TheRock/rocm-libraries/projects/composablekernel/example/03_gemm_bias_relu

GEMM with Bias and ReLU Activation Fusion

Theory

This example demonstrates GEMM fused with bias addition and ReLU activation. This is the core pattern for fully connected (dense) neural network layers and the feed-forward blocks in transformers.

Mathematical Formulation:

E = \text{ReLU}(A \times B + \text{bias})

• A: [M, K] input matrix
• B: [K, N] weight matrix
• \text{bias}: [N] bias vector (broadcasted)
• E: [M, N] output

Algorithmic Background:

• The GEMM result is kept in registers, bias is added, and ReLU is applied before writing to global memory.
• This fusion eliminates intermediate memory traffic and is a standard optimization in deep learning frameworks.

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/03_gemm_bias_relu
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j

# Example run
./gemm_bias_relu_xdl -M 2048 -N 8192 -K 2048 --verify=1 --time=1

Source Code Structure

Directory Layout

example/03_gemm_bias_relu/
├── gemm_bias_relu_xdl.cpp         # Main example: sets up, runs, and verifies GEMM+Bias+ReLU
include/ck/tensor_operation/gpu/device/
│   └── device_gemm_multiple_d.hpp         # Device-level API for multi-tensor GEMM
include/ck/tensor_operation/gpu/device/impl/
│   └── device_gemm_xdl_cshuffle_v3.hpp    # XDL with C-Shuffle epilogue
│   └── device_gemm_bias_relu_impl.hpp     # Specialized bias+ReLU implementation
include/ck/tensor_operation/gpu/grid/
│   └── gridwise_gemm_xdl_cshuffle.hpp     # Grid-level GEMM with epilogue
include/ck/tensor_operation/gpu/element/
    └── element_wise_operation.hpp         # Elementwise operation definitions

Key Classes and Functions

• DeviceGemmMultipleD (in device_gemm_multiple_d.hpp):
  Device API for GEMM with auxiliary tensors and fused epilogues.
• gridwise_gemm_xdl_cshuffle (in gridwise_gemm_xdl_cshuffle.hpp):
  Implements the tiled/blocking GEMM kernel with fused epilogue.
• element_wise_operation (in element_wise_operation.hpp):
  Defines bias addition and ReLU activation.

This example demonstrates the standard epilogue fusion concept that enables efficient neural network layers in modern deep learning.