The AI CUDA Engineer 👷

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch as th


def module_fn(
    x: torch.Tensor,
    clusters: torch.Tensor,
    clusters2: torch.Tensor,
    bn_weight: torch.Tensor,
    bn_bias: torch.Tensor,
    bn_mean: torch.Tensor,
    bn_var: torch.Tensor,
    is_training: bool,
    cluster_size: int,
    feature_size: int,
) -> torch.Tensor:
    """
    Functional version of the NetVLAD with ghost clusters

    Args:
        x: Input tensor of shape (batch_size, num_features, feature_size)
        clusters: Weight tensor for cluster assignments
        clusters2: Weight tensor for visual words
        bn_weight: BatchNorm weight
        bn_bias: BatchNorm bias
        bn_mean: BatchNorm running mean
        bn_var: BatchNorm running var
        is_training: Whether in training mode
        cluster_size: Number of clusters (K)
        feature_size: Feature dimension (D)

    Returns:
        Output tensor of shape (batch_size, cluster_size * feature_size)
    """
    max_sample = x.size()[1]
    x = x.view(-1, feature_size)  # B x N x D -> BN x D

    if x.device != clusters.device:
        msg = f"x.device {x.device} != cluster.device {clusters.device}"
        raise ValueError(msg)

    assignment = th.matmul(x, clusters)  # (BN x D) x (D x (K+G)) -> BN x (K+G)
    assignment = F.batch_norm(
        assignment, bn_mean, bn_var, bn_weight, bn_bias, is_training
    )

    assignment = F.softmax(assignment, dim=1)  # BN x (K+G) -> BN x (K+G)
    # remove ghost assigments
    assignment = assignment[:, :cluster_size]
    assignment = assignment.view(-1, max_sample, cluster_size)  # -> B x N x K
    a_sum = th.sum(assignment, dim=1, keepdim=True)  # B x N x K -> B x 1 x K
    a = a_sum * clusters2

    assignment = assignment.transpose(1, 2)  # B x N x K -> B x K x N

    x = x.view(-1, max_sample, feature_size)  # BN x D -> B x N x D
    vlad = th.matmul(assignment, x)  # (B x K x N) x (B x N x D) -> B x K x D
    vlad = vlad.transpose(1, 2)  # -> B x D x K
    vlad = vlad - a

    # L2 intra norm
    vlad = F.normalize(vlad)

    # flattening + L2 norm
    vlad = vlad.reshape(-1, cluster_size * feature_size)  # -> B x DK
    vlad = F.normalize(vlad)
    return vlad  # B x DK


class Model(nn.Module):
    def __init__(self, cluster_size, feature_size, ghost_clusters):
        super(Model, self).__init__()

        self.feature_size = feature_size
        self.cluster_size = cluster_size
        self.ghost_clusters = ghost_clusters

        init_sc = 1 / math.sqrt(feature_size)
        clusters = cluster_size + ghost_clusters

        # The `clusters` weights are the `(w,b)` in the paper
        self.clusters = nn.Parameter(init_sc * th.randn(feature_size, clusters))

        # Extract batchnorm parameters
        bn = nn.BatchNorm1d(clusters)
        self.bn_weight = nn.Parameter(bn.weight.data.clone())
        self.bn_bias = nn.Parameter(bn.bias.data.clone())
        self.bn_mean = nn.Parameter(bn.running_mean.data.clone())
        self.bn_var = nn.Parameter(bn.running_var.data.clone())

        # The `clusters2` weights are the visual words `c_k` in the paper
        self.clusters2 = nn.Parameter(init_sc * th.randn(1, feature_size, cluster_size))
        self.out_dim = self.cluster_size * feature_size

    def forward(self, x, fn=module_fn):
        return fn(
            x,
            self.clusters,
            self.clusters2,
            self.bn_weight,
            self.bn_bias,
            self.bn_mean,
            self.bn_var,
            self.training,
            self.cluster_size,
            self.feature_size,
        )


batch_size = 32
num_features = 100
num_clusters = 32
feature_size = 512
ghost_clusters = 16


def get_inputs():
    return [torch.randn(batch_size, num_features, feature_size)]


def get_init_inputs():
    return [num_clusters, feature_size, ghost_clusters]

#include <torch/extension.h>
#include <ATen/ATen.h>
#include <vector>

torch::Tensor forward(
    torch::Tensor x,
    torch::Tensor clusters,
    torch::Tensor clusters2,
    torch::Tensor bn_weight,
    torch::Tensor bn_bias,
    torch::Tensor bn_mean,
    torch::Tensor bn_var,
    bool is_training,
    int64_t cluster_size,
    int64_t feature_size
) {
    auto B = x.size(0);
    auto N = x.size(1);
    auto D = x.size(2);
    TORCH_CHECK(D == feature_size, "feature_size mismatch.");

    // Flatten x to [B*N, D] - no sync needed
    x = x.reshape({B * N, D});

    // Fuse operations that don't require sync
    auto assignment = at::matmul(x, clusters);
    assignment = at::batch_norm(
        assignment, bn_weight, bn_bias, bn_mean, bn_var,
        is_training, 0.1, 1e-5, true
    );
    
    // Single sync point after softmax
    assignment = at::softmax(assignment, 1);
    assignment = assignment.narrow(1, 0, cluster_size);
    assignment = assignment.reshape({B, N, cluster_size});

    // Fuse sum and multiplication operations
    auto a_sum = assignment.sum(1, true);
    auto a = a_sum * clusters2;

    // Optimize transpose and reshape operations
    assignment = assignment.transpose(1, 2).contiguous();
    x = x.reshape({B, N, D});

    // Fuse matrix multiplication and transpose
    auto vlad = at::bmm(assignment, x).transpose(1, 2);
    vlad = vlad - a;

    // Combine normalization operations
    vlad = vlad / (vlad.norm(2, {1}, true) + 1e-12);
    vlad = vlad.reshape({B, D * cluster_size});
    vlad = vlad / (vlad.norm(2, {1}, true) + 1e-12);

    return vlad;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "NetVLAD with ghost clusters (CUDA)");
}