mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-07-01 04:07:56 +00:00
15d7637f89
GH-2368 Minor edits GH-2368 Adding missing READMEs and standardization. resolving readme updates GH-2368 Minor improvements to documentation. Improving some readmes. Further improvement for readmes. Cleaned up the documentation in 'client_example' (#2468) Update for PR Update ACRONYMS.md to remove trivial terms Update ACRONYMS.md to provide detailed explanations for BF16 and BF8 formats Apply suggestion from @spolifroni-amd Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com> Apply suggestion from @spolifroni-amd Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com> Update README.md to clarify CK Tile API description and remove outdated references to the Tile Engine. revise 37_transpose readme revise 36_copy readme Remove references to the Tile Engine in README files for 19_gemm_multi_d and 35_batched_transpose, and update distribution links for clarity. Remove references to the Tile Engine in multiple README files and update distribution links for consistency and clarity. Remove references to the Tile Engine in README files across multiple examples
Client Example: N-Dimensional Convolution Forward
Theory
This client example demonstrates N-dimensional convolution forward for 3D inputs, supporting multiple data types (FP16, FP32, FP8 composite). Convolution is a fundamental operation in deep learning, especially in convolutional neural networks (CNNs) for images, audio, and volumetric data.
Mathematical Formulation:
Given input X, weights W:
Y = \text{Conv}(X, W)
- Supports 3D convolution (ND can be extended).
- Utilizes implicit GEMM for efficient computation.
Algorithmic Background:
- The forward convolution operation is implemented as a convolution with transformed coordinates.
- Used in inference and training pipelines for 3D CNNs, medical imaging, and volumetric data.
How to Run
Prerequisites
cd composable_kernel/build
make -j install
Build and Execute
cd composable_kernel/client_example/16_convnd_fwd
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j
# Example run (3D forward, FP16)
./conv3d_fwd_fp16
# Example run (3D forward, FP32)
./conv3d_fwd_fp32
# Example run (3D forward, FP16 compute with FP8)
./conv3d_fwd_fp16_comp_fp8
Source Code Structure
Directory Layout
client_example/16_convnd_fwd/
├── conv3d_fwd_fp16.cpp # 3D convolution forward (FP16)
├── conv3d_fwd_fp32.cpp # 3D convolution forward (FP32)
├── conv3d_fwd_fp16_comp_fp8.cpp # 3D convolution forward (FP16 compute, FP8)
├── common.hpp # Common utilities for convolution
├── CMakeLists.txt # Build configuration for the example
Key Functions
- main() (in each
.cpp):
Sets up input/output tensors, configures convolution parameters, launches the forward kernel, and verifies the result. - Forward convolution kernel invocation:
Uses the Composable Kernel device API to launch convolution forward for different data types.
Additional Details
- Supports FP16, FP32, and FP8 composite for 3D convolution.
- Example parameters can be adjusted in the source for different workloads.
Related Examples
- 09_convnd_fwd: N-dimensional convolution in the main example directory
- 30_grouped_conv_fwd_multiple_d: Grouped convolution forward with multiple D