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Client Example: Batch Normalization (Forward, Backward, Inference)
Theory
This client example demonstrates batch normalization in forward, backward, and inference modes for NHWC tensors. Batch normalization is used in deep neural networks to normalize activations across the batch and spatial dimensions, improving training stability and convergence.
Mathematical Formulation:
Given input X[N, H, W, C]:
- Mean:
\mu_c = \frac{1}{NHW} \sum_{n,h,w} X_{n,h,w,c} - Variance:
\sigma^2_c = \frac{1}{NHW} \sum_{n,h,w} (X_{n,h,w,c} - \mu_c)^2 - Normalized:
\hat{X}_{n,h,w,c} = \frac{X_{n,h,w,c} - \mu_c}{\sqrt{\sigma^2_c + \epsilon}} - Output:
Y_{n,h,w,c} = \gamma_c \hat{X}_{n,h,w,c} + \beta_c
\gamma_c, \beta_c are learnable scale and shift parameters per channel.
Algorithmic Background:
- Forward pass computes mean, variance, normalization, and affine transformation.
- Backward pass computes gradients with respect to input, gamma, and beta.
- Inference uses running mean and variance for normalization.
How to Run
Prerequisites
cd composable_kernel/build
make -j install
Build and Execute
cd composable_kernel/client_example/13_batchnorm
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j
# Example run (forward)
./batchnorm_fwd_nhwc
# Example run (backward)
./batchnorm_bwd_nhwc
# Example run (inference)
./batchnorm_infer_nhwc
Source Code Structure
Directory Layout
client_example/13_batchnorm/
├── batchnorm_fwd_nhwc.cpp # Batchnorm forward (NHWC)
├── batchnorm_bwd_nhwc.cpp # Batchnorm backward (NHWC)
├── batchnorm_infer_nhwc.cpp # Batchnorm inference (NHWC)
├── CMakeLists.txt # Build configuration for the example
Key Functions
- main() (in each
.cpp):
Sets up input tensors, configures batchnorm parameters, launches the forward, backward, or inference kernel, and verifies the result. - BatchNorm kernel invocation:
Uses the Composable Kernel device API to launch batch normalization for different modes.
Additional Details
- Supports NHWC layout for image and vision models.
- Example parameters can be adjusted in the source for different workloads.
Related Examples
- 34_batchnorm: Batch normalization in the main example directory