🧱 Mojo Opset

Overview

Mojo Opset is a domain specialized opset for LLMs and multimodal models that provides operator suites for both inference acceleration and training acceleration. It supports multiple hardware accelerators and diverse operator implementations, while abstracting away the differences and complexity of implementation strategies and hardware backends for users. The goal is to help users quickly build LLM models with Mojo Opset and achieve state-of-the-art performance across different accelerators.

Backend Implementations

Torch native

Mojo Opset provides a baseline implementation built on PyTorch native ops. This implementation serves as the golden reference for different backends and also functions as the fallback backend while other backends are being developed.

🔥🔥🔥 Triton-x (TTX for short)

TTX is a triton implementation for Mojo Opset.

Supported Hardware:

• Ascend NPU 910B/C

TTX now is compatible with torch.compile. You can control the run mode via the MOJO_RUN_MODE environment variable. The supported modes are EAGER and COMPILE; EAGER is enabled by default. The COMPILE mode requires the current Torch version to be >= 2.7.0; otherwise, an error will be raised.

# If you want the current Triton kernel to be registered in torch.library and captured by torch.dynamo
# to enable longer-term optimizations (default mode).
export MOJO_RUN_MODE="COMPILE"

# If you want the current Triton kernel to be invoked directly rather than registered in torch.library
# (this can slightly reduce PyTorch overhead in eager mode).
export MOJO_RUN_MODE="EAGER"

source code: mojo_opset/backends/ttx/kernels

Ixformer

Ixformer is a backend powered by the ixformer library, providing high-performance fused operator implementations for Iluvatar GPUs.

Supported Hardware:

• Iluvatar GPU (MR/BI series)

source code: mojo_opset/backends/ixformer

Backend Selection

You can control the backend you want to use via the MOJO_BACKEND environment variable; the currently supported backends are list as below:

• "ixformer"
• "ttx"
• "torch_npu"
• "torch"

When multiple backends are added, Mojo Opset selects the backend implementation according to its internal priority order (We plan to add a tuner feature later to automatically choose the optimal implementation for the current scenario).

Op List

Mojo Operator List

Op Category	Op Name	torch native	torch_npu	ttx	ixformer
Activation	MojoGelu	✅	✅	✅	TBD
Activation	MojoSilu	✅	✅	✅	TBD
Activation	MojoSwiGlu	✅	✅	✅	TBD
Linear	MojoLinear	✅	TBD	TBD	TBD
Gemm	MojoGroupGemm	✅	✅	✅	TBD
Gemm	MojoQuantGroupLinearReduceSum	✅	✅	TBD	TBD
ComputeComm	MojoGemmAllReduce	✅	TBD	TBD	TBD
ComputeComm	MojoAllGatherGemm	✅	TBD	TBD	TBD
ComputeComm	MojoGemmAll2All	✅	TBD	TBD	TBD
ComputeComm	MojoGemmReduceScatter	✅	TBD	TBD	TBD
Attention	MojoSdpa	✅	TBD	✅	TBD
Attention	MojoPrefillGQA	✅	✅	✅	TBD
Attention	MojoPagedPrefillGQA	✅	✅	✅	TBD
Attention	MojoDecodeGQA	✅	TBD	TBD	TBD
Attention	MojoPagedDecodeGQA	✅	✅	🚧	TBD
Attention	MojoDecodeMLA	✅	TBD	TBD	TBD
Attention	MojoPagedDecodeMLA	✅	TBD	TBD	TBD
Attention	MojoPrefillMLA	✅	TBD	TBD	TBD
Attention	MojoPagedPrefillMLA	✅	TBD	TBD	TBD
Attention	MojoDecodeNSA	✅	TBD	TBD	TBD
Attention	MojoPagedDecodeNSA	✅	TBD	TBD	TBD
Attention	MojoPrefillNSA	✅	TBD	TBD	TBD
Attention	MojoPagedPrefillNSA	✅	TBD	TBD	TBD
Attention	MojoSlidingWindowAttention	TBD	TBD	TBD	TBD
MoE	MojoMoE	✅	TBD	TBD	TBD
MoE	MojoMoEGating	✅	TBD	TBD	TBD
MoE	MojoMoEDispatch	✅	TBD	TBD	TBD
MoE	MojoExperts	✅	TBD	TBD	TBD
MoE	MojoMoECombine	✅	TBD	TBD	TBD
Sampling	MojoTopKSampling	TBD	TBD	TBD	TBD
Sampling	MojoTopPSampling	✅	TBD	✅	TBD
Sampling	MojoRejectSampling	✅	TBD	✅	TBD
Sampling	MojoApplyPenaltiesTempurate	✅	TBD	✅	TBD
Quantize	MojoQuant	✅	TBD	TBD	TBD
Quantize	MojoDequant	✅	TBD	TBD	TBD
Quantize	MojoDynamicQuant	✅	✅	TBD	TBD
Quantize	MojoDequantSwiGLUQuant	✅	✅	TBD	TBD
Quantize	MojoGemmDequant	✅	✅	TBD	TBD
Norm	MojoRMSNorm	✅	✅	✅	✅
Norm	MojoLayerNorm	✅	TBD	✅	✅
Norm	MojoResidualAddRMSNorm	✅	✅	✅	✅
Norm	MojoResidualAddLayerNorm	✅	TBD	✅	✅
Norm	MojoRMSNormQuant	✅	✅	TBD	TBD
Norm	MojoLayerNormQuant	✅	✅	TBD	TBD
Norm	MojoResidualAddRMSNormQuant	✅	✅	TBD	TBD
Norm	MojoResidualAddLayerNormQuant	✅	✅	TBD	TBD
Norm	MojoChannelRMSNorm	✅	TBD	TBD	TBD
PositionEmb	MojoRoPE	✅	✅	✅	TBD
PositionEmb	MojoGridRoPE	✅	TBD	TBD	TBD
KVCache	MojoStorePagedKVCache	✅	TBD	✅	TBD
KVCache	MojoStorePagedMLAKVCache	✅	TBD	TBD	TBD
Embedding	MojoEmbedding	✅	TBD	TBD	TBD
Embedding	MojoParallelEmbedding	✅	TBD	TBD	TBD
Embedding	MojoRelativeEmbedding	✅	TBD	TBD	TBD

Mojo Function List

Op Category	Op Name	torch native	ttx
Attention	MojoSdpaFunc	✅	✅
Attention	MojoDiffusionAttentionFunc	✅	✅
PositionEmb	MojoRotaryEmbFunc	✅	✅
Activation	MojoSiluFunc	✅	✅
Activation	MojoSwiGluFunc	TBD	TBD
Norm	MojoRMSNormFunc	✅	✅
Gemm	MojoGemmAllReduce	TBD	TBD
Loss	MojoLinearCrossEntropyFunc	✅	✅

Usage

Apply mojo op

from mojo_opset import MojoSilu

silu = MojoSilu()

silu(torch.randn(128, 128))

Modeling with Mojo Opset

You can build the model using Mojo Opset in the following ways:

1. Build model from mojo opset

   You can also build your modeling by mojo opset directly, Mojo qwen3 dense modeling is an example.

   And you can try the LLM inference demo by running the following command:

   bash ./examples/run_llm.sh
   
   Prompt: 你好，请介绍一下你自己。
   ----------------------------------------
   ----------------------------------------
   Generated text:  你好！我是一个大型语言模型，名叫通义千问，由通义实验室研发。我能够进行多轮对话，回答各种问题，创作文字，比如写故事、写邮件、写剧本等，还能进行逻辑推理、表达观点，甚至编写和调试程序。我的训练数据来自于互联网上的大量文本，因此我具备广泛的知识和语言理解能力。我可以用多种语言与你交流，包括中文、英文、日文、韩文等。

2. Patch for transformers models.

   For hugging face transformers models, you can use Mojo Opset to build the model by monkey patching the original modeling code.

   # 1. Apply mojo opset to qwen3 model
   mojo_opsetutils.patching.apply_mojo_to_qwen3()
   
   # 2. Instantiate patched model
   model = transformers.AutoModelForCausalLM("path/to/qwen3/model")

   And you can try the example by running the following command:

   python -m examples.qwen3_patch

3. Run a DiT inference demo.

   For Wan2.2-based image or video generation demos, you can run:

   bash ./examples/run_dit.sh

Environment Variables

MOJO_DETERMINISTIC

Controls whether deterministic computation is enabled (only TTX backend supported for now).

Value	Description
0 (default)	Deterministic computation disabled. Best performance.
1	Deterministic computation enabled.

Usage:

export MOJO_DETERMINISTIC=1

MOJO_RUN_MODE

Controls the run mode for mojo kernels (only TTX backend supported for now).

Value	Description
EAGER (default)	Kernels are invoked directly. Reduces overhead in eager mode.
COMPILE	Kernels are registered in torch.library, requires Torch >= 2.7.0.

Usage:

export MOJO_RUN_MODE="COMPILE"

MOJO_BACKEND

Controls the backend implementation to use.

Value	Description
ixformer	Use ixformer implementation (Iluvatar GPU only).
ttx	Use Triton-x implementation.
torch	Use PyTorch native implementation.

Usage:

export MOJO_BACKEND="ttx"

MOJO_OPSET_VERBOSITY

Controls the logging verbosity level. Uses standard Python logging levels.

Value	Description
DEBUG	Show all messages including debug details.
INFO (default)	Show informational messages and above.
WARNING	Show warnings and errors only.
ERROR	Show errors only.
CRITICAL	Show critical errors only.

Usage:

export MOJO_OPSET_VERBOSITY="DEBUG"

🚧 Future Work

• Add more mojo ops.
• Support more backend implementations and support more Hardware accelerators.
  • Ascend NPU's official implementation using Ascend C language.
  • Support Cambircon MLU using triton language.
• Performance optimization.
  • A tuner for various backend implementations, ensure users can always get the best performance.
  • A compilation mechanism for replacement the original torch ops with mojo ops.