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README.md

MiniDXNN Examples

Example applications for GPU-accelerated MLP inference and training with DirectX 12 LinAlg Matrix.

Table of Contents

Example 01: Texture Inference

Loads a pre-trained MLP binary and reconstructs a texture on the GPU.

Workflow: Train & export (Python) → Load binary → GPU inference → Save PPM image

Step-by-Step

1. Train a model (Python)

pip install torch numpy matplotlib
cd scripts/reference
python texture_training.py            # defaults: 4 hidden layers, 64 neurons, leaky_relu
Training options
Option Default Description
--backbone-layers 4 Number of hidden layers
--hidden-dim 64 Neurons per hidden layer
--activation leaky_relu identity, sigmoid, tanh, relu, leaky_relu
--texture-pattern checkerboard gradient, checkerboard, stripes, circle, perlin
--epochs 30 Training iterations
--learning-rate 0.0025 Optimizer learning rate
--optimizer lion sgd, adam, lion
--dtype float float, half
--samples 200000 Number of training samples
--batch-size 2000 Training batch size
--no-display false Skip matplotlib display and exit automatically
--seed 987654321 Random seed
--texture-width 2048 Texture width
--texture-height 2048 Texture height

2. Run GPU inference

cd build/example
Release/01-texture-inference.exe ../../scripts/reference/texture-mlp-data.bin output.ppm
Argument Required Default Description
mlp-binary Yes — Path to MLP binary file
output No mlp-inference-output.ppm Output PPM image
--texture-width No 4096 Image width
--texture-height No 4096 Image height
--cpu No false Use CPU reference ML operations instead of GPU
--cpp-fallback No false Use C++ fallback (mlp.hlsl compiled as C++)
--software-linalg No false Use software linear algebra instead of LinAlg Matrix
--debug No false Verbose output

Example 02: Texture Training

Trains an MLP entirely on the GPU to learn a 2D texture pattern, then reconstructs the texture using the trained model.

Workflow: Init weights → GPU mini-batch training (SGD/Adam/Lion) → Reconstruct texture → Save PPM image

Run

cd build/example
Release/02-texture-training.exe --output-image training-result.ppm
Option Default Description
--backbone-layers 4 Number of hidden layers
--hidden-dim 64 Neurons per hidden layer
--activation leaky_relu identity, sigmoid, tanh, relu, leaky_relu
--bias / --no-bias true Enable/disable bias in MLP layers
--epochs 30 Training epochs
--batch-size 2000 Mini-batch size
--learning-rate 0.0025 Optimizer learning rate
--optimizer lion sgd, adam, lion
--samples 200000 Number of training samples
--texture-width 2048 Texture width
--texture-height 2048 Texture height
--texture-pattern checkerboard gradient, checkerboard, stripes, circle, perlin
--output-image mlp-training-output.ppm Output PPM image
--cpu false Use CPU reference ML operations instead of GPU
--cpp-fallback false Use C++ fallback (mlp.hlsl compiled as C++)
--software-linalg false Use software linear algebra instead of LinAlg Matrix
--debug false Enable debug mode for detailed output
--seed 987654321 Random seed

GPU Kernel Details

The training compute shaders (example/kernel/02_texture_training.comp) demonstrate:

  • Using mininn::TrainingLayerDataRef to bind weight, bias, gradient, and logits cache buffers
  • Calling mininn::forward() followed by mininn::backward() for a full training step
  • GPU-side optimizer kernels (SGD, Adam, Lion) that read gradients and update weights directly on the GPU
  • Shared optimizer implementations in kernel/optimizer.hlsl that work on packed byte buffers

Example 03: Texture Compression with Input Encoding

Trains an MLP on the GPU to map normalized UV coordinates (u, v) to RGB pixel values, optionally using positional or grid input encoding for higher-quality texture compression.

See 03_texture_compression_with_input_encoding/README.md for the full option list, input-encoding modes, and example recipes.

MLP Model Format

Binary format used by Example 01 (produced by the Python training script):

Header (12 bytes):
  int32  numHiddenLayers
  int32  hiddenLayerDim
  int32  activationType    (0=Identity, 1=Sigmoid, 2=Tanh, 3=ReLU, 4=LeakyReLU)

Per layer (numHiddenLayers + 1 layers):
  float32[outputDim × inputDim]   Weight matrix (row-major)
  float32[outputDim]              Bias vector

Layer ordering: Input(2)→Hidden₁→…→Hiddenₙ→Output(2). Hidden layers use the header's activation; the output layer always uses Sigmoid.

For HLSL API details, see MLP HLSL API Reference.

License

MIT License — see LICENSE.

Copyright (c) 2026 Advanced Micro Devices, Inc. All rights reserved.