora",
+    "merge_lora",
+    "save_lora",
+    "load_lora",
+]

astrai/model/automodel.py

@@ -0,0 +1,130 @@
+"""
+AutoModel base class for model loading and saving.
+"""
+
+from contextlib import contextmanager
+from pathlib import Path
+from typing import Union
+
+import torch.nn as nn
+
+from astrai.config.model_config import BaseModelConfig, ConfigFactory
+from astrai.factory import BaseFactory
+from astrai.serialization import (
+    HF_MODEL_TYPES,
+    adapt_config,
+    convert_hf_weights,
+    load_model_config,
+    load_model_weights,
+    looks_like_hf_state_dict,
+    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 ModelFactory(BaseFactory[nn.Module]):
+    """Pure factory for model dispatch, separated from nn.Module state."""
+
+
+class AutoModel(nn.Module):
+    """Model base class with loading/saving 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,
+        weights_format: str = "auto",
+    ) -> nn.Module:
+        """Load a model directory.
+
+        Args:
+            path: Directory containing ``config.json`` and optionally
+                ``model.safetensors``.
+            disable_random_init: Replace parameter initializers with no-ops
+                while building the model.
+            strict: Passed to ``load_state_dict``.
+            weights_format: ``"auto"`` detects HuggingFace checkpoints
+                (LLaMA-style keys and ``model_type``) and converts them;
+                ``"astrai"`` skips conversion; ``"hf"`` forces it.
+        """
+        if weights_format not in ("auto", "astrai", "hf"):
+            raise ValueError(
+                f"weights_format must be one of 'auto', 'astrai', 'hf', "
+                f"got {weights_format!r}"
+            )
+
+        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))
+        is_hf_config = weights_format == "hf" or (
+            weights_format == "auto" and raw.get("model_type") in HF_MODEL_TYPES
+        )
+        if is_hf_config:
+            raw = adapt_config(raw)
+
+        config = ConfigFactory.load(raw)
+        model_type = config.model_type or "autoregressive_lm"
+
+        actual_cls = ModelFactory.get_component_class(model_type)
+
+        with _disable_random_init(enable=disable_random_init):
+            model = actual_cls(config)
+
+        weights_path = model_path / "model.safetensors"
+        index_path = model_path / "model.safetensors.index.json"
+        if weights_path.exists() or index_path.exists():
+            state_dict = load_model_weights(str(model_path))
+            is_hf_weights = is_hf_config or (
+                weights_format == "auto" and looks_like_hf_state_dict(state_dict)
+            )
+            if is_hf_weights:
+                state_dict = convert_hf_weights(state_dict, config)
+            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),
+        )

astrai/model/components/__init__.py

@@ -0,0 +1,25 @@
+from astrai.extension.backend.rotary import apply_rotary_emb
+from astrai.model.components.attention import GQA, MLA
+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, DeepSeekMoE
+from astrai.model.components.norm import RMSNorm
+from astrai.model.components.rope import (
+    RotaryEmbedding,
+    get_rotary_emb,
+)
+
+__all__ = [
+    "Linear",
+    "RMSNorm",
+    "MLP",
+    "DeepSeekMoE",
+    "Embedding",
+    "GQA",
+    "MLA",
+    "DecoderBlock",
+    "RotaryEmbedding",
+    "apply_rotary_emb",
+    "get_rotary_emb",
+]

astrai/model/components/attention.py

@@ -0,0 +1,181 @@
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+
+from astrai.extension.backend import apply_rotary_emb, attention
+from astrai.factory import BaseFactory
+from astrai.inference.cache import KVCache
+from astrai.model.components.linear import Linear
+from astrai.model.components.norm import RMSNorm
+
+
+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:
+        return x.reshape(*x.shape[:-1], n_heads, self.head_dim)
+
+    def forward(
+        self,
+        x: Tensor,
+        rotary_emb: Tensor,
+        attn_mask: Tensor = None,
+        kv_cache: Optional[KVCache] = None,
+        is_causal: bool = False,
+        fwd: Optional[str] = None,
+    ) -> 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)
+
+        sdqa_out = attention(
+            q, k, v, kv_cache, self.layer_id, attn_mask, is_causal, fwd
+        ).reshape(*x.shape[:-1], self.dim)
+
+        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,
+        kv_cache: Optional[KVCache] = None,
+        is_causal: bool = False,
+        fwd: Optional[str] = None,
+    ) -> Tensor:
+        q = self.q_proj(x)
+        q = q.reshape(*x.shape[:-1], 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.reshape(*x.shape[:-1], 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)
+
+        attn_out = attention(
+            q, k, v, kv_cache, self.layer_id, attn_mask, is_causal, fwd
+        ).reshape(*x.shape[:-1], self.dim)
+
+        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 +1,76 @@
+from dataclasses import asdict
+from typing import Optional, TypedDict
+
+import torch.nn as nn
+from torch import Tensor
+
+from astrai.inference.cache import KVCache
+from astrai.model.components.attention import AttnFactory
+from astrai.model.components.mlp import FFNFactory, RouterStats
+from astrai.model.components.norm import RMSNorm
+
+
+class DecoderOutput(TypedDict):
+    hidden_states: Tensor
+    aux_loss: Optional[Tensor]
+    router_stats: Optional[RouterStats]
+
+
+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)
+        ffn_type = self._resolve_ffn_type(config, layer_id)
+        self.mlp = FFNFactory.create(ffn_type, **cfg)
+
+    @staticmethod
+    def _resolve_ffn_type(config, layer_id: int) -> str:
+        if config.ffn_type != "moe":
+            return config.ffn_type
+        mlp_only = config.mlp_only_layers or []
+        if layer_id in mlp_only:
+            return "mlp"
+        if config.decoder_sparse_step > 1:
+            if (layer_id + 1) % config.decoder_sparse_step != 0:
+                return "mlp"
+        return "moe"
+
+    def forward(
+        self,
+        x: Tensor,
+        rotary_emb: Tensor,
+        attention_mask: Optional[Tensor] = None,
+        kv_cache: Optional[KVCache] = None,
+        is_causal: bool = False,
+        fwd: Optional[str] = None,
+    ) -> DecoderOutput:
+        attn_output = self.attention(
+            self.input_norm(x),
+            rotary_emb,
+            attention_mask,
+            kv_cache,
+            is_causal,
+            fwd,
+        )
+        x = attn_output + x
+        normalized = self.post_attention_norm(x)
+        mlp_output = self.mlp(normalized)
+        x = mlp_output["hidden_states"] + x
+
+        return {
+            "hidden_states": x,
+            "aux_loss": mlp_output["aux_loss"],
+            "router_stats": mlp_output.get("router_stats"),
+        }

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 +1,199 @@
+import logging
+from dataclasses import asdict
+from pathlib import Path
+from typing import Optional, Set
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from pydantic.dataclasses import dataclass
+
+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 +1,172 @@
+from typing import Optional, TypedDict
+
+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
+
+
+class RouterStats(TypedDict):
+    """Per-layer MoE routing statistics for training diagnostics.
+
+    Both tensors are detached monitoring data produced during forward.
+    """
+
+    probs: Tensor
+    topk_indices: Tensor
+
+
+class FFNOutput(TypedDict):
+    hidden_states: Tensor
+    aux_loss: Optional[Tensor]
+    router_stats: Optional[RouterStats]
+
+
+@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) -> FFNOutput:
+        gated = self.up(x) * F.silu(self.gate(x))
+        out = self.down(gated)
+        return {"hidden_states": out, "aux_loss": None, "router_stats": None}
+
+
+@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,
+        moe_intermediate_size: Optional[int] = None,
+        shared_expert_intermediate_size: Optional[int] = None,
+        norm_topk_prob: bool = True,
+    ):
+        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.norm_topk_prob = norm_topk_prob
+
+        expert_dim_ffn = (
+            moe_intermediate_size if moe_intermediate_size is not None else dim_ffn
+        )
+        shared_dim_ffn = (
+            shared_expert_intermediate_size
+            if shared_expert_intermediate_size is not None
+            else dim_ffn
+        )
+
+        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, shared_dim_ffn, down_init_std=down_init_std)
+                for _ in range(n_shared_experts)
+            ]
+        )
+        self.routed_experts = nn.ModuleList(
+            [
+                MLP(dim, expert_dim_ffn, down_init_std=down_init_std)
+                for _ in range(n_routed_experts)
+            ]
+        )
+
+    def forward(self, x: Tensor) -> FFNOutput:
+        include_aux_loss = self.training and torch.is_grad_enabled()
+        shape = x.shape
+        dim = shape[-1]
+        x_flat = x.view(-1, dim)
+
+        shared_out = self._shared_forward(x_flat)
+        routed_output = self._routed_forward(x_flat, include_aux_loss)
+
+        out = (shared_out + routed_output["hidden_states"]).view(shape)
+        return {
+            "hidden_states": out,
+            "aux_loss": routed_output["aux_loss"],
+            "router_stats": routed_output["router_stats"],
+        }
+
+    def _shared_forward(self, x: Tensor) -> Tensor:
+        if self.n_shared_experts == 0:
+            return torch.zeros_like(x)
+        return (
+            sum(e(x)["hidden_states"] for e in self.shared_experts)
+            / self.n_shared_experts
+        )
+
+    def _routed_forward(self, x: Tensor, include_aux_loss: bool) -> FFNOutput:
+        N, D = x.shape
+        K = self.n_activated_experts
+        E = self.n_routed_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, sorted=False)
+        if self.norm_topk_prob:
+            topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+
+        aux_loss = None
+        router_stats = None
+        if include_aux_loss:
+            expert_load = F.one_hot(topk_indices, num_classes=E).float()
+            expert_load = expert_load.mean(dim=(0, 1))
+            router_prob = router_probs.float().mean(dim=0)
+            aux_loss = E * (expert_load * router_prob).sum()
+            router_stats = {
+                "probs": router_probs.detach(),
+                "topk_indices": topk_indices,
+            }
+
+        # Grouped dispatch: sort (token, slot) pairs by expert so each expert
+        # consumes one contiguous slice instead of a per-expert mask scan.
+        flat_experts = topk_indices.reshape(-1)
+        sorted_experts, order = torch.sort(flat_experts)
+        flat_tokens = x.repeat_interleave(K, dim=0)[order]
+        flat_weights = topk_weights.reshape(-1, 1)[order]
+        boundaries = torch.cumsum(
+            torch.bincount(sorted_experts, minlength=E), dim=0
+        ).tolist()
+
+        output = torch.zeros(N, D, device=x.device, dtype=x.dtype)
+        start = 0
+        for expert_idx, end in enumerate(boundaries):
+            if end == start:
+                continue
+            expert_output = self.routed_experts[expert_idx](flat_tokens[start:end])[
+                "hidden_states"
+            ]
+            output.index_add_(
+                0,
+                order[start:end] // K,
+                expert_output * flat_weights[start:end],
+            )
+            start = end
+
+        return {
+            "hidden_states": output,
+            "aux_loss": aux_loss,
+            "router_stats": router_stats,
+        }

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 +1,76 @@
+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:
+    """Precompute cos/sin tables for rotary embedding.
+
+    Returns:
+        [max_len, dim/2, 2] (f32) — [cos, sin] pairs.
+    """
+    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.stack([cos, sin], dim=-1)
+
+
+def ntk_base(base: float, dim: int, factor: float) -> float:
+    return base * (factor ** (dim / (dim - 2)))
+
+
+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):
+        freqs_cis = get_rotary_emb(self.dim, max_len, self.base)
+        self.register_buffer("freqs_cis", freqs_cis, persistent=False)
+
+    def forward(self, x: Tensor, position_ids: Optional[Tensor] = None) -> Tensor:
+        """Lookup cos/sin for the given positions.
+
+        Args:
+            x: [batch, seq_len, ...] — only batch and seq_len are used.
+            position_ids: [batch, seq_len] optional position indices.
+
+        Returns:
+            [batch, seq_len, dim/2, 2] (f32) — [cos, sin] pairs.
+        """
+        if position_ids is None:
+            if x.ndim == 2:
+                position_ids = torch.arange(x.size(0), device=x.device)
+            else:
+                position_ids = (
+                    torch.arange(x.size(1), device=x.device)
+                    .unsqueeze(0)
+                    .expand(x.size(0), -1)
+                )
+        return self.freqs_cis[position_ids].float()

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, ModelFactory
+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
+
+
+@ModelFactory.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"]
+
+        hidden_states = self.norm(x)
+
+        if self.pooling_type == "cls":
+            pooled = hidden_states[:, 0]
+        elif self.pooling_type == "last":
+            if input_mask is not None:
+                lengths = input_mask.sum(dim=1) - 1
+                pooled = hidden_states[torch.arange(B, device=x.device), lengths]
+            else:
+                pooled = hidden_states[:, -1]
+        else:
+            if input_mask is not None:
+                mask = input_mask.unsqueeze(-1).to(dtype=hidden_states.dtype)
+                pooled = (hidden_states * mask).sum(dim=1) / mask.sum(dim=1).clamp(
+                    min=1.0
+                )
+            else:
+                pooled = hidden_states.mean(dim=1)
+
+        if self.normalize_embeddings:
+            pooled = torch.nn.functional.normalize(pooled, p=2, dim=-1)
+
+        return pooled

astrai/model/transformer.py

@@ -0,0 +1,152 @@
+from typing import Any, Dict, Mapping, Optional
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+
+from astrai.config.model_config import AutoRegressiveLMConfig
+from astrai.inference.cache import KVCache
+from astrai.model.automodel import AutoModel, ModelFactory
+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
+
+
+@ModelFactory.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,
+        kv_cache: Optional[KVCache] = None,
+        position_ids: Optional[Tensor] = None,
+        fwd: Optional[str] = None,
+    ) -> Dict[str, Tensor]:
+        if fwd is None:
+            if input_ids.ndim != 2:
+                raise ValueError("training input_ids must be [batch, seq_len]")
+            if kv_cache is not None:
+                raise ValueError("training forward does not accept a KV cache")
+        elif fwd in ("prefill", "decode"):
+            if input_ids.ndim != 1:
+                raise ValueError("inference input_ids must be packed [tokens]")
+            if kv_cache is None:
+                raise ValueError("inference forward requires a KV cache")
+        else:
+            raise ValueError(f"unsupported forward mode: {fwd}")
+
+        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
+
+        aux_losses = []
+        router_stats_list = []
+        for layer in self.layers:
+            layer_output = layer(
+                x,
+                rotary_emb,
+                attn_mask,
+                kv_cache,
+                use_sdpa_causal_mask,
+                fwd,
+            )
+            x = layer_output["hidden_states"]
+            stats = layer_output.get("router_stats")
+            if stats is not None:
+                aux_losses.append(layer_output["aux_loss"])
+                router_stats_list.append(stats)
+
+        hidden_states = self.norm(x)
+        logits = self.lm_head(hidden_states)
+
+        output = {"logits": logits, "hidden_states": hidden_states}
+        if aux_losses:
+            output["aux_loss"] = torch.stack(aux_losses).mean()
+            output["router_stats"] = router_stats_list
+        return output

astrai/optim/__init__.py

@@ -0,0 +1,38 @@
+"""Optimizer implementations and factory registration."""
+
+from astrai.optim.composite import (
+    OptimizerFactory,
+    composite_state_dict,
+    composite_step,
+    composite_zero_grad,
+    refresh_param_groups,
+)
+from astrai.optim.mano_adamw import Mano, ManoAdamW
+from astrai.optim.muon_adamw import MuonAdamW
+from astrai.optim.nora_nadamw import (
+    NAdamW,
+    Nora,
+    NoraNAdamW,
+    OptimizerParameterGroups,
+    nora_direction,
+    nora_lr_scale,
+    partition_optimizer_parameters,
+)
+
+__all__ = [
+    "Mano",
+    "ManoAdamW",
+    "MuonAdamW",
+    "NAdamW",
+    "Nora",
+    "NoraNAdamW",
+    "OptimizerFactory",
+    "OptimizerParameterGroups",
+    "composite_state_dict",
+    "composite_step",
+    "composite_zero_grad",
+    "nora_direction",
+    "nora_lr_scale",
+    "partition_optimizer_parameters",
+    "refresh_param_groups",
+]

astrai/optim/composite.py

@@ -0,0 +1,71 @@
+"""Shared infrastructure for the optim package.
+
+This module hosts two things:
+
+* ``OptimizerFactory`` — the registry for built-in optimizers. Defining it
+  here (rather than in ``__init__.py``) lets each optimizer module import it
+  and register itself with a decorator, avoiding circular imports.
+* Composite-optimizer helpers — ``step``/``zero_grad``/``state_dict``/
+  ``param_groups`` delegation shared by every optimizer that routes different
+  parameter groups through distinct sub-optimizers.
+"""
+
+from typing import Any
+
+import torch
+from torch.optim import Optimizer
+
+from astrai.factory import BaseFactory
+
+
+class OptimizerFactory(BaseFactory[Optimizer]):
+    """Factory for built-in training optimizers."""
+
+
+def composite_step(
+    sub_optimizers: list[Optimizer],
+    closure=None,
+) -> torch.Tensor | None:
+    """Run ``step`` on every sub-optimizer, invoking the closure once.
+
+    The closure (if given) is executed inside ``torch.enable_grad`` exactly
+    once before any sub-optimizer steps, matching the contract of a single
+    ``Optimizer.step``. Sub-optimizers receive ``None`` so they do not
+    re-execute it.
+    """
+    loss = None
+    if closure is not None:
+        with torch.enable_grad():
+            loss = closure()
+    for sub in sub_optimizers:
+        sub.step()
+    return loss
+
+
+def composite_zero_grad(
+    sub_optimizers: list[Optimizer],
+    set_to_none: bool = True,
+) -> None:
+    for sub in sub_optimizers:
+        sub.zero_grad(set_to_none=set_to_none)
+
+
+def composite_state_dict(
+    named_sub_optimizers: dict[str, Optimizer | None],
+) -> dict[str, Any]:
+    """Serialize sub-optimizers, preserving ``None`` slots."""
+    return {
+        name: sub.state_dict() if sub is not None else None
+        for name, sub in named_sub_optimizers.items()
+    }
+
+
+def refresh_param_groups(
+    sub_optimizers: list[Optimizer],
+) -> list[dict]:
+    """Concatenate param_groups from every non-None sub-optimizer."""
+    groups: list[dict] = []
+    for sub in sub_optimizers:
+        if sub is not None:
+            groups.extend(sub.param_groups)
+    return groups

astrai/optim/mano_adamw.py

@@ -0,0 +1,214 @@
+"""Mano manifold optimizer combined with AdamW.
+
+Mano projects the momentum onto the tangent space of the Oblique manifold
+(axis-wise tangent projection) and normalizes it, replacing the expensive
+Newton-Schulz iteration in Muon with a cheaper manifold normalization.
+
+Reference: https://arxiv.org/abs/2601.23000
+"""
+
+import math
+
+import torch
+from torch import nn, optim
+from torch.optim import Optimizer
+
+from astrai.optim.composite import (
+    OptimizerFactory,
+    composite_state_dict,
+    composite_step,
+    composite_zero_grad,
+    refresh_param_groups,
+)
+from astrai.optim.nora_nadamw import partition_optimizer_parameters
+
+
+class Mano(Optimizer):
+    """Manifold Normalized Optimizer for two-dimensional matrices.
+
+    Each step alternates the projection axis (dim 0 / dim 1) to restrike the
+    manifold along both rows and columns. The tangent momentum is computed
+    without normalizing the parameter itself (v2 simplification) and the
+    epsilon is added (not clamped) to the norm denominator.
+    """
+
+    def __init__(
+        self,
+        params,
+        lr: float = 1e-3,
+        weight_decay: float = 0.1,
+        momentum: float = 0.95,
+        nesterov: bool = True,
+        eps: float = 1e-8,
+    ):
+        if lr < 0:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if weight_decay < 0:
+            raise ValueError(f"Invalid weight decay: {weight_decay}")
+        if not 0 <= momentum <= 1:
+            raise ValueError(f"Invalid momentum: {momentum}")
+        if eps <= 0:
+            raise ValueError(f"Invalid epsilon: {eps}")
+
+        defaults = {
+            "lr": lr,
+            "weight_decay": weight_decay,
+            "momentum": momentum,
+            "nesterov": nesterov,
+            "eps": eps,
+            "steps": 0,
+        }
+        super().__init__(params, defaults)
+        for group in self.param_groups:
+            for param in group["params"]:
+                if param.ndim != 2:
+                    raise ValueError(
+                        f"Mano only supports 2D matrices, got shape {tuple(param.shape)}"
+                    )
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            lr = group["lr"]
+            weight_decay = group["weight_decay"]
+            momentum = group["momentum"]
+            nesterov = group["nesterov"]
+            eps = group["eps"]
+            dim = int(group["steps"] % 2)
+
+            for param in group["params"]:
+                if param.grad is None:
+                    continue
+                if param.grad.is_sparse:
+                    raise RuntimeError("Mano does not support sparse gradients")
+
+                grad = param.grad
+                state = self.state[param]
+                momentum_buffer = state.get("momentum_buffer")
+                if momentum_buffer is None:
+                    momentum_buffer = torch.zeros_like(grad)
+                momentum_buffer.mul_(momentum).add_(grad)
+                update = (
+                    grad.add(momentum_buffer, alpha=momentum)
+                    if nesterov
+                    else momentum_buffer
+                )
+
+                tangent = update - (
+                    torch.sum(update * param.data, dim=dim, keepdim=True) * param.data
+                )
+                direction = tangent / (
+                    torch.norm(tangent, p=2, dim=dim, keepdim=True) + eps
+                )
+
+                if weight_decay != 0:
+                    param.mul_(1 - lr * weight_decay)
+                adjusted_lr = lr * 0.2 * math.sqrt(direction.shape[dim])
+                param.add_(direction, alpha=-adjusted_lr)
+                state["momentum_buffer"] = momentum_buffer
+
+            group["steps"] += 1
+
+        return loss
+
+
+@OptimizerFactory.register("mano_adamw")
+class ManoAdamW(Optimizer):
+    """Mano for internal linear weights and AdamW for remaining parameters."""
+
+    optimizer_name = "mano_adamw"
+
+    def __init__(
+        self,
+        model: nn.Module,
+        lr: float = 3e-4,
+        weight_decay: float = 0.1,
+        momentum: float = 0.95,
+        nesterov: bool = True,
+    ):
+        groups = partition_optimizer_parameters(model)
+        all_params = [
+            *groups.nora,
+            *groups.nadamw_decay,
+            *groups.nadamw_no_decay,
+        ]
+        if not all_params:
+            raise ValueError(
+                "Cannot build an optimizer for a model with no trainable parameters"
+            )
+        super().__init__(all_params, {})
+
+        self.mano = (
+            Mano(
+                groups.nora,
+                lr=lr,
+                weight_decay=weight_decay,
+                momentum=momentum,
+                nesterov=nesterov,
+            )
+            if groups.nora
+            else None
+        )
+
+        adamw_groups = []
+        if groups.nadamw_decay:
+            adamw_groups.append(
+                {"params": groups.nadamw_decay, "weight_decay": weight_decay}
+            )
+        if groups.nadamw_no_decay:
+            adamw_groups.append({"params": groups.nadamw_no_decay, "weight_decay": 0.0})
+        self.adamw = (
+            optim.AdamW(
+                adamw_groups,
+                lr=lr,
+                betas=(0.9, 0.95),
+                fused=True,
+            )
+            if adamw_groups
+            else None
+        )
+        self.param_groups = refresh_param_groups([self.mano, self.adamw])
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        return composite_step(
+            [opt for opt in (self.mano, self.adamw) if opt is not None],
+            closure,
+        )
+
+    def zero_grad(self, set_to_none: bool = True):
+        composite_zero_grad(
+            [opt for opt in (self.mano, self.adamw) if opt is not None],
+            set_to_none,
+        )
+
+    def state_dict(self) -> dict:
+        return composite_state_dict({"mano": self.mano, "adamw": self.adamw})
+
+    def load_state_dict(self, state_dict: dict):
+        if "muon" in state_dict or "nora" in state_dict:
+            raise ValueError(
+                "Checkpoint uses a different optimizer; select the matching "
+                "--optimizer to resume it"
+            )
+        if "mano" not in state_dict or "adamw" not in state_dict:
+            raise ValueError(
+                "Checkpoint optimizer state is not compatible with mano_adamw"
+            )
+
+        saved_mano = state_dict["mano"]
+        saved_adamw = state_dict["adamw"]
+        if (self.mano is None) != (saved_mano is None):
+            raise ValueError("Checkpoint Mano parameter groups do not match the model")
+        if (self.adamw is None) != (saved_adamw is None):
+            raise ValueError("Checkpoint AdamW parameter groups do not match the model")
+        if self.mano is not None:
+            self.mano.load_state_dict(saved_mano)
+        if self.adamw is not None:
+            self.adamw.load_state_dict(saved_adamw)
+        self.param_groups = refresh_param_groups([self.mano, self.adamw])

astrai/optim/muon_adamw.py

@@ -0,0 +1,95 @@
+"""Legacy Muon + AdamW combined optimizer."""
+
+from typing import Any
+
+import torch
+from torch import Tensor, nn, optim
+
+from astrai.optim.composite import (
+    OptimizerFactory,
+    composite_state_dict,
+    composite_step,
+    composite_zero_grad,
+    refresh_param_groups,
+)
+
+
+@OptimizerFactory.register("muon_adamw")
+class MuonAdamW(optim.Optimizer):
+    """Combined Muon (matrix) + AdamW (non-matrix) optimizer."""
+
+    optimizer_name = "muon_adamw"
+
+    def __init__(
+        self,
+        model: nn.Module,
+        lr: float = 3e-4,
+        weight_decay: float = 0.1,
+        momentum: float = 0.95,
+        nesterov: bool = True,
+        ns_steps: int = 5,
+        adjust_lr_fn: str = "match_rms_adamw",
+    ):
+        defaults = {
+            "lr": lr,
+            "weight_decay": weight_decay,
+            "momentum": momentum,
+            "nesterov": nesterov,
+            "ns_steps": ns_steps,
+            "adjust_lr_fn": adjust_lr_fn,
+        }
+        params = [param for param in model.parameters() if param.requires_grad]
+        super().__init__(params, defaults)
+
+        matrix_params: list[Tensor] = []
+        other_params: list[Tensor] = []
+        for name, param in model.named_parameters():
+            if not param.requires_grad:
+                continue
+            if (
+                param.dim() >= 2
+                and "norm" not in name
+                and "bias" not in name
+                and "embed" not in name
+                and "lm_head" not in name
+            ):
+                matrix_params.append(param)
+            else:
+                other_params.append(param)
+
+        self.muon = optim.Muon(
+            matrix_params,
+            lr=lr,
+            weight_decay=weight_decay,
+            momentum=momentum,
+            nesterov=nesterov,
+            ns_steps=ns_steps,
+            adjust_lr_fn=adjust_lr_fn,
+        )
+        self.adamw = optim.AdamW(
+            [{"params": other_params, "weight_decay": 0.0}],
+            lr=lr,
+            betas=(0.9, 0.95),
+            fused=True,
+        )
+
+        self.param_groups = refresh_param_groups([self.muon, self.adamw])
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        return composite_step([self.muon, self.adamw], closure)
+
+    def zero_grad(self, set_to_none: bool = True):
+        composite_zero_grad([self.muon, self.adamw], set_to_none)
+
+    def state_dict(self) -> dict[str, Any]:
+        return composite_state_dict({"muon": self.muon, "adamw": self.adamw})
+
+    def load_state_dict(self, state_dict: dict[str, Any]):
+        if "muon" not in state_dict or "adamw" not in state_dict:
+            raise ValueError(
+                "Checkpoint optimizer state is not compatible with muon_adamw"
+            )
+        self.muon.load_state_dict(state_dict["muon"])
+        self.adamw.load_state_dict(state_dict["adamw"])
+        self.param_groups = refresh_param_groups([self.muon, self.adamw])

astrai/optim/nora_nadamw.py

@@ -0,0 +1,372 @@
+"""Nora matrix optimizer combined with Nesterov AdamW."""
+
+import math
+from dataclasses import dataclass
+from typing import Any
+
+import torch
+from torch import Tensor, nn
+from torch.distributed.tensor import DTensor, Shard
+from torch.optim import Optimizer
+
+from astrai.model.components.embedding import Embedding
+from astrai.model.components.linear import Linear
+from astrai.model.components.lora import LoRALinear
+from astrai.model.components.norm import RMSNorm
+from astrai.optim.composite import (
+    OptimizerFactory,
+    composite_state_dict,
+    composite_step,
+    composite_zero_grad,
+    refresh_param_groups,
+)
+
+NORA_EPS = 1e-10
+
+
+def _row_normalize(tensor: Tensor, eps: float) -> Tensor:
+    return tensor / tensor.norm(dim=-1, keepdim=True).clamp(min=eps)
+
+
+def nora_direction(update: Tensor, param: Tensor, eps: float = NORA_EPS) -> Tensor:
+    """Project an update onto each parameter row's tangent space and normalize."""
+    theta_hat = _row_normalize(param.to(torch.float32), eps)
+    update_fp32 = update.to(torch.float32)
+    radial = (update_fp32 * theta_hat).sum(dim=-1, keepdim=True) * theta_hat
+    direction = _row_normalize(update_fp32 - radial, eps)
+    return direction.to(update.dtype)
+
+
+def nora_lr_scale(lr: float, shape: torch.Size) -> float:
+    """Scale Nora's LR for tall ``[d_out, d_in]`` linear weights."""
+    return lr * math.sqrt(max(1.0, shape[-2] / shape[-1]))
+
+
+def _validate_complete_rows(param: Tensor) -> None:
+    if not isinstance(param, DTensor):
+        return
+    last_dim = param.ndim - 1
+    for placement in param.placements:
+        if isinstance(placement, Shard) and placement.dim % param.ndim == last_dim:
+            raise ValueError(
+                "Nora requires complete parameter rows, but this DTensor is sharded "
+                "along its last dimension"
+            )
+
+
+class Nora(Optimizer):
+    """Normalized Orthogonal Row Alignment for two-dimensional matrices."""
+
+    def __init__(
+        self,
+        params,
+        lr: float = 5e-3,
+        weight_decay: float = 0.0,
+        momentum: float = 0.95,
+        beta: float = 0.95,
+        nesterov: bool = True,
+        eps: float = NORA_EPS,
+    ):
+        if lr < 0:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if weight_decay < 0:
+            raise ValueError(f"Invalid weight decay: {weight_decay}")
+        if not 0 <= momentum <= 1:
+            raise ValueError(f"Invalid momentum: {momentum}")
+        if not 0 <= beta < 1:
+            raise ValueError(f"Invalid beta: {beta}")
+        if eps <= 0:
+            raise ValueError(f"Invalid epsilon: {eps}")
+
+        defaults = {
+            "lr": lr,
+            "weight_decay": weight_decay,
+            "momentum": momentum,
+            "beta": beta,
+            "nesterov": nesterov,
+            "eps": eps,
+        }
+        super().__init__(params, defaults)
+        for group in self.param_groups:
+            for param in group["params"]:
+                if param.ndim != 2:
+                    raise ValueError(
+                        f"Nora only supports 2D matrices, got shape {tuple(param.shape)}"
+                    )
+                _validate_complete_rows(param)
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            lr = group["lr"]
+            weight_decay = group["weight_decay"]
+            momentum = group["momentum"]
+            beta = group["beta"]
+            nesterov = group["nesterov"]
+            eps = group["eps"]
+            for param in group["params"]:
+                if param.grad is None:
+                    continue
+                if param.grad.is_sparse:
+                    raise RuntimeError("Nora does not support sparse gradients")
+
+                grad = param.grad
+                state = self.state[param]
+                momentum_buffer = state.get("momentum_buffer")
+                if momentum_buffer is None:
+                    momentum_buffer = torch.zeros_like(grad)
+                momentum_buffer.lerp_(grad, 1 - beta)
+                update = (
+                    grad.lerp(momentum_buffer, momentum)
+                    if nesterov
+                    else momentum_buffer
+                )
+                direction = nora_direction(update, param, eps)
+
+                if weight_decay != 0:
+                    param.mul_(1 - lr * weight_decay)
+                param.add_(direction, alpha=-nora_lr_scale(lr, param.shape))
+                state["momentum_buffer"] = momentum_buffer
+
+        return loss
+
+
+class NAdamW(Optimizer):
+    """AdamW using the reference Nesterov first-moment update."""
+
+    def __init__(
+        self,
+        params,
+        lr: float = 3e-4,
+        betas: tuple[float, float] = (0.9, 0.999),
+        eps: float = 1e-8,
+        weight_decay: float = 0.1,
+    ):
+        beta1, beta2 = betas
+        if lr < 0:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if not 0 <= beta1 < 1 or not 0 <= beta2 < 1:
+            raise ValueError(f"Invalid betas: {betas}")
+        if eps <= 0:
+            raise ValueError(f"Invalid epsilon: {eps}")
+        if weight_decay < 0:
+            raise ValueError(f"Invalid weight decay: {weight_decay}")
+        defaults = {
+            "lr": lr,
+            "betas": betas,
+            "eps": eps,
+            "weight_decay": weight_decay,
+        }
+        super().__init__(params, defaults)
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            beta1, beta2 = group["betas"]
+            eps = group["eps"]
+            lr = group["lr"]
+            weight_decay = group["weight_decay"]
+            for param in group["params"]:
+                if param.grad is None:
+                    continue
+                if param.grad.is_sparse:
+                    raise RuntimeError("NAdamW does not support sparse gradients")
+
+                grad = param.grad
+                state = self.state[param]
+                if not state:
+                    state["step"] = 0
+                    state["m"] = torch.zeros_like(param)
+                    state["v"] = torch.zeros_like(param)
+
+                state["step"] += 1
+                first_moment = state["m"]
+                second_moment = state["v"]
+                first_moment.mul_(beta1).add_(grad, alpha=1 - beta1)
+                second_moment.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
+
+                bias_correction1 = 1 - beta1 ** state["step"]
+                bias_correction2 = 1 - beta2 ** state["step"]
+                nesterov_moment = (
+                    beta1 * first_moment + (1 - beta1) * grad
+                ) / bias_correction1
+                corrected_second_moment = second_moment / bias_correction2
+
+                if weight_decay != 0:
+                    param.mul_(1 - lr * weight_decay)
+                param.addcdiv_(
+                    nesterov_moment,
+                    corrected_second_moment.sqrt().add_(eps),
+                    value=-lr,
+                )
+
+        return loss
+
+
+@dataclass
+class OptimizerParameterGroups:
+    nora: list[Tensor]
+    nadamw_decay: list[Tensor]
+    nadamw_no_decay: list[Tensor]
+
+
+def partition_optimizer_parameters(model: nn.Module) -> OptimizerParameterGroups:
+    """Partition trainable parameters by module role and parameter identity."""
+    nora_ids: set[int] = set()
+    no_decay_ids: set[int] = set()
+
+    for module_name, module in model.named_modules():
+        if isinstance(module, LoRALinear):
+            for param in module.parameters(recurse=False):
+                if param.requires_grad:
+                    no_decay_ids.add(id(param))
+            continue
+
+        if isinstance(module, (Embedding, RMSNorm)):
+            for param in module.parameters(recurse=False):
+                if param.requires_grad:
+                    no_decay_ids.add(id(param))
+            continue
+
+        if not isinstance(module, Linear):
+            continue
+
+        if module.bias is not None and module.bias.requires_grad:
+            no_decay_ids.add(id(module.bias))
+        if not module.weight.requires_grad:
+            continue
+        if module_name.rsplit(".", 1)[-1] == "lm_head":
+            no_decay_ids.add(id(module.weight))
+        elif module.weight.ndim == 2:
+            nora_ids.add(id(module.weight))
+
+    nora: list[Tensor] = []
+    nadamw_decay: list[Tensor] = []
+    nadamw_no_decay: list[Tensor] = []
+    seen: set[int] = set()
+    for param in model.parameters():
+        param_id = id(param)
+        if not param.requires_grad or param_id in seen:
+            continue
+        seen.add(param_id)
+        if param_id in no_decay_ids or param.ndim <= 1:
+            nadamw_no_decay.append(param)
+        elif param_id in nora_ids:
+            nora.append(param)
+        else:
+            nadamw_decay.append(param)
+
+    trainable_ids = {id(param) for param in model.parameters() if param.requires_grad}
+    grouped_ids = {id(param) for param in [*nora, *nadamw_decay, *nadamw_no_decay]}
+    if grouped_ids != trainable_ids:
+        missing = len(trainable_ids - grouped_ids)
+        extra = len(grouped_ids - trainable_ids)
+        raise RuntimeError(
+            f"Optimizer parameter partition is incomplete: missing={missing}, extra={extra}"
+        )
+
+    return OptimizerParameterGroups(nora, nadamw_decay, nadamw_no_decay)
+
+
+@OptimizerFactory.register("nora_nadamw")
+class NoraNAdamW(Optimizer):
+    """Nora for internal linear weights and NAdamW for remaining parameters."""
+
+    optimizer_name = "nora_nadamw"
+
+    def __init__(
+        self,
+        model: nn.Module,
+        lr: float = 3e-4,
+        weight_decay: float = 0.1,
+        nora_lr: float = 5e-3,
+        nora_weight_decay: float = 0.0,
+        nora_beta: float = 0.95,
+        nora_momentum: float = 0.95,
+    ):
+        groups = partition_optimizer_parameters(model)
+        all_params = [
+            *groups.nora,
+            *groups.nadamw_decay,
+            *groups.nadamw_no_decay,
+        ]
+        if not all_params:
+            raise ValueError(
+                "Cannot build an optimizer for a model with no trainable parameters"
+            )
+        super().__init__(all_params, {})
+
+        self.nora = (
+            Nora(
+                groups.nora,
+                lr=nora_lr,
+                weight_decay=nora_weight_decay,
+                momentum=nora_momentum,
+                beta=nora_beta,
+            )
+            if groups.nora
+            else None
+        )
+
+        nadamw_groups = []
+        if groups.nadamw_decay:
+            nadamw_groups.append(
+                {"params": groups.nadamw_decay, "weight_decay": weight_decay}
+            )
+        if groups.nadamw_no_decay:
+            nadamw_groups.append(
+                {"params": groups.nadamw_no_decay, "weight_decay": 0.0}
+            )
+        self.nadamw = NAdamW(nadamw_groups, lr=lr) if nadamw_groups else None
+        self.param_groups = refresh_param_groups([self.nora, self.nadamw])
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        return composite_step(
+            [opt for opt in (self.nora, self.nadamw) if opt is not None],
+            closure,
+        )
+
+    def zero_grad(self, set_to_none: bool = True):
+        composite_zero_grad(
+            [opt for opt in (self.nora, self.nadamw) if opt is not None],
+            set_to_none,
+        )
+
+    def state_dict(self) -> dict[str, Any]:
+        return composite_state_dict({"nora": self.nora, "nadamw": self.nadamw})
+
+    def load_state_dict(self, state_dict: dict[str, Any]):
+        if "muon" in state_dict or "adamw" in state_dict:
+            raise ValueError(
+                "Checkpoint uses muon_adamw state; select optimizer='muon_adamw' "
+                "to resume it"
+            )
+        if "nora" not in state_dict or "nadamw" not in state_dict:
+            raise ValueError(
+                "Checkpoint optimizer state is not compatible with nora_nadamw"
+            )
+
+        saved_nora = state_dict["nora"]
+        saved_nadamw = state_dict["nadamw"]
+        if (self.nora is None) != (saved_nora is None):
+            raise ValueError("Checkpoint Nora parameter groups do not match the model")
+        if (self.nadamw is None) != (saved_nadamw is None):
+            raise ValueError(
+                "Checkpoint NAdamW parameter groups do not match the model"
+            )
+        if self.nora is not None:
+            self.nora.load_state_dict(saved_nora)
+        if self.nadamw is not None:
+            self.nadamw.load_state_dict(saved_nadamw)
+        self.param_groups = refresh_param_groups([self.nora, self.nadamw])

astrai/parallel/__init__.py

@@ -0,0 +1,39 @@
+from astrai.parallel.executor import (
+    AccumOptimizer,
+    AccumScheduler,
+    BaseExecutor,
+    DDPExecutor,
+    ExecutorFactory,
+    FSDPExecutor,
+    GradientState,
+    NoneExecutor,
+    broadcast_state_dict,
+    create_ref_model,
+)
+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",
+    "ExecutorFactory",
+    "BaseExecutor",
+    "GradientState",
+    "AccumOptimizer",
+    "AccumScheduler",
+    "NoneExecutor",
+    "DDPExecutor",
+    "FSDPExecutor",
+    "create_ref_model",
+    "broadcast_state_dict",
+]

astrai/parallel/executor.py

@@ -0,0 +1,428 @@
+"""Unified training executor — parallel strategy + gradient accumulation."""
+
+import contextlib
+import logging
+import os
+from contextlib import contextmanager
+from typing import Any, Callable, Dict, Optional, Tuple
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from torch.distributed.fsdp import (
+    FSDPModule,
+    fully_shard,
+)
+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__)
+
+
+def broadcast_state_dict(
+    state_dict: Optional[Dict[str, torch.Tensor]],
+    src: int = 0,
+) -> Optional[Dict[str, torch.Tensor]]:
+    """Broadcast a state_dict from *src* rank to all ranks.
+
+    Tensors stay on their original device (GPU) for the broadcast.
+    All ranks must call this collectively.
+
+    On non-distributed runs, returns *state_dict* unchanged.
+    """
+    if not dist.is_initialized() or dist.get_world_size() == 1:
+        return state_dict
+
+    rank = dist.get_rank()
+
+    # Broadcast metadata (keys, shapes, dtypes, device) so non-src ranks
+    # can allocate matching empty tensors on the correct device.
+    if rank == src:
+        device = next(iter(state_dict.values())).device
+        metadata = [
+            (k, tuple(v.shape), v.dtype, str(device)) for k, v in state_dict.items()
+        ]
+    else:
+        metadata = None
+    metadata_list = [metadata]
+    dist.broadcast_object_list(metadata_list, src=src)
+    metadata = metadata_list[0]
+
+    # Non-src ranks allocate empty tensors with the broadcasted metadata.
+    if rank != src:
+        state_dict = {
+            k: torch.empty(s, dtype=d, device=torch.device(dev))
+            for k, s, d, dev in metadata
+        }
+
+    # Broadcast each tensor in-place.
+    for tensor in state_dict.values():
+        dist.broadcast(tensor, src=src)
+
+    return state_dict
+
+
+def create_ref_model(
+    model_fn: Callable[[], nn.Module],
+    executor: Optional["BaseExecutor"] = None,
+    model: Optional[nn.Module] = None,
+    state_dict: Optional[Dict[str, torch.Tensor]] = None,
+    device: Optional[str] = None,
+) -> Optional[nn.Module]:
+    """Create a frozen reference model from executor or state dict.
+
+    In distributed mode (FSDP), ``unwrap_model`` returns ``None`` on
+    non-rank-0.  The state_dict is broadcast from rank-0 to all ranks
+    so every rank gets a complete copy.
+    """
+    if state_dict is None and executor is not None and model is not None:
+        state_dict = executor.unwrap_model(model)
+
+    # FSDP's unwrap_model returns None on non-rank-0. Broadcast from
+    # rank-0 so every rank receives a complete state_dict.
+    if executor is not None and executor.use_distributed:
+        state_dict = broadcast_state_dict(state_dict)
+
+    if state_dict is None:
+        return None
+
+    ref_model = model_fn()
+    ref_model.load_state_dict(state_dict)
+    ref_model.requires_grad_(False)
+    ref_model.eval()
+    if device is not None:
+        ref_model = ref_model.to(device=device)
+    return ref_model
+
+
+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,
+        after_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)
+        if after_wrap is not None:
+            model = after_wrap(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):
+    """FSDP 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
+    ``fully_shard``'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 = False,
+    ):
+        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("FSDP 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(
+            "FSDP 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 not self.use_distributed:
+            return super().clip_grad_norm(model, max_norm)
+
+        # FSDP params are DTensors (sharded across ranks).
+        # torch.nn.utils.clip_grad_norm_ computes LOCAL norm per rank,
+        # so we must all-reduce to get the global norm before clipping.
+        local_norm = torch.nn.utils.get_total_norm(
+            [p.grad for p in model.parameters() if p.grad is not None],
+        )
+        if isinstance(local_norm, DTensor):
+            local_norm = local_norm.to_local()
+        total_norm_sq = local_norm**2
+        dist.all_reduce(total_norm_sq, op=dist.ReduceOp.SUM)
+        total_norm = total_norm_sq.sqrt()
+
+        clip_coef = max_norm / (total_norm + 1e-6)
+        clip_coef_clamped = torch.clamp(clip_coef, max=1.0)
+        for p in model.parameters():
+            if p.grad is not None:
+                p.grad.mul_(clip_coef_clamped)
+
+        return total_norm.item()
+
+    def unwrap_model(self, model: nn.Module):
+        if not self.use_distributed:
+            return model.state_dict()
+
+        # unshard() and full_tensor() are collective ops — all ranks must
+        # participate. Non-rank-0 ranks still call them but discard results.
+        for module in model.modules():
+            if isinstance(module, FSDPModule):
+                module.unshard()
+
+        state_dict = model.state_dict()
+        result = {}
+        for k, v in state_dict.items():
+            if isinstance(v, DTensor):
+                full = v.full_tensor()
+                if get_rank() == 0:
+                    result[k] = full
+            elif get_rank() == 0:
+                result[k] = v
+
+        for module in model.modules():
+            if isinstance(module, FSDPModule):
+                module.reshard()
+
+        if get_rank() != 0:
+            return None
+
+        return result

astrai/parallel/setup.py

@@ -0,0 +1,281 @@
+import logging
+import os
+import signal
+import socket
+import threading
+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
+
+from astrai.signal_handler import install_early_signal_handlers
+
+logger = logging.getLogger(__name__)
+
+
+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,
+):
+    install_early_signal_handlers()
+    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):
+        install_early_signal_handlers()
+        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
+
+        install_early_signal_handlers()
+        ctx = mp.start_processes(
+            _run_single_rank,
+            args=args,
+            nprocs=self.world_size,
+            start_method=self.start_method,
+            join=False,
+        )
+
+        parent_stop = threading.Event()
+        original_handlers = {}
+
+        def _parent_handler(signum, frame):
+            sig = signal.Signals(signum)
+            logger.warning(
+                "Parent (pid=%d) received %s, forwarding to children...",
+                os.getpid(),
+                sig.name,
+            )
+            parent_stop.set()
+            for p in ctx.processes:
+                if p.is_alive():
+                    p.terminate()
+
+        for sig in (signal.SIGTERM, signal.SIGINT):
+            prev = signal.signal(sig, _parent_handler)
+            if prev not in (signal.SIG_DFL, signal.SIG_IGN, None, _parent_handler):
+                original_handlers[sig] = prev
+
+        try:
+            while not ctx.join() and not parent_stop.is_set():
+                pass
+        except BaseException:
+            logger.warning(
+                "Parent received unexpected exception, terminating children..."
+            )
+            for p in ctx.processes:
+                if p.is_alive():
+                    p.terminate()
+            raise
+        finally:
+            for sig, handler in original_handlers.items():
+                signal.signal(sig, handler)
+
+            for p in ctx.processes:
+                p.join()
+
+            ctx.join()
+
+
+def _is_external_launcher() -> bool:
+    """Whether an external launcher (torchrun/elastic/manual env) started us."""
+    if dist.is_torchelastic_launched():
+        return True
+    if "LOCAL_WORLD_SIZE" in os.environ:
+        return True
+    if "RANK" in os.environ and "WORLD_SIZE" in os.environ:
+        return True
+    return False
+
+
+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()
+    if _is_external_launcher():
+        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 +1,542 @@
+"""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 = {}
+        required_outputs = {
+            output_key
+            for output_key, spec in sources_spec.items()
+            if spec.get("sections")
+        }
+
+        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
+                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
+                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
+
+        if not required_outputs or not required_outputs.issubset(result):
+            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]
+        required_outputs = {
+            output_key
+            for output_key, spec in sources_spec.items()
+            if spec.get("sections")
+        }
+        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 required_outputs and required_outputs.issubset(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 ``.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 +1,56 @@
+"""Storage writer strategies for pipeline output.
+
+The :class:`StoreWriter` abstraction decouples the pipeline from the
+concrete storage format (bin).  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
+
+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

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 +1,53 @@
+"""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,
+    save_bin,
+)
+from astrai.serialization.hf_adapter import (
+    HF_MODEL_TYPES,
+    adapt_config,
+    convert_hf_config,
+    convert_hf_weights,
+    looks_like_hf_state_dict,
+)
+
+__all__ = [
+    "Checkpoint",
+    "HF_MODEL_TYPES",
+    "adapt_config",
+    "convert_hf_config",
+    "convert_hf_weights",
+    "looks_like_hf_state_dict",
+    "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",
+    "save_bin",
+]

astrai/serialization/checkpoint.py

@@ -0,0 +1,209 @@
+"""Model checkpoint serialization helpers."""
+
+import io
+import json
+import time
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Callable, 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 _broadcast_load(loader: Callable[[], dict], broadcast: bool) -> dict:
+    """Load on rank 0 and broadcast the object to all ranks."""
+    if not broadcast or not dist.is_initialized():
+        return loader()
+    rank = get_rank()
+    if rank == 0:
+        data = loader()
+    else:
+        data = {}
+    tmp = [data]
+    dist.broadcast_object_list(tmp, src=0)
+    return tmp[0]
+
+
+def load_safetensors(path: Union[str, Path], broadcast: bool = False) -> dict:
+    return _broadcast_load(lambda: st.load_file(str(path)), broadcast)
+
+
+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:
+    return _broadcast_load(lambda: json.loads(Path(path).read_text()), broadcast)
+
+
+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:
+    save_path = Path(save_directory)
+    weights_file = save_path / _WEIGHTS_FILE
+    if weights_file.exists():
+        return load_state_dict(weights_file)
+
+    index_path = save_path / "model.safetensors.index.json"
+    if index_path.exists():
+        index = load_json(index_path)
+        weight_map = index.get("weight_map", {})
+        state_dict = {}
+        for shard in sorted(set(weight_map.values())):
+            state_dict.update(load_state_dict(save_path / shard))
+        return state_dict
+
+    raise FileNotFoundError(f"No model weights found in {save_directory}")
+
+
+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)
+
+        weights_path = save_path / _WEIGHTS_FILE
+        index_path = save_path / "model.safetensors.index.json"
+        if weights_path.exists() or index_path.exists():
+            state_dict = load_model_weights(save_dir)
+            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 +1,82 @@
+"""Dataset storage serialization helpers (memory-mapped binary)."""
+
+import json
+import os
+from typing import Any, Dict, List, Optional
+
+import numpy as np
+import torch
+from torch import Tensor
+
+
+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 JSONL 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 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 (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/serialization/hf_adapter.py

@@ -0,0 +1,271 @@
+"""HuggingFace checkpoint adaptation for LLaMA-style decoder models.
+
+AstrAI stores weights with its own key names (``layers.<i>.input_norm``,
+``layers.<i>.mlp.gate``), while HuggingFace decoder-only checkpoints use
+``model.layers.<i>.input_layernorm`` / ``model.layers.<i>.mlp.gate_proj``.
+This module translates HF configs and state dicts so external checkpoints
+can be loaded directly.
+
+Supported families (LLaMA layout, dense and MoE):
+- dense FFN: llama, mistral, qwen2, gemma, gemma2, phi3
+- MoE FFN (Mixtral / Qwen2-MoE / DeepSeek-V3 layout): router
+  ``mlp.gate``, routed experts ``mlp.experts.<j>``, shared experts
+  ``mlp.shared_experts.<j>``
+
+Not supported:
+- MLA attention (DeepSeek-V2/V3 ``kv_a_proj_with_mqa``) uses a different
+  KV factorization and cannot be converted numerically.
+- Attention/MLP bias (``attention_bias`` / ``mlp_bias``) — AstrAI
+  projections are bias-free.
+"""
+
+import logging
+import re
+from typing import Any, Dict, Mapping
+
+import torch
+
+from astrai.config.base import BaseConfig
+
+logger = logging.getLogger(__name__)
+
+HF_MODEL_TYPES = frozenset(
+    {
+        "llama",
+        "mistral",
+        "mixtral",
+        "qwen2",
+        "qwen2_moe",
+        "gemma",
+        "gemma2",
+        "phi3",
+    }
+)
+
+_EMBED = re.compile(r"^model\.embed_tokens\.weight$")
+_ATTN = re.compile(r"^model\.layers\.(\d+)\.self_attn\.(q|k|v|o)_proj\.(weight|bias)$")
+_Q_NORM = re.compile(r"^model\.layers\.(\d+)\.self_attn\.q_norm\.weight$")
+_K_NORM = re.compile(r"^model\.layers\.(\d+)\.self_attn\.k_norm\.weight$")
+_INPUT_NORM = re.compile(r"^model\.layers\.(\d+)\.input_layernorm\.weight$")
+_POST_NORM = re.compile(r"^model\.layers\.(\d+)\.post_attention_layernorm\.weight$")
+_FINAL_NORM = re.compile(r"^model\.norm\.weight$")
+_LM_HEAD = re.compile(r"^lm_head\.weight$")
+_DENSE_MLP = re.compile(
+    r"^model\.layers\.(\d+)\.mlp\.(gate|up|down)_proj\.(weight|bias)$"
+)
+_MOE_ROUTER = re.compile(r"^model\.layers\.(\d+)\.mlp\.gate\.weight$")
+_MOE_EXPERTS = re.compile(
+    r"^model\.layers\.(\d+)\.mlp\.experts\.(\d+)\.(gate|up|down)_proj\.(weight|bias)$"
+)
+_MOE_SHARED = re.compile(
+    r"^model\.layers\.(\d+)\.mlp\.shared_expert(?:s)?\.(\d+)\."
+    r"(gate|up|down)_proj\.(weight|bias)$"
+)
+
+_ASTR_PREFIXES = ("embed_tokens.", "layers.", "norm.", "lm_head.")
+
+
+def looks_like_hf_state_dict(state_dict: Mapping[str, Any]) -> bool:
+    """Return True if *state_dict* uses HuggingFace key names."""
+    return any(
+        key.startswith("model.")
+        or "self_attn." in key
+        or "input_layernorm" in key
+        or "mlp.experts." in key
+        for key in state_dict
+    )
+
+
+def _is_dense_mlp_layer(config: BaseConfig, layer_id: int) -> bool:
+    """Return whether a layer uses dense MLP instead of routed experts."""
+    if getattr(config, "ffn_type", "mlp") != "moe":
+        return True
+    mlp_only = getattr(config, "mlp_only_layers", None) or []
+    if layer_id in mlp_only:
+        return True
+    step = getattr(config, "decoder_sparse_step", 1) or 1
+    return step > 1 and (layer_id + 1) % step != 0
+
+
+def adapt_config(raw: Dict[str, Any]) -> Dict[str, Any]:
+    """Translate *raw* for AstrAI if it looks like an HF model config."""
+    if raw.get("model_type") in HF_MODEL_TYPES:
+        return convert_hf_config(raw)
+    return raw
+
+
+def convert_hf_config(raw: Dict[str, Any]) -> Dict[str, Any]:
+    """Convert an HF LLaMA-style config dict to AstrAI field names."""
+    if raw.get("attention_bias") or raw.get("mlp_bias"):
+        raise NotImplementedError(
+            "attention_bias / mlp_bias checkpoints are not supported; "
+            "AstrAI projections are bias-free"
+        )
+
+    cfg: Dict[str, Any] = {}
+    for key in (
+        "vocab_size",
+        "hidden_size",
+        "num_hidden_layers",
+        "intermediate_size",
+        "rms_norm_eps",
+        "tie_word_embeddings",
+        "max_position_embeddings",
+        "rope_theta",
+        "rope_scaling",
+        "num_attention_heads",
+        "num_key_value_heads",
+        "use_qk_norm",
+        "use_gated_attention",
+        "kv_lora_rank",
+        "qk_nope_head_dim",
+        "qk_rope_head_dim",
+        "moe_intermediate_size",
+        "shared_expert_intermediate_size",
+        "topk_method",
+        "norm_topk_prob",
+        "moe_aux_loss_coef",
+        "decoder_sparse_step",
+        "mlp_only_layers",
+        "neftune_alpha",
+    ):
+        if key in raw:
+            cfg[key] = raw[key]
+
+    if "qk_norm" in raw and "use_qk_norm" not in cfg:
+        cfg["use_qk_norm"] = raw["qk_norm"]
+    if (
+        raw.get("model_type") in ("gemma", "gemma2")
+        and "use_qk_norm" not in cfg
+        and "qk_norm" not in raw
+    ):
+        # Gemma/Gemma2 always apply RMSNorm to Q and K before attention.
+        cfg["use_qk_norm"] = True
+
+    n_heads = raw.get("num_attention_heads")
+    if cfg.get("num_key_value_heads") is None and n_heads is not None:
+        cfg["num_key_value_heads"] = n_heads
+
+    if raw.get("head_dim") is not None and n_heads and raw.get("hidden_size"):
+        expected = raw["hidden_size"] // n_heads
+        if raw["head_dim"] != expected:
+            raise NotImplementedError(
+                f"HF head_dim={raw['head_dim']} differs from the computed "
+                f"head dim {expected}; AstrAI derives head_dim from "
+                "hidden_size / num_attention_heads"
+            )
+
+    if "kv_lora_rank" in raw:
+        cfg["attn_type"] = "mla"
+
+    n_experts = raw.get("num_local_experts") or raw.get("n_routed_experts")
+    if n_experts:
+        cfg["ffn_type"] = "moe"
+        cfg["n_routed_experts"] = n_experts
+        if "num_experts_per_tok" in raw:
+            cfg["n_activated_experts"] = raw["num_experts_per_tok"]
+        if "n_activated_experts" in raw:
+            cfg["n_activated_experts"] = raw["n_activated_experts"]
+        if "n_shared_experts" in raw:
+            cfg["n_shared_experts"] = raw["n_shared_experts"]
+        else:
+            # Mixtral has no shared experts; AstrAI defaults to one.
+            cfg["n_shared_experts"] = 0
+        if cfg.get("moe_intermediate_size") is None and "intermediate_size" in raw:
+            # MoE configs store the per-expert FFN size in intermediate_size.
+            cfg["moe_intermediate_size"] = raw["intermediate_size"]
+        first_k_dense = raw.get("first_k_dense_replace")
+        if isinstance(first_k_dense, int) and first_k_dense > 0:
+            cfg["mlp_only_layers"] = list(range(first_k_dense))
+            cfg["decoder_sparse_step"] = 1
+
+    cfg["model_type"] = "autoregressive_lm"
+    return cfg
+
+
+def convert_hf_weights(
+    state_dict: Mapping[str, Any],
+    config: BaseConfig,
+) -> Dict[str, torch.Tensor]:
+    """Rename HF state dict keys to AstrAI names.
+
+    Keys that are already AstrAI-style pass through unchanged; unmapped
+    HF keys are dropped with a warning. Use with ``strict=True`` to fail
+    loudly when the checkpoint does not match the config.
+    """
+    if getattr(config, "attn_type", "gqa") == "mla":
+        if any("kv_a_proj_with_mqa" in key for key in state_dict):
+            raise NotImplementedError(
+                "MLA attention (DeepSeek-V2/V3 kv_a_proj_with_mqa) uses a "
+                "different KV factorization and cannot be converted"
+            )
+
+    ffn_type = getattr(config, "ffn_type", "mlp")
+    converted: Dict[str, torch.Tensor] = {}
+    skipped: list[str] = []
+    for key, tensor in state_dict.items():
+        if key.startswith(_ASTR_PREFIXES):
+            converted[key] = tensor
+            continue
+
+        new_key = None
+        if ffn_type == "moe":
+            m = _MOE_ROUTER.match(key)
+            if m:
+                new_key = f"layers.{m.group(1)}.mlp.router.weight"
+            else:
+                m = _MOE_EXPERTS.match(key)
+                if m:
+                    new_key = (
+                        f"layers.{m.group(1)}.mlp.routed_experts.{m.group(2)}."
+                        f"{m.group(3)}.{m.group(4)}"
+                    )
+                else:
+                    m = _MOE_SHARED.match(key)
+                    if m:
+                        new_key = (
+                            f"layers.{m.group(1)}.mlp.shared_experts.{m.group(2)}."
+                            f"{m.group(3)}.{m.group(4)}"
+                        )
+            if new_key is None:
+                m = _DENSE_MLP.match(key)
+                if m and _is_dense_mlp_layer(config, int(m.group(1))):
+                    new_key = f"layers.{m.group(1)}.mlp.{m.group(2)}.{m.group(3)}"
+        else:
+            m = _DENSE_MLP.match(key)
+            if m:
+                new_key = f"layers.{m.group(1)}.mlp.{m.group(2)}.{m.group(3)}"
+
+        if new_key is None:
+            m = _ATTN.match(key)
+            if m:
+                new_key = (
+                    f"layers.{m.group(1)}.attention.{m.group(2)}_proj.{m.group(3)}"
+                )
+            elif (m := _Q_NORM.match(key)) is not None:
+                new_key = f"layers.{m.group(1)}.attention.q_norm.weight"
+            elif (m := _K_NORM.match(key)) is not None:
+                new_key = f"layers.{m.group(1)}.attention.k_norm.weight"
+            elif (m := _INPUT_NORM.match(key)) is not None:
+                new_key = f"layers.{m.group(1)}.input_norm.weight"
+            elif (m := _POST_NORM.match(key)) is not None:
+                new_key = f"layers.{m.group(1)}.post_attention_norm.weight"
+            elif (m := _EMBED.match(key)) is not None:
+                new_key = "embed_tokens.weight"
+            elif (m := _FINAL_NORM.match(key)) is not None:
+                new_key = "norm.weight"
+            elif (m := _LM_HEAD.match(key)) is not None:
+                new_key = "lm_head.weight"
+
+        if new_key is None:
+            skipped.append(key)
+        else:
+            converted[new_key] = tensor
+
+    if skipped:
+        logger.warning(
+            "Dropped %d unmapped HuggingFace weight key(s): %s",
+            len(skipped),
+            ", ".join(sorted(skipped)[:10]),
+        )
+    return converted

astrai/signal_handler.py

@@ -0,0 +1,53 @@
+import logging
+import os
+import signal
+import threading
+
+logger = logging.getLogger(__name__)
+
+_early_stop = threading.Event()
+_active_context = None
+
+
+def _early_handler(signum: int, frame):
+    sig = signal.Signals(signum)
+    logger.warning(
+        "Received %s (pid=%d), requesting graceful training stop...",
+        sig.name,
+        os.getpid(),
+    )
+    _early_stop.set()
+    if _active_context is not None:
+        _active_context.request_stop()
+
+
+def install_early_signal_handlers():
+    for sig in (signal.SIGTERM, signal.SIGINT):
+        signal.signal(sig, _early_handler)
+    _unblock_signals()
+
+
+def _unblock_signals():
+    try:
+        mask = signal.pthread_sigmask(signal.SIG_BLOCK, set())
+        blocked = {signal.SIGTERM, signal.SIGINT} & mask
+        if blocked:
+            signal.pthread_sigmask(signal.SIG_UNBLOCK, blocked)
+    except (AttributeError, OSError):
+        pass
+
+
+def register_signal_handlers(context):
+    global _active_context
+    _active_context = context
+    for sig in (signal.SIGTERM, signal.SIGINT):
+        signal.signal(sig, _early_handler)
+    if _early_stop.is_set():
+        context.request_stop()
+        logger.warning("Signal was received during initialization, stopping...")
+
+
+def unregister_signal_handlers():
+    global _active_context
+    _active_context = None
+    _early_stop.clear()

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 +1,102 @@
+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.  :meth:`__getstate__` drops the cached template so
+        that pickle serialises only ``template_str``; each worker rebuilds
+        the cache on first render.
+        """
+        return Template(self.template_str)
+
+    def __getstate__(self) -> Dict[str, Any]:
+        """Exclude the cached Jinja2 template from pickling.
+
+        ``Template.root_render_func`` is a dynamically generated closure
+        that cannot be pickled by reference.  Dropping ``_compiled`` here
+        lets :class:`cached_property` rebuild it on first access after
+        unpickle.
+        """
+        state = self.__dict__.copy()
+        state.pop("_compiled", None)
+        return state
+
+    def __setstate__(self, state: Dict[str, Any]) -> None:
+        self.__dict__.update(state)
+
+    @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 +1,289 @@
+"""
+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"""
+
+    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)
+
+    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