composable_kernel/client_example/06_softmax/README.md

Client Example: 4D Softmax

Theory

This client example demonstrates Softmax computation over 4D tensors. Softmax is a key operation in deep learning, especially in attention mechanisms and classification, converting logits into normalized probabilities.

Mathematical Formulation: Given input X and axis a:

\text{softmax}(X)_i = \frac{\exp(X_i)}{\sum_j \exp(X_j)}

Algorithmic Background:

• Softmax is implemented using a numerically stable algorithm:
  1. Subtract the maximum value for numerical stability.
  2. Exponentiate and sum.
  3. Normalize by the sum.
• Efficient parallel Softmax requires careful reduction and memory access patterns.
• This example demonstrates Softmax over a 4D tensor, as used in attention and vision models.

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/06_softmax
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j

# Example run
./softmax4d

Source Code Structure

Directory Layout

client_example/06_softmax/
├── softmax4d.cpp         # Main client example: sets up, runs, and verifies 4D softmax
├── CMakeLists.txt        # Build configuration for the example

Key Functions

• main() (in softmax4d.cpp):
  Sets up input tensors, configures Softmax parameters, launches the Softmax kernel, and verifies the result.
• Softmax kernel invocation:
  Uses the Composable Kernel device API to launch the Softmax operation.

This client example provides a demonstration of efficient, numerically stable Softmax for 4D tensors in deep learning models.