The AI CUDA Engineer 👷

import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    stride: int,
    padding: int,
    conv_transpose: torch.Tensor,
    conv_transpose_bias: torch.Tensor,
) -> torch.Tensor:
    """
    Applies a 3D transposed convolution operation followed by two max pooling layers and a sum operation.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_channels, depth, height, width)
        stride (int): Stride of the transposed convolution
        padding (int): Padding of the transposed convolution
        conv_transpose (torch.Tensor): Transposed convolution weight tensor
        conv_transpose_bias (torch.Tensor): Bias tensor for transposed convolution

    Returns:
        torch.Tensor: Output tensor after applying transposed convolution, max pooling and sum reduction
    """
    x = F.conv_transpose3d(
        x, conv_transpose, bias=conv_transpose_bias, stride=stride, padding=padding
    )
    x = F.max_pool3d(x, kernel_size=2)
    x = F.max_pool3d(x, kernel_size=3)
    x = torch.sum(x, dim=1, keepdim=True)
    return x


class Model(nn.Module):
    """
    Model that performs a 3D transposed convolution, followed by two max pooling layers and a sum operation.
    """

    def __init__(self, in_channels, out_channels, kernel_size, stride, padding):
        super(Model, self).__init__()
        conv = nn.ConvTranspose3d(in_channels, out_channels, kernel_size)
        self.conv_transpose_parameter = nn.Parameter(conv.weight)
        self.conv_transpose_bias = nn.Parameter(conv.bias)

    def forward(self, x, stride, padding, fn=module_fn):
        return fn(
            x, stride, padding, self.conv_transpose_parameter, self.conv_transpose_bias
        )


batch_size = 16
in_channels = 8
out_channels = 16
depth, height, width = 16, 32, 32
kernel_size = 3
stride = 2
padding = 1


def get_inputs():
    return [torch.randn(batch_size, in_channels, depth, height, width), stride, padding]


def get_init_inputs():
    return [in_channels, out_channels, kernel_size, stride, padding]

#include <torch/extension.h>
#include <ATen/ATen.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cfloat>

// This kernel fuses the two max pooling operations by manually unrolling the loops
// for the 2x2x2 and 3x3x3 pooling windows using #pragma unroll. The fixed iteration
// counts allow the compiler to eliminate loop overhead and enable aggressive optimization.

__global__ void manual_unroll_maxpool_kernel(
    const float* __restrict__ input,
    float* __restrict__ output,
    const int N, const int C,
    const int D1, const int H1, const int W1,  // dimensions after conv_transpose3d
    const int D2, const int H2, const int W2,  // dimensions after 2x2x2 pooling
    const int D3, const int H3, const int W3)  // dimensions after 3x3x3 pooling
{
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    int total = N * C * D3 * H3 * W3;
    if (idx >= total) return;

    // Decode global index into (n, c, d3, h3, w3)
    int w3 = idx % W3;
    int h3 = (idx / W3) % H3;
    int d3 = (idx / (W3 * H3)) % D3;
    int c  = (idx / (W3 * H3 * D3)) % C;
    int n  = idx / (W3 * H3 * D3 * C);

    // Compute the starting indices for the 3x3x3 pooling window over the results of the
    // first 2x2x2 pooling. Each output element here corresponds to a 3x3 region in the pooled tensor.
    int start_d2 = d3 * 3;
    int start_h2 = h3 * 3;
    int start_w2 = w3 * 3;

    float final_max = -FLT_MAX;

    // Unroll the 3x3x3 pooling over the first pooling output
    #pragma unroll
    for (int d2_offset = 0; d2_offset < 3; d2_offset++) {
        int d2 = start_d2 + d2_offset;
        if (d2 >= D2) continue;
        #pragma unroll
        for (int h2_offset = 0; h2_offset < 3; h2_offset++) {
            int h2 = start_h2 + h2_offset;
            if (h2 >= H2) continue;
            #pragma unroll
            for (int w2_offset = 0; w2_offset < 3; w2_offset++) {
                int w2 = start_w2 + w2_offset;
                if (w2 >= W2) continue;

                // For each element in the 3x3x3 window, perform a 2x2x2 maxpool on the original input
                float local_max = -FLT_MAX;
                int start_d1 = d2 * 2;
                int start_h1 = h2 * 2;
                int start_w1 = w2 * 2;

                #pragma unroll
                for (int d1_offset = 0; d1_offset < 2; d1_offset++) {
                    int d1 = start_d1 + d1_offset;
                    if (d1 >= D1) continue;
                    #pragma unroll
                    for (int h1_offset = 0; h1_offset < 2; h1_offset++) {
                        int h1 = start_h1 + h1_offset;
                        if (h1 >= H1) continue;
                        #pragma unroll
                        for (int w1_offset = 0; w1_offset < 2; w1_offset++) {
                            int w1 = start_w1 + w1_offset;
                            if (w1 >= W1) continue;
                            int input_idx = ((n * C + c) * D1 + d1) * H1 * W1 + h1 * W1 + w1;
                            float val = input[input_idx];
                            local_max = fmaxf(local_max, val);
                        }
                    }
                }
                final_max = fmaxf(final_max, local_max);
            }
        }
    }
    output[idx] = final_max;
}


torch::Tensor forward(
    torch::Tensor x,
    int64_t stride,
    int64_t padding,
    torch::Tensor conv_transpose,
    torch::Tensor conv_transpose_bias) {

    x = x.contiguous();
    conv_transpose = conv_transpose.contiguous();
    conv_transpose_bias = conv_transpose_bias.contiguous();

    TORCH_CHECK(x.is_cuda(), "Input x must be a CUDA tensor");
    TORCH_CHECK(conv_transpose.is_cuda(), "conv_transpose must be a CUDA tensor");
    TORCH_CHECK(conv_transpose_bias.is_cuda(), "conv_transpose_bias must be a CUDA tensor");

    // Apply the 3D transposed convolution
    x = at::conv_transpose3d(
        x,
        conv_transpose,
        conv_transpose_bias,
        {stride, stride, stride},
        {padding, padding, padding}
    );

    // Get dimensions after conv_transpose3d
    auto sizes = x.sizes();
    int N = sizes[0];
    int C = sizes[1];
    int D1 = sizes[2];
    int H1 = sizes[3];
    int W1 = sizes[4];

    // Dimensions after first maxpool (2x2x2 pooling)
    int D2 = D1 / 2;
    int H2 = H1 / 2;
    int W2 = W1 / 2;

    // Dimensions after second maxpool (3x3x3 pooling)
    int D3 = D2 / 3;
    int H3 = H2 / 3;
    int W3 = W2 / 3;

    auto output = torch::empty({N, C, D3, H3, W3}, x.options());

    int total = N * C * D3 * H3 * W3;
    int threads = 256;
    int blocks = (total + threads - 1) / threads;

    manual_unroll_maxpool_kernel<<<blocks, threads>>>(
        x.data_ptr<float>(),
        output.data_ptr<float>(),
        N, C,
        D1, H1, W1,
        D2, H2, W2,
        D3, H3, W3
    );

    // Sum over the channel dimension (dim=1) and keep the dimension
    return output.sum(1, /*keepdim=*/true);
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Forward pass with manually unrolled maxpool operations.");
}