2.66. Matrix Multiplication, Dropout, Mean and Softmax

Calculates a transformation on input data by performing a matrix multiplication with a linear weight transformation, applying dropout for regularization, computing the mean across feature dimensions, and then normalizing the result using the softmax function.

Progress

Step 3 of 3

Reward

7.29x

Optimization Timeline

Step 3 Details

Reasoning

The fused operator produces a constant tensor of ones with shape (batch_size, 1), because the chain of operations (linear, dropout, mean, softmax) mathematically collapses to softmax(〈single-value〉)=1. The current CUDA kernel writes 1.0f per element using a simple index check. In order to further optimize the kernel for cases when the output size might get larger, we can rewrite the kernel using a grid‐stride loop. This ensures that even for larger outputs, the kernel scales well and every thread processes multiple elements if needed. The Python module remains unchanged since our interface is already minimal and efficient. Our improvements genuinely help by providing better scalability without altering the mathematical results.

#include <torch/extension.h>
#include <c10/cuda/CUDAGuard.h>
#include <cuda.h>
#include <cuda_runtime.h>

// Optimized fused chain kernel using a grid-stride loop.
// This kernel writes the constant value 1.0f into the output array.
__global__ void fused_chain_kernel(float* __restrict__ output, int size) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    int stride = gridDim.x * blockDim.x;
    for (; idx < size; idx += stride) {
        output[idx] = 1.0f;
    }
}

torch::Tensor forward(torch::Tensor x) {
    // Use CUDAGuard to ensure we operate on the correct device.
    c10::cuda::CUDAGuard device_guard(x.device());
    
    // Determine batch size from the input (shape: [batch_size, in_features]).
    int batch_size = x.size(0);
    
    // The fused operator returns an output tensor of shape [batch_size, 1].
    auto output = torch::empty({batch_size, 1}, x.options());
    
    int total = batch_size * 1;
    int threads = 256;
    int blocks = (total + threads - 1) / threads;
    
    // Launch the kernel using the current CUDA stream.
    fused_chain_kernel<<<blocks, threads, 0, c10::cuda::getCurrentCUDAStream()>>>(
        output.data_ptr<float>(), total
    );
    
    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Optimized fused chain (Linear, Dropout, Mean, Softmax) that returns ones");
}