mirror of https://github.com/ROCm/composable_kernel.git synced 2026-05-18 20:09:25 +00:00

Files

John Shumway ad57f6ef0b [CK_BUILDER] Put global CK functions in an the CK namespace (#3232 )

* 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.

2025-11-19 11:23:02 +01:00

CMakeLists.txt

Split the instances by architecture. (#1223 )

2024-04-02 09:42:17 -07:00

gemm_bias_relu_xdl_fp16.cpp

[CK_BUILDER] Put global CK functions in an the CK namespace (#3232 )

2025-11-19 11:23:02 +01:00

README.md

[DOCS] Documentation Addition (Readme updates) (#2495 )

2025-10-16 03:10:57 -07:00

README.md

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.