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Back to supported operations

Composable Kernel Grouped Convolution

Grouped Convolution Backward Weight

Grouped convolution operation for 1D, 2D or 3D spatial dimensions. Convolution utilizes GEMM kernel after tensor coordinate transform. Backward weight version uses splitK feature (due to large GEMM K dimension). In CK Grouped Convolution Backward Weight operation is called as DeviceGroupedConvBwdWeight and requires following types as template parameters:

  • NumDimSpatial - number of spatial dimensions (1D, 2D, 3D).
  • InLayout - input layout (NHWGC, GNHWC, NGCHW).
  • WeiLayout - weight layout (GKYXC).
  • OutLayout - output layout (NHWGK, GNHWK, NGKHW).
  • InDataType - input data type.
  • WeiDataType - weight data type.
  • OutDataType - output data type.
  • InElementwiseOperation - fused operation on tensor input.
  • WeiElementwiseOperation - fused operation on tensor weight.
  • OutElementwiseOperation - fused operation on tensor output.
  • ComputeTypeA - compute data type of tensor A for mfma instruction (ADataType by default).
  • ComputeTypeB - compute data type of tensor B for mfma instruction (ComputeTypeA by default).

For fused operations with additional tensor there is DeviceGroupedConvBwdWeightMultipleD operation which requires following parameters:

  • DsLayout - layouts for additional tensors for fused operations.
  • DsDataType - data types for additional tensors for fused operations.

Grouped convolution backward weight doesn't supports tensors larger than 2GB.

List of the device operations for grouped convolution backward weight in CK:

  • DeviceGroupedConvBwdWeight_Xdl_CShuffle - Device operation with XDL instructions.
  • DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle - Device operation with XDL instructions. Optimized for small C or K.
  • DeviceGroupedConvBwdWeight_Wmma_CShuffle - Device operation with WMMA instructions.
  • DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle - Device operation with XDL instructions and support of fused operations to output.
  • DeviceGroupedConvBwdWeight_Dl - Device operation with DL instructions.

Table of supported cases by instance factory with XDL instruction:

NHWGC/GKYXC/NHWGK NGCHW/GKYXC/NGKHW NGCHW/GKCYX/NGKHW GNHWC/GKYXC/GNHWK
bf16 2D, 3D 2D, 3D 2D, 3D ✗
bf16(fp32 for weight) 2D, 3D ✗ ✗ 1D, 2D, 3D
fp16 2D, 3D 2D, 3D 2D, 3D 1D, 2D, 3D
fp32 2D, 3D 2D, 3D 2D, 3D 1D, 2D, 3D

Table of supported cases by instance factory with WMMA instruction:

NHWGC/GKYXC/NHWGK NGCHW/GKYXC/NGKHW GNHWC/GKYXC/GNHWK
fp16 3D ✗ 3D
int8 3D ✗ 3D

Table of supported cases by instance factory with DL instruction:

NHWGC/GKYXC/NHWGK NGCHW/GKYXC/NGKHW GNHWC/GKYXC/GNHWK
bf16(fp32 for weight) 1D, 2D, 3D ✗ 1D, 2D, 3D
fp16 1D, 2D, 3D ✗ 1D, 2D, 3D
fp32 1D, 2D, 3D ✗ 1D, 2D, 3D

Table of supported cases by instance factory with fused elementwise operation:

  • Bilinear - 3D, NHWGC, bf16(fp32 for weight)/fp16/fp32
  • Scale - 3D, NHWGC, bf16(fp32 for weight)/fp16/fp32

Theory

Grouped convolution backward weight computes the gradient of the weights with respect to the loss, given the input and output gradients, for each group independently. This is essential for training CNNs and grouped/expert models.

Mathematical Formulation: For each group $g$: $$ \text{WeightGrad}^g = \text{ConvBwdWeight}(\text{Input}^g, \text{OutputGrad}^g) $$

  • Supports 1D, 2D, and 3D grouped convolutions.
  • Utilizes implicit GEMM for efficient computation.
  • Supports fused elementwise operations (e.g., bilinear, scale).
  • Uses splitK for large GEMM K dimensions.

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/11_grouped_conv_bwd_weight
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j

# Example run (2D grouped convolution backward weight, FP16)
./grouped_conv2d_bwd_weight_fp16

# Example run (3D grouped convolution backward weight, FP32)
./grouped_conv3d_bwd_weight_fp32

Source Code Structure

Directory Layout

client_example/11_grouped_conv_bwd_weight/
├── grouped_conv1d_bwd_weight_fp16.cpp         # 1D grouped convolution backward weight (FP16)
├── grouped_conv2d_bwd_weight_fp16.cpp         # 2D grouped convolution backward weight (FP16)
├── grouped_conv3d_bwd_weight_fp16.cpp         # 3D grouped convolution backward weight (FP16)
├── grouped_conv3d_bwd_weight_fp32.cpp         # 3D grouped convolution backward weight (FP32)
├── grouped_conv3d_bwd_weight_fp16_comp_bf8_fp8.cpp # 3D grouped convolution backward weight (FP16, BF8/FP8 mixed)
├── common.hpp                                 # Common utilities for grouped convolution
├── CMakeLists.txt                             # Build configuration for the example

Key Functions

  • main() (in each .cpp):
    Sets up input/output tensors, configures grouped convolution parameters, launches the backward weight kernel, and verifies the result.
  • Grouped convolution backward weight kernel invocation:
    Uses the Composable Kernel device API to launch grouped convolution backward weight for different dimensions and data types.

This client example provides a comprehensive demonstration of grouped convolution backward weight for efficient CNN and vision transformer training.