TheRock/rocm-libraries/projects/composablekernel/example/27_layernorm2d_fwd/README.md

2D Layer Normalization Forward

Theory

This example demonstrates 2D layer normalization forward pass. Layer normalization is used in transformers and other neural networks to normalize activations across the feature dimension, improving training stability.

Mathematical Formulation: Given input X[N, C, H, W]:

• Mean: \mu = \frac{1}{CHW} \sum_{c,h,w} X_{n,c,h,w}
• Variance: \sigma^2 = \frac{1}{CHW} \sum_{c,h,w} (X_{n,c,h,w} - \mu)^2
• Normalized: \hat{X}_{n,c,h,w} = \frac{X_{n,c,h,w} - \mu}{\sqrt{\sigma^2 + \epsilon}}
• Output: Y_{n,c,h,w} = \gamma \hat{X}_{n,c,h,w} + \beta

\gamma, \beta are learnable scale and shift parameters.

Algorithmic Background:

• Computes mean and variance per sample (across all features).
• Applies normalization and affine transformation.
• Used in transformer blocks and normalization layers.

How to Run

Prerequisites

Please follow the instructions in the main Build Guide section as a prerequisite to building and running this example.

Build and run

cd composable_kernel/example/27_layernorm2d_fwd
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j

# Example run
./layernorm2d_fwd_xdl --verify=1 --time=1

Source Code Structure

Directory Layout

example/27_layernorm2d_fwd/
├── layernorm2d_fwd_xdl.cpp         # Main example: sets up, runs, and verifies 2D layernorm
include/ck/tensor_operation/gpu/device/
│   └── device_layernorm_fwd.hpp       # Device-level layernorm API
include/ck/tensor_operation/gpu/device/impl/
│   └── device_layernorm_fwd_impl.hpp  # Implementation
include/ck/tensor_operation/gpu/grid/
    └── gridwise_layernorm_fwd.hpp     # Grid-level kernel

Key Classes and Functions

• DeviceLayernormFwd (in device_layernorm_fwd.hpp):
  Device API for layer normalization.
• gridwise_layernorm_fwd (in gridwise_layernorm_fwd.hpp):
  Implements the tiled/blocking layernorm kernel.

This example demonstrates how Composable Kernel implements efficient layer normalization for transformer and deep learning models.