mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-14 18:17:44 +00:00
ad57f6ef0b
* 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.
N-Dimensional Convolution Forward
Theory
This example demonstrates the N-dimensional convolution forward pass using Composable Kernel. Convolution is a fundamental operation in deep learning, especially in convolutional neural networks (CNNs) for images, audio, and volumetric data.
Mathematical Formulation: Given:
- Input tensor:
X[N, C_{in}, D_1, D_2, ..., D_n] - Weight tensor:
W[C_{out}, C_{in}, K_1, K_2, ..., K_n] - Output tensor:
Y[N, C_{out}, O_1, O_2, ..., O_n]
The convolution computes:
Y[n, c_{out}, o_1, ..., o_n] = \sum_{c_{in}} \sum_{k_1} ... \sum_{k_n} X[n, c_{in}, o_1 + k_1, ..., o_n + k_n] \cdot W[c_{out}, c_{in}, k_1, ..., k_n]
Stride, padding, and dilation parameters control the mapping between input and output indices.
Algorithmic Background:
- Composable Kernel implements convolution as an implicit GEMM (matrix multiplication) for efficiency.
- The input and weight tensors are transformed into matrices, and the convolution is performed as a GEMM.
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/09_convnd_fwd
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j
Run example_convnd_fwd_xdl
#arg1: verification (0=no, 1=yes)
#arg2: initialization (0=no init, 1=integer value, 2=decimal value)
#arg3: run kernel # of times (>1)
#arg4: N spatial dimensions (default 2)
#Following arguments (depending on number of spatial dims):
# N, K, C,
# <filter spatial dimensions>, (ie Y, X for 2D)
# <input image spatial dimensions>, (ie Hi, Wi for 2D)
# <strides>, (ie Sy, Sx for 2D)
# <dilations>, (ie Dy, Dx for 2D)
# <left padding>, (ie LeftPy, LeftPx for 2D)
# <right padding>, (ie RightPy, RightPx for 2D)
./bin/example_convnd_fwd_xdl 0 1 100
Source Code Structure
Directory Layout
example/09_convnd_fwd/
├── convnd_fwd_xdl.cpp # Main example: sets up, runs, and verifies N-D convolution
include/ck/tensor_operation/gpu/device/
│ └── device_convnd_fwd.hpp # Device-level convolution API
include/ck/tensor_operation/gpu/device/impl/
│ └── device_convnd_fwd_xdl.hpp # XDL-based convolution implementation
include/ck/tensor_operation/gpu/grid/
│ └── gridwise_convnd_fwd_xdl.hpp # Grid-level convolution kernel
include/ck/tensor_operation/gpu/block/
└── blockwise_convnd_fwd_xdl.hpp # Block-level convolution
Key Classes and Functions
- DeviceConvNdFwd (in
device_convnd_fwd.hpp):
Device API for N-dimensional convolution. - gridwise_convnd_fwd_xdl (in
gridwise_convnd_fwd_xdl.hpp):
Implements the tiled/blocking convolution kernel. - blockwise_convnd_fwd_xdl (in
blockwise_convnd_fwd_xdl.hpp):
Handles block-level computation and shared memory tiling.
This example demonstrates how Composable Kernel implements efficient N-dimensional convolution using implicit GEMM, supporting a wide range of deep learning applications.