CuteDSL - Accelerating ML Inference with Fused Triton and CUDA Kernels

Production ML inference is bottlenecked by memory bandwidth, not compute. Every PyTorch operation launches a separate GPU kernel, allocates intermediate tensors, and round-trips through global memory. CuteDSL fixes this by fusing multiple operations into single Triton/CUDA kernels – delivering up to 24x speedups while maintaining output equivalence.

The Problem

A standard transformer forward pass is death by a thousand cuts. RMS norm writes a normalized tensor to global memory, then the next linear layer reads it back. Multiply that by 12 layers, add attention, MLP, preprocessing, and postprocessing – you get dozens of unnecessary memory round-trips per inference call.

Kernel Fusion

The core idea: combine multiple sequential operations into a single GPU kernel. Instead of launching separate kernels for RMS norm, then Q/K/V projection, CuteDSL fuses them so the normalized values never leave registers.

Each kernel eliminates intermediate tensor allocations and reduces global memory traffic.

CuteChronos2: 24x Faster Time Series Forecasting

The first model is Amazon Chronos-2, a state-of-the-art time series forecasting model. CuteChronos2 is a from-scratch reimplementation with custom Triton kernels for every major operation, plus C++/CUDA preprocessing kernels for NaN-aware normalization and patching.

Benchmarks

On an RTX 5090 with Chronos-2 base (768 d_model, 12 layers), batch=1, length=512:

Output equivalence maintained throughout: max absolute error < 1e-4.

The compiled mode uses torch.compile(mode="reduce-overhead") which captures CUDA graphs for near-zero kernel launch overhead.

Usage

Drop-in replacement API matching HuggingFace upstream:

1
2
3
4
5
6
7
8
9

import torch
from cutechronos.model import CuteChronos2Model

model = CuteChronos2Model.from_pretrained("amazon/chronos-bolt-base")
model = model.to("cuda", torch.bfloat16)

context = torch.randn(1, 512, device="cuda")
with torch.inference_mode():
    quantile_preds = model(context)

For maximum performance with torch.compile:

1
2
3
4

model = CuteChronos2Model.from_pretrained_compiled(
    "amazon/chronos-bolt-base",
    compile_mode="reduce-overhead",
)

There’s also a pipeline API that handles variable-length batching:

1
2
3
4
5
6
7
8

from cutechronos.pipeline import CuteChronos2Pipeline

pipe = CuteChronos2Pipeline.from_pretrained(
    "amazon/chronos-bolt-base",
    device="cuda",
    dtype=torch.bfloat16,
)
predictions = pipe.predict(torch.randn(512), prediction_length=30)

CuteZImage: Accelerated Text-to-Image

The second model is Z-Image Turbo, a fast text-to-image diffusion model. CuteZImage reimplements the transformer backbone (30 main layers + 2 refiner layers, dim=3840, 30 heads) with:

• Fused SiLU-gated FFN – eliminates the 10240-wide intermediate allocation
• Fused AdaLN + RMS Norm – timestep conditioning fused with normalization
• Complex-valued RoPE kernel – fused reshape + complex multiply + flatten for multi-axis rotations
• from_diffusers() weight loading – load directly from any HuggingFace Z-Image checkpoint

Design Philosophy

CuteDSL provides pure PyTorch fallbacks for every fused kernel, so models run on CPU without Triton. On GPU, kernels are swapped in transparently. No vendor lock-in – if a kernel doesn’t load, the fallback activates silently.

Autoresearch Bots

Much of this work is driven by automated research bots. They profile models, identify fusion opportunities, generate candidate Triton kernels, and validate output equivalence – all while maintaining eval metrics. The bots search the space of possible kernel fusions and keep what works. CuteDSL’s kernel fusion approach has been particularly effective as a target for this automated optimization pipeline.

Links

• GitHub: github.com/lee101/cutedsl