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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(scales, zero-pointz)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
cd composable_kernel/build
make -j install
Build and Execute
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.template <typename InLayout, typename WeiLayout, typename OutLayout, typename InDataType, typename WeiDataType, typename OutDataType, typename AccDataType, typename QuantizationScheme, typename InElementwiseOperation, typename WeiElementwiseOperation, typename OutElementwiseOperation, typename ConvSpecialization> struct DeviceConv2dFwdQuantization : public BaseOperator - 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.