composable_kernel/client_example/23_elementwise_transpose

Client Example: Elementwise Operation with 3D Transpose

Theory

This client example demonstrates elementwise operations fused with 3D tensor transpose. This pattern is used in deep learning for applying activation functions or scaling while simultaneously reordering tensor dimensions (e.g., for layout conversion or attention head reshaping).

Mathematical Formulation:

• Elementwise: Z = f(X) or Z = f(X, Y)
• Transpose: Y_{i_0, i_1, i_2} = Z_{i_{\pi(0)}, i_{\pi(1)}, i_{\pi(2)}}
  • \pi is a permutation of the axes.

Algorithmic Background:

• The elementwise operation and transpose are fused in a single kernel.
• Intermediate results are kept in registers, not written to global memory.
• Used for layout conversion with activation, attention head reshaping, and more.

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/client_example/23_elementwise_transpose
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j

# Example run (elementwise + 3D transpose)
./elementwise_transpose_3d

Source Code Structure

Directory Layout

client_example/23_elementwise_transpose/
├── elementwise_transpose_3d.cpp         # Main client example: elementwise + 3D transpose
├── CMakeLists.txt                       # Build configuration for the example

Key Functions

• main() (in elementwise_transpose_3d.cpp):
  Sets up input tensors, configures elementwise and transpose parameters, launches the fused kernel, and verifies the result.
• Fused kernel invocation:
  Uses the Composable Kernel device API to launch the elementwise+transpose operation.

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Additional Details

• Supports fusion of elementwise operations with 3D transpose.
• Example parameters can be adjusted in the source for different workloads.

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Related Examples

• 44_elementwise_permute: Elementwise operation with permutation in the main example directory

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