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* Wrap ck host utitlies in CK namespace. The CK and CK-Tile source code bases are incompatible because CK is not properly using namespaces everywhere. In particular, we need to put hip_check_error in the ck namespace. Move all functions in include/ck_/host_utility that were in global namespace into the ck namespace. There may be additional namespace problems like this, and it's possible we'll have namespace clashes. But it is good design to properly guard our to code bases (CK and CKTile) so that they can both coexist. Moreover, estabilishing this compatiblity is essential if we are going to allow the builder to instantiate kernels from either template library. * Add using declarations to test code. After moving some of the untils into the ck namespace, most examples and a few tests had to be updated to recognize the new namespace declarations. We add using declarations to individual compute units for functions that were previously in the global namespace. * Add using declarations to client examples.
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
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.