TheRock/rocm-libraries/projects/composablekernel/example/40_conv2d_fwd_quantization

2D Convolution Forward with Quantization

Theory

This example demonstrates 2D convolution forward with quantized weights or activations. Quantization is used to reduce memory and computation by representing values with lower-precision integer types (e.g., int8), enabling efficient inference in deep learning.

Mathematical Formulation:

• Quantized convolution: Y = \text{dequant}(X_q) * \text{dequant}(W_q)
• X_q, W_q: quantized input and weight tensors (e.g., int8)
• \text{dequant}(x_q) = (x_q - z) \cdot s (scale s, zero-point z)
• Y: output tensor (often in higher precision, e.g., float32 or float16)

Algorithmic Background:

• Quantized values are dequantized on-the-fly during convolution.
• Accumulation is performed in higher precision for accuracy.
• Supports symmetric and asymmetric quantization.
• Convolution is implemented as implicit GEMM for efficiency.

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/example/40_conv2d_fwd_quantization
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j

# Example run
./conv2d_fwd_quantization_xdl --verify=1 --time=1

Source Code Structure

Directory Layout

example/40_conv2d_fwd_quantization/
├── conv2d_fwd_quantization_xdl.cpp         # Main example: sets up, runs, and verifies quantized conv2d
include/ck/tensor_operation/gpu/device/
│   └── device_conv2d_fwd_quantization.hpp       # Device-level quantized conv2d API
include/ck/tensor_operation/gpu/device/impl/
│   └── device_conv2d_fwd_quantization_impl.hpp  # Implementation
include/ck/tensor_operation/gpu/grid/
│   └── gridwise_conv2d_fwd_quantization.hpp     # Grid-level quantized conv2d kernel
include/ck/tensor_operation/gpu/element/
    └── quantization_operations.hpp              # Quantization/dequantization utilities

Key Classes and Functions

• DeviceConv2dFwdQuantization (in device_conv2d_fwd_quantization.hpp):
  Device API for quantized 2D convolution.
• gridwise_conv2d_fwd_quantization (in gridwise_conv2d_fwd_quantization.hpp):
  Implements the tiled/blocking quantized conv2d kernel.
• quantization_operations (in quantization_operations.hpp):
  Defines quantization and dequantization functions.

This example demonstrates how Composable Kernel supports efficient quantized convolution for deep learning inference.