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Client Example: Pooling Operations (2D Max, 3D Avg)

Theory

This client example demonstrates pooling operations for 2D max pooling and 3D average pooling, including both forward and backward passes. Pooling is used in convolutional neural networks (CNNs) for spatial downsampling, translation invariance, and reducing computation.

Mathematical Formulation:

  • Max Pooling (2D): Y_{n,c,h,w} = \max_{i,j} X_{n,c,h \cdot s_H + i, w \cdot s_W + j}
  • Average Pooling (3D): Y_{n,c,d,h,w} = \frac{1}{k_D k_H k_W} \sum_{i,j,k} X_{n,c,d \cdot s_D + i, h \cdot s_H + j, w \cdot s_W + k}

Where s_H, s_W, s_D are strides, k_H, k_W, k_D are kernel sizes.

Algorithmic Background:

  • Forward pass computes the pooled output.
  • Backward pass computes the gradient with respect to the input.
  • Handles padding and boundary conditions.

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/19_pool
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j

# Example run (2D max pool forward)
./max_pool2d_fwd

# Example run (2D max pool backward)
./max_pool2d_bwd

# Example run (3D avg pool forward)
./avg_pool3d_fwd

# Example run (3D avg pool backward)
./avg_pool3d_bwd

Source Code Structure

Directory Layout

client_example/19_pool/
├── max_pool2d_fwd.cpp         # 2D max pooling forward
├── max_pool2d_bwd.cpp         # 2D max pooling backward
├── avg_pool3d_fwd.cpp         # 3D average pooling forward
├── avg_pool3d_bwd.cpp         # 3D average pooling backward
├── CMakeLists.txt             # Build configuration for the example

Key Functions

  • main() (in each .cpp):
    Sets up input tensors, configures pooling parameters, launches the forward or backward kernel, and verifies the result.
  • Pooling kernel invocation:
    Uses the Composable Kernel device API to launch pooling operations for different modes.

Additional Details

  • Supports both max and average pooling, forward and backward.
  • Example parameters can be adjusted in the source for different workloads.


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