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* Wrap ck host utitlies in CK namespace.
The CK and CK-Tile source code bases are incompatible because CK is not properly using namespaces everywhere. In particular, we need to put hip_check_error in the ck namespace.
Move all functions in include/ck_/host_utility that were in global namespace into the ck namespace.
There may be additional namespace problems like this, and it's possible we'll have namespace clashes. But it is good design to properly guard our to code bases (CK and CKTile) so that they can both coexist. Moreover, estabilishing this compatiblity is essential if we are going to allow the builder to instantiate kernels from either template library.
* Add using declarations to test code.
After moving some of the untils into the ck namespace, most examples and a few tests had to be updated to recognize the new namespace declarations. We add using declarations to individual compute units for functions that were previously in the global namespace.
* Add using declarations to client examples.
[ROCm/composable_kernel commit: ad57f6ef0b]
GEMM with Add and Multiply Fusion
Theory
This 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_1D_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/example/46_gemm_add_multiply
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j
Run example_gemm_add_multiply_dl_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 11: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD0, StrideD1, StrideE"
./bin/example_gemm_add_multiply_dl_fp16 1 1 1
Source Code Structure
Directory Layout
example/46_gemm_add_multiply/
├── gemm_add_multiply_xdl.cpp # Main example: sets up, runs, and verifies GEMM+Add+Multiply
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_add_multiply_impl.hpp # Add+Multiply implementation
include/ck/tensor_operation/gpu/grid/
│ └── gridwise_gemm_multiple_d_xdl.hpp # Grid-level multi-stage GEMM
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. - gridwise_gemm_multiple_d_xdl (in
gridwise_gemm_multiple_d_xdl.hpp):
Implements the tiled/blocking GEMM kernel with multi-stage epilogue. - element_wise_operation (in
element_wise_operation.hpp):
Defines addition, multiplication, and other elementwise operations.
This example demonstrates how Composable Kernel supports efficient fusion of addition and multiplication with GEMM for deep learning and scientific computing.