Kernel Details - netvlad_fused_modular_edit

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_running_mean: torch.Tensor,
    bn_running_var: torch.Tensor,
    feature_size: int,
    cluster_size: int,
    is_training: bool,
) -> torch.Tensor:
    """
    Functional version of the NetVLAD without 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_running_mean: BatchNorm running mean
        bn_running_var: BatchNorm running var
        feature_size: Size of each feature
        cluster_size: Number of clusters (excluding ghost clusters)
        is_training: Whether in training mode

    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_running_mean,
        bn_running_var,
        bn_weight,
        bn_bias,
        training=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_running_mean = nn.Parameter(bn.running_mean.data.clone())
        self.bn_running_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_running_mean,
            self.bn_running_var,
            self.feature_size,
            self.cluster_size,
            self.training,
        )


batch_size = 32
num_features = 100
num_clusters = 32
feature_size = 512
ghost_clusters = 0


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


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

# Copyright 2018 Antoine Miech All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
Code modified from here
https://github.com/albanie/collaborative-experts/blob/master/model/net_vlad.py
"""


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


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))
        self.batch_norm = nn.BatchNorm1d(clusters)
        # 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, mask=None):
        """Aggregates feature maps into a fixed size representation.  In the following
        notation, B = batch_size, N = num_features, K = num_clusters, D = feature_size.

        Args:
            x (th.Tensor): B x N x D

        Returns:
            (th.Tensor): B x DK
        """
        max_sample = x.size()[1]
        x = x.view(-1, self.feature_size)  # B x N x D -> BN x D

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

        assignment = th.matmul(x, self.clusters)  # (BN x D) x (D x (K+G)) -> BN x (K+G)
        assignment = self.batch_norm(assignment)

        assignment = F.softmax(assignment, dim=1)  # BN x (K+G) -> BN x (K+G)
        # remove ghost assigments
        assignment = assignment[:, :self.cluster_size]
        assignment = assignment.view(-1, max_sample, self.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 * self.clusters2

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

        x = x.view(-1, max_sample, self.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, self.cluster_size * self.feature_size)  # -> B x DK
        vlad = F.normalize(vlad)
        return vlad  # B x DK

batch_size = 32
num_features = 100
num_clusters = 32
feature_size = 512
ghost_clusters = 0

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

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

Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name	47_NetVladNoGhostClusters
Level ID	3
Task ID	47
Kernel Name	netvlad_fused_modular_edit_1
CUDA Speedup (Native)	1.315x
CUDA Speedup (Compile)	0.833x
CUDA Runtime	0.090 ms
PyTorch Runtime (Native)	0.118 ms
PyTorch Runtime (Compile)	0.075 ms
Correct	True
Max Diff (vs. Reference)	0.000000
Model	gpt-4o-mini-2024-07-18
Temperature	0.00

View Experiment Progress Details

Related Kernels (Level 3, Task 47 • 47_NetVladNoGhostClusters)

Rank	Kernel Name	Runtime (ms)	Speedup Native	Speedup Compile
🥇	netvlad_fused_streams_edit_1	0.07	1.79	1.14
🥈	netvlad_fused_assign_edit_1	0.07	1.77	1.12
🥈	netvlad_fused_assign_warp_reduce_edit_1	0.07	1.77	1.12
4	netvlad_fused_assign_base	0.07	1.71	1.09
5	netvlad_stream_overlap_edit_1	0.07	1.69	1.07
6	netvlad_fused_modular_base	0.09	1.39	0.88
7	netvlad_fused_modular_edit_1	0.09	1.31	0.83
8	netvlad_stride_fused_edit_1	0.10	1.23	0.78
9	47_NetVladNoGhostClusters	0.10	1.17	0.74
9	netvlad_stride_fused_base	0.10	1.17	0.74
11	tiled_index_opt_base	0.11	1.11	0.70
11	tiled_unroll_block_optimization_base	0.11	1.11	0.70
13	tiled_matmul_unified_base	0.11	1.10	0.69
13	hybrid_netvlad_matmul_base	0.11	1.10	0.69
13	47_netvlad_noghostclusters_unroll_base_base	0.11	1.10	0.69
16	47_netvlad_noghostclusters_shared_base	0.11	1.09	0.69
16	tiled_reduced_sync_base_base	0.11	1.09	0.69
16	tiled_unroll_min_sync_base	0.11	1.09	0.69
16	tiled_unroll_min_sync_optimized_base	0.11	1.09	0.69
20	optimized_tiled_assignment_base	0.11	1.08	0.68

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

#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x " must be a CUDA tensor")
#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)

constexpr int TILE_SIZE = 128;

// Device function for computing dot product
__device__ float dot_product(const float* x, const float* cluster, int D) {
    float sum = 0.0f;
    int tid = threadIdx.x;

    for (int i = tid; i < D; i += TILE_SIZE) {
        sum += x[i] * cluster[i];
    }

    return sum;
}

// Device function for applying batch normalization
__device__ float batch_norm(float val, float mean, float var, float weight, float bias) {
    return (val - mean) * weight / sqrtf(var + 1e-5f) + bias;
}

// Device function for applying softmax
__device__ float softmax_reduction(float val, const float* row_cache, int KplusG) {
    float max_val = -INFINITY;
    float sum_exp = 0.0f;

    for (int i = 0; i < KplusG; ++i) {
        max_val = fmaxf(max_val, row_cache[i]);
    }

    __syncthreads();

    val = __expf(val - max_val);

    for (int i = 0; i < KplusG; ++i) {
        sum_exp += __expf(row_cache[i] - max_val);
    }

    __syncthreads();

    return val / sum_exp;
}

__global__ void modular_fused_assignment_kernel(
    const float* __restrict__ x,
    const float* __restrict__ clusters,
    const float* bn_weight,
    const float* bn_bias,
    const float* bn_mean,
    const float* bn_var,
    float* output,
    int64_t BxN,
    int64_t D,
    int64_t KplusG,
    bool is_training) {

    int row = blockIdx.x * blockDim.y + threadIdx.y;
    int col = threadIdx.y;

    __shared__ float smem_cache[TILE_SIZE * TILE_SIZE];

    if (row >= BxN) return;

    extern __shared__ float shared[];
    float* row_cache = shared;

    // Compute dot product
    float dot = dot_product(x + row * D, clusters + col * D, D);
    row_cache[col] = dot;

    __syncthreads();

    // Apply BN, if in inference mode
    if (!is_training) {
        row_cache[col] = batch_norm(row_cache[col], bn_mean[col], bn_var[col], bn_weight[col], bn_bias[col]);
    }

    __syncthreads();

    // Apply softmax
    output[row * KplusG + col] = softmax_reduction(row_cache[col], row_cache, KplusG);
}

void call_modular_fused_assignment_kernel(
    torch::Tensor x,
    torch::Tensor clusters,
    torch::Tensor bn_weight,
    torch::Tensor bn_bias,
    torch::Tensor bn_running_mean,
    torch::Tensor bn_running_var,
    torch::Tensor assignment,
    int64_t BxN,
    int64_t D,
    int64_t KplusG,
    bool is_training) {

    dim3 block(TILE_SIZE, TILE_SIZE);
    dim3 grid((BxN + TILE_SIZE - 1) / TILE_SIZE);
    size_t shared_mem = KplusG * sizeof(float);

    modular_fused_assignment_kernel<<<grid, block, shared_mem>>>(
        x.data_ptr<float>(),
        clusters.data_ptr<float>(),
        bn_weight.data_ptr<float>(),
        bn_bias.data_ptr<float>(),
        bn_running_mean.data_ptr<float>(),
        bn_running_var.data_ptr<float>(),
        assignment.data_ptr<float>(),
        BxN,
        D,
        KplusG,
        is_training);

    cudaDeviceSynchronize();
}

// Forward function
// Refactor code to invoke modular kernel call

torch::Tensor forward(
    torch::Tensor x,
    torch::Tensor clusters,
    torch::Tensor clusters2,
    torch::Tensor bn_weight,
    torch::Tensor bn_bias,
    torch::Tensor bn_running_mean,
    torch::Tensor bn_running_var,
    int64_t feature_size,
    int64_t cluster_size,
    bool is_training) {

    CHECK_INPUT(x);
    CHECK_INPUT(clusters);
    CHECK_INPUT(clusters2);
    CHECK_INPUT(bn_weight);
    CHECK_INPUT(bn_bias);
    CHECK_INPUT(bn_running_mean);
    CHECK_INPUT(bn_running_var);

    int64_t B = x.size(0);
    int64_t N = x.size(1);
    int64_t D = feature_size;
    int64_t K = cluster_size;
    int64_t KplusG = clusters.size(1);
    int64_t BxN = B * N;

    x = x.reshape({-1, D});
    auto assignment = torch::empty({BxN, KplusG}, x.options());

    call_modular_fused_assignment_kernel(
        x,
        clusters,
        bn_weight,
        bn_bias,
        bn_running_mean,
        bn_running_var,
        assignment,
        BxN,
        D,
        KplusG,
        is_training);

    assignment = assignment.narrow(1, 0, K).reshape({B, N, K});

    auto a_sum = assignment.sum(1, true);
    clusters2 = clusters2.expand({B, D, K});
    auto a = clusters2 * a_sum;

    assignment = assignment.transpose(1, 2);
    x = x.reshape({B, N, D});
    auto vlad = torch::bmm(assignment, x).transpose(1, 2) - a;

    vlad = torch::nn::functional::normalize(
        vlad, torch::nn::functional::NormalizeFuncOptions().p(2).dim(1));
    vlad = vlad.reshape({B, D * K});
    vlad = torch::nn::functional::normalize(
        vlad, torch::nn::functional::NormalizeFuncOptions().p(2).dim(1));

    return vlad;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "NetVLAD forward");
}

Performance Metrics

Metric	Value	Unit	Variance	Samples

Analysis Rules

Rule	Description

Operation / Metric	Value	Unit
aten::zero_
CPU Time	111272.43	μs
Device Time	1108920.16	μs
Self CPU Time	25278.06	μs
Self Device Time	0.00	μs
CPU Memory Usage	0	B
Device Memory Usage	0	B
Self CPU Memory Usage	0	B
Self Device Memory Usage	0	B
aten::fill_
CPU Time	86016.39	μs
Device Time	1108920.16	μs
Self CPU Time	30389.83	μs
Self Device Time	1108920.16	μs
CPU Memory Usage	0	B
Device Memory Usage	0	B
Self CPU Memory Usage	0	B
Self Device Memory Usage	0	B
aten::reshape
CPU Time	282117.36	μs
Device Time	79679.84	μs
Self CPU Time	63354.23	μs
Self Device Time	0.00	μs
CPU Memory Usage	0	B
Device Memory Usage	0	B
Self CPU Memory Usage	0	B
Self Device Memory Usage	0	B
cudaLaunchKernel
CPU Time	675685.51	μs
Device Time	110301.77	μs
Self CPU Time	675685.51	μs
Self Device Time	110301.77	μs
CPU Memory Usage	0	B
Device Memory Usage	0	B
Self CPU Memory Usage	0	B
Self Device Memory Usage	0	B
cudaDeviceSynchronize
CPU Time	922473.80	μs
Device Time	36.77	μs
Self CPU Time	922473.80	μs
Self Device Time	36.77	μs
CPU Memory Usage	0	B
Device Memory Usage	0	B
Self CPU Memory Usage	0	B
Self Device Memory Usage	0	B
aten::bmm
CPU Time	325179.25	μs
Device Time	145735.34	μs
Self CPU Time	220084.95	μs
Self Device Time	145735.34	μs
CPU Memory Usage	0	B
Device Memory Usage	0	B
Self CPU Memory Usage	0	B
Self Device Memory Usage	0	B
aten::norm
CPU Time	369100.38	μs
Device Time	160833.40	μs
Self CPU Time	113160.57	μs
Self Device Time	0.00	μs
CPU Memory Usage	0	B
Device Memory Usage	0	B
Self CPU Memory Usage	0	B
Self Device Memory Usage	0	B
aten::linalg_vector_norm
CPU Time	255939.82	μs
Device Time	160833.40	μs
Self CPU Time	122552.91	μs
Self Device Time	160833.40	μs
CPU Memory Usage	0	B
Device Memory Usage	0	B
Self CPU Memory Usage	0	B
Self Device Memory Usage	0	B
void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor<int>, at::detail::Array<char, 1> >(int, at::native::FillFunctor<int>, at::detail::Array<char, 1>)
CPU Time	0.00	μs
Device Time	1108920.16	μs
Self CPU Time	0.00	μs
Self Device Time	1108920.16	μs
CPU Memory Usage	0	B
Device Memory Usage	0	B
Self CPU Memory Usage	0	B
Self Device Memory Usage	0	B

Status: Completed

45304 warnings generated when compiling for host.
Suppressed 45330 warnings (45283 in non-user code, 47 NOLINT).
Use -header-filter=.* to display errors from all non-system headers. Use -system-headers to display errors from system headers as well.

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:7:35 bugprone-macro-parentheses

7 | #define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x " must be a CUDA tensor")

| ^

| ()

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:8:41: warning: macro argument should be enclosed in parentheses [bugprone-macro-parentheses]

8 | #define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")

| ^

| ()

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:16:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

16 | int tid = threadIdx.x;

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:60:5: warning: 3 adjacent parameters of 'modular_fused_assignment_kernel' of similar type ('int64_t') are easily swapped by mistake [bugprone-easily-swappable-parameters]

60 | int64_t BxN,

| ^~~~~~~~~~~~

61 | int64_t D,

| ~~~~~~~~~~

62 | int64_t KplusG,

| ~~~~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:60:13: note: the first parameter in the range is 'BxN'

60 | int64_t BxN,

| ^~~

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:62:13: note: the last parameter in the range is 'KplusG'

62 | int64_t KplusG,

| ^~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:65:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

65 | int row = blockIdx.x * blockDim.y + threadIdx.y;

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:66:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

66 | int col = threadIdx.y;

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:76:62: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

76 | float dot = dot_product(x + row * D, clusters + col * D, D);

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:89:79: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

89 | output[row * KplusG + col] = softmax_reduction(row_cache[col], row_cache, KplusG);

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:93:19: warning: the parameter 'x' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]

93 | torch::Tensor x,

| ^

| const &

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:94:19: warning: the parameter 'clusters' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]

94 | torch::Tensor clusters,

| ^

| const &

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:95:19: warning: the parameter 'bn_weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]

95 | torch::Tensor bn_weight,

| ^

| const &

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:96:19: warning: the parameter 'bn_bias' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]

96 | torch::Tensor bn_bias,

| ^

| const &

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:97:19: warning: the parameter 'bn_running_mean' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]

97 | torch::Tensor bn_running_mean,

| ^

| const &

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:98:19: warning: the parameter 'bn_running_var' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]

98 | torch::Tensor bn_running_var,

| ^

| const &

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:99:19: warning: the parameter 'assignment' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]

99 | torch::Tensor assignment,

| ^

| const &

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:130:19: warning: the parameter 'clusters' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]

130 | torch::Tensor clusters,

| ^

| const &

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:132:19: warning: the parameter 'bn_weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]

132 | torch::Tensor bn_weight,

| ^

| const &

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:133:19: warning: the parameter 'bn_bias' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]

133 | torch::Tensor bn_bias,

| ^

| const &

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:134:19: warning: the parameter 'bn_running_mean' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]

134 | torch::Tensor bn_running_mean,

| ^

| const &

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:135:19: warning: the parameter 'bn_running_var' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]

135 | torch::Tensor bn_running_var,

| ^

| const &

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:136:5: warning: 2 adjacent parameters of 'forward' of similar type ('int64_t') are easily swapped by mistake [bugprone-easily-swappable-parameters]

136 | int64_t feature_size,

| ^~~~~~~~~~~~~~~~~~~~~

137 | int64_t cluster_size,

| ~~~~~~~~~~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:136:13: note: the first parameter in the range is 'feature_size'

136 | int64_t feature_size,

| ^~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_47/b4_s0_netvlad_fused_modular/edit_1/edit_1.cu:137:13: note: the last parameter in the range is 'cluster_size'

137 | int64_t cluster_size,

| ^~~~~~~~~~~~

The AI CUDA Engineer 👷

`47_NetVladNoGhostClusters` • `netvlad_fused_modular_edit_1`

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Related Kernels (Level 3, Task 47 • 47_NetVladNoGhostClusters)

The AI CUDA Engineer 👷

47_NetVladNoGhostClusters • netvlad_fused_modular_edit_1

Kernel Information

Related Kernels (Level 3, Task 47 • 47_NetVladNoGhostClusters)

`47_NetVladNoGhostClusters` • `netvlad_fused_modular_edit_1`