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* Wrap ck host utitlies in CK namespace.
The CK and CK-Tile source code bases are incompatible because CK is not properly using namespaces everywhere. In particular, we need to put hip_check_error in the ck namespace.
Move all functions in include/ck_/host_utility that were in global namespace into the ck namespace.
There may be additional namespace problems like this, and it's possible we'll have namespace clashes. But it is good design to properly guard our to code bases (CK and CKTile) so that they can both coexist. Moreover, estabilishing this compatiblity is essential if we are going to allow the builder to instantiate kernels from either template library.
* Add using declarations to test code.
After moving some of the untils into the ck namespace, most examples and a few tests had to be updated to recognize the new namespace declarations. We add using declarations to individual compute units for functions that were previously in the global namespace.
* Add using declarations to client examples.
[ROCm/composable_kernel commit: ad57f6ef0b]
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.