composable_kernel/client_example/09_quantization

Client Example: Quantization for GEMM and Conv2D

Theory

This client example demonstrates quantized GEMM and 2D convolution operations, including per-layer and per-channel quantization, and fusion with bias and activation functions. Quantization reduces memory and computation by representing values with lower-precision integer types (e.g., int8), enabling efficient inference in deep learning.

Mathematical Formulation:

• Quantized GEMM: C = \text{dequant}(A_q) \times \text{dequant}(B_q)
• Quantized Conv2D: Y = \text{dequant}(X_q) * \text{dequant}(W_q)
• \text{dequant}(x_q) = (x_q - z) \cdot s (scale s, zero-point z)
• Per-layer: one scale/zero-point per tensor
• Per-channel: scale/zero-point per output channel

Algorithmic Background:

• Quantized values are dequantized on-the-fly during computation.
• Accumulation is performed in higher precision for accuracy.
• Supports bias addition and activation fusion (ReLU, Tanh).
• Per-channel quantization improves accuracy for convolutional layers.

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/09_quantization
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j

# Example run (GEMM quantization)
./gemm_quantization

# Example run (Conv2D per-layer quantization)
./conv2d_fwd_perlayer_quantization

# Example run (Conv2D per-channel quantization)
./conv2d_fwd_perchannel_quantization

# Example run (Conv2D + bias + ReLU + per-channel quantization)
./conv2d_fwd_bias_relu_perchannel_quantization

Source Code Structure

Directory Layout

client_example/09_quantization/
├── gemm_quantization.cpp                         # Quantized GEMM
├── conv2d_fwd_perlayer_quantization.cpp          # Conv2D per-layer quantization
├── conv2d_fwd_perchannel_quantization.cpp        # Conv2D per-channel quantization
├── conv2d_fwd_bias_relu_perlayer_quantization.cpp # Conv2D + bias + ReLU + per-layer quantization
├── conv2d_fwd_bias_relu_perchannel_quantization.cpp # Conv2D + bias + ReLU + per-channel quantization
├── conv2d_fwd_bias_tanh_perlayer_quantization.cpp # Conv2D + bias + Tanh + per-layer quantization
├── conv2d_fwd_bias_tanh_perchannel_quantization.cpp # Conv2D + bias + Tanh + per-channel quantization
├── CMakeLists.txt                                # Build configuration for the example

Key Functions

• main() (in each .cpp):
  Sets up input tensors, configures quantization parameters, launches the quantized kernel, and verifies the result.
• Quantization kernel invocation:
  Uses the Composable Kernel device API to launch quantized GEMM or Conv2D with optional bias and activation.

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Additional Details

• Supports int8 quantization, per-layer and per-channel scaling.
• Demonstrates fusion with bias and activation (ReLU, Tanh).
• Example parameters can be adjusted in the source for different workloads.

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Related Examples

• 01_gemm: GEMM for quantized matrix multiplication
• 14_gemm_quantization: GEMM quantization in the main example directory
• 40_conv2d_fwd_quantization: Conv2D quantization in the main example directory

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