composable_kernel/example/02_gemm_bilinear

Composable Kernel GEMM Bilinear Example

Introduction

This example demonstrates GEMM (General Matrix Multiplication) fused with bilinear operations on auxiliary tensors using Composable Kernel (CK). Bilinear fusion patterns are widely used in neural networks for gating, attention, and multimodal feature fusion, where the output of a matrix multiplication is combined elementwise with one or more additional tensors.

---

Theory

Mathematical Formulation:

F = \text{BilinearOp}(A \times B, D, E)

• A: [M, K] input matrix
• B: [K, N] weight matrix
• D, E: [M, N] auxiliary tensors (or broadcastable)
• F: [M, N] output

Examples:

• Elementwise: F = (A \times B) \odot D \odot E
• Gated: F = (A \times B) \odot \sigma(D) + E
• Weighted: F = \alpha (A \times B) + \beta (D \odot E)

The GEMM result is kept in registers and combined with auxiliary tensors in the epilogue, avoiding intermediate writes to global memory. This pattern is common in attention, gating, and feature interaction layers.

---

CK GEMM Bilinear API Overview

CK provides a composable API for GEMM with multiple auxiliary tensors via the DeviceGemmMultipleD operation.

Template Parameters

• ALayout - A matrix layout (RowMajor/ColumnMajor)
• BLayout - B matrix layout (RowMajor/ColumnMajor)
• DsLayout - Layouts for auxiliary tensors (tuple)
• ELayout - Output matrix layout (RowMajor/ColumnMajor)
• ADataType - A matrix data type
• BDataType - B matrix data type
• DsDataType - Data types for auxiliary tensors (tuple)
• EDataType - Output matrix data type
• AElementwiseOperation - Fused operation on tensor A before GEMM
• BElementwiseOperation - Fused operation on tensor B before GEMM
• CDEElementwiseOperation - Fused operation on C, D, E after GEMM

Supported Data Types and Layouts

• Supports fp16, int8, and other types depending on the device operation.
• Supports RowMajor and ColumnMajor layouts for all tensors.

Supported Device Operations

• DeviceGemmMultipleD: Standard multi-tensor GEMM
• DeviceGemmMultipleD_Bilinear: GEMM with bilinear fusion in the epilogue

---

How to Run

Build and Execute

cd composable_kernel/example/02_gemm_bilinear
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j

Run example_gemm_bilinear_xdl_fp16

#arg1: verification (0=no, 1=yes)
#arg2: initialization (0=no init, 1=integer value, 2=decimal value)
#arg3: time kernel (0=no, 1=yes)
#arg4 to 10: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD, StrideE
#arg11 to 12: alpha, beta
./bin/example_gemm_bilinear_xdl_fp16 1 1 1 3840 4096 4096 4096 4096 4096 4096 0.5 0.5

---

Source Code Structure

example/02_gemm_bilinear/
├── gemm_bilinear_xdl.cpp         # Main example: sets up, runs, and verifies GEMM with bilinear fusion
├── gemm_bilinear_wmma_fp16.cpp   # WMMA FP16 variant
├── gemm_bilinear_wmma_int8.cpp   # WMMA int8 variant
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_bilinear_impl.hpp    # Bilinear operation implementation
include/ck/tensor_operation/gpu/grid/
│   └── gridwise_gemm_multiple_d.hpp     # Grid-level multi-tensor GEMM kernel
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 multiple auxiliary tensors and fused epilogues.

  template <typename ALayout, typename BLayout, typename DsLayout, typename ELayout,
            typename ADataType, typename BDataType, typename DsDataType,
            typename EDataType, typename AElementwiseOperation,
            typename BElementwiseOperation, typename CDEElementwiseOperation>
  struct DeviceGemmMultipleD : public BaseOperator
  

• gridwise_gemm_multiple_d (in gridwise_gemm_multiple_d.hpp):
  Implements the tiled/blocking GEMM kernel with multi-tensor epilogue.
• element_wise_operation (in element_wise_operation.hpp):
  Defines bilinear and other elementwise operations.

---

This example demonstrates how Composable Kernel supports complex multi-tensor fusion patterns for advanced neural network architectures.