mirror of https://github.com/ROCm/composable_kernel.git synced 2026-06-30 19:57:40 +00:00

Files

Vidyasagar Ananthan 15d7637f89 GH-2368 Adding a basic glossary

GH-2368 Minor edits

GH-2368 Adding missing READMEs and standardization.

resolving readme updates

GH-2368 Minor improvements to documentation.

Improving some readmes.

Further improvement for readmes.

Cleaned up the documentation in 'client_example' (#2468)

Update for PR

Update ACRONYMS.md to remove trivial terms

Update ACRONYMS.md to provide detailed explanations for BF16 and BF8 formats

Apply suggestion from @spolifroni-amd

Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com>

Apply suggestion from @spolifroni-amd

Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com>

Update README.md to clarify CK Tile API description and remove outdated references to the Tile Engine.

revise 37_transpose readme

revise 36_copy readme

Remove references to the Tile Engine in README files for 19_gemm_multi_d and 35_batched_transpose, and update distribution links for clarity.

Remove references to the Tile Engine in multiple README files and update distribution links for consistency and clarity.

Remove references to the Tile Engine in README files across multiple examples

2025-10-02 10:53:25 -07:00

2.9 KiB

Raw Blame History

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

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.

2.9 KiB Raw Blame History