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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.
GEMM with Add, Add, and FastGELU Activation
Theory
This example demonstrates a GEMM operation fused with two addition operations and FastGELU activation. This pattern is used in transformer feed-forward networks and other neural architectures where a linear transformation is followed by bias addition, residual addition, and a non-linear activation.
Mathematical Formulation:
E = \text{FastGELU}((A \times B) + D_0 + D_1)
A: [M, K] input matrixB: [K, N] weight matrixD_0: [N] bias vector (broadcasted)D_1: [M, N] residual tensorE: [M, N] output
FastGELU is an efficient approximation of GELU:
\text{FastGELU}(x) = x \cdot \sigma(1.702 \cdot x)
where \sigma is the sigmoid function.
Algorithmic Background:
- The GEMM result is kept in registers, bias and residual are added, and FastGELU is applied before writing to global memory.
- No intermediate results are written to global memory.
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/04_gemm_add_add_fastgelu
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j
# Example run
./gemm_add_add_fastgelu_xdl -M 2048 -N 8192 -K 2048 --verify=1 --time=1
Source Code Structure
Directory Layout
example/04_gemm_add_add_fastgelu/
├── gemm_add_add_fastgelu_xdl.cpp # Main example: sets up, runs, and verifies GEMM+Add+Add+FastGELU
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_fastgelu_impl.hpp # FastGELU-specific 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 FastGELU and other elementwise operations.
This example demonstrates how Composable Kernel supports complex multi-stage epilogue fusion for advanced neural network architectures.