Pre-release

This release is a pre-release and may not be stable for production use.

MLBricks Kit

MLBricks Kit is a modular neural-network toolkit for building and experimenting with efficient AI systems across PyTorch and optimized native execution paths.

It provides unified auto, native, and pytorch backend dispatch together with reusable components and architectures including ESA, Bolt, BoltModel, VisionBolt, VESA, Bricks, FFNBrick, ResController, SOUP, and ElasticBit. MLBricks also includes native C++/CUDA execution paths, adaptive runtime planning, model lifecycle utilities, and production-oriented inference/training APIs.

Core components

ESA — Entangled State Attention for recurrent/state-based sequence processing.
Bolt / BoltAttention — optimized causal attention component.
BoltModel / BoltConfig — ready-made Bolt language-model interface.
FFNBricks — StateAwareFFN, VirtualStateAwareFFN, and MicroVirtualFFN.
ResController — adaptive residual control.
SOUP — state-oriented sequence processing with layerwise ESA/Bolt mixer selection.
ElasticBit — adaptive 4–32-bit CUDA runtime plus PyTorch-compatible quantization helpers.
VESA — ESA-based vision models.
VisualBolt — Bolt-based vision models through VisionBolt.
Bricks / Brick — heterogeneous model construction from MLBricks components.
Execution planner — automatic backend and execution-route planning.
Native acceleration — optional C++/CUDA kernels where supported.

The PyPI distribution is mlbricks-kit; the public Python import package remains mlbricks.

Version

import mlbricks
print(mlbricks.__version__)
# 1.0.0b2

Installation

Install mlbricks-kit from PyPI; use import mlbricks in Python.

From PyPI:

pip install mlbricks-kit

For development or installation directly from the repository root:

pip install -e .

For a CPU-only native build:

MLBRICKS_FORCE_CPU=1 pip install -e .

Quick start

import torch
from mlbricks import ESA, Bolt, StateAwareFFN, ResController

x = torch.randn(2, 128, 384)

esa = ESA(embd=384, head=6)
bolt = Bolt(d_model=384, num_heads=6, latent_dim=32)
ffn = StateAwareFFN(d_model=384)
residual = ResController(update_ratio=0.18)

MLBricks components use backend="auto" by default. The public backend choices are:

auto — qualify each element independently (for example ESA, Bolt, SAFFN, a vision scan, or ElasticLinear). PyTorch is used as the one-time correctness reference; native must match it before both routes are benchmarked. The fastest valid route is then frozen for that element. Composite models can mix routes.
native — require a supported MLBricks native implementation.
pytorch — force the PyTorch/reference path.

Example:

bolt.set_backend("native")
bolt.set_backend("pytorch")
bolt.set_backend("auto")

Unified model lifecycle

Saving, loading, training, resume, inference, compilation, and quantization are package-level MLBricks APIs. They are not owned by ESA or any other individual architecture.

import mlbricks as mlb

mlb.save(model, "my_model")
model = mlb.load("my_model", device="auto")
info = mlb.inspect("my_model")

The same calls work for ESA models, Bolt models, SOUP, VESA, Bricks, and mixed models built from multiple MLBricks components.

Train with the generic trainer:

trainer = mlb.Trainer(
    model,
    optimizer="adamw",
    lr=3e-4,
    checkpoint_dir="checkpoints",
    save_every=1000,
)

trainer.fit(
    train_loader,
    steps=10_000,
    val_loader=val_loader,
    validate_every=500,
)

Or use the one-call convenience API:

trainer = mlb.train(
    model,
    train_loader,
    steps=10_000,
    optimizer="adamw",
    lr=3e-4,
)

Resume an interrupted run with model weights, optimizer state, training step, scaler state, scheduler state (when supplied), and RNG state:

trainer = mlb.Trainer.resume("checkpoints/last", device="auto")
trainer.fit(train_loader, steps=20_000)

Unified inference and optimization helpers are also available:

y = mlb.predict(model, x)
text_or_tokens = mlb.generate(model, prompt, max_new_tokens=128)
model = mlb.compile(model)
model = mlb.quantize(model, method="elasticbit", bits=4)

ESAModel.save(), ESAModel.load(), and mlbricks.esa.Trainer are no longer public APIs.

ESA

from mlbricks import ESA

esa = ESA(
    embd=384,
    head=6,
)

For the ready-made ESA language-model architecture:

from mlbricks import ESAModel, ESAModelConfig

Bolt

Bolt and BoltAttention are the public attention names in MLBricks Kit 1.0.0b2.

1.0.0b2: native FP16 AMP training now supports FP32 master Parameters in the compound Stage-1 backward path; model equations, checkpoints and cache format are unchanged.

CUDA FP16 Bolt now uses a compound Stage-1 execution path when the native extension is available: one packed Q/U/G GEMM followed by one fused gate/RMS postprocess emits only Q, C, and FP32 rho. Training keeps the same parameters/equations and uses normalized-key PyTorch SDPA; use_sdpa=False remains the explicit reference route.

from mlbricks import Bolt, BoltAttention

bolt = Bolt(
    d_model=384,
    num_heads=6,
    latent_dim=32,
)

You can also import directly from the component package:

from mlbricks.bolt import Bolt, BoltAttention

Bolt language model

from mlbricks import BoltModel

model = BoltModel(
    vocab_size=50_257,
    context=2048,
    layers=12,
    dim=768,
    heads=12,
    latent_dim=32,
    position="rope",
    ffn="saffn",
    residual="rescontroller",
    norm="rmsnorm",
)

FFNBricks

from mlbricks import StateAwareFFN, VirtualStateAwareFFN, MicroVirtualFFN

ffn = StateAwareFFN(
    d_model=384,
    state_dim=128,
    depth_embedding_dim=32,
    layer_index=0,
    total_layers=6,
)

ResController

from mlbricks import ResController

controller = ResController(update_ratio=0.18)

SOUP

from mlbricks import SOUP

model = SOUP(
    dim=512,
    width=[1116, 1116],
    depth=2,
    mixer=["esa", "bolt"],
    ffn=["saffn", "saffn"],
    backend="auto",
)

SOUP keeps backend="auto" element-wise. It does not force one backend for the whole SOUP model: an ESA layer can freeze to native while a Bolt layer freezes to PyTorch, and backend-aware FFN/residual elements make their own one-time decisions. Each native candidate must first match its PyTorch reference output; only parity-qualified routes are benchmarked for speed. Per-layer mixer and FFN choices remain supported.

ElasticBit

from mlbricks import (
    ElasticBit,
    ElasticBitConfig,
    ElasticLinear,
    ElasticEmbedding,
    quantize_tensor,
    dequantize_tensor,
)

ElasticBit now also exposes the standalone 0.2 adaptive 4–32-bit CUDA API through the MLBricks namespace:

from mlbricks import ElasticBit

analysis = ElasticBit.bitsAnaliser(weights, calibration, threshold=0.01)
matrix = ElasticBit.RuntimeMatrix(weights, analysis["selected_bits"], "compact")
y = matrix.forward(x)

The existing ElasticLinear, tensor quantization, and module-conversion helpers remain available as a PyTorch-compatible fallback surface.

VESA

from mlbricks import Vesa

model = Vesa(
    image_size=224,
    patch_size=16,
    dim=384,
    depth=12,
    engine="Serpentine",
    scan="cross",
    position=None,
    backend="auto",
)

Supported vision engine names include:

Serpentine
ViT / VisionTransformer
CNN
Diffusion
AR

VisualBolt

VisualBolt is exposed through the VisionBolt API:

from mlbricks import VisionBolt

model = VisionBolt(
    image_size=224,
    patch_size=16,
    dim=384,
    depth=12,
    heads=6,
    latent_dim=32,
    engine="Serpentine",
    scan="cross",
    position=None,
    backend="auto",
)

VESA and VisualBolt share the same high-level vision-engine choices while keeping their respective ESA and Bolt mixers.

Build a custom model with Bricks

import torch
from mlbricks import (
    Bricks,
    Brick,
    ESA,
    Bolt,
    Attention,
    StateAwareFFN,
    FFN,
    ResController,
)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = Bricks(
    vocab_size=50_257,
    dim=384,
    context=2048,
    position="sinusoidal",
    layers=[
        Brick(
            # In a composed model, let Bricks own placement and move the parent once.
            mixer=ESA(embd=384, head=6, device=None),
            ffn=StateAwareFFN(
                d_model=384,
                state_dim=128,
                depth_embedding_dim=32,
                layer_index=0,
                total_layers=2,
            ),
            residual=ResController(update_ratio=0.18),
            dim=384,
        ),
        Brick(
            mixer=Bolt(384, 6, latent_dim=32),
            ffn=FFN(384, 1536, activation="gelu"),
            dim=384,
        ),
    ],
).to(device)

# Place inputs on the same device as the parent model.
input_ids = torch.randint(0, 50_257, (1, 32), device=device)
logits = model(input_ids)

ESA(device="auto") is convenient when ESA is used by itself. Inside Bricks or another parent nn.Module, prefer ESA(..., device=None) and move the complete parent model with .to(device) so embeddings, residual paths, mixers, FFNs, and inputs remain on one device.

Native vision runtime

VESA and VisualBolt can use the shared MLBricks vision runtime when the compiled backend is available. The public backend contract remains auto | native | pytorch.

Native-supported operations include selected scan/reorder paths, positional operations, normalization/dataflow helpers, recurrent ESA updates, and Bolt sequence paths. Operations already efficiently provided by PyTorch libraries such as GEMM, convolution, and standard tensor operations continue to use the corresponding PyTorch/ATen vendor implementations.

API documentation

See API.md for the public API reference and examples/ for runnable examples.

Component licenses

MLBricks Kit 1.0.0b2 contains component-specific license notices in addition to the repository-level licensing documents:

ESA — mlbricks/esa/LICENSE_ESA.txt
Bolt — mlbricks/bolt/LICENSE_BOLT.txt
ElasticBit — mlbricks/elasticbit/LICENSE_ELASTICBIT.txt
VESA — mlbricks/vesa/LICENSE_VESA.txt
VisualBolt — mlbricks/LICENSE_VISUALBOLT.txt
FFNBricks — mlbricks/ffnbrick/LICENSE_FFNBRICK.txt
ResController — mlbricks/residualbrick/LICENSE_RESIDUALBRICK.txt
SOUP — mlbricks/soup/LICENSE_SOUP.txt

Also see the repository-level LICENSE.md, LICENSING_NOTICE.md, and COMMERCIAL_LICENSE.md.

Licensing

MLBricks Kit 1.0.0b2 is source-available software distributed under the PolyForm Noncommercial License 1.0.0.

The public license permits noncommercial use, including personal use, education, academic study, noncommercial research, experimentation, benchmarking, and hobby projects, subject to the complete PolyForm license terms.

Commercial use is not granted by the public license and requires a separate written commercial license. This includes commercial products, paid software, SaaS/cloud services, enterprise deployment, paid client work, and other commercial or revenue-generating use where applicable.

Commercial licensing inquiries: licensing@mlbricks.io

See LICENSE.md, LICENSING_NOTICE.md, and COMMERCIAL_LICENSE.md.

Release

MLBricks Kit 1.0.0b2 is the beta release line for the package and its current component APIs.

Training compilation

The default remains training_compile_mode="default".

Bolt native build control

To install MLBricks without compiling the optional Bolt native extension, set:

MLBRICKS_BUILD_BOLT_NATIVE=0

Bolt remains available through its PyTorch/fallback implementation; this flag only disables native extension compilation during installation.

SOUP component license

The bundled SOUP component license is shipped at mlbricks/soup/LICENSE_SOUP.txt.

Versioning note

MLBricks Kit is released as 1.0.0b2. Experimental SOUP retains its independent component version 0.1.0a3.

Beta native wheel distribution

MLBricks 1.0.0b2 is distributed with prebuilt native wheels for supported Linux, Windows, and macOS targets plus a py3-none-any fallback wheel. On a matching platform, pip selects the platform-specific native wheel; otherwise it installs the portable PyTorch fallback instead of compiling C++/CUDA locally.

For release builds, GitHub CI compiles CUDA wheels with a fat architecture list covering NVIDIA compute capabilities 7.0, 7.5, 8.0, 8.6, 8.9, 9.0, 10.0, and 12.0+PTX. The beta native ABI is validated against PyTorch 2.10.x; the package therefore requires torch>=2.10,<2.11 until the native bindings migrate to the PyTorch stable ABI.

Source installations default native compilation off. Developers who intentionally want to compile from source can set the relevant MLBRICKS_BUILD_*_NATIVE=1 flags. The corresponding =0 values explicitly disable each extension.

Beta native platform support

For 1.0.0b2, official prebuilt native wheels target Linux x86_64 with CUDA 12.8, Windows x86_64 with CUDA 12.8, and macOS Apple Silicon (arm64) for CPU-native acceleration. Intel macOS is not included because the PyTorch 2.10 binary line used by this beta does not provide the required current x86_64 macOS wheels.

Windows CUDA-native users must use the CUDA 12.8 PyTorch build from the official PyTorch cu128 index. The MLBricks wheel itself remains precompiled, so MLBricks does not compile native code on the user's machine. If CUDA-native prerequisites are unavailable, MLBricks' PyTorch implementation remains the portable fallback.

Optional native build switches

Source installations keep optional native extension builds configurable. Set any of these to 0 before installation to skip that optional native extension:

MLBRICKS_BUILD_CORE_NATIVE=0
MLBRICKS_BUILD_BOLT_NATIVE=0
MLBRICKS_BUILD_VESA_NATIVE=0
MLBRICKS_BUILD_VISION_NATIVE=0
MLBRICKS_BUILD_FFNBRICK_NATIVE=0
MLBRICKS_BUILD_RESIDUALBRICK_NATIVE=0
MLBRICKS_BUILD_ELASTICBIT_NATIVE=0

Official native-wheel CI enables the required native build flags explicitly.

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This release

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1.0.0b2 This release

Sep 5, 2026 14 files

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1.0.0b1

Sep 4, 2026 14 files

mlbricks-kit 1.0.0b2

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