composable_kernel/client_example/29_gemm_add_multiply

Client Example: GEMM with Add and Multiply Fusion

Theory

This client example demonstrates GEMM fused with addition and multiplication operations. This pattern is used in neural networks for bias addition, scaling, gating, and other elementwise transformations after a linear layer.

Mathematical Formulation:

• GEMM: Y = A \times B
• Add: Z = Y + D_0
• Multiply: E = Z \odot D_1
  • D_0, D_1: auxiliary tensors (e.g., bias, scale, gate)

Algorithmic Background:

• The GEMM result is kept in registers, addition and multiplication are fused in the epilogue.
• No intermediate results are written to global memory.
• Used for bias+scale, gating, and other fused epilogue patterns.

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/29_gemm_add_multiply
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j

# Example run
./gemm_add_multiply

Source Code Structure

Directory Layout

client_example/29_gemm_add_multiply/
├── gemm_add_multiply.cpp         # Main client example: GEMM+Add+Multiply
├── CMakeLists.txt                # Build configuration for the example

Key Functions

• main() (in gemm_add_multiply.cpp):
  Sets up input matrices, configures GEMM and epilogue parameters, launches the fused kernel, and verifies the result.
• Fused kernel invocation:
  Uses the Composable Kernel device API to launch the GEMM with fused addition and multiplication.

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Additional Details

• Supports fusion of multiple elementwise operations with GEMM.
• Example parameters can be adjusted in the source for different workloads.

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Related Examples

• 02_gemm_bilinear: Multi-tensor bilinear operations
• 46_gemm_add_multiply: GEMM with add and multiply in the main example directory

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