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MASE-Triton

Software-emulation & acceleration triton kernels for MASE.

Install

Please ensure you are using Python 3.11 or later, and run MASE-Triton on CUDA-enabled GPU.

PyPI

pip install mase-triton

Build from Source

  1. Install uv

  2. Build the package

    uv build

    The wheel file can be found in dist/ folder. You can install it by pip install path/to/wheel/file.whl

Functionality

  • Random Bitflip
    • functional APIs: random bitflip function with backward support.
    • layers.py: subclasses of torch.nn.Module that can be used in neural networks.
      • RandomBitflipDropout
      • RandomBitflipLinear
  • Optical Transformer
    • functional APIs: optical transformer function with backward support.
      • ot_quantize
      • ot_linear
      • ot_matmul
    • layers.py: subclasses of torch.nn.Module that can be used in neural networks.
      • OpticalTransformerLinear
  • MXFP: Simulate MXFP formats on CPU & GPU using PyTorch & Triton.
    • functional
      • extract_mxfp_tensor: Cast a tensor to MXFP format (extracting the shared exponent and Minifloat elements).
      • compose_mxfp_tensor: Cast an MXFP tensor to FP format (composing MXFP components).
      • mxfp_linear: functional linear operation with MXFP support.
      • mxfp_matmul: functional matrix multiplication with MXFP support.
    • layers
      • MXFPLinearPTQ: Linear layer with MXFP support for post-training quantization (no back propagation support).
  • Minifloat: Simulate minifloat formats on CPU & GPU using PyTorch & Triton.
    • functional
      • extract_minifloat_component: Extract minifloat components from a tensor.
      • compose_minifloat_component: Compose minifloat components back to a tensor.
      • quantize_dequantize: Quantize and dequantize tensors using minifloat format.
      • minifloat_linear: functional linear operation with minifloat support.
      • minifloat_matmul: functional matrix multiplication with minifloat support.
    • layers
      • MinifloatLinearPTQ: Linear layer with minifloat support for post-training quantization (no back propagation support).

Dev

  1. Install uv

  2. Install dependencies for development

    uv sync

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Triton kernels for MASE

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