composable_kernel/client_example/21_grouped_gemm_bias

Client Example: Grouped GEMM with Bias

Theory

This client example demonstrates grouped GEMM fused with bias addition. Grouped GEMM performs multiple independent GEMM operations (with potentially different shapes) in a single kernel launch, and bias addition is a standard pattern in neural network layers.

Mathematical Formulation: For G groups, each with its own A_g, B_g, b_g:

• GEMM: Y_g = A_g \times B_g
• Bias: E_g = Y_g + b_g

Algorithmic Background:

• Each group can have different matrix sizes and strides.
• The kernel launches a grid covering all groups, with each block assigned to a group.
• Bias is added in the epilogue for each group.

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/21_grouped_gemm_bias
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j

# Example run (grouped GEMM with bias, FP16)
./grouped_gemm_fixed_nk_bias_fp16

Source Code Structure

Directory Layout

client_example/21_grouped_gemm_bias/
├── grouped_gemm_fixed_nk_bias_fp16.cpp         # Main client example: grouped GEMM + bias (FP16)
├── CMakeLists.txt                              # Build configuration for the example

Key Functions

• main() (in grouped_gemm_fixed_nk_bias_fp16.cpp):
  Sets up input matrices for each group, configures GEMM and bias parameters, launches the grouped kernel, and verifies the result.
• Grouped GEMM kernel invocation:
  Uses the Composable Kernel device API to launch grouped GEMM with bias addition.

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Additional Details

• Supports multiple groups with different matrix shapes.
• Example parameters can be adjusted in the source for different workloads.

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Related Examples

• 15_grouped_gemm: Grouped GEMM in the main example directory
• 11_convnd_fwd_bias: Convolution with bias fusion

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