TheRock/rocm-libraries/projects/composablekernel/client_example/16_convnd_fwd

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

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/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.

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Additional Details

• Supports FP16, FP32, and FP8 composite for 3D convolution.
• Parameters can be adjusted in the source files for different workloads. The following parameters are configurable:
  • NumDimSpatial: Number of spatial dimensions (default: 3 for 3D convolution)
  • G: Number of groups (default: 1)
  • N: Batch size (default: 64)
  • K: Number of output channels (default: 128)
  • C: Number of input channels (default: 64)
  • Z, Y, X: Filter/kernel dimensions (default: 3x3x3)
  • Di, Hi, Wi: Input dimensions - depth, height, width (default: 28x28x3)
  • Do, Ho, Wo: Output dimensions - depth, height, width (default: 28x28x3)

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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

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