refactor: dedupe fp8 meta state into per-operand rings

- collapse FP8TensorMeta's 12 slots + 6 copy-paste methods into three _ScaleRing objects (hist/idx/scale/initialized + update/seed)
- skip meta allocation entirely on the DynamicScaling path (zero rings, scales measured inline)
- drop write-only FP8State._last_device and unused E4M3_MAX alias

.dockerignore

@@ -4,6 +4,8 @@
 # Allow necessary files
 !astrai/
 !scripts/
-!assets/
+!docs/
+!csrc/
+!setup.py
 !pyproject.toml
 !README.md

.github/workflows/release.yml

@@ -26,30 +26,41 @@ jobs:
           if-no-files-found: error
 
   build-cuda-linux:
-    name: Build CUDA wheel (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 (CUDA 12.8)
+      - name: Install torch (${{ matrix.cuda_tag }})
         run: |
-          pip install torch --index-url https://download.pytorch.org/whl/cu128
+          pip install torch --index-url https://download.pytorch.org/whl/${{ matrix.cuda_tag }}
 
-      - name: Setup CUDA
+      - name: Setup CUDA (${{ matrix.cuda_ver }})
         uses: Jimver/cuda-toolkit@v0.2.35
         with:
-          cuda: "12.8.0"
+          cuda: "${{ 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
+          name: cuda-wheel-linux-${{ matrix.cuda_tag }}
           path: dist/*.whl
           if-no-files-found: error
 
@@ -66,10 +77,11 @@ jobs:
           name: pure-wheel
           path: release-assets/pure
 
-      - name: Download CUDA wheel
+      - name: Download CUDA wheels (all variants)
         uses: actions/download-artifact@v4
         with:
-          name: cuda-wheel-linux
+          pattern: cuda-wheel-linux-*
+          merge-multiple: true
           path: release-assets/cuda
 
       - name: Verify release assets
@@ -79,8 +91,7 @@ jobs:
           pure_wheels=(release-assets/pure/*.whl)
           cuda_wheels=(release-assets/cuda/*.whl)
           test "${#pure_wheels[@]}" -eq 1
-          test "${#cuda_wheels[@]}" -eq 1
-          test "$(basename "${pure_wheels[0]}")" != "$(basename "${cuda_wheels[0]}")"
+          test "${#cuda_wheels[@]}" -ge 1
 
       - name: Create release & upload assets
         uses: softprops/action-gh-release@v2

.gitignore

@@ -9,6 +9,7 @@
 !scripts/**/*.py
 !tests/**/*.py
 !csrc/**/*.py
+!csrc/CMakeLists.txt
 
 !csrc/**/*.cu
 !csrc/**/*.h
@@ -24,7 +25,7 @@
 !/.dockerignore
 !/Dockerfile
 !/docker-compose.yml
-!/assets/**
+!/docs/**
 !/CONTRIBUTING.md
 !/LICENSE
 !/pyproject.toml

CONTRIBUTING.md

@@ -20,9 +20,6 @@ Run the following checks **in order** — CI will reject if any fail.
 ruff format .
 ```
 
-> **Note**: `ruff format` may rename parameters (e.g. `mask` → `attn_mask`).  
-> Always review the diff after formatting.
-
 ### 2. Import sorting
 
 ```bash
@@ -42,22 +39,28 @@ ruff format .    # re-format after fix
 python -u -m pytest tests/ -v
 ```
 
-> Failed tests may leave orphan tempdirs under `%TEMP%`. Clean them manually if needed.
+> 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
+### 4. (Optional) Full pre-commit check script
 
-If you have Git Bash available:
+If you have `bash` available (Git Bash on Windows works too):
 
 ```bash
 bash scripts/pre_commit.sh
 ```
 
-This runs format check, import sort check, and tests in one go.
+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
 
 ```
-fix/feat/chore/docs/refactor/perf/test/style/ci/build/revert : short description (~50 chars)
+type: short description (~50 chars)
 
 - bullet point body (each ~60 chars)
 ```
@@ -73,7 +76,7 @@ fix/feat/chore/docs/refactor/perf/test/style/ci/build/revert : short description
 |---------|-------|-----|
 | `ruff check --select I` fails | Wrong import order | `ruff check . --select I --fix .` then `ruff format .` |
 | `ruff format` changed many files | Not formatted before commit | Review diff carefully before staging |
-| Pre-commit hook rejects | Tests or lint failed | Fix individually, do not `--no-verify` |
+| 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
@@ -93,7 +96,7 @@ fix/feat/chore/docs/refactor/perf/test/style/ci/build/revert : short description
 
 ## License
 
-By contributing, you agree that your contributions will be licensed under the [GPL-3.0 License](LICENSE).
+By contributing, you agree that your contributions will be licensed under the [Apache-2.0 License](LICENSE).
 
 ---
 

Dockerfile

@@ -1,8 +1,16 @@
 # 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
@@ -20,10 +28,12 @@ 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/cu128
+    --extra-index-url "https://download.pytorch.org/whl/${CUDA_TAG}"
 
 # Production stage
 FROM ubuntu:24.04 AS production
@@ -43,12 +53,17 @@ ENV PATH="/opt/venv/bin:$PATH"
 # Copy application code
 COPY astrai/ ./astrai/
 COPY scripts/ ./scripts/
-COPY assets/ ./assets/
+COPY docs/ ./docs/
 COPY pyproject.toml .
 COPY README.md .
 
-# Create non-root user
-RUN useradd -m astrai && chown -R astrai:astrai /app
+# 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 \

LICENSE

@@ -1,674 +1,201 @@
-                    GNU GENERAL PUBLIC LICENSE
-                       Version 3, 29 June 2007
-
- Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
- Everyone is permitted to copy and distribute verbatim copies
- of this license document, but changing it is not allowed.
-
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README.md

@@ -1,6 +1,6 @@
 <div align="center">
   
-  <img src="assets/images/logo.png" width="auto" alt="Logo">
+  <img src="docs/images/logo.png" width="auto" alt="Logo">
   <p>
     <strong>A lightweight Transformer training & inference framework</strong>
   </p>
@@ -8,7 +8,7 @@
 
 <div align="center">
   <img src="https://img.shields.io/badge/python-3.12+-blue.svg" alt="python">
-  <img src="https://img.shields.io/badge/license-GPL--3.0-blue.svg" alt="license">
+  <img src="https://img.shields.io/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">
@@ -17,7 +17,7 @@
 
 <div align="center">
   <a href="#english">English</a> •
-  <a href="assets/docs/README-zh-CN.md">中文</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>
@@ -27,7 +27,7 @@
 
 ## 📖 Table of Contents
 
-- [Features](#features)
+- [Overview](#overview)
 - [Getting Started](#getting-started)
 - [Demo](#demo)
 - [Documentation](#documentation)
@@ -40,15 +40,19 @@
 <a id="english"></a>
 ## English
 
-### Features
+### Overview
 
-- 🚀 **High Performance**: Optimized for both training and inference with efficient parallelization.
-- 🔧 **Flexible**: Support for seq/sft/dpo/grpo training, customizable model architectures.
-- 💡 **Easy to Use**: Simple API with comprehensive examples and demos.
-- 📦 **Lightweight**: Minimal dependencies, easy to deploy.
-- 🔬 **Research‑Friendly**: Modular design, easy to experiment with new ideas.
-- 🤗 **HuggingFace-Style API**: AutoModel/AutoTokenizer APIs inspired by HuggingFace for easy model and tokenizer loading.
-- 🔌 **Dual API Compatibility**: Supports both OpenAI and Anthropic chat completion APIs out of the box.
+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.
+
+| 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 |
 
 ### Getting Started
 
@@ -56,11 +60,14 @@ End-to-end walkthrough in 5 steps:
 
 **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
 git clone https://github.com/ViperEkura/AstrAI.git
 cd AstrAI
-pip install -e .                                          # pure PyTorch (no CUDA kernels)
-# CSRC_KERNELS=true pip install -e . --no-build-isolation  # optional: fused CUDA kernels
+pip install -e .                                          # 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)
 ```
 
@@ -132,7 +139,7 @@ Check out the demos in the `scripts/demo/` folder:
 # Download model weights (required before running demos)
 python scripts/demo/download.py                      # model → params/
 
-# Interactive streaming chat (multi-turn, maintains history)
+# Single-turn interactive streaming prompt loop (no conversation history)
 python scripts/demo/stream_chat.py
 # Type your message after >>, type !exit to quit
 
@@ -183,8 +190,11 @@ docker run --gpus all -v /path/to/data:/data -it astrai:latest
 # Docker Compose (GPU, default)
 docker compose up -d
 
-# Docker Compose (CPU only)
+# Docker Compose 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`.
@@ -213,18 +223,25 @@ curl -X POST http://localhost:8000/v1/messages \
 curl http://localhost:8000/health
 ```
 
-See [Inference Guide](assets/docs/inference.md) for SSE streaming format, error codes, and stats endpoint.
+See [Inference Guide](docs/guides/inference.md) for SSE streaming format, error codes, and stats endpoint.
 
 ### Documentation
 
 | Document | Description |
 |----------|-------------|
-| [CLI Reference](./assets/docs/params.md) | Parameters for all CLI tools (train, server, generate, preprocess) |
-| [Architecture](./assets/docs/architecture.md) | System architecture, class diagram & design patterns |
-| [Training](./assets/docs/training.md) | Training loop, strategies & formulas |
-| [Inference](./assets/docs/inference.md) | KVCache, continuous batching, sampling & HTTP API |
-| [Data Flow](./assets/docs/dataflow.md) | Data pipeline, storage backends & dataset architecture |
-| [Preprocessing](./assets/docs/preprocessing.md) | Declarative JSON-driven data preprocessing |
+| [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
 
@@ -245,10 +262,10 @@ For major changes, please open an issue first to discuss what you would like to
 
 ### License
 
-This project is licensed under the [GPL-3.0 License](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>
+</div>

assets/docs/dataflow.md

@@ -1,130 +0,0 @@
-# Data Flow
-
-This document describes the data pipeline: from raw text to model input tensors. For creating preprocessing configs, see [Preprocessing Guide](preprocessing.md).
-
-## Contents
-
-- [Overview](#overview)
-- [Data Preparation](#data-preparation) — tokenization, format detection, backends
-- [Data Keys by Training Type](#data-keys-by-training-type)
-- [Dataset Architecture](#dataset-architecture)
-- [Sampler](#sampler)
-- [DataLoader](#dataloader)
-
-## Overview
-
-```
-JSONL Lines → Pipeline (mask builder) → Tokenized Tensors
-                                              ↓
-                                      .h5 or .bin storage
-                                              ↓
-                                      Store.load()
-                                              ↓
-                                      Store.fetch(begin, end, keys)
-                                              ↓
-                                      BaseDataset.__getitem__(idx)
-                                              ↓
-                                      Sampler → DataLoader → Training / Inference
-```
-
-## Data Preparation
-
-Raw text is tokenized via `AutoTokenizer.encode()` and saved as HDF5 (`.h5`) or binary (`.bin` + `meta.json`) files with keyed tensor groups.
-
-### Tokenization
-
-The `Pipeline` reads JSONL lines, applies the mask builder (see [Preprocessing](preprocessing.md)), and produces flat token sequences:
-
-```python
-# Per JSONL line: messages → chat template → token IDs + loss mask
-tokens = tokenizer.encode(rendered_text)        # List[int]
-loss_mask = [0, 0, 0, 1, 1, 1, 1, 1, 1]        # 0=masked, 1=train
-# Stored as flat tensors, packed with other lines by packing strategy
-```
-
-The output `meta.json` records the storage format, key names, dtype, total token count, and tensor shapes for each shard.
-
-### Format Detection
-
-`detect_format(load_path)` inspects the path:
-
-- If `load_path` is a file: checks suffix — `.h5`/`.hdf5` → `"h5"`, `.jsonl` → `"jsonl"`, unknown suffix raises `ValueError`
-- If `load_path` is a directory: recursively globs for `*.h5`/`*.hdf5` files → `"h5"`, `*.bin` + `**/meta.json` → `"bin"`, or `*.jsonl` + `dataset_config.json` → `"jsonl"`
-
-### Store Backends
-
-Storage format is auto-detected by `detect_format()`; backends are dispatched via registry:
-
-```
-StoreFactory.create("h5")    → H5Store
-StoreFactory.create("bin")   → MmapStore
-StoreFactory.create("jsonl") → JsonlStore
-```
-
-All three inherit `Store` (base, owns `_data`/`_cum`/`_offsets`/`_normalize`) plus the `Streamable` and `Recordable` mixins, so every backend supports both `fetch(begin, end, keys)` (stream) and `fetch_record(index, keys)` (record) APIs.
-
-**H5Store**: Reads HDF5 files. Tensors are loaded into host memory and normalized into segmented storage. `segments_are_records=True` — each `data_i` dataset is one record.
-
-**MmapStore**: Memory-maps `.bin` files. OS page cache sharing is native — no explicit `share_memory_()` needed. Uses `torch.from_numpy(np.memmap(...))`. `segments_are_records=False` — bin segments are contiguous streams; record access is driven by `_offsets` (written when `save_bin(..., record_keys=...)` was used at preprocessing time).
-
-**JsonlStore**: On-the-fly tokenization of raw JSONL files at load time. Requires a `dataset_config.json` alongside the `.jsonl` files following the same `PipelineConfig` schema with an additional `tokenizer_path` field. Two modes: eager (default, applies `TokenizeTransform` to all records at load) and lazy (`processor=fn` given, defers tokenisation to `fetch_record` — used by DPO/GRPO).
-
-All backends normalise tensors into `Store._data[Dict[str, List[Tensor]]]` + `Store._cum[Dict[str, List[int]]]` (cumulative lengths for bisect-based stream indexing) + `Store._offsets[Dict[str, List[int]]]` (per-record offsets for record-mode indexing). Nested keys (GRPO `responses`/`masks` as `List[List[Tensor]]`) are stored as-is and excluded from both bookkeepings — they are only accessed record-by-record.
-
-## Data Keys by Training Type
-
-| Type | Storage Keys | Access Mode |
-|------|-------------|-------------|
-| `seq` | `sequence` (→ input_ids, target_ids via offset-by-1) | stream (`fetch`) |
-| `sft` | `sequence`, `loss_mask`, `position_ids` | stream (`fetch`) |
-| `dpo` | `chosen`, `rejected`, `chosen_mask`, `rejected_mask` | record (`fetch_record`) |
-| `grpo` | `prompts`, `responses`, `masks`, `rewards` | record (`fetch_record`) |
-
-## Dataset Architecture
-
-```
-DatasetFactory.load(train_type, load_path, window_size, stride=None,
-                    storage_type=None, tokenizer_path=None,
-                    max_position_embeddings=2048, store=None)
-  → BaseDataset.load(load_path, storage_type=None)
-    → detect_format(load_path)
-    → StoreFactory.create(storage_type)
-    → Store.load(load_path)
-      → _normalize(raw)  # base Store, shared by both backends
-        → Store._data[Dict[str, List[Tensor]]]
-          + _cum[Dict[str, List[int]]]   (stream mode)
-          + _offsets[Dict[str, List[int]]]  (record mode)
-
-Stream datasets (SEQ/SFT):
-  BaseDataset.__getitem__(idx)
-    → get_index(idx) → [begin, end)
-    → Store.fetch(begin, end, keys) → Tensor / Dict[str, Tensor]
-
-Record datasets (DPO/GRPO via RecordDataset):
-  RecordDataset.__getitem__(idx)
-    → Store.fetch_record(idx, keys) → Tensor / Dict[str, Tensor]
-```
-
-Class hierarchy: `BaseDataset` ← `SEQDataset` / `SFTDataset` (stream); `BaseDataset` ← `RecordDataset` ← `DPODataset` / `GRPODataset` (record).
-
-`window_size` = max input length, `stride` = step between consecutive samples (defaults to `window_size`, optional). Only meaningful for stream datasets — record datasets ignore both. `storage_type` defaults to `None` (auto-detect via `detect_format`).
-
-`tokenizer_path` triggers lazy on-the-fly tokenisation for record datasets on raw JSONL (DPO builds a `dpo_processor`; SEQ/SFT/pre-tokenised backends ignore it). `store` (pre-built `Store`) bypasses `load_path`/`storage_type`/`tokenizer_path` entirely — the caller controls Store construction.
-
-`Store.fetch(begin, end, keys)` (stream mode, on `Streamable`): accepts a single key (`str`) returning a `Tensor`, or a list of keys returning `Dict[str, Tensor]`. Internally uses `bisect` across multi-segment tensors. Raises `RuntimeError("Store not loaded")` if called before `load()`.
-
-`Store.fetch_record(index, keys)` (record mode, on `Recordable`): same key API. Uses `_offsets[key]` when present (bin layout with per-record offsets), otherwise indexes `_data[key]` directly (H5/JSONL where each segment is one record).
-
-## Sampler
-
-`ResumableDistributedSampler` supports checkpoint-aware distributed sampling:
-
-- Tracks `start_epoch` / `start_iter` for resume
-- Shuffle via `torch.Generator(seed + epoch)`
-- Per-replica index slicing for DDP
-
-## DataLoader
-
-Standard PyTorch `DataLoader` with configurable `batch_size`, `num_workers`, `pin_memory`, `prefetch_factor`. Sampler produces indices; dataloader fetches tensor batches via `__getitem__`.
-
-> Document Update Time: 2026-07-19

assets/docs/inference.md

@@ -1,252 +0,0 @@
-# Inference
-
-## Contents
-
-- [KV Cache](#kv-cache)
-- [KVCache System](#kvcache-system)
-- [Continuous Batching](#continuous-batching)
-- [Sampling](#sampling-strategy-pattern)
-- [Protocol Handlers](#protocol-handlers-strategy-pattern)
-- [Engine & GenerateResult](#engine--generateresult)
-- [HTTP API](#http-api) — endpoints, SSE, errors, stats
-- [Engine API](#engine-api)
-
-## KV Cache
-
-At decode time, only the last query token matters. All previous K/V are cached to avoid recomputation:
-
-$$
-o_n = \sum_j \text{softmax}\left(\frac{q_n k_j}{\sqrt{d_k}}\right) v_j
-$$
-
-RoPE is applied **before** KV cache write, not after — otherwise position encoding drift occurs.
-
-## KVCache System
-
-Seven classes working together, with two concrete cache implementations:
-
-### ContiguousCache (default)
-
-```
-ContiguousCache (simple contiguous per-slot cache)
-  ├── ContiguousCacheView  bundles k/v tensors + slot indices for attention layers
-```
-
-Created by default when no cache is passed to `InferenceScheduler`. Each task occupies a fixed slot of `[max_seq_len, num_key_value_heads, head_dim]`. Simple and efficient for small-to-medium batch sizes.
-
-### PageCache (paged with prefix sharing)
-
-```
-PageCache (paged KV cache with prefix sharing, alternative)
-  ├── PagePool               orchestrates page allocation + prefix matching
-  │     ├── Allocator         bitmask-based page allocator + ref-count + LRU
-  │     └── PrefixCache       hash-based prefix matching (page_hash via polynomial hash)
-  ├── TaskTable              maps task_id → page_table + cached token count
-  ├── Storage                k_cache / v_cache tensors (num_hidden_layers × n_pages × page_size × num_key_value_heads × head_dim)
-  └── PageCacheView          bundles Storage + page_table + total_len for attention layers
-```
-
-`isinstance(cache, KVCache)` checks dispatch to the correct view. Both implement the abstract `KVCache` interface used by `Executor` and `InferenceScheduler`.
-
-## Continuous Batching
-
-`InferenceScheduler` runs a daemon thread with a 4-phase loop:
-
-```
-1. Cleanup → Remove finished tasks, free KV cache slots/pages
-2. Refill  → Pop from waiting_queue, task_alloc resources, activate
-3. Prefill → Group by (prompt_len, start_pos), run full forward
-4. Decode  → Run single-token forward for each same-position group
-```
-
-## Sampling (Strategy Pattern)
-
-```
-BaseSamplingStrategy (ABC)
-  ├── TemperatureStrategy
-  ├── TopKStrategy
-  ├── TopPStrategy
-  └── SamplingPipeline
-```
-
-`SamplingPipeline` composes them: Temperature → Top-K → Top-P → softmax → multinomial.  
-`sample()` is a convenience shortcut for one-shot usage.
-
-## Protocol Handlers (Strategy Pattern)
-
-```python
-class ProtocolHandler:  # concrete orchestrator
-    def __init__(self, request, engine, builder): ...
-    async def handle(self):
-        prompt, ctx, stops = builder.prepare(request, engine)
-        agen = engine.generate_async(prompt, ...)
-        if stream: self._handle_stream(agen, ctx, stops)
-        else:      return await self._handle_non_stream(agen, ctx, stops)
-```
-
-`ResponseBuilder` (ABC): `prepare()`, `format_stream_start()`, `format_chunk()`, `format_stream_end()`, `format_response()`.
-
-`OpenAIResponseBuilder` → `/v1/chat/completions`, `AnthropicResponseBuilder` → `/v1/messages`.
-
-Adding a protocol = one builder file, no handler subclassing needed.
-
-## Engine & GenerateResult
-
-```
-InferenceEngine
-  ├── generate(prompt, stream, ...) → str | List[str] | Generator
-  ├── generate_with_request(req)    → same
-  ├── generate_async(prompt, ...)   → AsyncGenerator
-  ├── get_stats()                   → Dict
-  └── shutdown()
-```
-
-`GenerateResult` uses `Condition` for non-streaming (`wait_completion()`) and `Event` for streaming (`wait()`). Stream callback is `cb(token)`.
-
-## HTTP API
-
-```
-POST /v1/chat/completions   OpenAI
-POST /v1/messages            Anthropic
-GET  /health                 {"status":"ok","model_loaded":true}
-GET  /stats                  scheduler statistics
-```
-
-### OpenAI
-
-```bash
-curl -X POST http://localhost:8000/v1/chat/completions \
-  -H "Content-Type: application/json" \
-  -d '{"messages":[{"role":"user","content":"Hello"}],"max_tokens":512}'
-```
-
-Response:
-```json
-{
-  "id": "chatcmpl-abc123",
-  "object": "chat.completion",
-  "created": 1717000000,
-  "model": "astrai",
-  "choices": [{"index": 0, "message": {"role": "assistant", "content": "Hello!"}, "finish_reason": "stop"}],
-  "usage": {"prompt_tokens": 5, "completion_tokens": 10, "total_tokens": 15}
-}
-```
-
-Streaming SSE: `object: "chat.completion.chunk"` — starts with role delta, then token chunks, ends with finish chunk + usage stats, then `data: [DONE]`.
-
-### Anthropic
-
-```bash
-curl -X POST http://localhost:8000/v1/messages \
-  -H "Content-Type: application/json" \
-  -d '{"model":"astrai","system":"You are helpful.","messages":[{"role":"user","content":"Hello"}],"max_tokens":512}'
-```
-
-Supports `stop_sequences` and streaming via `event: content_block_delta`.
-
-### GenerationRequest Parameters
-
-| Param | Type | Default | Description |
-|-------|------|---------|-------------|
-| `messages` | List[dict] | required | Chat messages (role, content) |
-| `top_k` | int | 50 | Top-k count |
-| `top_p` | float | 1.0 | Nucleus threshold |
-| `temperature` | float | 1.0 | Sampling temperature (> 0.0) |
-| `max_tokens` | Optional[int] | None | Max generation length |
-| `stream` | bool | False | Stream output |
-
-### SSE Streaming Format
-
-**OpenAI** (`/v1/chat/completions`, `stream=true`):
-
-```
-data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":...,"model":"astrai",
-       "choices":[{"index":0,"delta":{"role":"assistant"},"finish_reason":null}]}
-
-data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":0,"model":"astrai",
-       "choices":[{"index":0,"delta":{"content":"Hello"},"finish_reason":null}]}
-
-data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":...,"model":"astrai",
-       "choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}
-
-data: {"prompt_tokens":5,"completion_tokens":1,"total_tokens":6}
-
-data: [DONE]
-```
-
-**Anthropic** (`/v1/messages`, `stream=true`):
-
-```
-event: message_start
-data: {"type":"message_start","message":{"id":"msg_...","model":"astrai","role":"assistant",
-       "content":[],"usage":{"input_tokens":0}}}
-
-event: content_block_start
-data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""}}
-
-event: content_block_delta
-data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"Hello"}}
-
-event: content_block_stop
-data: {"type":"content_block_stop","index":0}
-
-event: message_delta
-data: {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null},"usage":{...}}
-
-event: message_stop
-data: {"type":"message_stop"}
-```
-
-### Error Responses
-
-The server returns standard HTTP status codes. Pydantic validation errors (e.g. missing required fields)
-are handled automatically by FastAPI with 422 status. The only application-level error is engine initialization:
-
-| Status | Meaning |
-|--------|---------|
-| 200 | Success |
-| 422 | Unprocessable entity (Pydantic validation) |
-| 503 | Service unavailable (model not loaded, engine not ready) |
-
-Error response body (503):
-
-```json
-{
-    "detail": "Engine not initialized"
-}
-```
-
-### Stats Endpoint
-
-```
-GET /stats
-```
-
-Response:
-
-```json
-{
-    "total_tasks": 128,
-    "total_tokens": 10240,
-    "active_tasks": 3,
-    "waiting_queue": 2
-}
-```
-
-## Engine API
-
-```python
-# Non-streaming
-engine.generate("Hello", stream=False)          # -> str
-engine.generate(["A", "B"], stream=False)       # -> List[str]
-
-# Streaming
-engine.generate("Hello", stream=True)           # -> Generator[str]
-engine.generate(["A", "B"], stream=True)        # -> Generator[Tuple[int, str]]
-
-# Async
-async for token in engine.generate_async("Hello", ...):    # -> AsyncGenerator[str]
-    print(token)
-```
-
-> Document Update Time: 2026-07-09

astrai/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "1.3.11"
+__version__ = "1.3.13"
 __author__ = "ViperEkura"
 
 from astrai.config import (
@@ -17,15 +17,10 @@ from astrai.dataset import (
     StoreFactory,
 )
 from astrai.factory import BaseFactory
-from astrai.inference import (
-    GenerationRequest,
-    InferenceEngine,
-    ProtocolHandler,
-    SamplingPipeline,
-    get_app,
-    run_server,
-    sample,
-)
+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,
@@ -71,7 +66,6 @@ __all__ = [
     "EmbeddingEncoder",
     "EncoderConfig",
     "ExecutorFactory",
-    "GenerationRequest",
     "InferenceEngine",
     "LoRAConfig",
     "Pipeline",
@@ -94,5 +88,8 @@ __all__ = [
     "only_on_rank",
     "run_server",
     "sample",
+    "setup_logging",
     "spawn_parallel_fn",
 ]
+
+setup_logging()

astrai/config/base.py

@@ -1,92 +1,32 @@
 import json
-from dataclasses import MISSING, dataclass, fields
+from dataclasses import asdict
 from pathlib import Path
-from typing import Any, Dict, Optional, Self, Union, get_type_hints
+from typing import Any, Dict, Self, Union
+
+from pydantic import ConfigDict
+from pydantic.dataclasses import dataclass
 
 
-@dataclass
+@dataclass(config=ConfigDict(use_attribute_docstrings=True))
 class BaseConfig:
     def to_dict(self) -> Dict[str, Any]:
-        d = {}
-        for fld in fields(self):
-            v = getattr(self, fld.name)
-            if isinstance(v, (str, int, float, bool)):
-                d[fld.name] = v
-            elif v is None:
-                d[fld.name] = None
-            elif isinstance(v, (dict, list, tuple)):
-                try:
-                    val = list(v) if isinstance(v, tuple) else v
-                    json.dumps(val)
-                    d[fld.name] = val
-                except (TypeError, ValueError):
-                    pass
-            elif isinstance(v, BaseConfig):
-                d[fld.name] = v.to_dict()
-            elif hasattr(v, "__dataclass_fields__"):
-                sub = {}
-                for f in fields(v):
-                    a = getattr(v, f.name)
-                    sub[f.name] = list(a) if isinstance(a, tuple) else a
-                d[fld.name] = sub
-        return d
+        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:
-        hints = get_type_hints(cls)
-        inst = cls.__new__(cls)
-        for fld in fields(cls):
-            if fld.name in d:
-                v = d[fld.name]
-                target = cls._unwrap_optional(hints.get(fld.name))
-                if target is not None:
-                    try:
-                        v = cls._coerce(v, target)
-                    except (TypeError, ValueError):
-                        pass
-                object.__setattr__(inst, fld.name, v)
-            elif fld.default is not MISSING:
-                object.__setattr__(inst, fld.name, fld.default)
-            elif fld.default_factory is not MISSING:
-                object.__setattr__(inst, fld.name, fld.default_factory())
-            else:
-                object.__setattr__(inst, fld.name, None)
-        return inst
-
-    @staticmethod
-    def _unwrap_optional(tp) -> Optional[type]:
-        if tp is None:
-            return None
-        origin = getattr(tp, "__origin__", None)
-        if origin is not None:
-            args = getattr(tp, "__args__", ())
-            non_none = [a for a in args if a is not type(None)]
-            return non_none[0] if non_none else None
-        return tp
-
-    @staticmethod
-    def _coerce(value: Any, target_type: type) -> Any:
-        if target_type is bool and isinstance(value, bool):
-            return value
-        if (
-            target_type is int
-            and isinstance(value, (int, float))
-            and not isinstance(value, bool)
-        ):
-            return int(value)
-        if (
-            target_type is float
-            and isinstance(value, (int, float))
-            and not isinstance(value, bool)
-        ):
-            return float(value)
-        if target_type is str and isinstance(value, str):
-            return value
-        if isinstance(value, target_type):
-            return value
-        if isinstance(value, dict) and issubclass(target_type, BaseConfig):
-            return target_type.from_dict(value)
-        raise TypeError
+        return cls(**d)
 
     @classmethod
     def from_file(cls, path: Union[str, Path]) -> Self:

astrai/config/model_config.py

@@ -1,9 +1,14 @@
-from dataclasses import dataclass
 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``."""
@@ -17,7 +22,12 @@ class ConfigFactory(BaseFactory[BaseConfig]):
 
 @dataclass
 class BaseModelConfig(BaseConfig):
-    """Base config with ``model_type`` dispatch and file I/O."""
+    """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
@@ -26,7 +36,39 @@ class BaseModelConfig(BaseConfig):
 @dataclass
 @ConfigFactory.register("autoregressive_lm")
 class AutoRegressiveLMConfig(BaseModelConfig):
-    """Configuration for autoregressive language model."""
+    """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
@@ -34,49 +76,103 @@ class AutoRegressiveLMConfig(BaseModelConfig):
     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."""
+    """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

@@ -5,11 +5,19 @@ modes, both driven declaratively through ``input.sections`` or
 ``input.sources``.
 """
 
-from dataclasses import dataclass, field
+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):
@@ -25,6 +33,10 @@ class InputConfig(BaseConfig):
             "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
@@ -33,34 +45,17 @@ class InputConfig(BaseConfig):
 
 @dataclass
 class ProcessingConfig(BaseConfig):
-    """Processing configuration.
+    """Processing configuration for tokenization and packing.
 
-    Parameters
-    ----------
-    max_seq_len : int
-        Maximum sequence length (default: 2048).
-    min_chars : int
-        Minimum number of characters to keep (default: 50).
-    max_chars : int
-        Maximum number of characters to keep (default: 2_000_000).
-    max_items : Optional[int]
-        Maximum number of items to process (default: None, unlimited).
-    batch_size : int
-        Number of records tokenized together (default: 256).
-    packing_strategy : str
-        How to pack sequences into a contiguous stream.
-
-        - ``"simple"``: sequential concatenation (default, backward compatible).
-        - ``"bfd"``: best-fit decreasing bin packing, minimises wasted tokens.
-        - ``"bfd_split"``: BFD with over-length sequences split into chunks.
-    max_packed_len : int
-        Maximum length of a packed bin. Sequences longer than this are
-        truncated or split depending on ``packing_strategy`` (default: 8192).
-    truncation_mode : str
-        How to truncate sequences longer than ``max_packed_len``.
-
-        - ``"keep_start"``: keep the first ``max_packed_len`` tokens (default).
-        - ``"keep_end"``: keep the last ``max_packed_len`` tokens.
+    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
@@ -72,27 +67,45 @@ class ProcessingConfig(BaseConfig):
     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.
+    """Output configuration for storage.
 
-    Parameters
-    ----------
-    domain_key : Optional[str]
-        Domain key for the output store (default: None).
-    storage_format : str
-        Storage format, one of ``"bin"``, ``"jsonl"`` (default: ``"bin"``).
-    max_tokens_per_shard : int
-        Maximum tokens per shard before splitting (default: 100_000_000).
-    dtype : Dict[str, str]
-        Per-key dtype overrides, e.g. ``{"input_ids": "int32"}`` (default: {}).
-    position_ids_mode : Optional[str]
-        How to compute position_ids in packed sequences.
-
-         - ``"none"``: do not generate (default).
-        - ``"doc_reset"``: reset to 0 at each document boundary.
-        - ``"continuous"``: sequential 0, 1, 2, ... (pretrain, single doc).
+    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
@@ -101,9 +114,36 @@ class OutputConfig(BaseConfig):
     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)

astrai/config/train_config.py

@@ -1,7 +1,9 @@
-from dataclasses import dataclass, field, fields
+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
@@ -9,173 +11,210 @@ from torch.utils.data import Dataset
 from astrai.config.base import BaseConfig
 from astrai.model.components.lora import LoRAConfig
 
-
-def required(**kw):
-    return {"required": True, **kw}
+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
+@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
 class TrainConfig(BaseConfig):
-    # basic setting
-    model_fn: Callable[[], nn.Module] = field(
-        default=None, metadata=required(help="Model factory for training.")
-    )
-    strategy: str = field(default=None, metadata=required(help="Training strategy."))
-    dataset: Dataset = field(
-        default=None, metadata=required(help="Dataset for training.")
-    )
-    optimizer_fn: Callable[[nn.Module], Optimizer] = field(
-        default=None, metadata=required(help="Optimizer factory for training.")
-    )
-    scheduler_fn: Callable[[Optimizer], LRScheduler] = field(
-        default=None, metadata=required(help="Scheduler factory for training.")
-    )
-    n_epoch: int = field(default=1, metadata={"help": "Number of epochs for training."})
-    batch_per_device: int = field(
-        default=4, metadata={"help": "Batch size per device."}
-    )
-    grad_accum_steps: int = field(
-        default=1, metadata={"help": "Number of iterations between steps."}
-    )
-    max_grad_norm: Optional[float] = field(
-        default=1.0,
-        metadata={"help": "Maximum gradient norm. None disables clipping."},
-    )
-    gradient_checkpointing_modules: List[str] = field(
-        default_factory=list,
-        metadata={"help": "Module types to enable activation checkpointing for."},
-    )
+    """Training configuration.
 
-    # checkpoint setting
-    start_epoch: int = field(default=0, metadata={"help": "Start epoch for training."})
-    start_samples: int = field(
-        default=0,
-        metadata={
-            "help": "Start samples count (per rank). Superseded by checkpoint consumed_samples."
-        },
-    )
-    ckpt_dir: str = field(
-        default="./checkpoint", metadata={"help": "Checkpoint directory."}
-    )
-    ckpt_interval: int = field(
-        default=5000,
-        metadata={"help": "Number of optimizer steps between checkpoints."},
-    )
+    Combines hyperparameters with runtime objects (model_fn, dataset, etc.).
+    Only JSON-serializable fields are written to checkpoint meta via to_dict().
 
-    # lora setting
-    lora: Optional[LoRAConfig] = field(
-        default=None,
-        metadata={"help": "LoRA config. None means full fine-tuning."},
-    )
+    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 {}.
+    """
 
-    # metric setting
-    log_dir: str = field(
-        default="./checkpoint/logs", metadata={"help": "Directory for metric logs."}
-    )
-    metrics: List[str] = field(
-        default_factory=lambda: ["loss", "lr", "grad_norm"],
-        metadata={"help": "Metrics to record during training."},
-    )
+    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
 
-    # dataloader setting
-    random_seed: int = field(default=3407, metadata={"help": "Random seed."})
-    num_workers: int = field(
-        default=0, metadata={"help": "Number of workers for dataloader."}
-    )
-    prefetch_factor: Optional[int] = field(
-        default=None, metadata={"help": "Prefetch factor for dataloader."}
-    )
-    pin_memory: bool = field(
-        default=False, metadata={"help": "Pin memory for dataloader."}
-    )
-    collate_fn: Optional[Callable[[List[Any]], Any]] = field(
-        default=None,
-        metadata={"help": "Collate function for dataloader (e.g. dpo_collate_fn)."},
-    )
+    start_epoch: int = 0
+    start_samples: int = 0
+    ckpt_dir: str = "./checkpoint"
+    ckpt_interval: int = 5000
 
-    # distributed training
-    nprocs: int = field(
-        default=1, metadata={"help": "Number of processes for distributed training."}
-    )
-    backend: str = field(
-        default="nccl", metadata={"help": "Distributed training backend."}
-    )
-    master_addr: str = field(
-        default="localhost",
-        metadata={"help": "Master address for distributed training."},
-    )
-    master_port: str = field(
-        default="29500", metadata={"help": "Master port for distributed training."}
-    )
-    parallel_mode: str = field(
-        default="none",
-        metadata={"help": "Parallel strategy: none, ddp, fsdp."},
-    )
-    start_method: str = field(
-        default="spawn",
-        metadata={"help": "Multiprocessing start method (spawn/fork/forkserver)."},
-    )
+    lora: Optional[LoRAConfig] = None
 
-    # others
-    device_type: str = field(
-        default="cuda", metadata={"help": "Device type for distributed training."}
-    )
-    val_dataset: Optional[Dataset] = field(
-        default=None, metadata={"help": "Dataset for validation."}
-    )
-    val_split: Optional[float] = field(
-        default=None,
-        metadata={
-            "help": "Ratio to split from training dataset for validation (e.g. 0.05). Ignored if val_dataset is set."
-        },
-    )
-    val_step: int = field(
-        default=1000,
-        metadata={"help": "Number of optimizer steps between validation runs."},
-    )
-    neftune_alpha: float = field(
-        default=0.0,
-        metadata={"help": "NEFTune noise alpha (0=disabled, typical: 5.0)."},
-    )
+    metrics: List[str] = field(default_factory=lambda: ["loss", "lr", "grad_norm"])
 
-    # online rollout
-    rollout_interval: int = field(
-        default=512,
-        metadata={"help": "Number of optimizer steps between online rollouts."},
-    )
-    rollout_temperature: float = field(
-        default=0.7, metadata={"help": "Sampling temperature for online rollout."}
-    )
-    rollout_top_k: int = field(
-        default=0, metadata={"help": "Top-k filtering for online rollout (0=disable)."}
-    )
-    rollout_top_p: float = field(
-        default=0.9,
-        metadata={"help": "Top-p (nucleus) filtering for online rollout."},
-    )
-    rollout_max_tokens: int = field(
-        default=1024,
-        metadata={"help": "Maximum generated tokens per response in rollout."},
-    )
-    reward_model_fn: Optional[Callable] = field(
-        default=None,
-        metadata={
-            "help": "Factory for reward model (required for online RL strategies)."
-        },
-    )
+    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
 
-    executor_kwargs: Dict[str, Any] = field(
-        default_factory=dict,
-        metadata={"help": "Extra kwargs passed to ExecutorFactory.create()."},
-    )
-    extra_kwargs: Dict[str, Any] = field(
-        default_factory=dict, metadata={"help": "Other arguments."}
-    )
+    nprocs: int = 1
+    backend: str = "nccl"
+    master_addr: str = "localhost"
+    master_port: str = "29500"
+    parallel_mode: str = "none"
+    start_method: str = "spawn"
 
-    def __post_init__(self):
-        self.validate()
+    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
 
-    def validate(self):
-        for fld in fields(self):
-            if fld.metadata.get("required") and getattr(self, fld.name) is None:
-                raise ValueError(f"TrainConfig.{fld.name} is required but got None.")
+    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

@@ -6,7 +6,6 @@ from astrai.dataset.dataset import (
 )
 from astrai.dataset.sampler import RDSampler
 from astrai.dataset.storage import (
-    H5Store,
     JsonlStore,
     MmapStore,
     Recordable,
@@ -17,9 +16,7 @@ from astrai.dataset.storage import (
 )
 from astrai.serialization import (
     load_bin,
-    load_h5,
     save_bin,
-    save_h5,
 )
 
 __all__ = [
@@ -31,12 +28,9 @@ __all__ = [
     "Streamable",
     "Recordable",
     "StoreFactory",
-    "H5Store",
     "MmapStore",
     "JsonlStore",
     "detect_format",
-    "save_h5",
-    "load_h5",
     "save_bin",
     "load_bin",
     "RDSampler",

astrai/dataset/dataset.py

@@ -25,20 +25,50 @@ function (pure ``record -> Dict[str, Tensor]``) is forwarded to
 
 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,
@@ -314,7 +344,7 @@ class DatasetFactory(BaseFactory["BaseDataset"]):
                 stream datasets (SEQ/SFT).  Record datasets ignore it.
             stride: Stride between consecutive stream samples
                 (default: same as *window_size*).
-            storage_type: Storage backend ("h5", "bin", "jsonl") or
+            storage_type: Storage backend ("bin", "jsonl") or
                 None for auto-detection.
             tokenizer_path: Path to tokenizer for lazy JSONL
                 tokenisation (record datasets only).
@@ -349,16 +379,18 @@ class DatasetFactory(BaseFactory["BaseDataset"]):
         )
         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:
-            load_kwargs = dict(kwargs)
-            if (
-                tokenizer_path is not None
-                and storage_type == "jsonl"
-                and train_type in ("seq", "sft")
-                and "tokenizer_path" not in load_kwargs
-            ):
-                load_kwargs["tokenizer_path"] = tokenizer_path
-            store.load(load_path, **load_kwargs)
+            store.load(load_path, **kwargs)
 
         return cls.create(train_type, store=store)
 
@@ -384,7 +416,7 @@ class DatasetFactory(BaseFactory["BaseDataset"]):
         """Build an on-the-fly tokenisation processor if applicable.
 
         Only raw JSONL + record datasets (DPO/GRPO) need a processor;
-        pre-tokenised backends (H5/bin) and stream datasets (SEQ/SFT)
+        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":
@@ -451,7 +483,7 @@ class DPODataset(BaseDataset):
 
     Two loading paths (handled by :class:`DatasetFactory`):
 
-    - **Pre-tokenized** (H5/bin): ``store.load(path)`` reads per-record
+    - **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,
@@ -460,9 +492,6 @@ class DPODataset(BaseDataset):
 
     required_keys = ["chosen", "rejected", "chosen_mask", "rejected_mask"]
 
-    def make_processor(self, tokenizer, max_len: int):
-        return partial(dpo_processor, tokenizer=tokenizer, max_len=max_len)
-
     def __getitem__(self, index: int) -> Dict[str, Tensor]:
         return {
             "chosen": self.store.fetch_record(index, "chosen").to(dtype=torch.long),

astrai/dataset/storage.py

@@ -10,7 +10,6 @@ Architecture (composition over inheritance):
     Streamable (mixin)        — raw token slice  fetch(begin, end, keys)
     Recordable (mixin)        — raw record slice fetch_record(idx, keys)
 
-    H5Store(Store, Streamable, Recordable)
     MmapStore(Store, Streamable, Recordable)
     JsonlStore(Store, Streamable, Recordable)
 
@@ -36,9 +35,9 @@ control.  ``store.token_count`` is the total stream token count (what
 ``len(store)`` used to mean in the legacy stream-only API).
 
 ``segments_are_records`` (class attribute on each Store subclass)
-tells ``_normalize`` whether segments are inherently per-record (H5/
-JSONL) or opaque shards (bin).  Record access for bin relies on
-``_offsets`` instead.
+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
@@ -56,13 +55,10 @@ from typing import Callable, Dict, List, Optional, Tuple, Union
 import torch
 from torch import Tensor
 
-from astrai.config.preprocess_config import PipelineConfig
 from astrai.factory import BaseFactory
-from astrai.preprocessing.transform import TokenizeTransform
 from astrai.serialization import (
     load_bin,
     load_bin_offsets,
-    load_h5,
 )
 
 logger = logging.getLogger(__name__)
@@ -83,19 +79,10 @@ def detect_format(load_path: str) -> str:
     root = Path(load_path)
     if root.is_file():
         suffix = root.suffix.lower()
-        if suffix in (".h5", ".hdf5"):
-            return "h5"
         if suffix == ".jsonl":
             return "jsonl"
         raise ValueError(f"Unsupported file format: {suffix}")
 
-    h5_files = [
-        Path(p)
-        for pattern in ("*.h5", "*.hdf5")
-        for p in glob.glob(str(root / "**" / pattern), recursive=True)
-    ]
-    if h5_files:
-        return "h5"
     bin_files = [Path(p) for p in glob.glob(str(root / "**" / "*.bin"), recursive=True)]
     if bin_files:
         has_meta = (root / "meta.json").exists() or len(
@@ -185,7 +172,7 @@ class Store(ABC):
         """Number of records available via :meth:`fetch_record`.
 
         Non-zero only when the backing layout provides per-record
-        indexing (H5/JSONL segments or bin ``_offsets``).
+        indexing (JSONL segments or bin ``_offsets``).
         """
         return self._num_records
 
@@ -230,7 +217,7 @@ class Store(ABC):
         """
         if self._window_size <= 0:
             raise RuntimeError("sample_window() requires window_size > 0 (stream mode)")
-        if self._window_size <= 0 or self._length <= self._window_size:
+        if self._length <= self._window_size:
             raise IndexError(
                 f"Data too short for window: token_count={self._length}, "
                 f"window_size={self._window_size}"
@@ -269,7 +256,7 @@ class Store(ABC):
         Record mode: if *offsets* is provided (bin layout),
         ``_offsets[key]`` stores cumulative per-record offsets into the
         single concatenated segment.  Otherwise, when
-        ``segments_are_records`` is True (H5/JSONL), ``_data[key]`` is
+        ``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
@@ -305,7 +292,7 @@ class Store(ABC):
                     logger.warning(
                         "Key '%s' has %d segments with offsets — record mode "
                         "disabled for this key (multi-shard bin+offsets not "
-                        "supported). Merge shards or use H5/JSONL.",
+                        "supported). Merge shards or use JSONL.",
                         key,
                         len(segs),
                     )
@@ -330,7 +317,7 @@ class Streamable:
     Stateless trait relying on ``self._data``, ``self._cum``,
     ``self._length`` maintained by :class:`Store`.  Stream mode is
     active when the owning store has ``window_size > 0``; for stores
-    that can also serve record access (H5/JSONL/bin+offsets), the
+    that can also serve record access (JSONL/bin+offsets), the
     ``fetch_record`` API from :class:`Recordable` is used instead.
     """
 
@@ -415,33 +402,6 @@ class StoreFactory(BaseFactory["Store"]):
     """Factory for creating Store instances by type name."""
 
 
-@StoreFactory.register("h5")
-class H5Store(Store, Streamable, Recordable):
-    """HDF5-based storage backend (pre-tokenized data).
-
-    Each key is stored as a group of per-record datasets (``data_0``,
-    ``data_1``, …).  Supports both access modes:
-
-    - **Stream**: ``fetch(begin, end, key)`` and ``store[i]`` slice
-      across concatenated records via ``_cum`` — used by SEQ/SFT.
-    - **Record**: ``fetch_record(i, key)`` and ``store[i]`` (when
-      ``window_size == 0``) index ``_data[key]`` directly — used by
-      DPO/GRPO.
-    """
-
-    segments_are_records = True
-
-    def __init__(
-        self,
-        window_size: int = 0,
-        stride: Optional[int] = None,
-    ):
-        super().__init__(window_size=window_size, stride=stride)
-
-    def load(self, path: str, **kwargs):
-        self._normalize(load_h5(path))
-
-
 @StoreFactory.register("bin")
 class MmapStore(Store, Streamable, Recordable):
     """Memory-mapped binary storage backend.
@@ -574,19 +534,8 @@ class JsonlStore(Store, Streamable, Recordable):
       ``len(store)`` returns ``num_records``; stream primitives raise.
     """
 
-    CONFIG_NAME = "dataset_config.json"
     segments_are_records = True
 
-    _DEFAULT_MESSAGES_CONFIG = {
-        "version": 1,
-        "input": {
-            "sections": [{"field": "messages", "action": "$role", "template": True}]
-        },
-        "mask": {"system": "mask", "user": "mask", "assistant": "train"},
-        "mask_default": "mask",
-        "output": {"position_ids_mode": "doc_reset"},
-    }
-
     def __init__(
         self,
         window_size: int = 0,
@@ -607,22 +556,10 @@ class JsonlStore(Store, Streamable, Recordable):
             return
 
         if transform is None:
-            root = Path(path)
-            config_path = root / self.CONFIG_NAME if root.is_dir() else None
-            if config_path is not None and config_path.exists():
-                transform = TokenizeTransform.from_config_file(str(config_path))
-            else:
-                tokenizer_path = kwargs.get("tokenizer_path")
-                if not tokenizer_path:
-                    raise FileNotFoundError(
-                        f"JSONL dataset config not found. Expected "
-                        f"{self.CONFIG_NAME} alongside *.jsonl files, pass an "
-                        f"explicit transform, pass processor= for lazy "
-                        f"on-the-fly tokenisation, or pass tokenizer_path= to "
-                        f"use the built-in messages config."
-                    )
-                config = PipelineConfig.from_dict(self._DEFAULT_MESSAGES_CONFIG)
-                transform = TokenizeTransform(config, tokenizer_path)
+            raise ValueError(
+                "JsonlStore eager mode requires transform=. "
+                "Use DatasetFactory.load() which auto-constructs it."
+            )
 
         transformed = transform.apply(records)
         self._normalize(transformed)

astrai/extension/__init__.py

@@ -4,27 +4,52 @@ 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
 
-Interface (shared by all wrappers):
-    causal_offset: -1 = non-causal; >=0 = absolute position of first Q token
-    mask:       2D [batch, kv_len] or 3D [batch, q_len, kv_len] (bool, True = keep)
-    scale:      0.0 = auto (1/sqrt(head_dim)); >0 = explicit
-    layout:     "bhld" (default) or "blhd"
+Layout convention: all q/k/v are ``[batch, seq_len, n_heads, head_dim]``
+(blhd). Scale is always ``1/sqrt(head_dim)``.
 
-Causal and mask can coexist — both are applied simultaneously.
-
-Each wrapper dispatches to its compiled CUDA kernel (``astrai.extension.attn_*``)
-when available, otherwise falls back to ``torch.nn.functional.scaled_dot_product_attention``.
+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 attention, attn_decode, attn_paged_decode, attn_prefill
+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",
-    "attention",
     "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 +1,357 @@
+"""FP8 training: scaling recipes, per-tensor state, and aten::linear dispatch.
+
+Layered (see also ``ops/fp8.py`` for the CUDA interface adapter):
+
+1. Kernel interface: ``ops.fp8`` — the only module touching the pybind.
+2. Training state (this module): scaling *recipes* (TE-style delayed scaling
+   or dynamic current-amax scaling), per-tensor scales + amax history, and
+   the ``fp8_autocast`` context (like ``torch.autocast``).
+3. aten::linear integration (this module): registers the CUDA impl and the
+   dtype guard.
+
+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 wherever it is called: the
+    # forward captures the fmt/recipe/meta on the autograd node
+
+Importing this module registers the aten::linear CUDA and AutogradCUDA
+implementations.
+
+Format defaults follow the ecosystem consensus: E4M3 for the forward pass,
+E5M2 for the backward (gradient) pass ("hybrid"); every operand's scale is a
+quantization step derived from its amax history by the active recipe.
+"""
+
+from contextlib import contextmanager
+from dataclasses import dataclass
+from enum import Enum
+from typing import 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; the scale computation is the injection point.
+
+    ``scale_from_history`` receives the amax tensor for this operand (a ring
+    window for delayed scaling, the current amax for dynamic scaling) and
+    returns the quantization step: ``scale = (amax / FP8_MAX[fmt]) / 2^margin``.
+    """
+
+    history_len: int = 16
+    margin: int = 0
+
+    def scale_from_history(self, amax: torch.Tensor, fmt: str) -> torch.Tensor:
+        raise NotImplementedError
+
+
+@dataclass
+class DelayedScaling(FP8Recipe):
+    """TE-style delayed scaling: max over the amax history window.
+
+    The scale is computed from amax measured in *previous* steps (delayed one
+    step); the window length trades responsiveness against stability.
+    """
+
+    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 DynamicScaling(FP8Recipe):
+    """Current-amax scaling (torchao DYNAMIC): measure, then quantize.
+
+    No history — the scale is derived from the amax of the tensor being
+    quantized in the same step, at the cost of an extra reduction pass.
+    """
+
+    history_len: int = 1
+    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)
+
+
+class _ScaleRing:
+    """One operand's delayed-scaling state: amax history ring + derived scale.
+
+    The ring captures its recipe at construction; ``update`` records a fresh
+    amax and refreshes the scale for the *next* step (delayed one step).
+    """
+
+    __slots__ = ("recipe", "hist", "idx", "scale", "initialized")
+
+    def __init__(self, device: torch.device, recipe: FP8Recipe):
+        self.recipe = recipe
+        n = recipe.history_len
+        self.hist = torch.ones(n, device=device, dtype=torch.float32)
+        self.idx = 0
+        self.scale = torch.ones(1, device=device, dtype=torch.float32)
+        self.initialized = False
+
+    def update(self, amax: torch.Tensor, fmt: str) -> None:
+        self.hist[self.idx] = amax.reshape(())
+        self.idx = (self.idx + 1) % self.hist.numel()
+        self.scale.copy_(self.recipe.scale_from_history(self.hist, fmt))
+
+    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.
+
+    Holds the ``w`` / ``x`` / ``g`` rings; fused kernels record the 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 (``_dynamic_scale``), so it needs no history storage.
+    """
+
+    __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)
+
+
+class FP8State:
+    """Global fp8 training state: active recipe + per-tensor metas."""
+
+    def __init__(self):
+        self.enabled = False
+        self.recipe: FP8Recipe = DelayedScaling()
+        self.fp8_format: FP8Format = FP8Format.HYBRID
+        self._metas: dict[tuple, FP8TensorMeta] = {}
+
+    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.enabled = False
+        self._metas.clear()
+
+
+# Global singleton: autograd backward runs on the engine worker threads, so
+# thread-local state would lose the fp8 flag during loss.backward(). The GIL
+# protects Python-side mutation; the CUDA kernels take their own mutex.
+_state = FP8State()
+
+
+def fp8_state() -> FP8State:
+    return _state
+
+
+@contextmanager
+def fp8_autocast(
+    enabled: bool = True,
+    update_interval: int = 16,
+    recipe: Optional[FP8Recipe] = None,
+    fp8_format: str = "hybrid",
+    margin: int = 0,
+):
+    """Autocast-style context: fp8 linear dispatch on this thread.
+
+    Usage::
+
+        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
+
+    Args:
+        enabled: toggle fp8 dispatch for aten::linear.
+        update_interval: legacy alias for the delayed-scaling history window
+            (used only when ``recipe`` is not given).
+        recipe: scaling policy; defaults to ``DelayedScaling(update_interval)``.
+        fp8_format: ``"e4m3"`` / ``"e5m2"`` / ``"hybrid"`` (default) — hybrid
+            means E4M3 forward, E5M2 backward.
+        margin: scale headroom (``scale = (amax / FP8_MAX) / 2^margin``) used
+            with the default delayed recipe.
+    """
+    state = fp8_state()
+    prev = (state.enabled, state.recipe, state.fp8_format)
+    if recipe is None:
+        recipe = DelayedScaling(history_len=update_interval, margin=margin)
+    state.enabled = enabled
+    state.recipe = recipe
+    state.fp8_format = FP8Format(fp8_format)
+    try:
+        yield
+    finally:
+        state.enabled, state.recipe, state.fp8_format = prev
+
+
+# ---------------------------------------------------------------------------
+# 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)
+
+
+def fp8_linear_forward(x: torch.Tensor, w: torch.Tensor, bias=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, and feed the freshly measured amax back into the
+    delayed-scaling ring (dynamic scaling measures the current amax itself).
+    """
+    if bias is None:
+        bias = torch.empty(0, device=x.device, dtype=x.dtype)
+    state = fp8_state()
+    fmt = state.fp8_format.fwd()
+    if isinstance(state.recipe, DynamicScaling):
+        meta = None
+        sx = _dynamic_scale(x.reshape(-1, w.size(1)), state.recipe, fmt)
+        sw = _dynamic_scale(w, state.recipe, fmt)
+    else:
+        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)
+        sx, sw = meta.x.scale, meta.w.scale
+    out, amax_x, amax_w = linear_forward_fp8(x, w, bias, sx, sw, fmt)
+    if meta is not None:
+        meta.x.update(amax_x, fmt)
+        meta.w.update(amax_w, fmt)
+    return out
+
+
+class _LinearFp8(torch.autograd.Function):
+    """The fp8 linear forward/backward pair (standard Function style).
+
+    The forward runs inside the ``fp8_autocast`` region and captures the
+    active fmt/recipe/meta on ``ctx``; the backward reads only the captured
+    state, so ``loss.backward()`` may run after the context exits. The
+    gradient is quantized once (E5M2 in hybrid mode) and the dX / dW GEMMs
+    share that quantization; output masks come from ``needs_input_grad``.
+    """
+
+    @staticmethod
+    def forward(ctx, x, w, bias):
+        out = fp8_linear_forward(x, w, bias)
+        state = fp8_state()
+        ctx.save_for_backward(x, w)
+        ctx.fmt_bwd = state.fp8_format.bwd()
+        ctx.recipe = state.recipe
+        ctx.is_dynamic = isinstance(state.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 = ctx.saved_tensors
+        fmt = ctx.fmt_bwd
+        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)
+        else:
+            meta = ctx.meta
+            if not meta.g.initialized:
+                meta.g.seed(g, fmt)
+            sg, sw, sx = meta.g.scale, meta.w.scale, meta.x.scale
+        masks = list(ctx.needs_input_grad)
+        grad_x, grad_w, grad_b, amax_g = linear_backward_fp8(
+            g, x, w, masks, sg, sw, sx, fmt
+        )
+        if not ctx.is_dynamic:
+            ctx.meta.g.update(amax_g, fmt)
+        return grad_x, grad_w, grad_b if masks[2] else None
+
+
+# ---------------------------------------------------------------------------
+# aten::linear integration
+# ---------------------------------------------------------------------------
+
+
+def fp8_linear_enable(enabled: bool = True) -> None:
+    """Toggle fp8 dispatch for aten::linear (global; backward runs on engine
+    worker threads, so a thread-local flag would be lost during backward)."""
+    fp8_state().enabled = enabled
+
+
+def fp8_linear_enabled() -> bool:
+    return fp8_state().enabled
+
+
+def _fp8_supported(x: torch.Tensor, w: torch.Tensor) -> bool:
+    """Shape guard for the fp8 linear path.
+
+    Unlike a strict 16-alignment requirement, the fp8 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 (
+        fp8_linear_enabled()
+        and x.dtype == torch.bfloat16
+        and w.dtype == 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 its state captured at forward time,
+# so loss.backward() works wherever it is called; the same CUDA registration
+# still covers inference_mode, where autograd keys are skipped entirely.
+_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

@@ -1,36 +1,83 @@
 """Dynamic discovery and loading of compiled CUDA kernel modules.
 
-Each kernel is registered in ``csrc/build.py`` and built into a ``.so`` placed
-in this package directory. On import we try to load each one; kernels that
-failed to build (or are running on a CPU-only machine) are marked unavailable
-so the wrapper functions can fall back to ``torch`` SDPA.
+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__)
 
-KERNEL_NAMES = ["attn_decode", "attn_prefill", "attn_paged_decode"]
+_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] = {}
 
-for _name in KERNEL_NAMES:
-    try:
-        _mod = importlib.import_module(f".{_name}", package=__package__)
-        _available[_name] = True
-        _modules[_name] = _mod
-    except ImportError:
-        _available[_name] = False
-        _modules[_name] = None
+
+def _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`` was loaded."""
+    """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``, or ``None`` if unavailable."""
-    return _modules.get(name)
+    """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.py

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

astrai/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 +1,179 @@
+"""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, 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.
+"""
+
+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: torch.Tensor | None = 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"):
+    """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: torch.Tensor | None = 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, w, bias, sx, sw, fmt: str = "e4m3", bias_scale=None):
+    """Pure FP8 linear forward: quantize x/w to ``fmt``, pre-quantized GEMM.
+
+    Returns ``(out, amax_x, amax_w)``. ``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.
+    """
+    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
+    )
+
+
+def linear_backward_fp8(g, x, w, masks, sg, sw, sx, fmt: str = "e5m2"):
+    """FP8 linear backward; returns ``(grad_input, grad_weight, grad_bias, amax_g)``.
+
+    The gradient (and the transposed w/x operands) are quantized to ``fmt``
+    (default E5M2 — larger dynamic range for gradients) and the two GEMMs run
+    as FP8 tensor-core products sharing a single gradient quantization.
+    """
+    if not (
+        g.dtype == torch.bfloat16
+        and x.dtype == torch.bfloat16
+        and w.dtype == torch.bfloat16
+    ):
+        raise TypeError(
+            f"fp8 backward requires bf16 inputs, got {g.dtype}/{x.dtype}/{w.dtype}"
+        )
+    return get_module("fp8_ops").linear_backward_fp8(
+        g, x, w, list(masks), sg, sw, sx, _fmt_int(fmt)
+    )

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

@@ -13,41 +13,63 @@ from typing import (
     Type,
     TypeVar,
     Union,
+    get_args,
+    get_origin,
 )
-from typing import get_args as _get_args
-from typing import get_origin as _get_origin
 
 T = TypeVar("T")
 
 
-def _resolve_type(
+def _resolve_base_type(
     arg: Union[Type, str, ForwardRef], factory_cls: type
 ) -> Optional[Type]:
-    """Resolve a generic type-arg (str forward-ref, ForwardRef, or class)."""
-    if not isinstance(arg, (str, ForwardRef)):
+    """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
 
-    name = arg if isinstance(arg, str) else arg.__forward_arg__
-    if name == factory_cls.__name__:
-        return factory_cls
+    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
-    ns = vars(mod)
+    try:
+        return eval(name, vars(mod))  # noqa: S307
+    except NameError:
+        return None
 
-    if isinstance(arg, ForwardRef):
-        return arg._evaluate(ns, None, recursive_guard=frozenset())
 
-    return ns.get(name)
+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 component registration.
+    """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):
             ...
@@ -64,13 +86,10 @@ class BaseFactory(ABC, Generic[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)
+            if get_origin(orig_base) is BaseFactory:
+                (arg,) = get_args(orig_base)
                 cls._entries = {}
-                try:
-                    cls._component_base = _resolve_type(arg, cls)
-                except Exception:
-                    cls._component_base = None
+                cls._component_base = _resolve_base_type(arg, cls)
                 return
 
     @classmethod
@@ -82,7 +101,7 @@ class BaseFactory(ABC, Generic[T]):
         """
 
         def decorator(component_cls: Type[T]) -> Type[T]:
-            cls._validate_component(component_cls)
+            _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
@@ -95,12 +114,11 @@ class BaseFactory(ABC, Generic[T]):
         """Create a component instance by name, filtering kwargs to match
         the component's ``__init__`` signature.
         """
-        entry = cls._entries.get(name)
-        if entry is None:
+        component_cls = cls._entries.get(name)
+        if component_cls is None:
             raise ValueError(
                 f"Unknown component: '{name}'. Supported types: {sorted(cls._entries)}"
             )
-        component_cls = entry
         sig = inspect.signature(component_cls.__init__)
         has_var_kwargs = any(
             p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values()
@@ -114,18 +132,6 @@ class BaseFactory(ABC, Generic[T]):
             kwargs = {k: v for k, v in kwargs.items() if k in valid}
         return component_cls(*args, **kwargs)
 
-    @classmethod
-    def _validate_component(cls, component_cls: Type[T]):
-        """Validate the decorated class inherits from the factory's base type.
-
-        Override for custom validation beyond ``issubclass``.
-        """
-        base = cls._component_base
-        if base is not None and not issubclass(component_cls, base):
-            raise TypeError(
-                f"{component_cls.__name__} must inherit from {base.__name__}"
-            )
-
     @classmethod
     def get_component_class(cls, name: str) -> Type[T]:
         """Get the registered component class without instantiating it."""

astrai/inference/__init__.py

@@ -1,105 +1,33 @@
 """Inference module for continuous batching.
 
-Layers:
-  - core/:        Core inference loop (cache, executor, scheduler, task)
-  - api/:         HTTP orchestration (ProtocolHandler, server)
-  - protocols/:   Response builders (OpenAI, Anthropic)
-  - transport/:   SSE transport utilities
-  - engine.py:    Facade (InferenceEngine), Value Object (GenerationRequest)
-  - sample.py:    Strategy pattern (TemperatureStrategy, TopKStrategy, TopPStrategy, FrequencyPenaltyStrategy)
+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.api import (
-    AnthropicMessage,
-    BaseToolParser,
-    ChatCompletionRequest,
-    ChatMessage,
-    FunctionDef,
-    GenContext,
-    MessagesRequest,
-    ProtocolHandler,
-    SimpleJsonToolParser,
-    StopChecker,
-    ToolDef,
-    ToolParserFactory,
-    get_app,
-    run_server,
-)
-from astrai.inference.api.anthropic import AnthropicResponseBuilder
-from astrai.inference.api.openai import OpenAIResponseBuilder
-from astrai.inference.core import (
-    STOP,
-    Allocator,
-    CacheView,
-    ContiguousCache,
-    ContiguousCacheView,
-    Executor,
-    InferenceScheduler,
-    KVCache,
-    PageCache,
-    PageCacheView,
-    PagePool,
-    PrefixCache,
-    Storage,
-    Task,
-    TaskManager,
-    TaskStatus,
-    TaskTable,
-    page_hash,
-)
-from astrai.inference.engine import GenerationRequest, InferenceEngine
-from astrai.inference.sample import (
-    BaseSamplingStrategy,
-    FrequencyPenaltyStrategy,
-    SamplingPipeline,
-    TemperatureStrategy,
-    TopKStrategy,
-    TopPStrategy,
-    sample,
-)
+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",
-    "GenerationRequest",
     "InferenceScheduler",
     "Executor",
     "STOP",
     "Task",
     "TaskManager",
     "TaskStatus",
-    "Allocator",
-    "CacheView",
-    "KVCache",
-    "ContiguousCache",
-    "ContiguousCacheView",
-    "PageCache",
-    "PageCacheView",
-    "PagePool",
-    "PrefixCache",
-    "Storage",
-    "TaskTable",
-    "page_hash",
     "sample",
-    "BaseSamplingStrategy",
-    "TemperatureStrategy",
-    "TopKStrategy",
-    "TopPStrategy",
-    "FrequencyPenaltyStrategy",
-    "SamplingPipeline",
-    "ProtocolHandler",
-    "StopChecker",
-    "GenContext",
-    "BaseToolParser",
-    "SimpleJsonToolParser",
-    "ToolParserFactory",
-    "OpenAIResponseBuilder",
-    "AnthropicResponseBuilder",
-    "ChatMessage",
-    "ChatCompletionRequest",
-    "FunctionDef",
-    "ToolDef",
-    "AnthropicMessage",
-    "MessagesRequest",
     "get_app",
     "run_server",
 ]

astrai/inference/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/core/__init__.py

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

astrai/inference/core/cache.py

@@ -1,525 +0,0 @@
-import threading
-from abc import ABC, abstractmethod
-from collections import OrderedDict
-from typing import Callable, Dict, List, Optional, Tuple
-
-import torch
-from torch import Tensor
-
-
-def page_hash(token_ids: List[int], page_idx: int, page_size: int) -> int:
-    start = page_idx * page_size
-    end = min(start + page_size, len(token_ids))
-    h = 0
-    for i in range(start, end):
-        h = (h * 31 + token_ids[i]) & 0xFFFFFFFFFFFFFFFF
-    return h
-
-
-class Allocator:
-    """Bitmask-based page allocator with ref-counting and LRU eviction."""
-
-    def __init__(self, n_pages: int):
-        self._free_mask = (1 << n_pages) - 1
-        self._refs: List[int] = [0] * n_pages
-        self._lru: OrderedDict[int, None] = OrderedDict()
-        self.on_evict: Optional[Callable[[int], None]] = None
-        self._lock = threading.Lock()
-
-    def alloc(self) -> int:
-        with self._lock:
-            if self._free_mask:
-                lsb = self._free_mask & -self._free_mask
-                idx = lsb.bit_length() - 1
-                self._free_mask ^= lsb
-                self._refs[idx] = 1
-                return idx
-            if self._lru:
-                idx, _ = self._lru.popitem(last=False)
-                if self.on_evict:
-                    self.on_evict(idx)
-                self._refs[idx] = 1
-                self._free_mask &= ~(1 << idx)
-                return idx
-            return -1
-
-    def free(self, idx: int, keep_cached: bool = False):
-        with self._lock:
-            self._refs[idx] -= 1
-            if self._refs[idx] == 0:
-                if keep_cached:
-                    self._lru[idx] = None
-                else:
-                    self._free_mask |= 1 << idx
-
-    def inc_ref(self, idx: int):
-        with self._lock:
-            self._refs[idx] += 1
-            self._lru.pop(idx, None)
-
-    def ref_count(self, idx: int) -> int:
-        with self._lock:
-            return self._refs[idx]
-
-    def touch(self, idx: int):
-        with self._lock:
-            if idx in self._lru:
-                self._lru.move_to_end(idx)
-
-
-class PrefixCache:
-    """Hash-based prefix matching: maps page hashes to physical page indices."""
-
-    def __init__(self, page_size: int):
-        self._page_size = page_size
-        self._page_to_hash: Dict[int, int] = {}
-        self._hash_to_page: Dict[int, int] = {}
-        self._lock = threading.Lock()
-
-    def evict(self, idx: int):
-        with self._lock:
-            h = self._page_to_hash.pop(idx, None)
-            if h is not None:
-                self._hash_to_page.pop(h, None)
-
-    def has_page(self, idx: int) -> bool:
-        with self._lock:
-            return idx in self._page_to_hash
-
-    def lookup(self, token_ids: List[int]) -> List[int]:
-        with self._lock:
-            full_pages = len(token_ids) // self._page_size
-            hits: List[int] = []
-            for i in range(full_pages):
-                h = page_hash(token_ids, i, self._page_size)
-                p = self._hash_to_page.get(h)
-                if p is None:
-                    break
-                hits.append(p)
-            return hits
-
-    def record(self, page_idx: int, token_ids: List[int], logical_page_idx: int):
-        with self._lock:
-            h = page_hash(token_ids, logical_page_idx, self._page_size)
-            old_h = self._page_to_hash.pop(page_idx, None)
-            if old_h is not None:
-                self._hash_to_page.pop(old_h, None)
-            self._page_to_hash[page_idx] = h
-            self._hash_to_page[h] = page_idx
-
-
-class PagePool:
-    """Orchestrates allocator (page management) and PrefixCache (content addressing)."""
-
-    def __init__(self, allocator: Allocator, prefix: PrefixCache):
-        self._alloc = allocator
-        self._prefix = prefix
-        self._alloc.on_evict = prefix.evict
-
-    @property
-    def allocator(self) -> Allocator:
-        return self._alloc
-
-    @property
-    def prefix(self) -> PrefixCache:
-        return self._prefix
-
-    def alloc(self) -> int:
-        return self._alloc.alloc()
-
-    def free(self, idx: int):
-        keep = self._prefix.has_page(idx)
-        self._alloc.free(idx, keep_cached=keep)
-        if not keep:
-            self._prefix.evict(idx)
-
-    def inc_ref(self, idx: int):
-        self._alloc.inc_ref(idx)
-
-    def lookup(self, token_ids: List[int]) -> List[int]:
-        hits = self._prefix.lookup(token_ids)
-        for p in hits:
-            self._alloc.touch(p)
-        return hits
-
-    def record(self, page_idx: int, token_ids: List[int], logical_page_idx: int):
-        self._prefix.record(page_idx, token_ids, logical_page_idx)
-
-
-class TaskTable:
-    """Maps task_ids to page tables and cached token counts."""
-
-    def __init__(self, page_size: int):
-        self._page_size = page_size
-        self._pages: Dict[str, List[int]] = {}
-        self._cached: Dict[str, int] = {}
-        self._lock = threading.Lock()
-
-    def set(self, task_id: str, page_table: List[int], cached: int):
-        with self._lock:
-            self._pages[task_id] = page_table
-            self._cached[task_id] = cached
-
-    def get(self, task_id: str) -> List[int]:
-        with self._lock:
-            return self._pages.get(task_id, [])
-
-    def get_cached(self, task_id: str) -> int:
-        with self._lock:
-            return self._cached.get(task_id, 0)
-
-    def pop(self, task_id: str) -> Tuple[List[int], int]:
-        with self._lock:
-            pages = self._pages.pop(task_id, [])
-            cached = self._cached.pop(task_id, 0)
-            return pages, cached
-
-    def get_ref(self, task_id: str) -> List[int]:
-        with self._lock:
-            return self._pages.setdefault(task_id, [])
-
-    def table_tensor(self, task_ids: List[str], device: torch.device) -> Tensor:
-        with self._lock:
-            states = [self._pages.get(tid, []) for tid in task_ids]
-            max_pages = max((len(s) for s in states), default=0)
-            rows = [s + [-1] * (max_pages - len(s)) for s in states]
-            return torch.tensor(rows, dtype=torch.long, device=device)
-
-
-class Storage:
-    """KV-cache tensor storage with paged write/gather."""
-
-    def __init__(
-        self,
-        n_layers: int,
-        n_pages: int,
-        page_size: int,
-        n_kv_heads: int,
-        head_dim: int,
-        device: torch.device,
-        dtype: torch.dtype,
-    ):
-        self.page_size = page_size
-        self.k_cache = torch.empty(
-            (n_layers, n_pages, page_size, n_kv_heads, head_dim),
-            device=device,
-            dtype=dtype,
-        )
-        self.v_cache = torch.empty(
-            (n_layers, n_pages, page_size, n_kv_heads, head_dim),
-            device=device,
-            dtype=dtype,
-        )
-
-    def write(
-        self,
-        layer_id: int,
-        page_table: Tensor,
-        start_pos: int,
-        k: Tensor,
-        v: Tensor,
-    ):
-        seq_len = k.size(1)
-        if seq_len == 0:
-            return
-        page_size = self.page_size
-        written = 0
-        first_page = start_pos // page_size
-        last_page = (start_pos + seq_len - 1) // page_size
-        for pi in range(first_page, last_page + 1):
-            phys_pages = page_table[:, pi]
-            page_start = pi * page_size
-            write_start = max(page_start, start_pos)
-            write_end = min(page_start + page_size, start_pos + seq_len)
-            offset = write_start - page_start
-            chunk = write_end - write_start
-            valid = phys_pages >= 0
-            if not valid.all():
-                if valid.any():
-                    valid_pages = phys_pages[valid]
-                    self.k_cache[layer_id, valid_pages, offset : offset + chunk] = k[
-                        valid, written : written + chunk
-                    ]
-                    self.v_cache[layer_id, valid_pages, offset : offset + chunk] = v[
-                        valid, written : written + chunk
-                    ]
-                written += chunk
-                continue
-            self.k_cache[layer_id, phys_pages, offset : offset + chunk] = k[
-                :, written : written + chunk
-            ]
-            self.v_cache[layer_id, phys_pages, offset : offset + chunk] = v[
-                :, written : written + chunk
-            ]
-            written += chunk
-
-    def gather(
-        self, layer_id: int, page_table: Tensor, total_len: int
-    ) -> Tuple[Tensor, Tensor]:
-        safe = page_table.clamp(min=0)
-        k = self.k_cache[layer_id, safe]
-        v = self.v_cache[layer_id, safe]
-        k = k.flatten(1, 2)
-        v = v.flatten(1, 2)
-        if (page_table < 0).any():
-            invalid = (
-                (page_table < 0)
-                .unsqueeze(-1)
-                .expand(-1, -1, self.page_size)
-                .flatten(1, 2)
-            )
-            invalid = invalid[:, :, None, None].expand_as(k)
-            k = k.masked_fill(invalid, 0.0)
-            v = v.masked_fill(invalid, 0.0)
-        k = k[:, :total_len]
-        v = v[:, :total_len]
-        return k, v
-
-
-class CacheView(ABC):
-    """Abstract view passed to attention layers for KV-cache I/O."""
-
-    @abstractmethod
-    def write(self, layer_id: int, k: Tensor, v: Tensor): ...
-
-    @abstractmethod
-    def gather(self, layer_id: int) -> Tuple[Tensor, Tensor]: ...
-
-
-class KVCache(ABC):
-    """Abstract KV-cache facade for scheduler/executor."""
-
-    @abstractmethod
-    def task_alloc(self, task_id: str, prompt_ids: List[int]) -> bool: ...
-
-    @abstractmethod
-    def task_free(self, task_id: str): ...
-
-    @abstractmethod
-    def task_extend(self, task_id: str, pos: int) -> bool: ...
-
-    @abstractmethod
-    def bind_tasks(
-        self,
-        task_ids: List[str],
-        total_len: int,
-        device: torch.device,
-        write_positions: Optional[Tensor] = None,
-    ) -> CacheView: ...
-
-    def task_cached(self, task_id: str) -> int:
-        return 0
-
-    def task_record_hashes(
-        self, task_id: str, prompt_ids: List[int], start_logical_page: int = 0
-    ): ...
-
-
-class PageCacheView(CacheView):
-    """Bundles Storage + page_table + total_len for attention layers."""
-
-    def __init__(self, storage: Storage, page_table: Tensor, total_len: int = 0):
-        self._storage = storage
-        self._page_table = page_table
-        self._total_len = total_len
-
-    def write(self, layer_id: int, k: Tensor, v: Tensor):
-        start_pos = self._total_len - k.size(1)
-        self._storage.write(layer_id, self._page_table, start_pos, k, v)
-
-    def gather(self, layer_id: int) -> Tuple[Tensor, Tensor]:
-        return self._storage.gather(layer_id, self._page_table, self._total_len)
-
-
-class PageCache(KVCache):
-    """Paged KV-cache with prefix sharing."""
-
-    def __init__(
-        self,
-        n_layers: int,
-        n_pages: int,
-        page_size: int,
-        n_kv_heads: int,
-        head_dim: int,
-        device: torch.device,
-        dtype: torch.dtype,
-    ):
-        self.page_size = page_size
-        self._pool = PagePool(Allocator(n_pages), PrefixCache(page_size))
-        self._table = TaskTable(page_size)
-        self._storage = Storage(
-            n_layers, n_pages, page_size, n_kv_heads, head_dim, device, dtype
-        )
-
-    def task_alloc(self, task_id: str, prompt_ids: List[int]) -> bool:
-        hits = self._pool.lookup(prompt_ids)
-        cached = len(hits) * self.page_size
-        for p in hits:
-            self._pool.inc_ref(p)
-
-        remaining = len(prompt_ids) - cached
-        n_new = (
-            (remaining + self.page_size - 1) // self.page_size if remaining > 0 else 0
-        )
-        new_pages: List[int] = []
-        if n_new > 0:
-            for _ in range(n_new):
-                p = self._pool.alloc()
-                if p < 0:
-                    for hp in hits:
-                        self._pool.free(hp)
-                    for np in new_pages:
-                        self._pool.free(np)
-                    return False
-                new_pages.append(p)
-
-        self._table.set(task_id, hits + new_pages, cached)
-        return True
-
-    def task_free(self, task_id: str):
-        page_table, _ = self._table.pop(task_id)
-        for idx in page_table:
-            self._pool.free(idx)
-
-    def task_extend(self, task_id: str, pos: int) -> bool:
-        page_table = self._table.get(task_id)
-        needed = (pos + 1 + self.page_size - 1) // self.page_size
-        while len(page_table) < needed:
-            p = self._pool.alloc()
-            if p < 0:
-                return False
-            page_table.append(p)
-        return True
-
-    def task_cached(self, task_id: str) -> int:
-        return self._table.get_cached(task_id)
-
-    def task_record_hashes(
-        self, task_id: str, prompt_ids: List[int], start_logical_page: int = 0
-    ):
-        page_table = self._table.get(task_id)
-        full_pages = len(prompt_ids) // self.page_size
-        for i in range(start_logical_page, full_pages):
-            self._pool.record(page_table[i], prompt_ids, i)
-
-    def bind_tasks(
-        self,
-        task_ids: List[str],
-        total_len: int,
-        device: torch.device,
-        write_positions: Optional[Tensor] = None,
-    ) -> PageCacheView:
-        page_table = self._table.table_tensor(task_ids, device)
-        return PageCacheView(self._storage, page_table, total_len)
-
-
-class ContiguousCacheView(CacheView):
-    """Contiguous KV-cache view for attention layers."""
-
-    def __init__(
-        self,
-        cache: "ContiguousCache",
-        batch_indices: Tensor,
-        total_len: int = 0,
-        write_positions: Optional[Tensor] = None,
-    ):
-        self._cache = cache
-        self._batch_indices = batch_indices
-        self._total_len = total_len
-        self._write_positions = write_positions
-
-    def write(self, layer_id: int, k: Tensor, v: Tensor):
-        seq_len = k.size(1)
-        indices = self._batch_indices
-        if self._write_positions is not None and seq_len == 1:
-            pos = self._write_positions
-            self._cache.k[layer_id, indices, pos] = k.squeeze(1)
-            self._cache.v[layer_id, indices, pos] = v.squeeze(1)
-        else:
-            start_pos = self._total_len - seq_len
-            self._cache.k[layer_id, indices, start_pos : start_pos + seq_len] = k
-            self._cache.v[layer_id, indices, start_pos : start_pos + seq_len] = v
-
-    def gather(self, layer_id: int) -> Tuple[Tensor, Tensor]:
-        max_len = self._total_len
-        indices = self._batch_indices
-        k = self._cache.k[layer_id, indices, :max_len]
-        v = self._cache.v[layer_id, indices, :max_len]
-        return k, v
-
-
-class ContiguousCache(KVCache):
-    """Contiguous per-slot KV cache (default implementation)."""
-
-    def __init__(
-        self,
-        n_layers: int,
-        max_batch_size: int,
-        max_seq_len: int,
-        n_kv_heads: int,
-        head_dim: int,
-        device: torch.device,
-        dtype: torch.dtype,
-    ):
-        self.max_seq_len = max_seq_len
-        self.k = torch.zeros(
-            n_layers,
-            max_batch_size,
-            max_seq_len,
-            n_kv_heads,
-            head_dim,
-            device=device,
-            dtype=dtype,
-        )
-        self.v = torch.zeros(
-            n_layers,
-            max_batch_size,
-            max_seq_len,
-            n_kv_heads,
-            head_dim,
-            device=device,
-            dtype=dtype,
-        )
-        self._slot_len: Dict[int, int] = {}
-        self._task_slot: Dict[str, int] = {}
-        self._free_slots = list(range(max_batch_size))
-        self._device = device
-
-    def task_alloc(self, task_id: str, prompt_ids: List[int]) -> bool:
-        if not self._free_slots:
-            return False
-        slot = self._free_slots.pop(0)
-        self._task_slot[task_id] = slot
-        self._slot_len[slot] = 0
-        return True
-
-    def task_free(self, task_id: str):
-        slot = self._task_slot.pop(task_id, None)
-        if slot is not None:
-            self._slot_len.pop(slot, None)
-            self._free_slots.append(slot)
-
-    def task_extend(self, task_id: str, pos: int) -> bool:
-        return pos < self.max_seq_len
-
-    def task_cached(self, task_id: str) -> int:
-        slot = self._task_slot.get(task_id)
-        if slot is None:
-            return 0
-        return self._slot_len.get(slot, 0)
-
-    def bind_tasks(
-        self,
-        task_ids: List[str],
-        total_len: int,
-        device: torch.device,
-        write_positions: Optional[Tensor] = None,
-    ) -> ContiguousCacheView:
-        slots = [self._task_slot[tid] for tid in task_ids]
-        batch_indices = torch.tensor(slots, dtype=torch.long, device=device)
-        for slot in slots:
-            if total_len > self._slot_len.get(slot, 0):
-                self._slot_len[slot] = total_len
-        return ContiguousCacheView(
-            self, batch_indices, total_len, write_positions=write_positions
-        )

astrai/inference/core/executor.py

@@ -1,166 +0,0 @@
-import logging
-from typing import List, Optional
-
-import torch
-
-from astrai.inference.core.cache import KVCache
-from astrai.inference.core.task import Task
-from astrai.inference.sample import sample
-from astrai.model.automodel import AutoModel
-from astrai.tokenize.tokenizer import AutoTokenizer
-
-logger = logging.getLogger(__name__)
-
-
-class Executor:
-    """Model forward passes for prefill and decode phases."""
-
-    def __init__(
-        self,
-        model: AutoModel,
-        tokenizer: AutoTokenizer,
-        kv_cache: KVCache,
-        device: Optional[str] = None,
-        dtype: Optional[torch.dtype] = None,
-    ):
-        self.model = model
-        self.tokenizer = tokenizer
-        self.kv_cache = kv_cache
-        self.device = device or next(model.parameters()).device
-        self.dtype = dtype or next(model.parameters()).dtype
-
-    def execute_prefill(self, tasks: List[Task], prompt_len: int, start_pos: int = 0):
-        if start_pos >= prompt_len:
-            return
-
-        tasks = sorted(tasks, key=lambda t: t.task_id)
-        batch_sz = len(tasks)
-
-        input_ids = torch.tensor(
-            [t.prompt_ids[start_pos:prompt_len] for t in tasks],
-            dtype=torch.long,
-            device=self.device,
-        )
-
-        task_ids = [t.task_id for t in tasks]
-        position_ids = (
-            torch.arange(start_pos, prompt_len, dtype=torch.long, device=self.device)
-            .unsqueeze(0)
-            .expand(batch_sz, -1)
-        )
-        input_mask = position_ids.unsqueeze(-1) >= torch.arange(
-            prompt_len, device=self.device
-        )
-
-        with torch.inference_mode():
-            self.model(
-                input_ids,
-                input_mask=input_mask,
-                position_ids=position_ids,
-                paged_cache=self.kv_cache.bind_tasks(task_ids, prompt_len, self.device),
-            )
-
-    def execute_decode(
-        self, tasks: List[Task], return_logprobs: bool = False
-    ) -> List[int]:
-        """Decode next token for each task.
-
-        Args:
-            return_logprobs: When ``True``, also record (and return)
-                the log-probability of each sampled token under the
-                post-strategy sampling distribution.  The logprob is
-                appended to ``task.output_logprobs`` and the return
-                list becomes ``List[Tuple[int, float]]``.
-
-        Returns:
-            ``List[int]`` of sampled token IDs, or
-            ``List[Tuple[int, float]]`` of ``(token_id, logprob)`` when
-            ``return_logprobs`` is ``True``.
-        """
-        if not tasks:
-            return []
-
-        input_ids = torch.tensor(
-            [t.output_ids[-1] if t.output_ids else t.prompt_ids[-1] for t in tasks],
-            dtype=torch.long,
-            device=self.device,
-        )
-
-        position_ids = torch.tensor(
-            [t.next_pos for t in tasks], dtype=torch.long, device=self.device
-        )
-        total_len = max(t.next_pos for t in tasks) + 1
-        input_mask = position_ids[:, None, None] >= torch.arange(
-            total_len, device=self.device
-        )
-
-        task_ids = [t.task_id for t in tasks]
-
-        temperatures = torch.tensor([t.temperature for t in tasks], device=self.device)
-        top_ks = torch.tensor([t.top_k for t in tasks], device=self.device)
-        top_ps = torch.tensor([t.top_p for t in tasks], device=self.device)
-        freq_penalties = torch.tensor(
-            [t.frequency_penalty for t in tasks], device=self.device
-        )
-
-        history_lists = []
-        history_lens = []
-        for t in tasks:
-            window = t.rep_window
-            prompt_part = t.prompt_ids[-window:]
-            ids = prompt_part + t.output_ids
-            history_lists.append(ids)
-            history_lens.append(len(ids))
-
-        max_len = max(history_lens) if history_lens else 0
-        padded_ids = torch.zeros(
-            len(tasks), max_len, dtype=torch.long, device=self.device
-        )
-        padded_mask = torch.zeros(
-            len(tasks), max_len, dtype=torch.bool, device=self.device
-        )
-        for i, h in enumerate(history_lists):
-            L = history_lens[i]
-            padded_ids[i, :L] = torch.as_tensor(h, dtype=torch.long, device=self.device)
-            padded_mask[i, :L] = True
-
-        with torch.inference_mode():
-            outputs = self.model(
-                input_ids.unsqueeze(1),
-                input_mask=input_mask,
-                paged_cache=self.kv_cache.bind_tasks(
-                    task_ids,
-                    total_len,
-                    self.device,
-                    write_positions=position_ids,
-                ),
-                position_ids=position_ids.unsqueeze(1),
-            )
-            logits = outputs["logits"][:, -1, :]
-
-        if return_logprobs:
-            tokens, logprobs = sample(
-                logits,
-                temperature=temperatures,
-                top_k=top_ks,
-                top_p=top_ps,
-                frequency_penalty=freq_penalties,
-                input_ids=padded_ids,
-                input_mask=padded_mask,
-                return_logprobs=True,
-            )
-            tokens_list = tokens.tolist()
-            logprobs_list = logprobs.tolist()
-            for t, lp in zip(tasks, logprobs_list):
-                t.output_logprobs.append(float(lp))
-            return list(zip(tokens_list, logprobs_list))
-
-        return sample(
-            logits,
-            temperature=temperatures,
-            top_k=top_ks,
-            top_p=top_ps,
-            frequency_penalty=freq_penalties,
-            input_ids=padded_ids,
-            input_mask=padded_mask,
-        ).tolist()

astrai/inference/core/scheduler.py

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

astrai/inference/engine.py

@@ -8,9 +8,10 @@ from typing import Any, AsyncGenerator, Dict, Generator, List, Optional, Tuple,
 import torch
 import torch.nn as nn
 
-from astrai.inference.core.cache import KVCache
-from astrai.inference.core.scheduler import InferenceScheduler
-from astrai.inference.core.task import STOP
+from astrai.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
 
 
@@ -64,44 +65,6 @@ class GenerateResult:
             return self.results.copy()
 
 
-class GenerationRequest:
-    """Request parameters for text generation."""
-
-    def __init__(
-        self,
-        messages: List[Dict[str, str]],
-        top_k: int = 50,
-        top_p: float = 1.0,
-        temperature: float = 1.0,
-        max_tokens: Optional[int] = None,
-        frequency_penalty: float = 0.0,
-        rep_window: int = 64,
-        stream: bool = False,
-    ):
-        if not (isinstance(top_k, int) and top_k >= 0):
-            raise ValueError("top_k must be a non-negative integer")
-        if not (0.0 <= top_p <= 1.0):
-            raise ValueError("top_p must be a float between 0.0 and 1.0")
-        if not (isinstance(temperature, (int, float)) and temperature >= 0):
-            raise ValueError("temperature must be a non-negative number")
-        if not (
-            isinstance(frequency_penalty, (int, float))
-            and -2.0 <= frequency_penalty <= 2.0
-        ):
-            raise ValueError("frequency_penalty must be between -2.0 and 2.0")
-        if not (isinstance(rep_window, int) and rep_window > 0):
-            raise ValueError("rep_window must be a positive integer")
-
-        self.messages = messages
-        self.top_k = top_k
-        self.top_p = top_p
-        self.temperature = temperature
-        self.max_tokens = max_tokens
-        self.frequency_penalty = frequency_penalty
-        self.rep_window = rep_window
-        self.stream = stream
-
-
 class InferenceEngine:
     """Unified inference engine backed by continuous-batching scheduler."""
 
@@ -111,9 +74,9 @@ class InferenceEngine:
         tokenizer: AutoTokenizer,
         max_batch_size: int = 1,
         max_seq_len: Optional[int] = None,
-        max_prompt_len: int = 2048,
-        page_size: int = 128,
-        cache: Optional[KVCache] = None,
+        cache: Optional[PagePool] = None,
+        enable_cuda_graph: bool = True,
+        backend: Optional[Union[str, ATTN_BACKEND, AttentionBackend, type]] = None,
     ):
         self.model = model
         self.tokenizer = tokenizer
@@ -122,8 +85,9 @@ class InferenceEngine:
             tokenizer=self.tokenizer,
             max_batch_size=max_batch_size,
             max_seq_len=max_seq_len,
-            max_prompt_len=max_prompt_len,
             cache=cache,
+            enable_cuda_graph=enable_cuda_graph,
+            backend=backend,
         )
 
         self.scheduler.start()
@@ -149,28 +113,23 @@ class InferenceEngine:
         is_batch = isinstance(prompt, list)
         prompts = prompt if is_batch else [prompt]
 
-        if stream:
-            return self._generate_streaming(
-                prompts,
-                is_batch,
-                max_tokens,
-                temperature,
-                top_p,
-                top_k,
-                frequency_penalty,
-                rep_window,
-            )
-        else:
-            return self._generate_non_streaming(
-                prompts,
-                is_batch,
-                max_tokens,
-                temperature,
-                top_p,
-                top_k,
-                frequency_penalty,
-                rep_window,
-            )
+        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,
@@ -182,9 +141,10 @@ class InferenceEngine:
         frequency_penalty: float = 0.0,
         rep_window: int = 64,
     ) -> AsyncGenerator[str, None]:
-        sync_gen = self._generate_streaming(
+        sync_gen = self._generate(
             [prompt],
             False,
+            True,
             max_tokens,
             temperature,
             top_p,
@@ -196,51 +156,30 @@ class InferenceEngine:
         async def _agen():
             loop = asyncio.get_event_loop()
             while True:
-                token = await loop.run_in_executor(None, self._next_token, sync_gen)
+                token = await loop.run_in_executor(None, next, sync_gen, None)
                 if token is None:
                     break
                 yield token
 
         return _agen()
 
-    @staticmethod
-    def _next_token(gen: Generator) -> Optional[str]:
-        try:
-            return next(gen)
-        except StopIteration:
-            return None
-
-    def generate_with_request(
-        self, request: GenerationRequest
-    ) -> Union[Generator[str, None, None], str, List[str]]:
-        prompt = self.tokenizer.apply_chat_template(request.messages, tokenize=False)
-        return self.generate(
-            prompt=prompt,
-            stream=request.stream,
-            max_tokens=request.max_tokens,
-            temperature=request.temperature,
-            top_p=request.top_p,
-            top_k=request.top_k,
-            frequency_penalty=request.frequency_penalty,
-            rep_window=request.rep_window,
-        )
-
-    def _submit_tasks(
+    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,
-    ) -> Tuple[GenerateResult, List[str]]:
+    ) -> Union[Generator, str, List[str]]:
         n = len(prompts)
+        request_backend = get_backend(use_default=False)
         result = GenerateResult(count=n)
-        task_ids = []
-        for i, p in enumerate(prompts):
-            cb = self._make_callback(result, i)
-            task_id = self.scheduler.add_task(
+        task_ids = [
+            self.scheduler.add_task(
                 prompt=p,
                 max_tokens=max_tokens,
                 temperature=temperature,
@@ -248,99 +187,54 @@ class InferenceEngine:
                 top_k=top_k,
                 frequency_penalty=frequency_penalty,
                 rep_window=rep_window,
-                stream_callback=cb,
+                backend=request_backend,
+                stream_callback=lambda token, idx=i: result.append(token, idx),
             )
-            task_ids.append(task_id)
-        return result, task_ids
+            for i, p in enumerate(prompts)
+        ]
 
-    @staticmethod
-    def _make_callback(result: GenerateResult, idx: int):
-        def cb(token):
-            result.append(token, idx)
+        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]
 
-        return cb
-
-    def _generate_streaming(
-        self,
-        prompts: List[str],
-        is_batch: bool,
-        max_tokens: Optional[int],
-        temperature: float,
-        top_p: float,
-        top_k: int,
-        frequency_penalty: float,
-        rep_window: int,
-    ) -> Generator:
-        result, task_ids = self._submit_tasks(
-            prompts,
-            max_tokens,
-            temperature,
-            top_p,
-            top_k,
-            frequency_penalty,
-            rep_window,
-        )
-        n = len(prompts)
         remaining = n
         finished = [False] * n
 
         def gen():
             nonlocal remaining
-            try:
-                while remaining > 0:
-                    items = result.pop_all()
-                    for idx, token in items:
-                        if token is STOP:
-                            if not finished[idx]:
-                                finished[idx] = True
-                                remaining -= 1
-                        else:
-                            yield (idx, token) if is_batch else token
-                    if remaining > 0:
-                        result.wait(timeout=0.05)
-            finally:
-                for tid in task_ids:
-                    self.scheduler.remove_task(tid)
+            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 _generate_non_streaming(
-        self,
-        prompts: List[str],
-        is_batch: bool,
-        max_tokens: Optional[int],
-        temperature: float,
-        top_p: float,
-        top_k: int,
-        frequency_penalty: float,
-        rep_window: int,
-    ) -> Union[str, List[str]]:
-        result, task_ids = self._submit_tasks(
-            prompts,
-            max_tokens,
-            temperature,
-            top_p,
-            top_k,
-            frequency_penalty,
-            rep_window,
-        )
-
-        try:
-            result.wait_completion()
-        except TimeoutError:
-            for tid in task_ids:
-                self.scheduler.remove_task(tid)
-            raise
-
-        for tid in task_ids:
-            self.scheduler.remove_task(tid)
-
-        res = result.get_results()
-        return res if is_batch else res[0]
-
     def get_stats(self) -> Dict[str, Any]:
         return self.scheduler.get_stats()
 
+    @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():

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

@@ -4,8 +4,7 @@
 lazy singleton FastAPI instance.
 """
 
-from astrai.inference.api.protocol import GenContext, ProtocolHandler, StopChecker
-from astrai.inference.api.server import (
+from astrai.inference.network.app import (
     AnthropicMessage,
     ChatCompletionRequest,
     ChatMessage,
@@ -15,7 +14,8 @@ from astrai.inference.api.server import (
     get_app,
     run_server,
 )
-from astrai.inference.api.tool_parser import (
+from astrai.inference.network.protocol import GenContext, ProtocolHandler, StopChecker
+from astrai.inference.network.tool_parser import (
     BaseToolParser,
     SimpleJsonToolParser,
     ToolParserFactory,

astrai/inference/network/anthropic.py

@@ -6,13 +6,13 @@ from typing import Any, Dict, List, Tuple, Union
 
 from pydantic import BaseModel
 
-from astrai.inference.api.protocol import (
+from astrai.inference.engine import InferenceEngine
+from astrai.inference.network.protocol import (
     GenContext,
     ResponseBuilder,
     StopInfo,
     sse_event,
 )
-from astrai.inference.engine import InferenceEngine
 
 
 def _extract_text(content: Union[str, List[Dict[str, Any]]]) -> str:

astrai/inference/network/app.py

@@ -18,10 +18,10 @@ import uvicorn
 from fastapi import APIRouter, FastAPI, HTTPException
 from pydantic import BaseModel, Field
 
-from astrai.inference.api.anthropic import AnthropicResponseBuilder
-from astrai.inference.api.openai import OpenAIResponseBuilder
-from astrai.inference.api.protocol import ProtocolHandler
 from astrai.inference.engine import InferenceEngine
+from astrai.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
 
@@ -110,6 +110,7 @@ def _create_engine(
     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}")
@@ -123,6 +124,7 @@ def _create_engine(
         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
@@ -186,6 +188,7 @@ def run_server(
     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 = {
@@ -193,6 +196,7 @@ def run_server(
         "dtype": dtype,
         "param_path": param_path,
         "max_batch_size": max_batch_size,
+        "max_seq_len": max_seq_len,
     }
     uvicorn.run(
         app,

astrai/inference/network/openai.py

@@ -7,14 +7,14 @@ from typing import Any, Dict, List, Optional, Tuple, Union
 
 from pydantic import BaseModel
 
-from astrai.inference.api.protocol import (
+from astrai.inference.engine import InferenceEngine
+from astrai.inference.network.protocol import (
     GenContext,
     ResponseBuilder,
     StopInfo,
     sse_event,
 )
-from astrai.inference.api.tool_parser import BaseToolParser, ToolParserFactory
-from astrai.inference.engine import InferenceEngine
+from astrai.inference.network.tool_parser import BaseToolParser, ToolParserFactory
 
 logger = logging.getLogger(__name__)
 

astrai/inference/network/protocol.py

@@ -181,12 +181,10 @@ class ProtocolHandler:
         self, agen: AsyncGenerator, ctx: GenContext, stop_sequences: List[str]
     ) -> Dict[str, Any]:
         checker = StopChecker(stop_sequences)
-        chunks: List[str] = []
         body = ""
         matched = None
 
         async for token in agen:
-            chunks.append(token)
             body += token
 
             matched = checker.check(body)
@@ -195,6 +193,5 @@ class ProtocolHandler:
 
             ctx.completion_tokens += 1
 
-        content = "".join(chunks)
         stop = StopInfo(matched=matched, body=body)
-        return self.builder.format_response(ctx, content, stop)
+        return self.builder.format_response(ctx, body, stop)

astrai/inference/network/tool_parser.py

@@ -22,13 +22,10 @@ class BaseToolParser(ABC):
     Maintains streaming state internally so that each call to :meth:`feed`
     can diff against previously emitted content.
 
-    Parameters
-    ----------
-    tools : list of dict, optional
-        Tool definitions from the request.
-    tool_choice : str
-        ``"auto"`` / ``"required"`` / ``"none"`` or a named tool choice
-        dict.
+    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"):
@@ -51,14 +48,12 @@ class BaseToolParser(ABC):
 
         Returns an empty list when nothing new should be emitted.
 
-        Parameters
-        ----------
-        body : str
-            The complete accumulated generated text so far.
-        current_token_ids : list of int, optional
-            All token IDs decoded into *body* (cumulative).
-        delta_token_ids : list of int, optional
-            Only the token IDs for this chunk.
+        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

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

@@ -266,7 +266,7 @@ class SamplingPipeline(BaseSamplingStrategy):
     @staticmethod
     def _is_greedy(temperature: Union[float, Tensor]) -> bool:
         if isinstance(temperature, Tensor):
-            return temperature.numel() == 1 and temperature.item() == 0
+            return bool((temperature == 0).all())
         return temperature == 0
 
     @torch.inference_mode()
@@ -305,12 +305,12 @@ class SamplingPipeline(BaseSamplingStrategy):
             return tokens, chosen
 
         transformed = self.apply(logits, filter_value, input_ids, input_mask)
-        log_probs = torch.log_softmax(transformed.float(), dim=-1)
         tokens = torch.multinomial(
             torch.softmax(transformed, dim=-1), num_samples=1
         ).squeeze(-1)
         if not return_logprobs:
             return tokens
+        log_probs = torch.log_softmax(transformed.float(), dim=-1)
         chosen = torch.gather(log_probs, -1, tokens.unsqueeze(-1)).squeeze(-1)
         return tokens, chosen
 
@@ -343,6 +343,10 @@ def sample(
     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
@@ -359,14 +363,21 @@ def sample(
         ``True`` — a ``(token_ids, chosen_logprobs)`` tuple where
         ``chosen_logprobs`` has shape ``[batch]``.
     """
-    return SamplingPipeline(
-        [
-            TemperatureStrategy(temperature),
-            TopKStrategy(top_k),
-            TopPStrategy(top_p),
-            FrequencyPenaltyStrategy(frequency_penalty),
-        ]
-    ).sample(
+    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,

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

@@ -1,50 +1,46 @@
-import logging
 import threading
 import time
 import uuid
 from collections import deque
 from enum import Enum
-from typing import Any, Callable, Deque, Dict, List, Optional
+from 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
 
-logger = logging.getLogger(__name__)
+if TYPE_CHECKING:
+    from astrai.extension import AttentionBackend
 
 STOP = object()
 
 
 class StreamDecoder:
-    """Incremental decoder for byte-level BPE streaming.
+    """Incremental decoder backed by the tokenizers library's DecodeStream.
 
-    Byte-level BPE may split a single Unicode character (e.g. em-dash,
-    smart quotes) across multiple tokens. Decoding such a token in
-    isolation produces U+FFFD (replacement char). This decoder
-    accumulates token IDs and only emits text once the trailing
-    characters are complete, buffering incomplete multi-byte sequences
-    until the next token arrives.
+    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__ = ("_tokenizer", "_ids", "_emitted")
+    __slots__ = ("_stream", "_tok")
 
     def __init__(self, tokenizer: AutoTokenizer):
-        self._tokenizer = tokenizer
-        self._ids: List[int] = []
-        self._emitted: str = ""
+        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.
         """
-        self._ids.append(token_id)
-        full = self._tokenizer.decode(self._ids, skip_special_tokens=True)
-        if full.endswith("\ufffd"):
-            return ""
-        if len(full) > len(self._emitted):
-            diff = full[len(self._emitted) :]
-            self._emitted = full
-            return diff
-        return ""
+        chunk = self._stream.step(self._tok, token_id)
+        return chunk or ""
 
 
 class TaskStatus(Enum):
@@ -69,6 +65,7 @@ class Task:
         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
@@ -78,16 +75,25 @@ class Task:
         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.arrival_time = time.time()
-        self.finish_time: Optional[float] = None
+        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.
@@ -98,26 +104,15 @@ class Task:
             self._decoder = StreamDecoder(tokenizer)
         return self._decoder.push(self.output_ids[-1])
 
-    def flush_remaining(self, tokenizer: AutoTokenizer) -> str:
-        """Emit any text still buffered in the decoder.
-
-        Called when generation terminates (max_tokens reached, stop
-        sequence, or external removal) to avoid dropping a final
-        incomplete-looking fragment that is actually complete when
-        adjacent to the stop token.
-        """
-        if self._decoder is None or not self.output_ids:
-            return ""
-        full = tokenizer.decode(self.output_ids, skip_special_tokens=True)
-        if len(full) > len(self._decoder._emitted):
-            diff = full[len(self._decoder._emitted) :]
-            self._decoder._emitted = full
-            return diff
-        return ""
-
     @property
     def next_pos(self) -> int:
-        return self.input_tokens + len(self.output_ids)
+        """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:
@@ -135,12 +130,11 @@ class TaskManager:
         tokenizer: AutoTokenizer,
         max_batch_size: int = 16,
         max_seq_len: int = 8192,
-        max_prompt_len: int = 512,
+        metrics: Optional["MetricsCollector"] = None,
     ):
         self.tokenizer = tokenizer
         self.max_batch_size = max_batch_size
         self.max_seq_len = max_seq_len
-        self.max_prompt_len = max_prompt_len
 
         self.waiting_queue: Deque[Task] = deque()
         self.active_tasks: List[Task] = []
@@ -152,6 +146,8 @@ class TaskManager:
         self._total_tasks = 0
         self._total_tokens = 0
 
+        self._metrics = metrics
+
     def add_task(
         self,
         prompt: str,
@@ -161,17 +157,13 @@ class TaskManager:
         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_prompt_len:
-            prompt_ids = prompt_ids[-self.max_prompt_len :]
-
-        if len(prompt_ids) >= self.max_seq_len:
-            if stream_callback:
-                stream_callback(STOP)
-            return task_id
+        if 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)
@@ -187,6 +179,7 @@ class TaskManager:
             top_k=top_k,
             frequency_penalty=frequency_penalty,
             rep_window=rep_window,
+            backend=backend,
         )
 
         with self._lock:
@@ -195,6 +188,9 @@ class TaskManager:
             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
 
@@ -214,26 +210,33 @@ class TaskManager:
             cb(token)
 
     def get_stats(self) -> Dict[str, Any]:
-        return {
+        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:
-                    task.finish_time = time.time()
                     finished.append(task)
                 elif task.is_finished(stop_ids):
                     task.status = TaskStatus.FINISHED
-                    task.finish_time = time.time()
                     finished.append(task)
                     self._total_tokens += task.output_tokens
 
+            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

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

@@ -9,7 +9,7 @@ from astrai.model.components.lora import (
     merge_lora,
     save_lora,
 )
-from astrai.model.components.mlp import MLP
+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
@@ -19,6 +19,7 @@ __all__ = [
     "Linear",
     "RMSNorm",
     "MLP",
+    "DeepSeekMoE",
     "GQA",
     "DecoderBlock",
     # Models

astrai/model/automodel.py

@@ -4,13 +4,21 @@ AutoModel base class for model loading and saving.
 
 from contextlib import contextmanager
 from pathlib import Path
-from typing import Self, Union
+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 load_model_config, load_model_weights, save_model
+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
@@ -40,11 +48,12 @@ def _disable_random_init(enable: bool = True):
             setattr(nn.init, n, fn)
 
 
-class AutoModel(BaseFactory["AutoModel"], nn.Module):
-    """
-    Autoregressive language model base class.
-    Provides model loading/saving, registration, and generation.
-    """
+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__()
@@ -56,7 +65,25 @@ class AutoModel(BaseFactory["AutoModel"], nn.Module):
         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)
 
@@ -65,17 +92,29 @@ class AutoModel(BaseFactory["AutoModel"], nn.Module):
             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 = AutoModel.get_component_class(model_type)
+        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"
-        if weights_path.exists():
+        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
@@ -89,7 +128,3 @@ class AutoModel(BaseFactory["AutoModel"], nn.Module):
             state_dict=self.state_dict(),
             save_directory=str(save_directory),
         )
-
-    def to(self, *args, **kwargs) -> Self:
-        """Move model to device/dtype."""
-        return super().to(*args, **kwargs)

astrai/model/components/__init__.py

@@ -1,12 +1,12 @@
-from astrai.model.components.attention import GQA, MLA, repeat_kv
+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
+from astrai.model.components.mlp import MLP, DeepSeekMoE
 from astrai.model.components.norm import RMSNorm
 from astrai.model.components.rope import (
     RotaryEmbedding,
-    apply_rotary_emb,
     get_rotary_emb,
 )
 
@@ -14,6 +14,7 @@ __all__ = [
     "Linear",
     "RMSNorm",
     "MLP",
+    "DeepSeekMoE",
     "Embedding",
     "GQA",
     "MLA",
@@ -21,5 +22,4 @@ __all__ = [
     "RotaryEmbedding",
     "apply_rotary_emb",
     "get_rotary_emb",
-    "repeat_kv",
 ]

astrai/model/components/attention.py

@@ -5,22 +5,11 @@ 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.core.cache import CacheView
+from astrai.inference.cache import KVCache
 from astrai.model.components.linear import Linear
 from astrai.model.components.norm import RMSNorm
-from astrai.model.components.rope import apply_rotary_emb
-
-
-def repeat_kv(x: Tensor, n_rep: int) -> Tensor:
-    bs, slen, n_heads, head_dim = x.shape
-    if n_rep == 1:
-        return x
-    return (
-        x[:, :, :, None, :]
-        .expand(bs, slen, n_heads, n_rep, head_dim)
-        .reshape(bs, slen, n_heads * n_rep, head_dim)
-    )
 
 
 class AttnFactory(BaseFactory[nn.Module]):
@@ -66,17 +55,16 @@ class GQA(nn.Module):
             self.gate = Linear(dim, dim)
 
     def _split_heads(self, x: Tensor, n_heads) -> Tensor:
-        batch_size, seq_len, _ = x.shape
-        x = x.reshape(batch_size, seq_len, n_heads, self.head_dim)
-        return x
+        return x.reshape(*x.shape[:-1], n_heads, self.head_dim)
 
     def forward(
         self,
         x: Tensor,
         rotary_emb: Tensor,
         attn_mask: Tensor = None,
-        paged_cache: Optional[CacheView] = 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)
@@ -86,19 +74,9 @@ class GQA(nn.Module):
         if self.use_qk_norm:
             q, k = self.q_norm(q), self.k_norm(k)
 
-        if paged_cache is not None:
-            paged_cache.write(self.layer_id, k, v)
-            k, v = paged_cache.gather(self.layer_id)
-
-        k, v = repeat_kv(k, self.n_rep), repeat_kv(v, self.n_rep)
-
-        q, k, v = q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3)
-        sdqa_out = (
-            F.scaled_dot_product_attention(q, k, v, attn_mask, is_causal=is_causal)
-            .permute(0, 2, 1, 3)
-            .contiguous()
-            .flatten(2)
-        )
+        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))
@@ -161,19 +139,18 @@ class MLA(nn.Module):
         x: Tensor,
         rotary_emb: Tensor,
         attn_mask: Tensor = None,
-        paged_cache: Optional[CacheView] = None,
+        kv_cache: Optional[KVCache] = None,
         is_causal: bool = False,
+        fwd: Optional[str] = None,
     ) -> Tensor:
-        bsz, seq_len, _ = x.size()
-
         q = self.q_proj(x)
-        q = q.view(bsz, seq_len, self.n_heads, self.head_dim)
+        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.view(bsz, seq_len, self.n_kv_heads, -1)
+        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
@@ -193,18 +170,9 @@ class MLA(nn.Module):
             q = self.q_norm(q)
             k = self.k_norm(k)
 
-        if paged_cache is not None:
-            paged_cache.write(self.layer_id, k, v)
-            k, v = paged_cache.gather(self.layer_id)
-
-        q = q.permute(0, 2, 1, 3)
-        k = k.permute(0, 2, 1, 3)
-        v = v.permute(0, 2, 1, 3)
-
-        attn_out = F.scaled_dot_product_attention(
-            q, k, v, attn_mask, is_causal=is_causal
-        )
-        attn_out = attn_out.permute(0, 2, 1, 3).contiguous().flatten(2)
+        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))

astrai/model/components/decoder_block.py

@@ -1,15 +1,21 @@
 from dataclasses import asdict
-from typing import Optional
+from typing import Optional, TypedDict
 
 import torch.nn as nn
 from torch import Tensor
 
-from astrai.inference.core.cache import CacheView
+from astrai.inference.cache import KVCache
 from astrai.model.components.attention import AttnFactory
-from astrai.model.components.mlp import FFNFactory
+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__()
@@ -26,24 +32,45 @@ class DecoderBlock(nn.Module):
         self.attention = AttnFactory.create(config.attn_type, **cfg, layer_id=layer_id)
         self.input_norm = RMSNorm(config.hidden_size, config.rms_norm_eps)
         self.post_attention_norm = RMSNorm(config.hidden_size, config.rms_norm_eps)
-        self.mlp = FFNFactory.create(config.ffn_type, **cfg)
+        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,
-        paged_cache: Optional[CacheView] = None,
+        kv_cache: Optional[KVCache] = None,
         is_causal: bool = False,
-    ) -> Tensor:
+        fwd: Optional[str] = None,
+    ) -> DecoderOutput:
         attn_output = self.attention(
             self.input_norm(x),
             rotary_emb,
             attention_mask,
-            paged_cache,
+            kv_cache,
             is_causal,
+            fwd,
         )
         x = attn_output + x
-        x = self.mlp(self.post_attention_norm(x)) + x
+        normalized = self.post_attention_norm(x)
+        mlp_output = self.mlp(normalized)
+        x = mlp_output["hidden_states"] + x
 
-        return x
+        return {
+            "hidden_states": x,
+            "aux_loss": mlp_output["aux_loss"],
+            "router_stats": mlp_output.get("router_stats"),
+        }

astrai/model/components/lora.py

@@ -1,11 +1,12 @@
 import logging
-from dataclasses import asdict, dataclass
+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 (

astrai/model/components/mlp.py

@@ -1,3 +1,5 @@
+from typing import Optional, TypedDict
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -11,6 +13,22 @@ 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):
@@ -19,10 +37,10 @@ class MLP(nn.Module):
         self.gate = Linear(dim, dim_ffn)
         self.down = Linear(dim_ffn, dim, init_std=down_init_std)
 
-    def forward(self, x: Tensor) -> Tensor:
+    def forward(self, x: Tensor) -> FFNOutput:
         gated = self.up(x) * F.silu(self.gate(x))
         out = self.down(gated)
-        return out
+        return {"hidden_states": out, "aux_loss": None, "router_stats": None}
 
 
 @FFNFactory.register("moe")
@@ -36,6 +54,9 @@ class DeepSeekMoE(nn.Module):
         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
@@ -43,6 +64,16 @@ class DeepSeekMoE(nn.Module):
         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
@@ -50,51 +81,92 @@ class DeepSeekMoE(nn.Module):
 
         self.shared_experts = nn.ModuleList(
             [
-                MLP(dim, dim_ffn, down_init_std=down_init_std)
+                MLP(dim, shared_dim_ffn, down_init_std=down_init_std)
                 for _ in range(n_shared_experts)
             ]
         )
         self.routed_experts = nn.ModuleList(
             [
-                MLP(dim, dim_ffn, down_init_std=down_init_std)
+                MLP(dim, expert_dim_ffn, down_init_std=down_init_std)
                 for _ in range(n_routed_experts)
             ]
         )
 
-    def forward(self, x: Tensor) -> Tensor:
-        bsz, seq_len, dim = x.shape
+    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_out = self._routed_forward(x_flat)
+        routed_output = self._routed_forward(x_flat, include_aux_loss)
 
-        out = (shared_out + routed_out).view(bsz, seq_len, dim)
-        return out
+        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) for e in self.shared_experts) / self.n_shared_experts
+        return (
+            sum(e(x)["hidden_states"] for e in self.shared_experts)
+            / self.n_shared_experts
+        )
 
-    def _routed_forward(self, x: Tensor) -> Tensor:
+    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)
-        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+        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)
-        for expert_idx in range(self.n_routed_experts):
-            expert_mask = topk_indices == expert_idx
-            token_idx, k_idx = expert_mask.nonzero(as_tuple=True)
-            if token_idx.numel() == 0:
+        start = 0
+        for expert_idx, end in enumerate(boundaries):
+            if end == start:
                 continue
-            expert_input = x[token_idx]
-            expert_output = self.routed_experts[expert_idx](expert_input)
-            weights = topk_weights[token_idx, k_idx].unsqueeze(-1)
-            output.index_add_(0, token_idx, expert_output * weights)
+            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 output
+        return {
+            "hidden_states": output,
+            "aux_loss": aux_loss,
+            "router_stats": router_stats,
+        }

astrai/model/components/rope.py

@@ -11,28 +11,23 @@ def get_rotary_emb(
     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.complex(cos, sin)
+    return torch.stack([cos, sin], dim=-1)
 
 
 def ntk_base(base: float, dim: int, factor: float) -> float:
     return base * (factor ** (dim / (dim - 2)))
 
 
-def apply_rotary_emb(x: torch.Tensor, freqs_cis: Tensor) -> Tensor:
-    dtype = x.dtype
-    x_ = x.float().reshape(*x.shape[:-1], -1, 2)
-    x_complex = torch.view_as_complex(x_)
-    freqs_cis = freqs_cis.unsqueeze(2)
-    x_rotated = x_complex * freqs_cis
-    x_out = torch.view_as_real(x_rotated).flatten(-2)
-    return x_out.to(dtype)
-
-
 class RotaryEmbedding(nn.Module):
     def __init__(
         self,
@@ -56,16 +51,26 @@ class RotaryEmbedding(nn.Module):
         self._set_rotary_buffer(self.max_len)
 
     def _set_rotary_buffer(self, max_len: int):
-        rotary_emb = get_rotary_emb(self.dim, max_len, self.base)
-        freqs_cis = torch.view_as_real(rotary_emb)
+        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:
-            position_ids = (
-                torch.arange(x.size(1), device=x.device)
-                .unsqueeze(0)
-                .expand(x.size(0), -1)
-            )
-        position_freq_cis = self.freqs_cis[position_ids].float()
-        return torch.view_as_complex(position_freq_cis)
+            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

@@ -5,7 +5,7 @@ import torch.nn as nn
 from torch import Tensor
 
 from astrai.config.model_config import EncoderConfig
-from astrai.model.automodel import AutoModel
+from astrai.model.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
@@ -13,7 +13,7 @@ from astrai.model.components.rope import RotaryEmbedding
 from astrai.model.transformer import process_attention_mask
 
 
-@AutoModel.register("embedding")
+@ModelFactory.register("embedding")
 class EmbeddingEncoder(AutoModel):
     def __init__(self, config: EncoderConfig):
         super().__init__(config)
@@ -70,7 +70,7 @@ class EmbeddingEncoder(AutoModel):
         attn_mask = process_attention_mask(input_mask)
 
         for layer in self.layers:
-            x = layer(x, rotary_emb, attn_mask)
+            x = layer(x, rotary_emb, attn_mask)["hidden_states"]
 
         hidden_states = self.norm(x)
 

astrai/model/transformer.py

@@ -5,8 +5,8 @@ import torch.nn as nn
 from torch import Tensor
 
 from astrai.config.model_config import AutoRegressiveLMConfig
-from astrai.inference.core.cache import CacheView
-from astrai.model.automodel import AutoModel
+from astrai.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
@@ -26,7 +26,7 @@ def process_attention_mask(
     return input_mask
 
 
-@AutoModel.register("autoregressive_lm")
+@ModelFactory.register("autoregressive_lm")
 class AutoRegressiveLM(AutoModel):
     """Autoregressive language model with paged KV cache."""
 
@@ -103,20 +103,50 @@ class AutoRegressiveLM(AutoModel):
         self,
         input_ids: Tensor,
         input_mask: Optional[Tensor] = None,
-        paged_cache: Optional[CacheView] = None,
+        kv_cache: Optional[KVCache] = None,
         position_ids: Optional[Tensor] = None,
+        fwd: Optional[str] = None,
     ) -> Dict[str, Tensor]:
-        assert input_ids.ndim == 2
+        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:
-            x = layer(x, rotary_emb, attn_mask, paged_cache, use_sdpa_causal_mask)
+            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)
 
-        return {"logits": logits, "hidden_states": 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

@@ -4,12 +4,12 @@ from astrai.parallel.executor import (
     BaseExecutor,
     DDPExecutor,
     ExecutorFactory,
-    FSDP2Executor,
     FSDPExecutor,
     GradientState,
     NoneExecutor,
+    broadcast_state_dict,
+    create_ref_model,
 )
-from astrai.parallel.module import ColumnParallelLinear, RowParallelLinear
 from astrai.parallel.setup import (
     get_current_device,
     get_rank,
@@ -26,8 +26,6 @@ __all__ = [
     "only_on_rank",
     "setup_parallel",
     "spawn_parallel_fn",
-    "RowParallelLinear",
-    "ColumnParallelLinear",
     "ExecutorFactory",
     "BaseExecutor",
     "GradientState",
@@ -36,5 +34,6 @@ __all__ = [
     "NoneExecutor",
     "DDPExecutor",
     "FSDPExecutor",
-    "FSDP2Executor",
+    "create_ref_model",
+    "broadcast_state_dict",
 ]

astrai/parallel/executor.py

@@ -4,18 +4,15 @@ import contextlib
 import logging
 import os
 from contextlib import contextmanager
-from typing import Any, Callable, Optional, Tuple
+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,
-    FullStateDictConfig,
-    StateDictType,
     fully_shard,
 )
-from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
 from torch.distributed.tensor import DTensor
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.optim import Optimizer
@@ -27,6 +24,82 @@ 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)
@@ -95,11 +168,14 @@ class BaseExecutor:
         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:
@@ -238,88 +314,11 @@ class DDPExecutor(BaseExecutor):
 
 @ExecutorFactory.register("fsdp")
 class FSDPExecutor(BaseExecutor):
-    def __init__(
-        self,
-        grad_accum_steps: int = 1,
-        process_group=None,
-        sharding_strategy=None,
-        cpu_offload=None,
-        auto_wrap_policy=None,
-        backward_prefetch=None,
-        mixed_precision=None,
-        ignored_modules=None,
-        param_init_fn=None,
-        sync_module_states: bool = False,
-        forward_prefetch: bool = False,
-        limit_all_gathers: bool = True,
-        ignored_states=None,
-        device_mesh=None,
-    ):
-        super().__init__(grad_accum_steps=grad_accum_steps)
-        self._fsdp_kwargs = {
-            k: v
-            for k, v in dict(
-                process_group=process_group,
-                sharding_strategy=sharding_strategy,
-                cpu_offload=cpu_offload,
-                auto_wrap_policy=auto_wrap_policy,
-                backward_prefetch=backward_prefetch,
-                mixed_precision=mixed_precision,
-                ignored_modules=ignored_modules,
-                param_init_fn=param_init_fn,
-                sync_module_states=sync_module_states,
-                forward_prefetch=forward_prefetch,
-                limit_all_gathers=limit_all_gathers,
-                use_orig_params=True,
-                ignored_states=ignored_states,
-                device_mesh=device_mesh,
-            ).items()
-            if v is not None
-        }
-        self._original_model: Optional[nn.Module] = None
-
-    def _prepare_model(self, model: nn.Module) -> nn.Module:
-        if not self.use_distributed:
-            logger.warning("FSDP backend selected but world_size=1, model not wrapped")
-            return model
-        self._original_model = model
-        device_id = torch.device("cuda", get_rank())
-        model = FSDP(model, device_id=device_id, **self._fsdp_kwargs)
-        logger.info("Model wrapped with FSDP (world_size=%d)", get_world_size())
-        return model
-
-    def _no_sync(self, model: nn.Module):
-        if isinstance(model, FSDP):
-            return model.no_sync()
-        return contextlib.nullcontext()
-
-    def clip_grad_norm(self, model: nn.Module, max_norm: float) -> float:
-        if isinstance(model, FSDP) and self.use_distributed:
-            total_norm = model.clip_grad_norm_(max_norm)
-            if isinstance(total_norm, torch.Tensor):
-                return total_norm.item()
-            return total_norm
-        return super().clip_grad_norm(model, max_norm)
-
-    def unwrap_model(self, model: nn.Module):
-        if isinstance(model, FSDP) and self.use_distributed:
-            with FSDP.state_dict_type(
-                model,
-                StateDictType.FULL_STATE_DICT,
-                FullStateDictConfig(offload_to_cpu=True, rank0_only=True),
-            ):
-                return model.state_dict()
-
-        return model.state_dict()
-
-
-@ExecutorFactory.register("fsdp2")
-class FSDP2Executor(BaseExecutor):
-    """FSDP2 executor using `torch.distributed.fsdp.fully_shard` (per-module API).
+    """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
-    FSDP2's dynamic ``__class__`` assignment fail at the CPython level.
+    ``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.
     """
@@ -329,7 +328,7 @@ class FSDP2Executor(BaseExecutor):
         grad_accum_steps: int = 1,
         mesh: Optional[Any] = None,
         mp_policy: Optional[Any] = None,
-        reshard_after_forward: bool = True,
+        reshard_after_forward: bool = False,
     ):
         super().__init__(grad_accum_steps=grad_accum_steps)
         self._mesh = mesh
@@ -338,7 +337,7 @@ class FSDP2Executor(BaseExecutor):
 
     def _prepare_model(self, model: nn.Module) -> nn.Module:
         if not self.use_distributed:
-            logger.warning("FSDP2 backend selected but world_size=1, model not wrapped")
+            logger.warning("FSDP backend selected but world_size=1, model not wrapped")
             return model
 
         kwargs = dict(
@@ -356,7 +355,7 @@ class FSDP2Executor(BaseExecutor):
                 fully_shard(child, **kwargs)
 
         logger.info(
-            "FSDP2 wrapping applied to %d direct children (root skipped for ABC compat)",
+            "FSDP wrapping applied to %d direct children (root skipped for ABC compat)",
             len(list(model.children())),
         )
         return model
@@ -376,32 +375,54 @@ class FSDP2Executor(BaseExecutor):
             yield
 
     def clip_grad_norm(self, model: nn.Module, max_norm: float) -> float:
-        if self.use_distributed:
-            total_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
-            if isinstance(total_norm, torch.Tensor):
-                return total_norm.item()
-            return total_norm
-        return super().clip_grad_norm(model, max_norm)
+        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()
 
-        if get_rank() != 0:
-            return None
-
+        # 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 = {
-            k: (v.full_tensor() if isinstance(v, DTensor) else v)
-            for k, v in state_dict.items()
-        }
+        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/module.py

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

astrai/parallel/setup.py

@@ -12,7 +12,7 @@ import torch
 import torch.distributed as dist
 import torch.multiprocessing as mp
 
-from astrai.parallel.signal_handler import install_early_signal_handlers
+from astrai.signal_handler import install_early_signal_handlers
 
 logger = logging.getLogger(__name__)
 
@@ -247,18 +247,15 @@ class LocalStrategy(LaunchStrategy):
             ctx.join()
 
 
-def _detect_launcher() -> str:
-    """Detect the distributed launcher from environment.
-
-    Returns one of: "torchelastic", "torchrun", "external", "local".
-    """
+def _is_external_launcher() -> bool:
+    """Whether an external launcher (torchrun/elastic/manual env) started us."""
     if dist.is_torchelastic_launched():
-        return "torchelastic"
+        return True
     if "LOCAL_WORLD_SIZE" in os.environ:
-        return "torchrun"
+        return True
     if "RANK" in os.environ and "WORLD_SIZE" in os.environ:
-        return "external"
-    return "local"
+        return True
+    return False
 
 
 def spawn_parallel_fn(
@@ -273,8 +270,7 @@ def spawn_parallel_fn(
 ):
     if master_port is None:
         master_port = find_free_port()
-    launcher = _detect_launcher()
-    if launcher in ("torchelastic", "torchrun", "external"):
+    if _is_external_launcher():
         strategy = TorchrunStrategy(
             world_size, backend, master_addr, master_port, device_type, start_method
         )

astrai/preprocessing/builder.py

@@ -416,7 +416,11 @@ class MultiOutputMaskBuilder(BaseMaskBuilder):
             return None
 
         result: dict = {}
-        any_output = False
+        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", [])
@@ -428,7 +432,6 @@ class MultiOutputMaskBuilder(BaseMaskBuilder):
                 if ids is None:
                     continue
                 result[output_key] = ids
-                any_output = True
                 continue
 
             list_field = spec.get("list_field", False)
@@ -444,7 +447,6 @@ class MultiOutputMaskBuilder(BaseMaskBuilder):
                 result[output_key] = ids
                 if mask is not None:
                     result[mask_key] = mask
-                any_output = True
                 continue
 
             ids, mask = self.renderer.process_sections(
@@ -460,9 +462,7 @@ class MultiOutputMaskBuilder(BaseMaskBuilder):
             elif "mask_key" in spec:
                 result[mask_key] = mask
 
-            any_output = True
-
-        if not any_output:
+        if not required_outputs or not required_outputs.issubset(result):
             return None
 
         result["domain"] = _extract_domain(item, config.output.domain_key)
@@ -474,6 +474,11 @@ class MultiOutputMaskBuilder(BaseMaskBuilder):
             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:
@@ -506,7 +511,7 @@ class MultiOutputMaskBuilder(BaseMaskBuilder):
 
         return [
             ({**result, "domain": _extract_domain(item, config.output.domain_key)})
-            if result
+            if required_outputs and required_outputs.issubset(result)
             else None
             for item, result in zip(items, results)
         ]

astrai/preprocessing/pipeline.py

@@ -1,7 +1,7 @@
 """Config-driven JSONL preprocessing pipeline.
 
 Composes a :class:`BaseMaskBuilder` (selected by ``input.type``) with
-sharding and flush to ``.h5`` / ``.bin`` storage.  Packing, position-id
+sharding and flush to ``.bin`` storage.  Packing, position-id
 generation and storage writing are each delegated to pluggable strategies,
 dispatched by configuration keys.
 

astrai/preprocessing/writer.py

@@ -1,7 +1,7 @@
 """Storage writer strategies for pipeline output.
 
 The :class:`StoreWriter` abstraction decouples the pipeline from the
-concrete storage format (bin / h5).  The pipeline builds a ``{key:
+concrete storage format (bin).  The pipeline builds a ``{key:
 List[Tensor]}`` dict and delegates the write to the writer selected
 by ``output.storage_format``.
 """
@@ -15,7 +15,7 @@ from typing import Dict, List
 import torch
 
 from astrai.factory import BaseFactory
-from astrai.serialization import save_bin, save_h5
+from astrai.serialization import save_bin
 
 logger = logging.getLogger(__name__)
 
@@ -54,22 +54,3 @@ class BinWriter(StoreWriter):
                 exc_info=True,
             )
             raise
-
-
-@StoreWriterFactory.register("h5")
-class H5Writer(StoreWriter):
-    def save(self, output_dir, domain, shard_idx, tensors):
-        chunk_dir = os.path.join(output_dir, domain)
-        file_path = os.path.join(chunk_dir, f"data_{shard_idx:04d}.h5")
-        try:
-            save_h5(chunk_dir, f"data_{shard_idx:04d}", tensors)
-        except Exception:
-            if os.path.exists(file_path):
-                os.remove(file_path)
-            logger.error(
-                "Failed to write shard %s/data_%04d.h5, cleaned up partial output",
-                domain,
-                shard_idx,
-                exc_info=True,
-            )
-            raise

astrai/serialization/__init__.py

@@ -20,13 +20,23 @@ from astrai.serialization.checkpoint import (
 from astrai.serialization.dataset import (
     load_bin,
     load_bin_offsets,
-    load_h5,
     save_bin,
-    save_h5,
+)
+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",
@@ -39,7 +49,5 @@ __all__ = [
     "save_torch",
     "load_bin",
     "load_bin_offsets",
-    "load_h5",
     "save_bin",
-    "save_h5",
 ]

astrai/serialization/checkpoint.py

@@ -5,7 +5,7 @@ import json
 import time
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Any, Dict, Optional, Union
+from typing import Any, Callable, Dict, Optional, Union
 
 import safetensors.torch as st
 import torch
@@ -22,39 +22,31 @@ def save_safetensors(state_dict: dict, path: Union[str, Path]):
     st.save_file(state_dict, str(path))
 
 
-def load_safetensors(path: Union[str, Path], broadcast: bool = False) -> dict:
+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 st.load_file(str(path))
-
+        return loader()
     rank = get_rank()
     if rank == 0:
-        state_dict = st.load_file(str(path))
+        data = loader()
     else:
-        state_dict = {}
-    tmp = [state_dict]
+        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:
-    if not broadcast or not dist.is_initialized():
-        with open(str(path), "r") as f:
-            return json.load(f)
-
-    rank = get_rank()
-    if rank == 0:
-        with open(str(path), "r") as f:
-            data = json.load(f)
-    else:
-        data = {}
-    tmp = [data]
-    dist.broadcast_object_list(tmp, src=0)
-    return tmp[0]
+    return _broadcast_load(lambda: json.loads(Path(path).read_text()), broadcast)
 
 
 def save_torch(obj: Any, path: Union[str, Path]):
@@ -99,7 +91,21 @@ def load_model_config(save_directory: str) -> dict:
 
 
 def load_model_weights(save_directory: str) -> dict:
-    return load_state_dict(Path(save_directory) / _WEIGHTS_FILE)
+    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:
@@ -190,8 +196,10 @@ class Checkpoint:
         if meta_path.exists():
             return cls.load(save_dir, broadcast=broadcast)
 
-        if weights_path.exists():
-            state_dict = load_state_dict(weights_path, 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():

astrai/serialization/dataset.py

@@ -1,55 +1,14 @@
-"""Dataset storage serialization helpers (HDF5 / memory-mapped binary)."""
+"""Dataset storage serialization helpers (memory-mapped binary)."""
 
 import json
 import os
-from pathlib import Path
 from typing import Any, Dict, List, Optional
 
-import h5py
 import numpy as np
 import torch
 from torch import Tensor
 
 
-def save_h5(file_path: str, file_name: str, tensor_group: Dict[str, List[Tensor]]):
-    os.makedirs(file_path, exist_ok=True)
-    full_file_path = os.path.join(file_path, f"{file_name}.h5")
-    with h5py.File(full_file_path, "w") as f:
-        for key, tensors in tensor_group.items():
-            grp = f.create_group(key)
-            for idx, tensor in enumerate(tensors):
-                arr = tensor.cpu().numpy()
-                grp.create_dataset(f"data_{idx}", data=arr)
-
-
-def load_h5(file_path: str, share_memory=True) -> Dict[str, List[Tensor]]:
-    tensor_group: Dict[str, List[Tensor]] = {}
-
-    root_path = Path(file_path)
-    if root_path.is_file() and root_path.suffix in (".h5", ".hdf5"):
-        h5_files = [root_path]
-    else:
-        h5_files = list(root_path.rglob("*.h5")) + list(root_path.rglob("*.hdf5"))
-
-    for h5_file in h5_files:
-        with h5py.File(h5_file, "r") as f:
-            for key in f.keys():
-                grp = f[key]
-                dsets = []
-                for dset_name in grp.keys():
-                    dset = grp[dset_name]
-                    tensor = torch.from_numpy(dset[:])
-                    if share_memory:
-                        tensor = tensor.share_memory_()
-                    dsets.append(tensor)
-
-                if tensor_group.get(key) is None:
-                    tensor_group[key] = []
-                tensor_group[key].extend(dsets)
-
-    return tensor_group
-
-
 def save_bin(
     file_path: str,
     tensor_group: Dict[str, List[Tensor]],
@@ -65,7 +24,7 @@ def save_bin(
     offsets, preserving backward compatibility.
 
     Nested keys (``List[List[Tensor]]`` such as GRPO ``responses``) are
-    not supported in bin format — use H5 for those.
+    not supported in bin format — use JSONL for those.
     """
     os.makedirs(file_path, exist_ok=True)
     record_keys = set(record_keys or [])
@@ -74,7 +33,7 @@ def save_bin(
         if tensors and isinstance(tensors[0], list):
             raise ValueError(
                 f"Nested key '{key}' (List[List[Tensor]]) is not supported "
-                f"in bin format. Use H5 or JSONL storage instead."
+                f"in bin format. Use JSONL storage instead."
             )
         cat = torch.cat(tensors, dim=0)
         entry: Dict[str, Any] = {
@@ -112,7 +71,7 @@ def load_bin_offsets(file_path: str) -> Dict[str, List[int]]:
 
     Returns an empty dict when no key has offsets (legacy bin files),
     in which case record-mode access falls back to per-record segment
-    indexing (H5/JSONL layout).
+    indexing (JSONL layout).
     """
     with open(os.path.join(file_path, "meta.json"), "r") as f:
         meta = json.load(f)

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/tokenize/chat_template.py

@@ -38,12 +38,27 @@ class ChatTemplate:
         The compiled :class:`~jinja2.Template` holds a dynamically-generated
         ``root`` render function whose ``__module__`` is ``None``; under
         ``pickle`` it falls back to ``__main__`` and breaks ``spawn``-based
-        multiprocessing.  By deferring compilation to first access, the
-        default pickle protocol serialises only ``template_str``; each
-        worker rebuilds the cache on first render.
+        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,

astrai/tokenize/tokenizer.py

@@ -20,8 +20,6 @@ Messages = List[Message]
 class AutoTokenizer:
     """Base tokenizer class with automatic loading support"""
 
-    TOKENIZER_CLASSES = {}  # Registry for auto-loading
-
     def __init__(
         self,
         path: Optional[Union[str, Path]] = None,
@@ -108,17 +106,6 @@ class AutoTokenizer:
         with open(save_path / "tokenizer_config.json", "w", encoding="utf-8") as f:
             json.dump(config, f, ensure_ascii=False, indent=2)
 
-    @classmethod
-    def register_tokenizer(cls, name: str, tokenizer_class: type):
-        """
-        Register a new tokenizer class.
-
-        Args:
-            name: Name to register the tokenizer class under
-            tokenizer_class: The tokenizer class to register
-        """
-        cls.TOKENIZER_CLASSES[name] = tokenizer_class
-
     def encode(
         self,
         tokens: Union[str, List[str]],

astrai/trainer/metric_util.py

@@ -1,3 +1,4 @@
+import math
 from typing import Dict
 
 import torch
@@ -22,6 +23,54 @@ def grad_norm(model: nn.Module, per_param: bool = False) -> float | Dict[str, fl
     return total_sq.sqrt().item()
 
 
+class GradSNRTracker:
+    """Track gradient signal-to-noise ratio via EMA of first/second moments.
+
+    SNR = E[g]^2 / Var(g) = E[g]^2 / (E[g^2] - E[g]^2)
+
+    The reported value is the power ratio in decibels: ``10 * log10(SNR)``.
+
+    The tracker accumulates per-parameter EMA moments across optimizer steps.
+    Call ``update`` after backward (before ``optimizer.step``) and read
+    ``snr`` to get the aggregate SNR across all parameters.
+    """
+
+    def __init__(self, beta: float = 0.999, eps: float = 1e-8):
+        self.beta = beta
+        self.eps = eps
+        self._first: Dict[int, torch.Tensor] = {}
+        self._second: Dict[int, torch.Tensor] = {}
+
+    @torch.no_grad()
+    def update(self, model: nn.Module) -> None:
+        beta = self.beta
+        for param in model.parameters():
+            if param.grad is None:
+                continue
+            pid = id(param)
+            g = param.grad.detach()
+            if pid not in self._first:
+                self._first[pid] = g.clone()
+                self._second[pid] = g.pow(2).clone()
+            else:
+                self._first[pid].mul_(beta).add_(g, alpha=1 - beta)
+                self._second[pid].mul_(beta).addcmul_(g, g, value=1 - beta)
+
+    @property
+    def snr(self) -> float:
+        if not self._first:
+            return 0.0
+        total_signal = 0.0
+        total_noise = 0.0
+        for m, v in zip(self._first.values(), self._second.values()):
+            signal = m.pow(2).sum().item()
+            noise = (v - m.pow(2)).clamp(min=0).sum().item()
+            total_signal += signal
+            total_noise += noise
+        snr = total_signal / (total_noise + self.eps)
+        return 10.0 * math.log10(max(snr, self.eps))
+
+
 def ctx_get_loss(ctx):
     return ctx.loss
 
@@ -36,3 +85,14 @@ def ctx_get_val_loss(ctx):
 
 def ctx_get_grad_norm(ctx):
     return ctx.grad_norm
+
+
+def ctx_get_grad_snr(ctx):
+    tracker = getattr(ctx, "grad_snr_tracker", None)
+    if tracker is None:
+        return None
+    return tracker.snr
+
+
+def ctx_get_moe_metric(ctx, key):
+    return ctx.strategy._moe_metrics.get(key)

astrai/trainer/rollout.py

@@ -6,7 +6,7 @@ Provides:
 - :class:`BaseRewardModel` — pluggable reward interface
 - :class:`RolloutGenerator` — KV-cache-backed generation of grouped
   responses + decoding (no reward); delegates the generation loop to
-  :class:`~astrai.inference.core.scheduler.InferenceScheduler.run_batch`
+  :class:`~astrai.inference.scheduler.InferenceScheduler.run_batch`
   so rollout and the production inference server share one code path
 - :class:`RolloutRunner` — orchestrates generation + scoring with a
   step-driven cache; its ``__call__`` returns ``(RolloutResult, is_fresh)``
@@ -20,7 +20,7 @@ from typing import Dict, List, Optional, Tuple
 import torch
 from torch import Tensor
 
-from astrai.inference.core.scheduler import InferenceScheduler
+from astrai.inference.scheduler import InferenceScheduler
 
 
 @dataclass(kw_only=True)
@@ -101,7 +101,7 @@ class RolloutGenerator:
     """Pure generation + decoding for a group of responses per prompt.
 
     Delegates the prefill/decode loop to
-    :meth:`~astrai.inference.core.scheduler.InferenceScheduler.run_batch`,
+    :meth:`~astrai.inference.scheduler.InferenceScheduler.run_batch`,
     which uses a real KV cache (no O(n²) recompute).  Has no dependency
     on any reward model; can be reused in isolation for offline
     generation, qualitative sampling, or eval pipelines.

astrai/trainer/schedule.py

@@ -2,7 +2,7 @@
 
 import math
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List
+from typing import List
 
 from torch.optim.lr_scheduler import LRScheduler
 
@@ -20,12 +20,6 @@ class BaseScheduler(LRScheduler, ABC):
         """Calculate the current learning rate."""
         raise NotImplementedError
 
-    def state_dict(self) -> Dict[str, Any]:
-        return super().state_dict()
-
-    def load_state_dict(self, state_dict: Dict[str, Any]):
-        super().load_state_dict(state_dict)
-
 
 class SchedulerFactory(BaseFactory["BaseScheduler"]):
     """Factory class for creating learning rate schedulers.

astrai/trainer/strategy.py

@@ -1,7 +1,7 @@
 """Training strategy implementations with factory pattern."""
 
-from abc import ABC, abstractmethod
-from typing import Callable, Dict, Union
+from abc import ABC
+from typing import Callable, Dict, List, Optional, TypedDict, Union
 
 import torch
 import torch.nn as nn
@@ -9,18 +9,20 @@ import torch.nn.functional as F
 from torch import Tensor
 
 from astrai.factory import BaseFactory
+from astrai.model.components.mlp import RouterStats
+from astrai.parallel.executor import broadcast_state_dict
 from astrai.trainer.rollout import RolloutResult
 
 
-def create_ref_model(
-    model_fn: Callable[[], nn.Module], state_dict: Dict[str, Tensor]
-) -> nn.Module:
-    """Create a frozen reference model from model_fn + full state dict."""
-    ref_model = model_fn()
-    ref_model.load_state_dict(state_dict)
-    ref_model.requires_grad_(False)
-    ref_model.eval()
-    return ref_model
+class LossOutput(TypedDict):
+    loss: Tensor
+    metrics: Dict[str, float]
+
+
+class LogprobsOutput(TypedDict):
+    logprobs: Tensor
+    aux_loss: Optional[Tensor]
+    router_stats: Optional[List[RouterStats]]
 
 
 def move_to_device(batch: Dict[str, Tensor], device: str) -> Dict[str, Tensor]:
@@ -34,7 +36,7 @@ def get_logprobs(
     attn_mask: Tensor,
     loss_mask: Tensor,
     reduction: str,
-) -> Tensor:
+) -> LogprobsOutput:
     """Compute token-wise log probabilities from model outputs.
 
     Args:
@@ -56,10 +58,11 @@ def get_logprobs(
     shifted_input_ids = input_ids[:, 1:]
     shifted_loss_mask = loss_mask[:, 1:]
 
-    logits = model(
+    outputs = model(
         input_ids[:, :-1],
         attn_mask[:, :, :-1, :-1] if attn_mask.dim() == 4 else attn_mask[:, :-1],
-    )["logits"]
+    )
+    logits = outputs["logits"]
     log_probs = torch.log_softmax(logits.float(), dim=-1)
 
     token_logprobs = torch.gather(
@@ -67,13 +70,18 @@ def get_logprobs(
     ).squeeze(-1)
 
     if reduction == "mean":
-        return (token_logprobs * shifted_loss_mask).sum(dim=-1) / shifted_loss_mask.sum(
+        logprobs = (token_logprobs * shifted_loss_mask).sum(
             dim=-1
-        ).clamp(min=1.0)
+        ) / shifted_loss_mask.sum(dim=-1).clamp(min=1.0)
     elif reduction == "sum":
-        return (token_logprobs * shifted_loss_mask).sum(dim=-1)
+        logprobs = (token_logprobs * shifted_loss_mask).sum(dim=-1)
     else:
-        return token_logprobs * shifted_loss_mask
+        logprobs = token_logprobs * shifted_loss_mask
+    return {
+        "logprobs": logprobs,
+        "aux_loss": outputs.get("aux_loss"),
+        "router_stats": outputs.get("router_stats"),
+    }
 
 
 def make_doc_boundary_mask(position_ids: Tensor) -> Tensor:
@@ -92,6 +100,68 @@ def make_doc_boundary_mask(position_ids: Tensor) -> Tensor:
     return (same_doc & causal).unsqueeze(1)
 
 
+def _collect_moe_diagnostics(
+    router_stats_list: List[RouterStats],
+) -> Dict[str, float]:
+    """Collect MoE routing diagnostic metrics from per-layer router stats.
+
+    Args:
+        router_stats_list: One :class:`RouterStats` dict per MoE layer with
+            keys ``probs`` (N, E) and ``topk_indices`` (N, K), both detached.
+
+    Returns:
+        Dict with keys: router_entropy, dead_expert_fraction,
+        load_imbalance_mean, load_imbalance_max.  Values are averaged
+        across layers.
+    """
+    layer_entropies: List[Tensor] = []
+    layer_dead_fractions: List[Tensor] = []
+    layer_imbalance_means: List[Tensor] = []
+    layer_imbalance_maxs: List[Tensor] = []
+
+    for stats in router_stats_list:
+        probs = stats["probs"].float()
+        topk_indices = stats["topk_indices"]
+        num_experts = probs.shape[-1]
+        if num_experts == 0:
+            continue
+        probs = probs.reshape(-1, num_experts)
+        if probs.numel() == 0:
+            continue
+
+        # Router entropy
+        entropy = -(probs * torch.log(probs.clamp_min(1e-8))).sum(dim=-1).mean()
+
+        # Load from the actual dispatch: one-hot sum of top-k assignments.
+        expert_counts = F.one_hot(topk_indices, num_experts).sum(dim=(0, 1)).float()
+        ideal_load = expert_counts.mean()  # N*K / E
+        load_ratios = expert_counts / max(float(ideal_load), 1.0)
+        imbalance_mean = (load_ratios - 1.0).abs().mean()
+        imbalance_max = load_ratios.max()
+        dead_fraction = (expert_counts == 0).float().mean()
+
+        layer_entropies.append(entropy)
+        layer_dead_fractions.append(dead_fraction)
+        layer_imbalance_means.append(imbalance_mean)
+        layer_imbalance_maxs.append(imbalance_max)
+
+    if not layer_entropies:
+        return {}
+
+    return {
+        "router_entropy": float(torch.stack(layer_entropies).mean().cpu().item()),
+        "dead_expert_fraction": float(
+            torch.stack(layer_dead_fractions).mean().cpu().item()
+        ),
+        "load_imbalance_mean": float(
+            torch.stack(layer_imbalance_means).mean().cpu().item()
+        ),
+        "load_imbalance_max": float(
+            torch.stack(layer_imbalance_maxs).mean().cpu().item()
+        ),
+    }
+
+
 class BaseStrategy(ABC):
     """Abstract base class for training strategies.
 
@@ -112,10 +182,11 @@ class BaseStrategy(ABC):
         self.model = model
         self.device = device
         self.executor = kwargs.pop("executor", None)
-        self.extra_kwargs = kwargs
+        self.moe_aux_loss_coef = kwargs.pop("moe_aux_loss_coef", 0.01)
+        self._moe_metrics: Dict[str, float] = {}
+        self.strategy_kwargs = kwargs
         self._rollout_runner = None
 
-    @abstractmethod
     def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
         """Compute loss for the given batch.
 
@@ -125,7 +196,36 @@ class BaseStrategy(ABC):
         Returns:
             Computed loss tensor
         """
-        raise NotImplementedError
+        return self.compute_loss_output(batch)["loss"]
+
+    def compute_loss_output(self, batch: Dict[str, Tensor]) -> LossOutput:
+        return self._normalize_output(self.compute_loss(batch))
+
+    def _loss_output(
+        self,
+        task_loss: Tensor,
+        metrics: Dict[str, Tensor],
+        aux_loss: Optional[Tensor] = None,
+        router_stats: Optional[List[RouterStats]] = None,
+    ) -> LossOutput:
+        total_loss = task_loss
+        if aux_loss is not None:
+            weighted_aux_loss = self.moe_aux_loss_coef * aux_loss
+            total_loss = total_loss + weighted_aux_loss
+            metrics["moe_aux_loss"] = aux_loss
+            metrics["moe_aux_loss_weighted"] = weighted_aux_loss
+            self._refresh_moe_diagnostics(aux_loss, router_stats)
+        metrics["loss"] = total_loss
+        return {
+            "loss": total_loss,
+            "metrics": {name: value.detach().item() for name, value in metrics.items()},
+        }
+
+    @staticmethod
+    def _normalize_output(output: Union[LossOutput, Tensor]) -> LossOutput:
+        if isinstance(output, dict):
+            return output
+        return {"loss": output, "metrics": {"loss": output.detach().item()}}
 
     def supports_online(self) -> bool:
         """Whether this strategy can operate with a rollout runner.
@@ -158,22 +258,36 @@ class BaseStrategy(ABC):
         """
         pass
 
+    def _refresh_moe_diagnostics(
+        self,
+        aux_loss: Tensor,
+        router_stats: Optional[List[RouterStats]] = None,
+    ) -> None:
+        """Collect MoE routing diagnostics from the latest forward pass.
+
+        Populates ``self._moe_metrics`` with router entropy, dead expert
+        fraction, load imbalance, and aux_loss.  Called from
+        :meth:`_loss_output` when an MoE aux loss is present.
+        """
+        self._moe_metrics = _collect_moe_diagnostics(router_stats or [])
+        self._moe_metrics["aux_loss"] = float(aux_loss.detach().cpu().item())
+
     def on_optimizer_step(self):
         """Advance online rollout state after a successful optimizer step."""
         if self._rollout_runner is not None:
             self._rollout_runner.step()
 
-    def __call__(self, batch: Dict[str, Tensor]) -> Tensor:
+    def __call__(self, batch: Dict[str, Tensor]) -> LossOutput:
         """Run offline or online forward depending on runner injection."""
         if self._rollout_runner is None:
-            return self.compute_loss(batch)
+            return self.compute_loss_output(batch)
 
         result, is_fresh = self._rollout_runner(batch)
         if is_fresh:
             self._on_rollout_refresh()
 
         train_batch = self.prepare_from_rollout(result)
-        return self.compute_loss(train_batch)
+        return self.compute_loss_output(train_batch)
 
 
 class StrategyFactory(BaseFactory["BaseStrategy"]):
@@ -200,6 +314,7 @@ class SEQStrategy(BaseStrategy):
     """Standard next-token prediction training strategy.
 
     Computes cross-entropy loss for next token prediction.
+    Optionally adds MoE load balancing auxiliary loss.
     """
 
     def __init__(
@@ -212,10 +327,11 @@ class SEQStrategy(BaseStrategy):
         super().__init__(model, device, **kwargs)
         self.label_smoothing = label_smoothing
 
-    def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
+    def compute_loss_output(self, batch: Dict[str, Tensor]) -> LossOutput:
         batch = move_to_device(batch, self.device)
         input_ids, target_ids = batch["input_ids"], batch["target_ids"]
-        logits = self.model(input_ids=input_ids)["logits"]
+        outputs = self.model(input_ids=input_ids)
+        logits = outputs["logits"]
 
         loss = F.cross_entropy(
             input=logits.flatten(0, 1).float(),
@@ -223,7 +339,12 @@ class SEQStrategy(BaseStrategy):
             label_smoothing=self.label_smoothing,
         )
 
-        return loss
+        return self._loss_output(
+            loss,
+            {"task_loss": loss},
+            outputs.get("aux_loss"),
+            outputs.get("router_stats"),
+        )
 
 
 @StrategyFactory.register("sft")
@@ -231,6 +352,7 @@ class SFTStrategy(BaseStrategy):
     """Supervised Fine-tuning strategy with loss masking.
 
     Applies cross-entropy loss only to tokens where loss_mask is True.
+    Optionally adds MoE load balancing auxiliary loss.
     """
 
     def __init__(
@@ -243,7 +365,7 @@ class SFTStrategy(BaseStrategy):
         super().__init__(model, device, **kwargs)
         self.label_smoothing = label_smoothing
 
-    def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
+    def compute_loss_output(self, batch: Dict[str, Tensor]) -> LossOutput:
         batch = move_to_device(batch, self.device)
         input_ids, target_ids, position_ids, loss_mask = (
             batch["input_ids"],
@@ -255,9 +377,10 @@ class SFTStrategy(BaseStrategy):
         ignore_index = -100
         input_mask = make_doc_boundary_mask(position_ids)
         target_ids = target_ids.masked_fill(~loss_mask, ignore_index)
-        logits = self.model(
+        outputs = self.model(
             input_ids=input_ids, position_ids=position_ids, input_mask=input_mask
-        )["logits"]
+        )
+        logits = outputs["logits"]
 
         loss = F.cross_entropy(
             input=logits.flatten(0, 1).float(),
@@ -266,7 +389,12 @@ class SFTStrategy(BaseStrategy):
             label_smoothing=self.label_smoothing,
         )
 
-        return loss
+        return self._loss_output(
+            loss,
+            {"task_loss": loss},
+            outputs.get("aux_loss"),
+            outputs.get("router_stats"),
+        )
 
 
 @StrategyFactory.register("dpo")
@@ -291,7 +419,7 @@ class DPOStrategy(BaseStrategy):
         self.beta = beta
         self.reduction = reduction
 
-    def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
+    def compute_loss_output(self, batch: Dict[str, Tensor]) -> LossOutput:
         batch = move_to_device(batch, self.device)
         chosen_ids, rejected_ids = batch["chosen"], batch["rejected"]
         chosen_mask, rejected_mask = batch["chosen_mask"], batch["rejected_mask"]
@@ -307,22 +435,25 @@ class DPOStrategy(BaseStrategy):
         )[None, None, :, :]  # [1, 1, S, S]
         full_mask = key_pad & causal  # [B*2, 1, S, S] — composed
 
-        log_pi = get_logprobs(
+        policy_output = get_logprobs(
             self.model,
             concat_ids,
             full_mask,
             concat_loss_mask,
             self.reduction,
         )
+        log_pi = policy_output["logprobs"]
+        aux_loss = policy_output["aux_loss"]
 
         with torch.no_grad():
-            log_ref = get_logprobs(
+            ref_output = get_logprobs(
                 self.ref_model,
                 concat_ids,
                 full_mask,
                 concat_loss_mask,
                 self.reduction,
             )
+            log_ref = ref_output["logprobs"]
 
         log_pi_chosen = log_pi[: chosen_ids.shape[0]]
         log_pi_rejected = log_pi[chosen_ids.shape[0] :]
@@ -335,7 +466,12 @@ class DPOStrategy(BaseStrategy):
         ratio_diff = pi_log_ratio - ref_log_ratio
         dpo_loss = -F.logsigmoid(self.beta * ratio_diff).mean()
 
-        return dpo_loss
+        return self._loss_output(
+            dpo_loss,
+            {"dpo_loss": dpo_loss},
+            aux_loss,
+            policy_output.get("router_stats"),
+        )
 
     def supports_online(self) -> bool:
         return True
@@ -401,9 +537,13 @@ class GRPOStrategy(BaseStrategy):
 
     def sync_old_model(self):
         """Copy current policy weights to old model."""
-        self.old_model.load_state_dict(self.executor.unwrap_model(self.model))
+        state_dict = self.executor.unwrap_model(self.model)
+        if self.executor.use_distributed:
+            state_dict = broadcast_state_dict(state_dict)
+        if state_dict is not None:
+            self.old_model.load_state_dict(state_dict)
 
-    def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
+    def compute_loss_output(self, batch: Dict[str, Tensor]) -> LossOutput:
         batch = move_to_device(batch, self.device)
         prompts = batch["prompts"]
         responses = batch["responses"]
@@ -444,16 +584,23 @@ class GRPOStrategy(BaseStrategy):
         # get_logprobs returns [B*G, S-1] (S = prompt_len + response_len).
         # Response token logprobs occupy the last ``response_len`` positions
         # (the first response token is predicted from the last prompt token).
-        token_log_probs_policy = get_logprobs(
+        policy_output = get_logprobs(
             self.model, full_sequences, attn_mask, full_masks, "none"
-        )[:, prompt_len - 1 :]
+        )
+        token_log_probs_policy = policy_output["logprobs"]
+        aux_loss = policy_output["aux_loss"]
+        token_log_probs_policy = token_log_probs_policy[:, prompt_len - 1 :]
         with torch.no_grad():
-            token_log_probs_old = get_logprobs(
+            old_output = get_logprobs(
                 self.old_model, full_sequences, attn_mask, full_masks, "none"
-            )[:, prompt_len - 1 :]
-            token_log_probs_ref = get_logprobs(
+            )
+            token_log_probs_old = old_output["logprobs"]
+            token_log_probs_old = token_log_probs_old[:, prompt_len - 1 :]
+            ref_output = get_logprobs(
                 self.ref_model, full_sequences, attn_mask, full_masks, "none"
-            )[:, prompt_len - 1 :]
+            )
+            token_log_probs_ref = ref_output["logprobs"]
+            token_log_probs_ref = token_log_probs_ref[:, prompt_len - 1 :]
 
         # Reshape to [B, G, response_len]
         token_log_probs_policy = token_log_probs_policy.view(batch_size, group_size, -1)
@@ -486,9 +633,13 @@ class GRPOStrategy(BaseStrategy):
         kl_per_token = r - torch.log(r + eps) - 1.0
         kl_penalty = self.kl_coef * (kl_per_token * token_masks).sum() / token_count
 
-        total_loss = policy_loss + kl_penalty
-
-        return total_loss
+        task_loss = policy_loss + kl_penalty
+        return self._loss_output(
+            task_loss,
+            {"policy_loss": policy_loss, "kl_loss": kl_penalty},
+            aux_loss,
+            policy_output.get("router_stats"),
+        )
 
     def supports_online(self) -> bool:
         return True
@@ -510,5 +661,5 @@ class GRPOStrategy(BaseStrategy):
 # Factory aliases: online variants use the same strategy class; the
 # ``RolloutRunner`` is injected by ``TrainContextBuilder`` to enable
 # online mode, so no separate subclass is needed.
-StrategyFactory._entries["online_grpo"] = GRPOStrategy
-StrategyFactory._entries["online_dpo"] = DPOStrategy
+StrategyFactory.register("online_grpo")(GRPOStrategy)
+StrategyFactory.register("online_dpo")(DPOStrategy)

astrai/trainer/train_callback.py

@@ -3,6 +3,7 @@ import logging
 import os
 import sys
 import time
+from functools import partial
 from pathlib import Path
 from typing import IO, Callable, List, Optional, Protocol, runtime_checkable
 
@@ -18,8 +19,10 @@ from astrai.parallel.setup import get_current_device
 from astrai.serialization import Checkpoint
 from astrai.trainer.metric_util import (
     ctx_get_grad_norm,
+    ctx_get_grad_snr,
     ctx_get_loss,
     ctx_get_lr,
+    ctx_get_moe_metric,
     ctx_get_val_loss,
 )
 from astrai.trainer.train_context import TrainContext
@@ -235,7 +238,7 @@ class ProgressBarCallback(TrainCallback):
 class MetricCallback(TrainCallback):
     def __init__(
         self,
-        log_dir: str,
+        ckpt_dir: str,
         save_interval: int,
         metrics: List[str] = None,
         val_step: int = 0,
@@ -246,8 +249,7 @@ class MetricCallback(TrainCallback):
         self.val_step = val_step
         self._next_val_step = 0
 
-        self.log_dir = Path(log_dir) if log_dir else Path.cwd() / "logs"
-        self.log_dir.mkdir(parents=True, exist_ok=True)
+        self.ckpt_dir = Path(ckpt_dir) if ckpt_dir else Path.cwd() / "checkpoint"
 
         self.log_cache = []
 
@@ -256,14 +258,41 @@ class MetricCallback(TrainCallback):
             "lr": ctx_get_lr,
             "val_loss": ctx_get_val_loss,
             "grad_norm": ctx_get_grad_norm,
+            "grad_snr": ctx_get_grad_snr,
+            "moe_aux_loss": partial(ctx_get_moe_metric, key="aux_loss"),
+            "router_entropy": partial(ctx_get_moe_metric, key="router_entropy"),
+            "dead_expert_fraction": partial(
+                ctx_get_moe_metric, key="dead_expert_fraction"
+            ),
+            "load_imbalance_mean": partial(
+                ctx_get_moe_metric, key="load_imbalance_mean"
+            ),
+            "load_imbalance_max": partial(ctx_get_moe_metric, key="load_imbalance_max"),
         }
 
     def _metrics(self, context: TrainContext, names):
-        return {
-            m: self._metric_funcs[m](context)
-            for m in names
-            if self._metric_funcs[m](context) is not None
-        }
+        metrics = dict(context.metrics)
+        for name in names:
+            metric_fn = self._metric_funcs.get(name)
+            if metric_fn is None:
+                continue
+            value = metric_fn(context)
+            if value is not None:
+                metrics[name] = value
+        selected = set(context.metrics) | set(names)
+        selected.discard("*")
+        result = {name: metrics[name] for name in selected if name in metrics}
+        if context.world_size > 1 and dist.is_initialized() and result:
+            metric_names = sorted(result)
+            values = torch.tensor(
+                [result[name] for name in metric_names],
+                dtype=torch.float32,
+                device=get_current_device(),
+            )
+            dist.all_reduce(values, op=dist.ReduceOp.SUM)
+            values /= context.world_size
+            result.update(zip(metric_names, values.tolist()))
+        return result
 
     @only_on_rank(0)
     def _append(self, event_type: str, context: TrainContext, **extra):
@@ -285,8 +314,8 @@ class MetricCallback(TrainCallback):
 
         with torch.no_grad():
             for batch in context.val_dataloader:
-                loss = context.strategy(batch)
-                total_loss += loss.item()
+                loss_output = context.strategy(batch)
+                total_loss += loss_output["loss"].item()
                 num_batches += 1
 
         if context.world_size > 1 and dist.is_initialized():
@@ -306,13 +335,15 @@ class MetricCallback(TrainCallback):
 
     @only_on_rank(0)
     def _flush(self, epoch, step):
-        log_file = self.log_dir / f"epoch_{epoch}_step_{step}_metric.jsonl"
+        log_file = self.ckpt_dir / f"epoch_{epoch}_step_{step}" / "metric.jsonl"
         log_file.parent.mkdir(parents=True, exist_ok=True)
         with open(log_file, "w") as f:
             for log in self.log_cache:
                 f.write(json.dumps(log) + "\n")
 
     def on_optimizer_step(self, context):
+        context.grad_snr_tracker.update(context.model)
+
         if (
             context.val_dataloader is not None
             and self.val_step > 0

astrai/trainer/train_context.py

@@ -1,3 +1,4 @@
+import logging
 import threading
 from dataclasses import dataclass, field
 from pathlib import Path
@@ -7,17 +8,27 @@ import torch
 import torch.nn as nn
 from torch.utils.data import DataLoader, random_split
 
+from astrai.config.model_config import ConfigFactory
 from astrai.config.train_config import TrainConfig
 from astrai.dataset import RDSampler
-from astrai.inference.core.scheduler import InferenceScheduler
+from astrai.inference.scheduler import InferenceScheduler
 from astrai.model.components.lora import inject_lora
-from astrai.parallel.executor import BaseExecutor, ExecutorFactory
+from astrai.parallel.executor import BaseExecutor, ExecutorFactory, create_ref_model
 from astrai.parallel.setup import get_current_device, get_rank, get_world_size
 from astrai.protocols import OptimizerProtocol, SchedulerProtocol
-from astrai.serialization import Checkpoint, load_json
+from astrai.serialization import (
+    Checkpoint,
+    adapt_config,
+    convert_hf_weights,
+    load_json,
+    looks_like_hf_state_dict,
+)
 from astrai.tokenize import AutoTokenizer
+from astrai.trainer.metric_util import GradSNRTracker
 from astrai.trainer.rollout import RolloutGenerator, RolloutRunner
-from astrai.trainer.strategy import BaseStrategy, StrategyFactory, create_ref_model
+from astrai.trainer.strategy import BaseStrategy, StrategyFactory
+
+logger = logging.getLogger(__name__)
 
 
 @dataclass
@@ -34,7 +45,9 @@ class TrainContext:
     epoch: int = field(default=0)
     consumed_samples: int = field(default=0)
     loss: float = field(default=0.0)
+    metrics: Dict[str, float] = field(default_factory=dict)
     grad_norm: Optional[float] = field(default=None)
+    grad_snr_tracker: GradSNRTracker = field(default_factory=GradSNRTracker)
     val_dataloader: Optional[DataLoader] = field(default=None)
     val_loss: Optional[float] = field(default=None)
 
@@ -60,6 +73,15 @@ class TrainContext:
         )
 
 
+@dataclass
+class _PreloadedState:
+    model_config: dict = field(default_factory=dict)
+    state_dict: Optional[dict] = None
+    epoch: int = 0
+    consumed_samples: int = 0
+    checkpoint: Optional[Checkpoint] = None
+
+
 class TrainContextBuilder:
     def __init__(
         self,
@@ -75,205 +97,241 @@ class TrainContextBuilder:
         return self
 
     def build(self) -> TrainContext:
-        cfg = self.config
-        device = get_current_device()
+        # Resolve persisted state.
+        preloaded_state = self._load_preloaded_state()
 
-        executor = ExecutorFactory.create(
+        # Build the core training components and restore their persisted state.
+        executor = self._create_executor()
+        context = self._create_context(preloaded_state, executor)
+        self._prepare_model(context, executor, preloaded_state)
+        self._restore_optimizer_state(context)
+
+        # Resolve datasets.
+        train_dataset, val_dataset = self._get_datasets()
+        self._create_dataloaders(context, train_dataset, val_dataset)
+
+        # Strategies depend on the prepared model; online rollout depends on both.
+        strategy_kwargs = self._create_strategy(context, executor)
+        self._configure_rollout(context, strategy_kwargs)
+
+        return context
+
+    def _create_executor(self) -> BaseExecutor:
+        cfg = self.config
+        return ExecutorFactory.create(
             cfg.parallel_mode,
             grad_accum_steps=cfg.grad_accum_steps,
             **cfg.executor_kwargs,
         )
 
-        model_config = {}
+    def _load_preloaded_state(self) -> _PreloadedState:
+        cfg = self.config
+        state = _PreloadedState(
+            epoch=cfg.start_epoch,
+            consumed_samples=cfg.start_samples * get_world_size(),
+        )
         if self._param_path:
             config_path = Path(self._param_path) / "config.json"
             if config_path.exists():
-                model_config = load_json(config_path)
-
-        preloaded_state_dict = None
-        preloaded_epoch = cfg.start_epoch
-        preloaded_consumed = cfg.start_samples * get_world_size()
-        preloaded_checkpoint = None
-        if self._param_path:
+                state.model_config = adapt_config(load_json(config_path))
             checkpoint = Checkpoint.load_any(self._param_path)
             if checkpoint is not None:
-                preloaded_state_dict = checkpoint.state_dict
                 if checkpoint.config:
-                    model_config = checkpoint.config
+                    checkpoint.config = adapt_config(checkpoint.config)
+                if checkpoint.state_dict and looks_like_hf_state_dict(
+                    checkpoint.state_dict
+                ):
+                    checkpoint.state_dict = convert_hf_weights(
+                        checkpoint.state_dict,
+                        ConfigFactory.load(checkpoint.config or state.model_config),
+                    )
+                state.state_dict = checkpoint.state_dict
+                state.model_config = checkpoint.config or state.model_config
                 if self._resume:
-                    preloaded_epoch = checkpoint.epoch or cfg.start_epoch
-                    if checkpoint.consumed_samples > 0:
-                        per_step = (
-                            cfg.batch_per_device
-                            * get_world_size()
-                            * cfg.grad_accum_steps
-                        )
-                        preloaded_consumed = (
-                            checkpoint.consumed_samples // per_step
-                        ) * per_step
-                    else:
-                        preloaded_consumed = cfg.start_samples * get_world_size()
-                    preloaded_checkpoint = checkpoint
+                    state.epoch = checkpoint.epoch
+                    per_step = (
+                        cfg.batch_per_device * get_world_size() * cfg.grad_accum_steps
+                    )
+                    state.consumed_samples = (
+                        checkpoint.consumed_samples // per_step * per_step
+                    )
+                    state.checkpoint = checkpoint
+        if not state.model_config:
+            model = cfg.model_fn()
+            if hasattr(model, "config"):
+                state.model_config = model.config.to_dict()
+        return state
 
-        if not model_config and hasattr(cfg.model_fn(), "config"):
-            model_config = cfg.model_fn().config.to_dict()
+    def _create_context(
+        self, state: _PreloadedState, executor: BaseExecutor
+    ) -> TrainContext:
+        return TrainContext(
+            world_size=get_world_size(),
+            rank=get_rank(),
+            config=self.config,
+            model_config=state.model_config,
+            executor=executor,
+            epoch=state.epoch,
+            consumed_samples=state.consumed_samples,
+            checkpoint=state.checkpoint,
+        )
 
-        def _before_wrap(m):
-            m = m.to(device=device)
+    def _prepare_model(
+        self, context: TrainContext, executor: BaseExecutor, state: _PreloadedState
+    ) -> None:
+        cfg = self.config
+        device = get_current_device()
+
+        def before_wrap(model):
+            model = model.to(device=device)
             if cfg.lora is not None:
                 inject_lora(
-                    m,
+                    model,
                     r=cfg.lora.r,
                     alpha=cfg.lora.alpha,
                     target_modules=set(cfg.lora.target_modules),
                 )
-            if preloaded_state_dict is not None:
-                m.load_state_dict(preloaded_state_dict, strict=False)
-            return m
+            if state.state_dict is not None:
+                model.load_state_dict(state.state_dict, strict=False)
+            return model
 
-        context = TrainContext(
-            world_size=get_world_size(),
-            rank=get_rank(),
-            config=cfg,
-            model_config=model_config,
-            executor=executor,
-            epoch=preloaded_epoch,
-            consumed_samples=preloaded_consumed,
-            checkpoint=preloaded_checkpoint,
-        )
+        def after_wrap(model):
+            if cfg.compile_mode is not None:
+                logger.info("torch.compile enabled (mode=%s)", cfg.compile_mode)
+                model = torch.compile(model, mode=cfg.compile_mode)
+            return model
 
         context.model, context.optimizer, context.scheduler = executor.prepare(
             cfg.model_fn,
             cfg.optimizer_fn,
             cfg.scheduler_fn,
-            before_wrap=_before_wrap,
+            before_wrap=before_wrap,
+            after_wrap=after_wrap,
         )
 
-        train_dataset = cfg.dataset
-        val_dataset = cfg.val_dataset
+    def _get_datasets(self):
+        cfg = self.config
+        if cfg.val_dataset is not None or cfg.val_split is None:
+            return cfg.dataset, cfg.val_dataset
+        n_val = max(1, int(len(cfg.dataset) * cfg.val_split))
+        generator = torch.Generator().manual_seed(cfg.random_seed)
+        return random_split(
+            cfg.dataset, [len(cfg.dataset) - n_val, n_val], generator=generator
+        )
 
-        if val_dataset is None and cfg.val_split is not None:
-            n_total = len(cfg.dataset)
-            n_val = max(1, int(n_total * cfg.val_split))
-            n_train = n_total - n_val
-            generator = torch.Generator().manual_seed(cfg.random_seed)
-            train_dataset, val_dataset = random_split(
-                cfg.dataset, [n_train, n_val], generator=generator
+    def _create_dataloaders(
+        self, context: TrainContext, train_dataset, val_dataset
+    ) -> None:
+        sampler_offset = context.consumed_samples // context.world_size
+        if self._resume and sampler_offset > 0:
+            samples_per_replica = (
+                len(train_dataset) + context.world_size - 1
+            ) // context.world_size
+            if samples_per_replica > 0:
+                context.epoch = sampler_offset // samples_per_replica
+        context.dataloader = self._create_dataloader(
+            train_dataset, context.epoch, sampler_offset
+        )
+        if val_dataset is not None:
+            context.val_dataloader = self._create_dataloader(
+                val_dataset, 0, 0, shuffle=False
             )
 
-        sampler_offset = context.consumed_samples // context.world_size
+    def _create_dataloader(
+        self, dataset, epoch: int, start_iter: int, shuffle: bool = True
+    ):
+        cfg = self.config
         sampler = RDSampler(
-            data_source=train_dataset,
-            start_epoch=context.epoch,
-            start_iter=sampler_offset,
+            dataset,
+            start_epoch=epoch,
+            start_iter=start_iter,
             seed=cfg.random_seed,
+            shuffle=shuffle,
         )
-        context.dataloader = DataLoader(
-            train_dataset,
+        loader_kwargs = dict(
+            dataset=dataset,
             batch_size=cfg.batch_per_device,
             sampler=sampler,
             num_workers=cfg.num_workers,
             pin_memory=cfg.pin_memory,
-            prefetch_factor=cfg.prefetch_factor,
             collate_fn=cfg.collate_fn,
         )
-
-        if val_dataset is not None:
-            val_sampler = RDSampler(
-                data_source=val_dataset,
-                start_epoch=0,
-                start_iter=0,
-                seed=cfg.random_seed,
-                shuffle=False,
-            )
-            context.val_dataloader = DataLoader(
-                val_dataset,
-                batch_size=cfg.batch_per_device,
-                sampler=val_sampler,
-                num_workers=cfg.num_workers,
-                pin_memory=cfg.pin_memory,
-                prefetch_factor=cfg.prefetch_factor,
-                collate_fn=cfg.collate_fn,
-            )
-
-        if context.checkpoint and context.checkpoint.extra:
-            extra = context.checkpoint.extra
-            for name in ("optimizer", "scheduler"):
-                if name in extra:
-                    obj = getattr(context, name, None)
-                    if obj is not None:
-                        obj.load_state_dict(extra[name])
-
-        strategy_kwargs = dict(cfg.extra_kwargs)
-
-        needs_ref = cfg.strategy in (
-            "dpo",
-            "grpo",
-            "online_grpo",
-            "online_dpo",
+        # PyTorch rejects prefetch_factor/persistent_workers when workers=0.
+        if cfg.num_workers > 0:
+            loader_kwargs["persistent_workers"] = cfg.persistent_workers
+            if cfg.prefetch_factor is not None:
+                loader_kwargs["prefetch_factor"] = cfg.prefetch_factor
+        return DataLoader(
+            **loader_kwargs,
         )
-        needs_old = cfg.strategy in ("grpo", "online_grpo")
 
-        if needs_ref:
-            ref_model = create_ref_model(
-                cfg.model_fn, executor.unwrap_model(context.model)
-            ).to(device=device)
-            strategy_kwargs["ref_model"] = ref_model
-
-        old_model = None
-        if needs_old:
-            old_model = create_ref_model(
-                cfg.model_fn, executor.unwrap_model(context.model)
-            ).to(device=device)
-            strategy_kwargs["old_model"] = old_model
+    def _restore_optimizer_state(self, context: TrainContext) -> None:
+        if context.checkpoint and context.checkpoint.extra:
+            for name in ("optimizer", "scheduler"):
+                if (
+                    name in context.checkpoint.extra
+                    and getattr(context, name, None) is not None
+                ):
+                    getattr(context, name).load_state_dict(
+                        context.checkpoint.extra[name]
+                    )
 
+    def _create_strategy(self, context: TrainContext, executor: BaseExecutor) -> dict:
+        cfg = self.config
+        kwargs = dict(cfg.strategy_kwargs)
+        kwargs.setdefault("moe_aux_loss_coef", cfg.moe_aux_loss_coef)
+        if cfg.strategy in ("dpo", "grpo", "online_grpo", "online_dpo"):
+            kwargs["ref_model"] = create_ref_model(
+                cfg.model_fn,
+                executor=executor,
+                model=context.model,
+                device=get_current_device(),
+            )
+        if cfg.strategy in ("grpo", "online_grpo"):
+            kwargs["old_model"] = create_ref_model(
+                cfg.model_fn,
+                executor=executor,
+                model=context.model,
+                device=get_current_device(),
+            )
         context.strategy = StrategyFactory.create(
             cfg.strategy,
             model=context.model,
-            device=device,
+            device=get_current_device(),
             executor=executor,
-            **strategy_kwargs,
+            **kwargs,
         )
+        return kwargs
 
-        # Enable online rollout when the train_type is an ``online_*`` variant.
-        is_online = cfg.strategy.startswith("online_")
-        if is_online:
-            if not context.strategy.supports_online():
-                raise ValueError(
-                    f"Strategy '{cfg.strategy}' does not support online rollout"
-                )
-            if cfg.reward_model_fn is None:
-                raise ValueError("reward_model_fn is required for online RL strategies")
-
-            tokenizer = AutoTokenizer.from_pretrained(self._param_path)
-            reward_model = cfg.reward_model_fn()
-
-            group_size = strategy_kwargs.get("group_size", 1)
-            rollout_batch_size = group_size * max(1, cfg.batch_per_device)
-            max_seq_len = getattr(context.model.config, "max_position_embeddings", None)
-
-            scheduler = InferenceScheduler(
-                model=context.model,
-                tokenizer=tokenizer,
-                max_batch_size=rollout_batch_size,
-                max_seq_len=max_seq_len,
-                max_prompt_len=max_seq_len or 4096,
+    def _configure_rollout(self, context: TrainContext, strategy_kwargs: dict) -> None:
+        cfg = self.config
+        if not cfg.strategy.startswith("online_"):
+            return
+        if not context.strategy.supports_online():
+            raise ValueError(
+                f"Strategy '{cfg.strategy}' does not support online rollout"
             )
-
-            generator = RolloutGenerator(
-                scheduler=scheduler,
-                tokenizer=tokenizer,
-                max_tokens=cfg.rollout_max_tokens,
-                group_size=group_size,
-                temperature=cfg.rollout_temperature,
-                top_k=cfg.rollout_top_k,
-                top_p=cfg.rollout_top_p,
-            )
-            runner = RolloutRunner(
+        tokenizer = AutoTokenizer.from_pretrained(self._param_path)
+        group_size = strategy_kwargs.get("group_size", 1)
+        scheduler = InferenceScheduler(
+            model=context.model,
+            tokenizer=tokenizer,
+            max_batch_size=group_size * max(1, cfg.batch_per_device),
+            max_seq_len=getattr(context.model.config, "max_position_embeddings", None),
+        )
+        generator = RolloutGenerator(
+            scheduler=scheduler,
+            tokenizer=tokenizer,
+            max_tokens=cfg.rollout_max_tokens,
+            group_size=group_size,
+            temperature=cfg.rollout_temperature,
+            top_k=cfg.rollout_top_k,
+            top_p=cfg.rollout_top_p,
+        )
+        context.strategy.set_rollout_runner(
+            RolloutRunner(
                 generator=generator,
-                reward_model=reward_model,
+                reward_model=cfg.reward_model_fn(),
                 rollout_interval=cfg.rollout_interval,
             )
-            context.strategy.set_rollout_runner(runner)
-
-        return context
+        )

astrai/trainer/trainer.py

@@ -5,7 +5,7 @@ import torch.distributed as dist
 
 from astrai.config import TrainConfig
 from astrai.parallel.setup import spawn_parallel_fn
-from astrai.parallel.signal_handler import (
+from astrai.signal_handler import (
     register_signal_handlers,
     unregister_signal_handlers,
 )
@@ -42,7 +42,7 @@ class Trainer:
             ),
             CallbackFactory.create(
                 "metric",
-                log_dir=cfg.log_dir,
+                ckpt_dir=cfg.ckpt_dir,
                 save_interval=cfg.ckpt_interval,
                 metrics=cfg.metrics,
                 val_step=cfg.val_step,
@@ -82,9 +82,10 @@ class Trainer:
                         break
                     with executor.accumulate(context.model):
                         self._call_callbacks("on_batch_begin", context)
-                        loss = context.strategy(batch)
-                        context.loss = loss.item()
-                        stand_loss = loss / executor.grad_accum_steps
+                        loss_output = context.strategy(batch)
+                        context.loss = loss_output["loss"].item()
+                        context.metrics = loss_output["metrics"]
+                        stand_loss = loss_output["loss"] / executor.grad_accum_steps
                         executor.backward(stand_loss)
                         context.consumed_samples += (
                             context.config.batch_per_device * context.world_size

csrc/CMakeLists.txt

@@ -0,0 +1,97 @@
+cmake_minimum_required(VERSION 3.18)
+project(astrai_kernels LANGUAGES CUDA CXX)
+
+set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_CXX_STANDARD_REQUIRED ON)
+set(CMAKE_CUDA_STANDARD 17)
+
+find_package(CUDAToolkit REQUIRED)
+
+if(NOT DEFINED TORCH_HOME)
+    set(TORCH_HOME "$ENV{TORCH_HOME}")
+endif()
+if(NOT TORCH_HOME)
+    message(FATAL_ERROR "TORCH_HOME must point at the torch install dir (site-packages/torch)")
+endif()
+
+if(NOT DEFINED PYTHON_INCLUDE_DIR)
+    set(PYTHON_INCLUDE_DIR "/usr/include/python${PYTHON_VERSION_MAJOR}.${PYTHON_VERSION_MINOR}")
+endif()
+
+if(NOT DEFINED ASTRAI_CUDA_ARCH)
+    if(DEFINED ENV{ASTRAI_CUDA_ARCH})
+        set(ASTRAI_CUDA_ARCH "$ENV{ASTRAI_CUDA_ARCH}")
+    else()
+        set(ASTRAI_CUDA_ARCH 80)
+    endif()
+endif()
+
+set(TORCH_LIB_DIR "${TORCH_HOME}/lib")
+set(CUDA_LIB_DIR "/usr/local/cuda/lib64")
+
+set(CXX_FLAGS -O3 -funroll-loops)
+set(NVCC_FLAGS -O3
+    --expt-relaxed-constexpr
+    --use_fast_math
+    "--ptxas-options=-O3,-v"
+    --extra-device-vectorization
+    --threads=16)
+
+set(TORCH_LIBS
+    "${TORCH_LIB_DIR}/libtorch_python.so"
+    "${TORCH_LIB_DIR}/libtorch_cuda.so"
+    "${TORCH_LIB_DIR}/libc10_cuda.so"
+    "${TORCH_LIB_DIR}/libtorch_cpu.so"
+    "${TORCH_LIB_DIR}/libtorch.so"
+    "${TORCH_LIB_DIR}/libc10.so"
+    CUDA::cudart)
+
+set(CMAKE_CUDA_ARCHITECTURES "${ASTRAI_CUDA_ARCH}")
+
+# Kernel registry — parallel lists of module names (.so / pybind names,
+# globally unique across families) and their per-family source paths under
+# kernels/. `loader.py` auto-discovers the .so files in astrai/extension/lib/,
+# so this CMake registry is the single place to register a new kernel.
+set(KERNEL_NAMES
+    attn_decode
+    attn_prefill
+    attn_paged_decode
+    attn_paged_prefill
+    rotary_emb
+    fp8_ops
+)
+set(KERNEL_SRCS
+    attention/decode.cu
+    attention/prefill.cu
+    attention/paged_decode.cu
+    attention/paged_prefill.cu
+    rotary/rotary_emb.cu
+    fp8/ops.cu
+)
+
+list(LENGTH KERNEL_NAMES _kernel_count)
+math(EXPR _kernel_last "${_kernel_count} - 1")
+foreach(i RANGE ${_kernel_last})
+    list(GET KERNEL_NAMES ${i} name)
+    list(GET KERNEL_SRCS ${i} src)
+    add_library(${name} MODULE "${CMAKE_CURRENT_SOURCE_DIR}/kernels/${src}")
+
+    target_compile_definitions(${name} PRIVATE TORCH_EXTENSION_NAME=${name})
+
+    target_include_directories(${name} PRIVATE
+        "${TORCH_HOME}/include"
+        "${TORCH_HOME}/include/torch/csrc/api/include"
+        "${PYTHON_INCLUDE_DIR}")
+
+    target_link_libraries(${name} PRIVATE ${TORCH_LIBS})
+    target_link_options(${name} PRIVATE "-Wl,-rpath,${TORCH_LIB_DIR}")
+
+    target_compile_options(${name} PRIVATE
+        $<$<COMPILE_LANGUAGE:CXX>:${CXX_FLAGS}>
+        $<$<COMPILE_LANGUAGE:CUDA>:${NVCC_FLAGS}>)
+
+    set_target_properties(${name} PROPERTIES
+        PREFIX ""
+        SUFFIX ".${PY_SOABI}.so"
+        LIBRARY_OUTPUT_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/../astrai/extension/lib")
+endforeach()

csrc/__init__.py

@@ -1,2 +1,2 @@
 # Source directory for CUDA kernels — build-time only.
-# Compiled .so files live in astrAI/_ext/.
+# Compiled .so files live in astrai/extension/lib/ (see csrc/CMakeLists.txt).

csrc/build.py

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

csrc/kernels/attention/common.h

@@ -0,0 +1,95 @@
+#pragma once
+
+// Pure POD header
+
+namespace astrai {
+namespace attention {
+
+// Tensor layout for Q/K/V tensors passed to attention kernels.
+// Internally, kernels always operate on BHLD [batch, n_heads, seq_len, head_dim].
+// When the caller passes BLHD, dims 1 and 2 are transposed at entry.
+enum TensorLayout : int {
+    BHLD = 0,  // [batch, n_heads, seq_len, head_dim]
+    BLHD = 1,  // [batch, seq_len, n_heads, head_dim]
+};
+
+// Split-KV workspace cap: max decode splits per (batch, q_head).
+constexpr int MAX_SPLITS = 32;
+
+
+// Unified attention params covering BOTH addressing modes:
+//   - Contiguous K/V: dense [batch, kv_head, kv_len, head_dim] tensors (k/v).
+//   - Paged (SGLang-style): flat pool [size, kv_head, head_dim] + req_to_token.
+// Each kernel selects the addressing via a KVSource policy (see
+// layout_policies.cuh); a given call only touches the fields of one mode, so
+// this is a POD shared by both paths rather than two parallel structs that
+// drift out of sync.
+//
+// Pointer/flag members carry default member initializers: the pointers gate
+// optional paths via null checks (new_k_ptr, mask, o_part, ...), so a stack
+// `AttentionParams<T> p;` left partially packed must never see garbage
+// non-null pointers or a garbage use_mask/causal_offset — that class of bug
+// reads through wild addresses. NSDMI keeps the struct an aggregate (C++17)
+// and trivially copyable, so `= {}`, memcpy-style packing and by-value kernel
+// params all behave exactly as before.
+template<typename T, typename AT = float>
+struct AttentionParams {
+    // Shape
+    int batch;
+    int q_head;
+    int kv_head;
+    int head_dim;
+    int q_len;   // Per-request in contiguous mode; total_q in paged mode.
+    int kv_len;  // Contiguous mode; paged mode uses kv_indptr.
+
+    // Attention behavior
+    float scale;
+    // -1 = non-causal; >=0 = absolute position of first Q token
+    int causal_offset = -1;
+    int use_mask = 0;
+
+    // pointers
+    const T* __restrict__ q_ptr = nullptr;
+    const T* __restrict__ k_ptr = nullptr;
+    const T* __restrict__ v_ptr = nullptr;
+    const T* __restrict__ new_k_ptr = nullptr;
+    const T* __restrict__ new_v_ptr = nullptr;
+    T* __restrict__ o_ptr = nullptr;
+    const bool* __restrict__ mask = nullptr;
+
+    // strides
+    int q_b_stride;
+    int q_h_stride;
+    int q_l_stride;
+    int q_d_stride;
+
+    int kv_b_stride;
+    int kv_h_stride;
+    int kv_l_stride;
+    int kv_d_stride;
+
+    int new_kv_b_stride;
+    int new_kv_h_stride;
+
+    int mask_b_stride;
+    int mask_h_stride;
+    int mask_l_stride;
+
+    // Paged K/V addressing
+    const int* __restrict__ req_to_token = nullptr;      // [num_reqs, max_context_len]
+    const int* __restrict__ req_pool_indices = nullptr;  // [batch]
+    const int* __restrict__ kv_indptr = nullptr;         // [batch + 1]
+    const int* __restrict__ qo_indptr = nullptr;         // [batch + 1] or nullptr for decode
+    const int* __restrict__ q_tile_to_batch = nullptr;   // [num_q_tiles], prefill only
+    const int* __restrict__ q_tile_to_index = nullptr;   // [num_q_tiles], prefill only
+    int num_q_tiles;
+    int max_context_len; // req_to_token stride (dim 1)
+
+    // Decode split-KV workspace
+    int num_splits;
+    AT* __restrict__ o_part = nullptr;
+    AT* __restrict__ ml_part = nullptr;
+};
+
+}  // namespace attention
+}  // namespace astrai

csrc/kernels/attention/decode.cu

@@ -0,0 +1,65 @@
+#include "dispatchers.cuh"
+#include "entry_utils.cuh"
+
+using namespace astrai::attention;
+
+torch::Tensor attn_decode(
+    torch::Tensor q,
+    torch::Tensor k,
+    torch::Tensor v,
+    c10::optional<torch::Tensor> mask,
+    int64_t causal_offset,
+    double scale,
+    int64_t layout,
+    c10::optional<torch::Tensor> o_part_buf,
+    c10::optional<torch::Tensor> ml_part_buf
+) {
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(q));
+    auto stream = at::cuda::getCurrentCUDAStream();
+
+    AttentionParams<bf16> p;
+    attn_pack_params(q, k, v, mask, causal_offset, scale, layout, p);
+    TORCH_CHECK(p.q_len == 1, "Q seq_len must be 1");
+    TORCH_CHECK(p.head_dim % 32 == 0, "head_dim must be multiple of 32");
+
+    auto O = torch::empty_strided(q.sizes(), q.strides(), q.options());
+    auto O_view = (layout == BLHD) ? O.transpose(1, 2) : O;
+    p.o_ptr = (bf16*)O_view.data_ptr();
+
+    if (o_part_buf.has_value() && ml_part_buf.has_value()
+        && o_part_buf->defined() && ml_part_buf->defined()) {
+        TORCH_CHECK(o_part_buf->scalar_type() == torch::kFloat32, "o_part_buf must be f32");
+        TORCH_CHECK(ml_part_buf->scalar_type() == torch::kFloat32, "ml_part_buf must be f32");
+        int64_t o_needed = (int64_t)p.batch * p.q_head * MAX_SPLITS * p.head_dim;
+        int64_t ml_needed = (int64_t)p.batch * p.q_head * MAX_SPLITS * 2;
+        TORCH_CHECK(o_part_buf->numel() >= o_needed,
+                     "o_part_buf too small: need ", o_needed, " got ", o_part_buf->numel());
+        TORCH_CHECK(ml_part_buf->numel() >= ml_needed,
+                     "ml_part_buf too small: need ", ml_needed, " got ", ml_part_buf->numel());
+        TORCH_CHECK(o_part_buf->is_cuda() && ml_part_buf->is_cuda(),
+                     "split buffers must be CUDA tensors");
+        TORCH_CHECK(o_part_buf->is_contiguous() && ml_part_buf->is_contiguous(),
+                     "split buffers must be contiguous");
+        p.o_part = (float*)o_part_buf->data_ptr();
+        p.ml_part = (float*)ml_part_buf->data_ptr();
+    } else {
+        alloc_split_partials(p);
+    }
+    DISPATCH_HEAD_DIM(p.head_dim, dispatch_decode, p, stream);
+    C10_CUDA_CHECK(cudaGetLastError());
+    return O;
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("attn_decode", &attn_decode,
+        py::arg("q"),
+        py::arg("k"),
+        py::arg("v"),
+        py::arg("mask") = py::none(),
+        py::arg("causal_offset") = -1,
+        py::arg("scale") = 0.0,
+        py::arg("layout") = (int64_t)BHLD,
+        py::arg("o_part_buf") = py::none(),
+        py::arg("ml_part_buf") = py::none(),
+        "GQA decode (tensor-core head-packing on sm_80+, scalar fallback)");
+}

csrc/kernels/attention/decode_split_kv.cuh

@@ -1,11 +1,21 @@
 #pragma once
 #include <cuda_bf16.h>
 #include <float.h>
-#include "attn_common.h"
-#include "attn_warp_utils.cuh"
+#include "common.h"
+#include "layout_policies.cuh"
+#include "../common/reduce.cuh"
+
+namespace astrai {
+namespace attention {
+
 constexpr int DC_CHUNK = 64;
 
-template <int HEAD_DIM, bool IsCausal, bool HasMask>
+// Scalar split-KV decode (fallback for sm < 80, no tensor cores), unified
+// across contiguous and paged (SGLang flat-pool) K/V via the KV template
+// parameter.  For decode the query is the last token, so its valid range
+// [0, seq_len) IS the causal range; KV::decode_attend_len expresses that
+// bound per addressing mode (contig clips to causal_offset, paged = seq_len).
+template <int HEAD_DIM, typename KV, bool IsCausal, bool HasMask>
 __global__ void attn_decode_split_kv_kernel(AttentionParams<bf16> p) {
     int batch = blockIdx.x / p.kv_head;
     int kv_head = blockIdx.x % p.kv_head;
@@ -15,40 +25,46 @@ __global__ void attn_decode_split_kv_kernel(AttentionParams<bf16> p) {
     int lane = threadIdx.x;
     int hd_per_thread = p.head_dim / 32;
 
+    const int seq_len = KV::kv_len(p, batch);
+    const KVContext kctx = KV::template make_ctx<HEAD_DIM>(p, batch, kv_head);
+
     // Q: [batch, q_head, q_len=1, head_dim] — stride-based
     float q_reg[8];
-    int q_off = batch * p.q_stride_b + q_head * p.q_stride_h
-              + lane * hd_per_thread * p.q_stride_d;
+    int q_off = KV::q_decode_base(p, batch, q_head)
+              + lane * hd_per_thread * p.q_d_stride;
     for (int i = 0; i < hd_per_thread; i++)
-        q_reg[i] = __bfloat162float(p.q[q_off + i * p.q_stride_d]);
+        q_reg[i] = __bfloat162float(p.q_ptr[q_off + i * p.q_d_stride]);
 
-    // KV: [batch, kv_head, kv_len, head_dim] — stride-based base
-    int kv_base = batch * p.kv_stride_b + kv_head * p.kv_stride_h;
-    int mask_base = batch * p.mask_b_stride;
+    int mask_base = batch * p.mask_b_stride + q_head * p.mask_h_stride;
 
     float m = -FLT_MAX, d = 0.0f, acc_reg[8] = {0.0f};
 
-    extern __shared__ __align__(16) bf16 k_smem[];
+    extern __shared__ __align__(16) bf16 smem[];
+    bf16* k_smem = smem;
+    bf16* v_smem = smem + DC_CHUNK * p.head_dim;
 
     // Split-KV: each split processes a contiguous subset of chunks
-    int chunks_total = (p.kv_len + DC_CHUNK - 1) / DC_CHUNK;
+    int chunks_total = (seq_len + DC_CHUNK - 1) / DC_CHUNK;
     int chunks_per_split = (chunks_total + p.num_splits - 1) / p.num_splits;
     int ch_begin = split * chunks_per_split;
     int ch_end = min(chunks_total, ch_begin + chunks_per_split);
 
     for (int ci = ch_begin; ci < ch_end; ci++) {
         int chunk_start = ci * DC_CHUNK;
-        int this_chunk = min(DC_CHUNK, p.kv_len - chunk_start);
+        int this_chunk = min(DC_CHUNK, seq_len - chunk_start);
 
-        // Load K into shared memory (gather from strided global)
+        // Load K and V into shared memory (addressing via KV policy;
+        // paged guards empty slots with zero-fill).
         int total = this_chunk * p.head_dim;
         for (int i = threadIdx.y * 32 + lane; i < total;
              i += blockDim.x * blockDim.y) {
             int s = i / p.head_dim;
             int d_dim = i % p.head_dim;
-            int kv_idx = chunk_start + s;
-            int g_off = kv_base + kv_idx * p.kv_stride_l + d_dim * p.kv_stride_d;
-            k_smem[i] = p.k[g_off];
+            int kc = chunk_start + s;
+            KVAddr a = KV::template decode_addr<1>(
+                p, kctx, batch, kv_head, kc, d_dim, true, true);
+            k_smem[i] = a.valid ? *reinterpret_cast<const bf16*>(a.k) : (bf16)0.f;
+            v_smem[i] = a.valid ? *reinterpret_cast<const bf16*>(a.v) : (bf16)0.f;
         }
         __syncthreads();
 
@@ -65,20 +81,19 @@ __global__ void attn_decode_split_kv_kernel(AttentionParams<bf16> p) {
                     partial = -FLT_MAX;
             }
             if constexpr (IsCausal) {
-                if (kv_idx > p.causal_offset)
+                if (kv_idx >= KV::decode_attend_len(p, batch))
                     partial = -FLT_MAX;
             }
 
             float new_m = fmaxf(m, partial);
-            float alpha = expf(m - new_m);
-            float beta  = expf(partial - new_m);
+            float alpha = __expf(m - new_m);
+            float beta  = __expf(partial - new_m);
             d = d * alpha + beta;
 
-            int v_off = kv_base + kv_idx * p.kv_stride_l
-                        + lane * hd_per_thread * p.kv_stride_d;
-            for (int i = 0; i < hd_per_thread; i++)
-                acc_reg[i] = fmaf(acc_reg[i], alpha,
-                                  __bfloat162float(p.v[v_off + i * p.kv_stride_d]) * beta);
+            for (int i = 0; i < hd_per_thread; i++) {
+                float vv = __bfloat162float(v_smem[s * p.head_dim + lane * hd_per_thread + i]);
+                acc_reg[i] = fmaf(acc_reg[i], alpha, vv * beta);
+            }
             m = new_m;
         }
         __syncthreads();
@@ -98,6 +113,10 @@ __global__ void attn_decode_split_kv_kernel(AttentionParams<bf16> p) {
     }
 }
 
+// Split-combine: merges the per-split partials (o_part/ml_part) into the
+// final normalised O.  KV selects the O addressing (contig batch stride vs
+// paged row stride).
+template <typename KV>
 __global__ void attn_decode_combine_kernel(AttentionParams<bf16> p) {
     int bh = blockIdx.x;
     int d = threadIdx.x;
@@ -116,14 +135,17 @@ __global__ void attn_decode_combine_kernel(AttentionParams<bf16> p) {
         if (mi <= -FLT_MAX) continue;
         float li = mlp[s * 2 + 1];
         float nm = fmaxf(m, mi);
-        float corr = expf(m - nm);
-        float e = expf(mi - nm);
+        float corr = __expf(m - nm);
+        float e = __expf(mi - nm);
         acc = fmaf(acc, corr, op[s * p.head_dim + d] * e);
         l = fmaf(l, corr, li * e);
         m = nm;
     }
 
     float inv = (l > 1e-20f) ? (1.0f / l) : 0.0f;
-    int o_off = batch * p.q_stride_b + q_head * p.q_stride_h + d * p.q_stride_d;
-    p.o[o_off] = __float2bfloat16(acc * inv);
+    int o_off = KV::q_decode_base(p, batch, q_head) + d * p.q_d_stride;
+    p.o_ptr[o_off] = __float2bfloat16(acc * inv);
 }
+
+}  // namespace attention
+}  // namespace astrai

csrc/kernels/attention/decode_split_kv_mma.cuh

@@ -1,20 +1,25 @@
 #pragma once
 #include <cfloat>
 #include <cuda_bf16.h>
-#include "attn_common.h"
-#include "attn_mma_utils.cuh"
-#include "attn_warp_utils.cuh"
+#include "common.h"
+#include "layout_policies.cuh"
+#include "mma_utils.cuh"
 
-// Split-K (FlashDecoding) tensor-core decode via GQA head-packing.
-// Decode has q_len == 1, so we pack G = q_head/kv_head query heads into the
-// M=16 rows of mma.sync.m16n8k16, turning G independent GEMVs into a single
-// GEMM that reuses each loaded K/V tile across all G heads.
+namespace astrai {
+namespace attention {
+
+// Split-K (FlashDecoding) tensor-core decode via GQA head-packing, unified
+// across contiguous and paged (SGLang flat-pool) K/V via the KV template
+// parameter.  Decode has q_len == 1, so we pack G = q_head/kv_head query
+// heads into the M=16 rows of mma.sync.m16n8k16, turning G independent GEMVs
+// into a single GEMM that reuses each loaded K/V tile across all G heads.
 //
+// KV = ContigKV (dense tensors) or PagedKV (flat pool + req_to_token).
 // IsCausal and HasMask are compile-time bools — no runtime branch in the
 // inner compute loop.
 //
-// Traits = KernelTraits<HEAD_DIM, BC=32, WARPS=1, STAGES=<2 or 1>>.
-template <typename Traits, bool IsCausal, bool HasMask>
+// Traits = KernelTraits<HEAD_DIM, BC=16, WARPS=1, STAGES=2>.
+template <typename Traits, typename KV, bool IsCausal, bool HasMask>
 __global__ void attn_decode_split_kv_mma_kernel(AttentionParams<bf16> p) {
     const int lane = threadIdx.x;
     const int gid = lane >> 2;
@@ -31,18 +36,21 @@ __global__ void attn_decode_split_kv_mma_kernel(AttentionParams<bf16> p) {
     const int G = min(MAX_G, G_total - g_begin);
     const int q_head0 = kv_head * G_total + g_begin;
 
+    // Per-request seq_len (paged reads kv_indptr; contig uses p.kv_len).
+    const int seq_len = KV::kv_len(p, batch);
+    const KVContext kctx = KV::template make_ctx<Traits::HEAD_DIM>(p, batch, kv_head);
+
     // Double-buffered shared memory for K/V (no sQ needed)
     __shared__ __align__(16) bf16 sK[Traits::STAGES * Traits::BC * Traits::LD];
     __shared__ __align__(16) bf16 sV[Traits::STAGES * Traits::BC * Traits::LD];
 
     // Load Q directly from global into mma A-operand registers.
-    // stride_row = p.q_stride_h for decode (q_len=1).
-    const int q_base = batch * p.q_stride_b + q_head0 * p.q_stride_h;
+    const int q_base = KV::q_decode_base(p, batch, q_head0);
     const int qra = gid;
     const int qrb = gid + 8;
     const bool va = qra < G, vb = qrb < G;
     unsigned Qa[Traits::KD][4];
-    load_q_mma_frags<Traits::KD>(p.q + q_base, p.q_stride_h, p.q_stride_d,
+    load_q_mma_frags<Traits::KD>(p.q_ptr + q_base, p.q_h_stride, p.q_d_stride,
                                   qra, qrb, va, vb, tid4, Qa);
 
     float Oacc[Traits::DN8][4];
@@ -51,13 +59,12 @@ __global__ void attn_decode_split_kv_mma_kernel(AttentionParams<bf16> p) {
         Oacc[j][0] = Oacc[j][1] = Oacc[j][2] = Oacc[j][3] = 0.0f;
     float m0 = -FLT_MAX, m1 = -FLT_MAX, l0 = 0.0f, l1 = 0.0f;
 
-    const int kv_base = batch * p.kv_stride_b + kv_head * p.kv_stride_h;
-    const int tiles_total = (p.kv_len + Traits::BC - 1) / Traits::BC;
+    const int tiles_total = (seq_len + Traits::BC - 1) / Traits::BC;
     const int tiles_per_split = (tiles_total + p.num_splits - 1) / p.num_splits;
     const int ti_begin = split * tiles_per_split;
     const int ti_end = min(tiles_total, ti_begin + tiles_per_split);
 
-    // ---- Load tile lambda: predicated cp.async ----
+    // ---- Load tile lambda: predicated cp.async (addressing via KV policy) ----
     auto load_tile = [&](int ti, int buf) {
         int kv0 = ti * Traits::BC;
         bf16* dK = sK + buf * Traits::BC * Traits::LD;
@@ -67,35 +74,29 @@ __global__ void attn_decode_split_kv_mma_kernel(AttentionParams<bf16> p) {
              i += Traits::NUM_THREADS * Traits::VEC) {
             int r = i / Traits::HEAD_DIM, d = i % Traits::HEAD_DIM;
             int kc = kv0 + r;
-            bool valid = kc < p.kv_len;
+            bool valid = kc < seq_len;
+            // All GQA passes consume new K/V directly. Only the first pass
+            // persists it, so no cross-block synchronization is required.
+            KVAddr a = KV::template decode_addr<Traits::VEC>(
+                p, kctx, batch, kv_head, kc, d, valid, pass == 0);
             int off = r * Traits::LD + swiz_col(d, r, Traits::SWIZ_MASK);
-            int g_off = kv_base + kc * p.kv_stride_l + d * p.kv_stride_d;
-            cp_async_16_pred(&dK[off], &p.k[g_off], valid);
-            cp_async_16_pred(&dV[off], &p.v[g_off], valid);
+            astrai::cp_async_16(&dK[off], a.k, a.valid);
+            astrai::cp_async_16(&dV[off], a.v, a.valid);
         }
-        cp_async_commit();
+        astrai::cp_async_commit_group();
     };
 
-    constexpr int BUF_MASK = (Traits::STAGES > 1) ? (Traits::STAGES - 1) : 0;
-
-    // Prologue
-    if (ti_begin < ti_end) {
-        load_tile(ti_begin, 0);
-    }
-
-    for (int ti = ti_begin; ti < ti_end; ti++) {
-        int buf = (ti - ti_begin) & BUF_MASK;
-
-        cp_async_wait_group<0>();
-        __syncwarp();
-        if constexpr (Traits::STAGES > 1) {
-            if (ti + 1 < ti_end)
-                load_tile(ti + 1, (ti + 1 - ti_begin) & BUF_MASK);
-        }
+    // ---- Multi-stage cp.async pipeline ----
+    // Prologue loads STAGES tiles; each loop iteration waits only for the
+    // oldest outstanding group (wait_group<STAGES-1>) so the STAGES-1 newer
+    // tile loads stay in flight and overlap with the current tile's compute.
+    constexpr int STAGES = Traits::STAGES;
+    const int ntiles = ti_end - ti_begin;
 
+    auto process_tile = [&](int it, int buf) {
         const bf16* bK = sK + buf * Traits::BC * Traits::LD;
         const bf16* bV = sV + buf * Traits::BC * Traits::LD;
-        int kv0 = ti * Traits::BC;
+        int kv0 = (ti_begin + it) * Traits::BC;
 
         float Sacc[Traits::NC8][4];
         mma_compute_scores<Traits>(Qa, bK, lane, Sacc);
@@ -105,22 +106,45 @@ __global__ void attn_decode_split_kv_mma_kernel(AttentionParams<bf16> p) {
             Sacc[n8][0] *= p.scale, Sacc[n8][1] *= p.scale,
             Sacc[n8][2] *= p.scale, Sacc[n8][3] *= p.scale;
 
-        // Decode: q_len=1, so qrow0=qrow1=0
-        int maxc = IsCausal ? min(p.kv_len, p.causal_offset + 1) : p.kv_len;
+        // Decode: q_len=1, so qrow0=qrow1=0.  Paged treats [0, seq_len) as
+        // the causal range (query is the last token); contig clips to the
+        // causal_offset bound.  Dead code eliminated when IsCausal == false.
+        int maxc = IsCausal ? KV::decode_attend_len(p, batch) : seq_len;
         mma_softmax_tile<Traits, HasMask>(kv0, maxc, maxc,
                                            0, 0,
-                                           p.mask_b_stride, 0,
-                                           batch,
-                                           p.mask,
-                                           Sacc, Oacc, m0, m1, l0, l1, lane);
+                                            p.mask_b_stride, p.mask_h_stride, p.mask_l_stride,
+                                            batch, q_head0 + gid, q_head0 + gid + 8,
+                                            p.mask,
+                                            va, vb,
+                                            Sacc, Oacc, m0, m1, l0, l1, lane);
 
         mma_pv_accumulate<Traits>(Sacc, bV, lane, Oacc);
-        __syncwarp();
+    };
 
-        if constexpr (Traits::STAGES == 1) {
-            if (ti + 1 < ti_end)
-                load_tile(ti + 1, 0);
+    if (ntiles >= STAGES) {
+        #pragma unroll
+        for (int i = 0; i < STAGES; i++)
+            load_tile(ti_begin + i, i);
+
+        for (int it = 0; it < ntiles; it++) {
+            if (it + 1 == ntiles)
+                astrai::cp_async_wait_group<0>();
+            else
+                astrai::cp_async_wait_group<STAGES - 1>();
+            __syncwarp();
+            process_tile(it, it & (STAGES - 1));
+            __syncwarp();
+            if (it + STAGES < ntiles)
+                load_tile(ti_begin + it + STAGES, (it + STAGES) & (STAGES - 1));
         }
+    } else {
+        // Fewer tiles than stages: load all, wait for all, process.
+        for (int i = 0; i < ntiles; i++)
+            load_tile(ti_begin + i, i);
+        astrai::cp_async_wait_all();
+        __syncwarp();
+        for (int it = 0; it < ntiles; it++)
+            process_tile(it, it);
     }
 
     // ---- write UN-normalised partials for this split ----
@@ -159,3 +183,6 @@ __global__ void attn_decode_split_kv_mma_kernel(AttentionParams<bf16> p) {
         }
     }
 }
+
+}  // namespace attention
+}  // namespace astrai

csrc/kernels/attention/dispatchers.cuh

@@ -0,0 +1,238 @@
+#pragma once
+// Shared attention dispatchers — used by both production .cu and test .cu.
+// No torch dependency; pure CUDA.
+//
+// The paged and contiguous kernels are unified by the KVSource policy
+// (ContigKV / PagedKV from layout_policies.cuh), so each launcher struct
+// below is templated on KV and the paged dispatch is just the same launcher
+// instantiated with PagedKV.  Only the grid/split math differs, and that is
+// covered by KV::host_q_len / KV::host_kv_len.
+
+#include <cuda_runtime.h>
+#include <algorithm>
+#include "layout_policies.cuh"
+#include "prefill_split_q.cuh"
+#include "decode_split_kv.cuh"
+#ifndef ASTRAI_NO_MMA
+#include "prefill_split_q_mma.cuh"
+#include "decode_split_kv_mma.cuh"
+#endif
+
+namespace astrai {
+namespace attention {
+
+// Split-KV: compute number of splits to fill all SMs for small-batch decode.
+// Caps splits so each split processes at least `min_tiles_per_split` tiles,
+// avoiding excessive loop/prologue overhead when tiles are small.
+//
+// Target total grid blocks (`TARGET_BLOCKS`) rather than scaling splits by SM
+// count.  Decode blocks are single-warp (32 threads) and a SM hosts ~11 of
+// them, so the old `2*sm/base` cap badly undersplit at large batch (B=16 got
+// 3 splits, optimal ~8).  Measured (L20, grid search): bandwidth saturates
+// near 256-512 total blocks; 512 minimizes worst-case latency across the
+// B x kv grid; more is pure oversplit overhead.
+constexpr int DECODE_TARGET_BLOCKS = 512;
+inline int compute_num_splits(int base_blocks, int tiles_total,
+                               int min_tiles_per_split = 1) {
+    int n = (DECODE_TARGET_BLOCKS + base_blocks - 1) / base_blocks;
+    int max_by_work = tiles_total / min_tiles_per_split;
+    return std::max(1, std::min(n, std::min(max_by_work, MAX_SPLITS)));
+}
+
+// Dispatch IsCausal × HasMask — eliminates the duplicated 4-way if/else
+// ladder that appeared in each dispatch_* function.  FN must be a function
+// template <int HEAD_DIM, bool IsCausal, bool HasMask>; HEAD_DIM is forwarded
+// as the first template argument so callers only spell it once.
+//
+// Usage:  DISPATCH_CAUSAL_MASK(is_causal, has_mask, launcher<KV>::template launch, HEAD_DIM, p, stream);
+#define DISPATCH_CAUSAL_MASK(is_causal, has_mask, FN, HEAD_DIM, ...) \
+    do { \
+        if (is_causal) { \
+            if (has_mask) FN<HEAD_DIM, true,  true>(__VA_ARGS__); \
+            else          FN<HEAD_DIM, true,  false>(__VA_ARGS__); \
+        } else { \
+            if (has_mask) FN<HEAD_DIM, false, true>(__VA_ARGS__); \
+            else          FN<HEAD_DIM, false, false>(__VA_ARGS__); \
+        } \
+    } while (0)
+
+// ======================================================================
+// Prefill launchers (KV selects ContigKV or PagedKV addressing)
+// ======================================================================
+
+#ifndef ASTRAI_NO_MMA
+template <int BC_>
+struct PrefillKernelConfig {
+    static constexpr int BC = BC_;
+    static constexpr int WARPS = 4;
+    static constexpr int STAGES = 2;
+};
+
+// Compile-time configuration map shared by contiguous and paged prefill.
+// Unsupported head dimensions intentionally have no mapping.
+template <int HEAD_DIM, bool IsCausal>
+struct PrefillConfigMap;
+
+template <> struct PrefillConfigMap<32, false>  : PrefillKernelConfig<32> {};
+template <> struct PrefillConfigMap<32, true>   : PrefillKernelConfig<64> {};
+template <> struct PrefillConfigMap<64, false>  : PrefillKernelConfig<32> {};
+template <> struct PrefillConfigMap<64, true>   : PrefillKernelConfig<64> {};
+template <> struct PrefillConfigMap<128, false> : PrefillKernelConfig<32> {};
+template <> struct PrefillConfigMap<128, true>  : PrefillKernelConfig<32> {};
+template <> struct PrefillConfigMap<256, false> : PrefillKernelConfig<16> {};
+template <> struct PrefillConfigMap<256, true>  : PrefillKernelConfig<16> {};
+
+template <typename QSchedule, typename KV>
+struct PrefillLauncherMMA {
+    template <int HEAD_DIM, bool IsCausal, bool HasMask>
+    static void launch(AttentionParams<bf16>& p, cudaStream_t stream) {
+        using Config = PrefillConfigMap<HEAD_DIM, IsCausal>;
+        using Traits = KernelTraits<HEAD_DIM, Config::BC, Config::WARPS, Config::STAGES>;
+        constexpr int ROWS = Traits::BR * Config::WARPS;
+        dim3 grid(QSchedule::host_q_blocks(p, ROWS), p.q_head,
+                  QSchedule::host_grid_batch(p));
+        dim3 block(Traits::NUM_THREADS);
+        attn_prefill_split_q_mma_kernel<Traits, QSchedule, KV, IsCausal, HasMask>
+            <<<grid, block, 0, stream>>>(p);
+    }
+};
+#endif
+
+template <typename QSchedule, typename KV>
+struct PrefillLauncherScalar {
+    template <int HEAD_DIM, bool IsCausal, bool HasMask>
+    static void launch(AttentionParams<bf16>& p, cudaStream_t stream) {
+        constexpr int G = (HEAD_DIM == 32) ? 4 : 8, ROWS = 64, P_BC = 32;
+        dim3 grid(QSchedule::host_q_blocks(p, ROWS), p.q_head,
+                  QSchedule::host_grid_batch(p));
+        dim3 block(G, ROWS);
+        attn_prefill_split_q_kernel_t<HEAD_DIM, QSchedule, KV, G, ROWS, P_BC,
+                                      IsCausal, HasMask>
+            <<<grid, block, 0, stream>>>(p);
+    }
+};
+
+template <int HEAD_DIM>
+static inline void dispatch_prefill(AttentionParams<bf16>& p, cudaStream_t stream) {
+    bool is_causal = (p.causal_offset >= 0);
+    bool has_mask = (p.use_mask && p.mask);
+
+#ifndef ASTRAI_NO_MMA
+    using Launcher = PrefillLauncherMMA<DenseQSchedule, ContigKV>;
+    DISPATCH_CAUSAL_MASK(is_causal, has_mask,
+                         Launcher::template launch,
+                         HEAD_DIM, p, stream);
+#else
+    using Launcher = PrefillLauncherScalar<DenseQSchedule, ContigKV>;
+    DISPATCH_CAUSAL_MASK(is_causal, has_mask,
+                         Launcher::template launch,
+                         HEAD_DIM, p, stream);
+#endif
+}
+
+template <int HEAD_DIM>
+static inline void dispatch_paged_prefill(AttentionParams<bf16>& p, cudaStream_t stream) {
+    bool is_causal = (p.causal_offset >= 0);
+    bool has_mask = (p.use_mask && p.mask);
+
+#ifndef ASTRAI_NO_MMA
+    using Launcher = PrefillLauncherMMA<PackedQSchedule, PagedKV>;
+    DISPATCH_CAUSAL_MASK(is_causal, has_mask,
+                         Launcher::template launch,
+                         HEAD_DIM, p, stream);
+#else
+    using Launcher = PrefillLauncherScalar<PackedQSchedule, PagedKV>;
+    DISPATCH_CAUSAL_MASK(is_causal, has_mask,
+                         Launcher::template launch,
+                         HEAD_DIM, p, stream);
+#endif
+}
+
+// ======================================================================
+// Decode launchers (KV selects ContigKV or PagedKV addressing)
+// ======================================================================
+
+#ifndef ASTRAI_NO_MMA
+// BC=16: halves smem (16KB vs 32KB) → doubles occupancy (6 vs 3 blocks/SM).
+// For D=256, BC=16 also reduces register pressure (fewer Sacc/PV frags),
+// enabling STAGES=2 (double-buffer) within the 32KB smem budget — eliminates
+// the 176-byte spill that STAGES=1+BC=32 suffered.
+template <typename KV>
+struct DecodeLauncherMMA {
+    template <int HEAD_DIM, bool IsCausal, bool HasMask>
+    static void launch(AttentionParams<bf16>& p, cudaStream_t stream) {
+        int G = p.q_head / p.kv_head;
+        constexpr int MAX_G = 16;
+        int num_passes = (G + MAX_G - 1) / MAX_G;
+        constexpr int BC = 16;
+        int kv_len = KV::host_kv_len(p);
+        int tiles_total = (kv_len + BC - 1) / BC;
+        p.num_splits = compute_num_splits(p.batch * p.kv_head * num_passes, tiles_total, 2);
+        constexpr int STAGES = 2;
+        using Traits = KernelTraits<HEAD_DIM, BC, 1, STAGES>;
+        dim3 grid(p.kv_head * num_passes, p.batch, p.num_splits);
+        attn_decode_split_kv_mma_kernel<Traits, KV, IsCausal, HasMask>
+            <<<grid, 32, 0, stream>>>(p);
+    }
+};
+#endif
+
+template <typename KV>
+struct DecodeLauncherScalar {
+    template <int HEAD_DIM, bool IsCausal, bool HasMask>
+    static void launch(AttentionParams<bf16>& p, cudaStream_t stream) {
+        int kv_len = KV::host_kv_len(p);
+        int chunks_total = (kv_len + DC_CHUNK - 1) / DC_CHUNK;
+        p.num_splits = compute_num_splits(p.batch * p.kv_head, chunks_total);
+        size_t smem = 2 * DC_CHUNK * p.head_dim * sizeof(bf16);
+        int group_size = p.q_head / p.kv_head;
+        int g = min(group_size, 32);  // cap at 32 to respect 1024-thread limit
+        dim3 grid(p.batch * p.kv_head, 1, p.num_splits);
+        dim3 block(32, g);
+        cudaFuncSetAttribute(
+            attn_decode_split_kv_kernel<HEAD_DIM, KV, IsCausal, HasMask>,
+            cudaFuncAttributeMaxDynamicSharedMemorySize,
+            smem);
+        attn_decode_split_kv_kernel<HEAD_DIM, KV, IsCausal, HasMask>
+            <<<grid, block, smem, stream>>>(p);
+    }
+};
+
+template <int HEAD_DIM>
+static inline void dispatch_decode(AttentionParams<bf16>& p, cudaStream_t stream) {
+    bool is_causal = (p.causal_offset >= 0);
+    bool has_mask = (p.use_mask && p.mask);
+
+#ifndef ASTRAI_NO_MMA
+    DISPATCH_CAUSAL_MASK(is_causal, has_mask,
+                         DecodeLauncherMMA<ContigKV>::template launch,
+                         HEAD_DIM, p, stream);
+#else
+    DISPATCH_CAUSAL_MASK(is_causal, has_mask,
+                         DecodeLauncherScalar<ContigKV>::template launch,
+                         HEAD_DIM, p, stream);
+#endif
+
+    attn_decode_combine_kernel<ContigKV><<<p.batch * p.q_head, p.head_dim, 0, stream>>>(p);
+}
+
+template <int HEAD_DIM>
+static inline void dispatch_paged_decode(AttentionParams<bf16>& p, cudaStream_t stream) {
+    bool is_causal = (p.causal_offset >= 0);
+    bool has_mask = (p.use_mask && p.mask);
+
+#ifndef ASTRAI_NO_MMA
+    DISPATCH_CAUSAL_MASK(is_causal, has_mask,
+                         DecodeLauncherMMA<PagedKV>::template launch,
+                         HEAD_DIM, p, stream);
+#else
+    DISPATCH_CAUSAL_MASK(is_causal, has_mask,
+                         DecodeLauncherScalar<PagedKV>::template launch,
+                         HEAD_DIM, p, stream);
+#endif
+
+    attn_decode_combine_kernel<PagedKV><<<p.batch * p.q_head, p.head_dim, 0, stream>>>(p);
+}
+
+}  // namespace attention
+}  // namespace astrai

csrc/kernels/attention/entry_utils.cuh

@@ -0,0 +1,363 @@
+#pragma once
+#include <float.h>
+#include <torch/extension.h>
+#include <c10/cuda/CUDAGuard.h>
+#include "common.h"
+
+// Dispatch head_dim: shared macro — avoids C++20 lambda template syntax.
+// Usage: DISPATCH_HEAD_DIM(hd, fn, args...)
+//   Expands to: fn<32>(args...); fn<64>(args...); etc.
+#define DISPATCH_HEAD_DIM(hd, fn, ...) \
+    switch (hd) { \
+        case 32:  fn<32>(__VA_ARGS__); break; \
+        case 64:  fn<64>(__VA_ARGS__); break; \
+        case 128: fn<128>(__VA_ARGS__); break; \
+        case 256: fn<256>(__VA_ARGS__); break; \
+        default: \
+            TORCH_CHECK(false, "unsupported head_dim ", hd, \
+                         " (supported: 32, 64, 128, 256)"); \
+    }
+
+namespace astrai {
+namespace attention {
+
+using bf16 = __nv_bfloat16;
+
+// The split kernel unconditionally writes every (batch, q_head, split) slot it
+// owns — including empty split ranges, which store m = -FLT_MAX so the combine
+// skips them. Allocators are therefore left uninitialized (torch::empty); the
+// per-call memset (torch::zeros / torch::full) was pure overhead.
+template<typename P>
+inline void alloc_split_partials(P& p) {
+    auto fopt = torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA);
+    auto o_part = torch::empty(at::IntArrayRef{p.batch, p.q_head, MAX_SPLITS, p.head_dim}, fopt);
+    auto ml_part = torch::empty(at::IntArrayRef{p.batch, p.q_head, MAX_SPLITS, 2}, fopt);
+    p.o_part = (float*)o_part.data_ptr();
+    p.ml_part = (float*)ml_part.data_ptr();
+}
+
+// ---- Shared Q-dims + strides extraction ----
+template <typename P>
+inline void extract_q_dims_and_strides(torch::Tensor& q, int64_t layout, P& p) {
+    if (layout == BLHD) q = q.transpose(1, 2);
+    p.batch = (int)q.size(0);
+    p.q_head = (int)q.size(1);
+    p.q_len = (int)q.size(2);
+    p.head_dim = (int)q.size(3);
+    p.q_b_stride = (int)q.stride(0);
+    p.q_h_stride = (int)q.stride(1);
+    p.q_l_stride = (int)q.stride(2);
+    p.q_d_stride = (int)q.stride(3);
+}
+
+// ---- Shared mask packing ----
+// Accepts 2D [batch, kv_len], 3D [batch, q_len, kv_len],
+// or 4D [batch, n_heads, q_len, kv_len].
+// Head/q dimensions with size 1 broadcast (stride set to 0).
+template <typename P>
+inline void pack_mask(const c10::optional<torch::Tensor>& mask, P& p) {
+    if (p.use_mask) {
+        auto m = mask.value();
+        TORCH_CHECK(m.is_cuda(), "mask must be on CUDA");
+        TORCH_CHECK(m.dtype() == torch::kBool, "mask must be bool");
+        TORCH_CHECK(m.size(0) == p.batch, "mask batch mismatch");
+        TORCH_CHECK(m.size(m.dim() - 1) == p.kv_len, "mask kv_len mismatch");
+        if (m.dim() == 2) {
+            p.mask_b_stride = (int)m.stride(0);
+            p.mask_h_stride = 0;
+            p.mask_l_stride = 0;
+        } else if (m.dim() == 3) {
+            TORCH_CHECK(m.size(1) == 1 || m.size(1) == p.q_len, "mask q_len mismatch");
+            p.mask_b_stride = (int)m.stride(0);
+            p.mask_h_stride = 0;
+            p.mask_l_stride = (m.size(1) == 1) ? 0 : (int)m.stride(1);
+        } else if (m.dim() == 4) {
+            TORCH_CHECK(m.size(2) == 1 || m.size(2) == p.q_len, "mask q_len mismatch");
+            p.mask_b_stride = (int)m.stride(0);
+            p.mask_h_stride = (m.size(1) == 1) ? 0 : (int)m.stride(1);
+            p.mask_l_stride = (m.size(2) == 1) ? 0 : (int)m.stride(2);
+        } else {
+            TORCH_CHECK(false, "mask must be 2D, 3D, or 4D");
+        }
+        p.mask = m.data_ptr<bool>();
+    } else {
+        p.mask = nullptr;
+        p.mask_b_stride = 0;
+        p.mask_h_stride = 0;
+        p.mask_l_stride = 0;
+    }
+}
+
+// ---- attn_pack_params (contiguous KV) ----
+template<typename T>
+inline void attn_pack_params(
+    torch::Tensor q,
+    torch::Tensor k,
+    torch::Tensor v,
+    c10::optional<torch::Tensor> mask,
+    int64_t causal_offset,
+    double scale,
+    int64_t layout,
+    AttentionParams<T>& p
+) {
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(q));
+
+    TORCH_CHECK(q.is_cuda() && k.is_cuda() && v.is_cuda());
+    TORCH_CHECK(q.dtype() == torch::kBFloat16);
+    TORCH_CHECK(k.dtype() == torch::kBFloat16);
+    TORCH_CHECK(v.dtype() == torch::kBFloat16);
+    TORCH_CHECK(k.sizes() == v.sizes(), "K and V must have identical shapes");
+    TORCH_CHECK(q.dim() == 4 && k.dim() == 4, "Q/K/V must be 4D");
+    extract_q_dims_and_strides(q, layout, p);
+
+    if (layout == BLHD) k = k.transpose(1, 2), v = v.transpose(1, 2);
+
+    p.kv_head = (int)k.size(1);
+    p.kv_len = (int)k.size(2);
+    TORCH_CHECK(p.q_head % p.kv_head == 0,
+                "q_head must be divisible by kv_head");
+    TORCH_CHECK(k.size(3) == p.head_dim, "K/V head_dim must match Q");
+    TORCH_CHECK(q.stride(3) == 1 && k.stride(3) == 1 && v.stride(3) == 1,
+                "Q/K/V head_dim must be contiguous");
+
+    p.kv_b_stride = (int)k.stride(0);
+    p.kv_h_stride = (int)k.stride(1);
+    p.kv_l_stride = (int)k.stride(2);
+    p.kv_d_stride = (int)k.stride(3);
+
+    p.causal_offset = (int)causal_offset;
+    p.use_mask = mask.has_value() ? 1 : 0;
+    p.scale = (scale > 0.0) ? (float)scale : 1.0f / sqrtf((float)p.head_dim);
+
+    p.q_ptr = (const T*)q.data_ptr();
+    p.k_ptr = (const T*)k.data_ptr();
+    p.v_ptr = (const T*)v.data_ptr();
+    p.new_k_ptr = nullptr;
+    p.new_v_ptr = nullptr;
+    p.o_ptr = nullptr;
+    p.o_part = nullptr;
+    p.ml_part = nullptr;
+
+    pack_mask(mask, p);
+}
+
+// ---- attn_pack_paged_decode_params ----
+// SGLang-style: flat KV pool + req_to_token indexing + variable
+// seq_lens via kv_indptr.  Q is [batch, q_head, head_dim] (q_len=1 per req).
+template<typename T>
+inline void attn_pack_paged_decode_params(
+    torch::Tensor q,
+    torch::Tensor k_cache,
+    torch::Tensor v_cache,
+    torch::Tensor req_to_token,
+    torch::Tensor req_pool_indices,
+    torch::Tensor kv_indptr,
+    const c10::optional<torch::Tensor>& new_k,
+    const c10::optional<torch::Tensor>& new_v,
+    c10::optional<torch::Tensor> mask,
+    int64_t causal_offset,
+    double scale,
+    AttentionParams<T>& p
+) {
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(q));
+
+    TORCH_CHECK(q.is_cuda() && k_cache.is_cuda() && v_cache.is_cuda());
+    TORCH_CHECK(req_to_token.is_cuda() && req_pool_indices.is_cuda() && kv_indptr.is_cuda());
+    TORCH_CHECK(q.dtype() == torch::kBFloat16, "q must be bf16");
+    TORCH_CHECK(k_cache.dtype() == torch::kBFloat16, "k_cache must be bf16");
+    TORCH_CHECK(v_cache.dtype() == torch::kBFloat16, "v_cache must be bf16");
+    TORCH_CHECK(req_to_token.dtype() == torch::kInt32, "req_to_token must be int32");
+    TORCH_CHECK(req_pool_indices.dtype() == torch::kInt32,
+                "req_pool_indices must be int32");
+    TORCH_CHECK(kv_indptr.dtype() == torch::kInt32, "kv_indptr must be int32");
+    TORCH_CHECK(k_cache.sizes() == v_cache.sizes(), "k_cache and v_cache must match");
+    TORCH_CHECK(k_cache.dim() == 3, "k_cache must be 3D [size, kv_head, head_dim]");
+    TORCH_CHECK(q.dim() == 3, "q must be 3D [batch, q_head, head_dim]");
+
+    p.batch = (int)q.size(0);
+    p.q_head = (int)q.size(1);
+    p.head_dim = (int)q.size(2);
+    p.kv_head = (int)k_cache.size(1);
+    TORCH_CHECK(k_cache.size(2) == p.head_dim, "k_cache head_dim mismatch");
+    TORCH_CHECK(q.stride(2) == 1 && k_cache.stride(2) == 1 && v_cache.stride(2) == 1,
+                "Q/K/V head_dim must be contiguous");
+    TORCH_CHECK(p.head_dim % 32 == 0, "head_dim must be multiple of 32");
+    TORCH_CHECK(p.q_head % p.kv_head == 0, "q_head must be divisible by kv_head");
+
+    p.q_l_stride = (int)q.stride(0);
+    p.q_h_stride = (int)q.stride(1);
+    p.q_d_stride = (int)q.stride(2);
+
+    p.k_ptr = (const T*)k_cache.data_ptr();
+    p.v_ptr = (const T*)v_cache.data_ptr();
+    p.q_ptr = (const T*)q.data_ptr();
+    p.req_to_token = req_to_token.data_ptr<int>();
+    p.req_pool_indices = req_pool_indices.data_ptr<int>();
+    p.kv_indptr = kv_indptr.data_ptr<int>();
+    p.qo_indptr = nullptr;
+    p.max_context_len = (int)req_to_token.size(1);
+
+    TORCH_CHECK(new_k.has_value() == new_v.has_value(),
+                "new_k and new_v must be provided together");
+    if (new_k.has_value()) {
+        auto nk = new_k.value();
+        auto nv = new_v.value();
+        TORCH_CHECK(nk.is_cuda() && nv.is_cuda(), "new K/V must be CUDA tensors");
+        TORCH_CHECK(nk.dtype() == torch::kBFloat16 && nv.dtype() == torch::kBFloat16,
+                    "new K/V must be bf16");
+        TORCH_CHECK(nk.dim() == 3 && nv.dim() == 3,
+                    "new K/V must be 3D [batch, kv_head, head_dim]");
+        TORCH_CHECK(nk.sizes() == nv.sizes(), "new K and V must have identical shapes");
+        TORCH_CHECK(nk.strides() == nv.strides(),
+                    "new K and V must have identical strides");
+        TORCH_CHECK(nk.size(0) == p.batch && nk.size(1) == p.kv_head
+                    && nk.size(2) == p.head_dim, "new K/V shape mismatch");
+        TORCH_CHECK(nk.stride(2) == 1 && nv.stride(2) == 1,
+                    "new K/V head_dim must be contiguous");
+        p.new_k_ptr = (const T*)nk.data_ptr();
+        p.new_v_ptr = (const T*)nv.data_ptr();
+        p.new_kv_b_stride = (int)nk.stride(0);
+        p.new_kv_h_stride = (int)nk.stride(1);
+    } else {
+        p.new_k_ptr = nullptr;
+        p.new_v_ptr = nullptr;
+        p.new_kv_b_stride = p.new_kv_h_stride = 0;
+    }
+
+    p.causal_offset = (int)causal_offset;
+    p.use_mask = (mask.has_value() && mask.value().defined()) ? 1 : 0;
+    p.scale = (scale > 0.0) ? (float)scale : 1.0f / sqrtf((float)p.head_dim);
+
+    if (p.use_mask) {
+        auto m = mask.value();
+        TORCH_CHECK(m.is_cuda() && m.dtype() == torch::kBool, "mask must be bool CUDA");
+        TORCH_CHECK(m.size(0) == p.batch, "mask batch mismatch");
+        p.mask_b_stride = (int)m.stride(0);
+        p.mask_h_stride = 0;
+        p.mask_l_stride = 0;
+        p.mask = m.data_ptr<bool>();
+    } else {
+        p.mask = nullptr;
+        p.mask_b_stride = 0;
+        p.mask_h_stride = 0;
+        p.mask_l_stride = 0;
+    }
+
+    p.o_ptr = nullptr;
+    p.o_part = nullptr;
+    p.ml_part = nullptr;
+}
+
+// ---- attn_pack_paged_prefill_params ----
+// SGLang-style: flat KV pool + req_to_token + ragged batch via qo_indptr.
+// Q is [total_q, q_head, head_dim] (flattened across all requests).
+template<typename T>
+inline void attn_pack_paged_prefill_params(
+    torch::Tensor 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,
+    c10::optional<torch::Tensor> mask,
+    int64_t causal_offset,
+    double scale,
+    AttentionParams<T>& p
+) {
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(q));
+
+    TORCH_CHECK(q.is_cuda() && k_cache.is_cuda() && v_cache.is_cuda());
+    TORCH_CHECK(req_to_token.is_cuda() && req_pool_indices.is_cuda());
+    TORCH_CHECK(kv_indptr.is_cuda() && qo_indptr.is_cuda());
+    TORCH_CHECK(q_tile_to_batch.is_cuda() && q_tile_to_index.is_cuda());
+    TORCH_CHECK(q.dtype() == torch::kBFloat16, "q must be bf16");
+    TORCH_CHECK(k_cache.dtype() == torch::kBFloat16, "k_cache must be bf16");
+    TORCH_CHECK(v_cache.dtype() == torch::kBFloat16, "v_cache must be bf16");
+    TORCH_CHECK(req_to_token.dtype() == torch::kInt32, "req_to_token must be int32");
+    TORCH_CHECK(req_pool_indices.dtype() == torch::kInt32,
+                "req_pool_indices must be int32");
+    TORCH_CHECK(kv_indptr.dtype() == torch::kInt32, "kv_indptr must be int32");
+    TORCH_CHECK(qo_indptr.dtype() == torch::kInt32, "qo_indptr must be int32");
+    TORCH_CHECK(q_tile_to_batch.dtype() == torch::kInt32,
+                "q_tile_to_batch must be int32");
+    TORCH_CHECK(q_tile_to_index.dtype() == torch::kInt32,
+                "q_tile_to_index must be int32");
+    TORCH_CHECK(k_cache.sizes() == v_cache.sizes(), "k_cache and v_cache must match");
+    TORCH_CHECK(k_cache.dim() == 3, "k_cache must be 3D [size, kv_head, head_dim]");
+    TORCH_CHECK(q.dim() == 3, "q must be 3D [total_q, q_head, head_dim]");
+
+    p.q_head = (int)q.size(1);
+    p.head_dim = (int)q.size(2);
+    p.q_len = (int)q.size(0);
+    p.kv_head = (int)k_cache.size(1);
+    p.batch = (int)req_pool_indices.size(0);
+    TORCH_CHECK(k_cache.size(2) == p.head_dim, "k_cache head_dim mismatch");
+    TORCH_CHECK(q.stride(2) == 1 && k_cache.stride(2) == 1 && v_cache.stride(2) == 1,
+                "Q/K/V head_dim must be contiguous");
+    TORCH_CHECK(p.head_dim % 16 == 0, "head_dim must be multiple of 16");
+    TORCH_CHECK(p.q_head % p.kv_head == 0, "q_head must be divisible by kv_head");
+    TORCH_CHECK(kv_indptr.size(0) == p.batch + 1, "kv_indptr must be [batch+1]");
+    TORCH_CHECK(qo_indptr.size(0) == p.batch + 1, "qo_indptr must be [batch+1]");
+    TORCH_CHECK(q_tile_to_batch.dim() == 1 && q_tile_to_index.dim() == 1,
+                "Q tile mappings must be 1D");
+    TORCH_CHECK(q_tile_to_batch.size(0) == q_tile_to_index.size(0),
+                "Q tile mappings must have equal length");
+
+    p.q_l_stride = (int)q.stride(0);
+    p.q_h_stride = (int)q.stride(1);
+    p.q_d_stride = (int)q.stride(2);
+
+    p.k_ptr = (const T*)k_cache.data_ptr();
+    p.v_ptr = (const T*)v_cache.data_ptr();
+    p.new_k_ptr = nullptr;
+    p.new_v_ptr = nullptr;
+    p.q_ptr = (const T*)q.data_ptr();
+    p.req_to_token = req_to_token.data_ptr<int>();
+    p.req_pool_indices = req_pool_indices.data_ptr<int>();
+    p.kv_indptr = kv_indptr.data_ptr<int>();
+    p.qo_indptr = qo_indptr.data_ptr<int>();
+    p.q_tile_to_batch = q_tile_to_batch.data_ptr<int>();
+    p.q_tile_to_index = q_tile_to_index.data_ptr<int>();
+    p.num_q_tiles = (int)q_tile_to_batch.size(0);
+    p.max_context_len = (int)req_to_token.size(1);
+
+    p.causal_offset = (int)causal_offset;
+    p.use_mask = (mask.has_value() && mask.value().defined()) ? 1 : 0;
+    if (p.use_mask) {
+        auto m = mask.value();
+        TORCH_CHECK(m.is_cuda() && m.dtype() == torch::kBool, "mask must be bool CUDA");
+        TORCH_CHECK(m.size(0) == p.batch, "mask batch mismatch");
+        if (m.dim() == 2) {
+            TORCH_CHECK(m.size(1) <= p.max_context_len, "mask kv_len mismatch");
+            p.mask_b_stride = (int)m.stride(0);
+            p.mask_h_stride = 0;
+            p.mask_l_stride = 0;
+        } else if (m.dim() == 4) {
+            TORCH_CHECK(m.size(1) == 1 || m.size(1) == p.q_head, "mask head mismatch");
+            TORCH_CHECK(m.size(2) > 0 && m.size(2) <= p.q_len, "mask q_len mismatch");
+            TORCH_CHECK(m.size(3) <= p.max_context_len, "mask kv_len mismatch");
+            p.mask_b_stride = (int)m.stride(0);
+            p.mask_h_stride = (m.size(1) == 1) ? 0 : (int)m.stride(1);
+            p.mask_l_stride = (m.size(2) == 1) ? 0 : (int)m.stride(2);
+        } else {
+            TORCH_CHECK(false, "mask must be 2D or 4D");
+        }
+        p.mask = m.data_ptr<bool>();
+    } else {
+        p.mask = nullptr;
+        p.mask_b_stride = 0;
+        p.mask_h_stride = 0;
+        p.mask_l_stride = 0;
+    }
+    p.scale = (scale > 0.0) ? (float)scale : 1.0f / sqrtf((float)p.head_dim);
+
+    p.o_ptr = nullptr;
+    p.o_part = nullptr;
+    p.ml_part = nullptr;
+}
+
+}  // namespace attention
+}  // namespace astrai