This client example demonstrates grouped convolution fused with various activation and elementwise operations. Grouped convolution splits the input and weights into groups and applies convolution independently to each group, while fusion with activation and scaling improves efficiency.

Mathematical Formulation: For each group g:

Convolution: Y^g = \text{Conv}(X^g, W^g)
Fused operations: E^g = f(Y^g, D_0^g, D_1^g, ...)
- f can be bilinear, scale, add, relu, etc.

Algorithmic Background:

Grouped convolution is used in efficient CNNs, depthwise separable convolutions, and expert models.
Fused epilogue operations (scale, add, relu, reduce) are performed in registers before writing to memory.
Supports 1D, 2D, and 3D grouped convolutions and a variety of fusion patterns.

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/24_grouped_conv_activation
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j

# Example run (grouped conv + scale)
./grouped_convnd_fwd_scale/grouped_convnd_fwd_scale

# Example run (grouped conv + bilinear)
./grouped_convnd_fwd_bilinear/grouped_convnd_fwd_bilinear

# Example run (grouped conv + scale + relu)
./grouped_convnd_fwd_convscale_relu/grouped_convnd_fwd_convscale_relu

# Example run (grouped conv + scale + add + relu)
./grouped_convnd_fwd_scaleadd_scaleadd_relu/grouped_convnd_fwd_scaleadd_scaleadd_relu

Source Code Structure

Directory Layout

client_example/24_grouped_conv_activation/
├── grouped_convnd_fwd_scale/                  # Grouped conv + scale
├── grouped_convnd_fwd_bilinear/               # Grouped conv + bilinear
├── grouped_convnd_fwd_convscale/              # Grouped conv + scale (convscale)
├── grouped_convnd_fwd_convscale_add/          # Grouped conv + scale + add
├── grouped_convnd_fwd_convscale_reduce/       # Grouped conv + scale + reduce
├── grouped_convnd_fwd_convscale_relu/         # Grouped conv + scale + relu
├── grouped_convnd_fwd_convinvscale/           # Grouped conv + inverse scale
├── grouped_convnd_fwd_scaleadd_ab/            # Grouped conv + scale + add (A/B)
├── grouped_convnd_fwd_scaleadd_scaleadd_relu/ # Grouped conv + scale + add + relu
├── grouped_convnd_bwd_data_bilinear/          # Grouped conv bwd data + bilinear
├── grouped_convnd_bwd_data_scale/             # Grouped conv bwd data + scale
├── grouped_convnd_bwd_weight_bilinear/        # Grouped conv bwd weight + bilinear
├── grouped_convnd_bwd_weight_scale/           # Grouped conv bwd weight + scale
├── CMakeLists.txt                             # Build configuration for the example

Key Functions

main() (in each subdirectory's .cpp):
Sets up input tensors, configures grouped convolution and fusion parameters, launches the kernel, and verifies the result.
Grouped convolution kernel invocation:
Uses the Composable Kernel device API to launch grouped convolution with various fused epilogue operations.

Additional Details

Supports a wide range of fusion patterns (bilinear, scale, add, relu, reduce, etc.).
Example parameters can be adjusted in the source for different workloads.

10_grouped_convnd_bwd_data: Grouped convolution backward data
11_grouped_conv_bwd_weight: Grouped convolution backward weight
30_grouped_conv_fwd_multiple_d: Grouped convolution forward with multiple D

Back to Client Examples

README.md

Client Example: Grouped Convolution with Activation and Fusion

Theory