← Back to Leaderboard

The AI CUDA Engineer 👷

21_Conv2d_Add_Scale_Sigmoid_GroupNorm • optimized_memory_access_kernel_base

Level 2 • Task 21
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    conv_weight: torch.Tensor,
    conv_bias: torch.Tensor,
    bias: torch.Tensor,
    scale: torch.Tensor,
    group_norm_weight: torch.Tensor,
    group_norm_bias: torch.Tensor,
    num_groups: int,
) -> torch.Tensor:
    """
    Applies convolution, bias addition, scaling, sigmoid activation and group normalization.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_channels, height, width)
        conv_weight (torch.Tensor): Convolution weight tensor
        conv_bias (torch.Tensor): Convolution bias tensor
        bias (torch.Tensor): Bias tensor for addition
        scale (torch.Tensor): Scale tensor for multiplication
        group_norm_weight (torch.Tensor): Group norm weight tensor
        group_norm_bias (torch.Tensor): Group norm bias tensor
        num_groups (int): Number of groups for group normalization

    Returns:
        torch.Tensor: Output tensor after applying convolution, bias, scale, sigmoid and group norm
    """
    x = F.conv2d(x, conv_weight, bias=conv_bias)
    x = x + bias
    x = x * scale
    x = torch.sigmoid(x)
    x = F.group_norm(x, num_groups, group_norm_weight, group_norm_bias)
    return x


class Model(nn.Module):
    """
    Model that performs a convolution, adds a bias term, scales, applies sigmoid, and performs group normalization.
    """

    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        num_groups,
        bias_shape,
        scale_shape,
    ):
        super(Model, self).__init__()
        conv = nn.Conv2d(in_channels, out_channels, kernel_size)
        self.conv_weight = conv.weight
        self.conv_bias = nn.Parameter(
            conv.bias + torch.ones_like(conv.bias) * 0.02
        )  # make sure its nonzero
        self.bias = nn.Parameter(torch.randn(bias_shape) * 0.02)
        self.scale = nn.Parameter(torch.randn(scale_shape) * 0.02)
        group_norm = nn.GroupNorm(num_groups, out_channels)
        self.group_norm_weight = group_norm.weight
        self.group_norm_bias = nn.Parameter(
            group_norm.bias + torch.ones_like(group_norm.bias) * 0.02
        )  # make sure its nonzero

    def forward(self, x, num_groups, fn=module_fn):
        return fn(
            x,
            self.conv_weight,
            self.conv_bias,
            self.bias,
            self.scale,
            self.group_norm_weight,
            self.group_norm_bias,
            num_groups,
        )


batch_size = 128
in_channels = 3
out_channels = 16
height, width = 32, 32
kernel_size = 3
num_groups = 8
bias_shape = (out_channels, 1, 1)
scale_shape = (out_channels, 1, 1)


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


def get_init_inputs():
    return [in_channels, out_channels, kernel_size, num_groups, bias_shape, scale_shape]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Model that performs a convolution, adds a bias term, scales, applies sigmoid, and performs group normalization.
    """
    def __init__(self, in_channels, out_channels, kernel_size, num_groups, bias_shape, scale_shape):
        super(Model, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
        self.conv.bias = nn.Parameter(self.conv.bias + torch.ones_like(self.conv.bias) * 0.02)
        self.bias = nn.Parameter(torch.randn(bias_shape)*0.02) 
        self.scale = nn.Parameter(torch.randn(scale_shape)*0.02)
        self.group_norm = nn.GroupNorm(num_groups, out_channels)
        self.group_norm.bias = nn.Parameter(self.group_norm.bias + torch.ones_like(self.group_norm.bias) * 0.02)

    def forward(self, x):
        x = self.conv(x)
        x = x + self.bias
        x = x * self.scale
        x = torch.sigmoid(x)
        x = self.group_norm(x)
        return x

batch_size = 128
in_channels = 3
out_channels = 16
height, width = 32, 32
kernel_size = 3
num_groups = 8
bias_shape = (out_channels, 1, 1)
scale_shape = (out_channels, 1, 1)

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

def get_init_inputs():
    return [in_channels, out_channels, kernel_size, num_groups, bias_shape, scale_shape]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 21_Conv2d_Add_Scale_Sigmoid_GroupNorm
Level ID 2
Task ID 21
Kernel Name optimized_memory_access_kernel_base
CUDA Speedup (Native) 1.744x
CUDA Speedup (Compile) 1.493x
CUDA Runtime 0.043 ms
PyTorch Runtime (Native) 0.075 ms
PyTorch Runtime (Compile) 0.064 ms
Correct True
Max Diff (vs. Reference) 0.008000
Model azure-gpt-4o-2024-08-06
Temperature 0.00
View Experiment Progress Details

Related Kernels (Level 2, Task 21 • 21_Conv2d_Add_Scale_Sigmoid_GroupNorm)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 shared_memory_coalesced_access_kernel_base 0.04 1.79 1.53
🥈 fused_elem_groupnorm_reduced_sync_base_base 0.04 1.74 1.49
🥈 fused_optimized_warp_base 0.04 1.74 1.49
🥈 optimized_memory_access_kernel_base 0.04 1.74 1.49
🥈 atomic_optimized_kernel_base_base 0.04 1.74 1.49
🥈 optimized_memory_access_kernel_base 0.04 1.74 1.49
🥈 optimized_fused_kernel_base 0.04 1.74 1.49
8 fused_warp_reduce_groupnorm_base 0.04 1.70 1.46
8 fused_warp_groupnorm_base 0.04 1.70 1.46
8 shared_memory_reuse_kernel_base 0.04 1.70 1.46
8 fused_lockfree_groupnorm_base_base 0.04 1.70 1.46
8 fused_stride_kernel_base 0.04 1.70 1.46
8 fused_elem_groupnorm_no_atomic_base 0.04 1.70 1.46
8 fused_elem_groupnorm_min_sync_base_base 0.04 1.70 1.46
8 unrolled_fused_kernel_base_base 0.04 1.70 1.46
16 optimized_modular_kernel_base 0.04 1.67 1.43
16 fused_strided_groupnorm_base_base 0.04 1.67 1.43
18 fused_sigmoid_groupnorm_base 0.05 1.63 1.40
19 fused_sigmoid_groupnorm_base 0.05 1.60 1.37
19 block_size_optimized_kernel_base_base 0.05 1.60 1.37 
#include <torch/extension.h>
#include <ATen/ATen.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <math.h>

#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)

// Optimized kernel with improved memory access patterns
__global__ void optimized_memory_access_kernel(
    const float* __restrict__ x,    // input tensor (result of conv2d), shape [N, C, H, W]
    float* __restrict__ y,          // output tensor, same shape
    const float* __restrict__ bias, // bias for elementwise op (either size 1 or C)
    const float* __restrict__ scale,// scale for elementwise op (either size 1 or C)
    const float* __restrict__ gn_weight, // group norm weight, shape [C]
    const float* __restrict__ gn_bias,   // group norm bias, shape [C]
    int N, int C, int H, int W,
    int num_groups,
    bool bias_broadcast,
    bool scale_broadcast,
    float eps) {

    // Determine the current sample and group from the block index
    int group_idx = blockIdx.x % num_groups;
    int sample_idx = blockIdx.x / num_groups;
    int channels_per_group = C / num_groups;
    int group_size = channels_per_group * H * W;  // total elements in this group

    // Compute offsets
    int sample_offset = sample_idx * C * H * W;
    int group_channel_offset = group_idx * channels_per_group;

    // Shared memory for reduction
    extern __shared__ float shared_mem[];
    float* shared_sum = shared_mem;
    float* shared_sum_sq = shared_mem + blockDim.x;

    float local_sum = 0.0f;
    float local_sum_sq = 0.0f;

    for (int i = threadIdx.x; i < group_size; i += blockDim.x) {
        int c_local = i / (H * W);
        int hw = i % (H * W);
        int c = group_channel_offset + c_local;
        int idx = sample_offset + c * (H * W) + hw;

        float in_val = __ldg(&x[idx]);
        float b_val = bias_broadcast ? __ldg(&bias[0]) : __ldg(&bias[c]);
        float s_val = scale_broadcast ? __ldg(&scale[0]) : __ldg(&scale[c]);
        float pre_act = (in_val + b_val) * s_val;
        float v = 1.0f / (1.0f + expf(-pre_act));  // sigmoid activation

        y[idx] = v;
        local_sum += v;
        local_sum_sq += v * v;
    }

    int tid = threadIdx.x;
    shared_sum[tid] = local_sum;
    shared_sum_sq[tid] = local_sum_sq;
    __syncthreads();

    for (int stride = blockDim.x / 2; stride > 0; stride /= 2) {
        if (tid < stride) {
            shared_sum[tid] += shared_sum[tid + stride];
            shared_sum_sq[tid] += shared_sum_sq[tid + stride];
        }
        __syncthreads();
    }

    float group_mean = 0.0f;
    float group_var = 0.0f;
    if (tid == 0) {
        group_mean = shared_sum[0] / group_size;
        group_var = shared_sum_sq[0] / group_size - group_mean * group_mean;
        shared_sum[0] = group_mean;
        shared_sum_sq[0] = group_var;
    }
    __syncthreads();

    group_mean = shared_sum[0];
    group_var = shared_sum_sq[0];
    float inv_std = 1.0f / sqrtf(group_var + eps);

    for (int i = threadIdx.x; i < group_size; i += blockDim.x) {
        int c_local = i / (H * W);
        int hw = i % (H * W);
        int c = group_channel_offset + c_local;
        int idx = sample_offset + c * (H * W) + hw;

        float v = y[idx];
        float normalized = (v - group_mean) * inv_std;
        float gamma = __ldg(&gn_weight[c]);
        float beta = __ldg(&gn_bias[c]);
        y[idx] = gamma * normalized + beta;
    }
}

// Launcher for the optimized kernel
void optimized_memory_access_cuda(
    at::Tensor x,          // Input from conv2d
    at::Tensor bias,       // Bias for elementwise op
    at::Tensor scale,      // Scale for elementwise op
    at::Tensor y,          // Output tensor
    at::Tensor gn_weight,  // Group normalization weight
    at::Tensor gn_bias,    // Group normalization bias
    int64_t num_groups,
    bool bias_broadcast,
    bool scale_broadcast,
    float eps) {

    int N = x.size(0);
    int C = x.size(1);
    int H = x.size(2);
    int W = x.size(3);

    int total_blocks = N * num_groups;
    int threads = 256;
    size_t shared_mem_size = 2 * threads * sizeof(float);

    optimized_memory_access_kernel<<<total_blocks, threads, shared_mem_size>>>(
        x.data_ptr<float>(),
        y.data_ptr<float>(),
        bias.data_ptr<float>(),
        scale.data_ptr<float>(),
        gn_weight.data_ptr<float>(),
        gn_bias.data_ptr<float>(),
        N, C, H, W,
        num_groups,
        bias_broadcast,
        scale_broadcast,
        eps);

    cudaError_t err = cudaGetLastError();
    if (err != cudaSuccess) {
        TORCH_CHECK(false, "CUDA kernel failed : ", cudaGetErrorString(err));
    }
}

// Forward function
at::Tensor module_fn_forward(
    at::Tensor x,
    at::Tensor conv_weight,
    at::Tensor conv_bias,
    at::Tensor bias,
    at::Tensor scale,
    at::Tensor gn_weight,
    at::Tensor gn_bias,
    int64_t num_groups) {

    CHECK_INPUT(x);
    CHECK_INPUT(conv_weight);
    if (conv_bias.defined()) CHECK_INPUT(conv_bias);
    CHECK_INPUT(bias);
    CHECK_INPUT(scale);
    CHECK_INPUT(gn_weight);
    CHECK_INPUT(gn_bias);

    x = at::conv2d(x, conv_weight, conv_bias);

    at::Tensor y = at::empty_like(x);

    bool bias_broadcast = (bias.numel() == 1);
    bool scale_broadcast = (scale.numel() == 1);

    float eps = 1e-5;

    optimized_memory_access_cuda(x, bias, scale, y, gn_weight, gn_bias,
                                 num_groups, bias_broadcast, scale_broadcast, eps);

    return y;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &module_fn_forward, "Optimized memory access kernel (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 2.530 inst/cycle 0.000 5
Executed Ipc Elapsed 2.004 inst/cycle 0.001 5
Issue Slots Busy 63.574 % 0.012 5
Issued Ipc Active 2.544 inst/cycle 0.000 5
SM Busy 63.574 % 0.012 5
Memory Throughput 496297386669.708 byte/second 35545246934961332224.000 5
Mem Busy 23.690 % 0.082 5
Max Bandwidth 28.810 % 0.141 5
L1/TEX Hit Rate 65.622 % 0.000 5
L2 Hit Rate 70.320 % 0.002 5
Mem Pipes Busy 19.926 % 0.063 5
Warp Cycles Per Issued Instruction 19.728 cycle 0.001 5
Warp Cycles Per Executed Instruction 19.822 cycle 0.001 5
Avg. Active Threads Per Warp 31.350 0.000 5
Avg. Not Predicated Off Threads Per Warp 27.460 0.000 5
Max Active Clusters 0.000 cluster 0.000 5
Max Cluster Size 8.000 block 0.000 5
Overall GPU Occupancy 0.000 % 0.000 5
Cluster Occupancy 0.000 % 0.000 5
Block Limit SM 32.000 block 0.000 5
Block Limit Registers 8.000 block 0.000 5
Block Limit Shared Mem 21.000 block 0.000 5
Block Limit Warps 8.000 block 0.000 5
Theoretical Active Warps per SM 64.000 warp 0.000 5
Theoretical Occupancy 100.000 % 0.000 5
Achieved Occupancy 78.334 % 0.007 5
Achieved Active Warps Per SM 50.134 warp 0.003 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (48.3%) based on active cycles, taking into account the rates of its different instructions. It executes integer and logic operations. It is well-utilized, but should not be a bottleneck.
WRN Occupancy This kernel's theoretical occupancy is not impacted by any block limit. The difference between calculated theoretical (100.0%) and measured achieved occupancy (78.3%) can be the result of warp scheduling overheads or workload imbalances during the kernel execution. Load imbalances can occur between warps within a block as well as across blocks of the same kernel. See the CUDA Best Practices Guide (https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html#occupancy) for more details on optimizing occupancy.
Operation / Metric Value Unit
aten::fill_
CPU Time 149741.76 μs
Device Time 1107112.77 μs
Self CPU Time 34050.94 μs
Self Device Time 1107112.77 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::zero_
CPU Time 175328.67 μs
Device Time 1107112.77 μs
Self CPU Time 25608.16 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::conv2d
CPU Time 1244470.23 μs
Device Time 392292.37 μs
Self CPU Time 23846.69 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::convolution
CPU Time 1220623.53 μs
Device Time 392292.37 μs
Self CPU Time 29276.36 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::_convolution
CPU Time 1191347.17 μs
Device Time 392292.37 μs
Self CPU Time 59022.52 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::cudnn_convolution
CPU Time 998628.06 μs
Device Time 281302.55 μs
Self CPU Time 228768.22 μs
Self Device Time 281282.91 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 951405.37 μs
Device Time 28762.38 μs
Self CPU Time 951405.37 μs
Self Device Time 28762.38 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 1107112.77 μs
Self CPU Time 0.00 μs
Self Device Time 1107112.77 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 
45314 warnings generated when compiling for host.
Suppressed 45328 warnings (45281 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/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.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/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.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/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:15:5: warning: 4 adjacent parameters of 'optimized_memory_access_kernel' of similar type ('const float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
15 | const float* __restrict__ bias, // bias for elementwise op (either size 1 or C)
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
16 | const float* __restrict__ scale,// scale for elementwise op (either size 1 or C)
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
17 | const float* __restrict__ gn_weight, // group norm weight, shape [C]
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
18 | const float* __restrict__ gn_bias, // group norm bias, shape [C]
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:15:31: note: the first parameter in the range is 'bias'
15 | const float* __restrict__ bias, // bias for elementwise op (either size 1 or C)
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:18:31: note: the last parameter in the range is 'gn_bias'
18 | const float* __restrict__ gn_bias, // group norm bias, shape [C]
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:19:5: warning: 2 adjacent parameters of 'optimized_memory_access_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
19 | int N, int C, int H, int W,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:19:9: note: the first parameter in the range is 'N'
19 | int N, int C, int H, int W,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:19:16: note: the last parameter in the range is 'C'
19 | int N, int C, int H, int W,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:19:26: warning: 2 adjacent parameters of 'optimized_memory_access_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
19 | int N, int C, int H, int W,
| ^~~~~~
20 | int num_groups,
| ~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:19:30: note: the first parameter in the range is 'W'
19 | int N, int C, int H, int W,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:20:9: note: the last parameter in the range is 'num_groups'
20 | int num_groups,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:26:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
26 | int group_idx = blockIdx.x % num_groups;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:27:22: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
27 | int sample_idx = blockIdx.x / num_groups;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:43:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
43 | for (int i = threadIdx.x; i < group_size; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:43:52: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
43 | for (int i = threadIdx.x; i < group_size; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:60:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
60 | int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:65:23: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
65 | for (int stride = blockDim.x / 2; stride > 0; stride /= 2) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:76:38: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
76 | group_mean = shared_sum[0] / group_size;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:77:40: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
77 | group_var = shared_sum_sq[0] / group_size - group_mean * group_mean;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:87:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
87 | for (int i = threadIdx.x; i < group_size; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:87:52: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
87 | for (int i = threadIdx.x; i < group_size; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:103:16: 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]
103 | at::Tensor x, // Input from conv2d
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:104:16: warning: the parameter 'bias' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
104 | at::Tensor bias, // Bias for elementwise op
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:105:16: warning: the parameter 'scale' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
105 | at::Tensor scale, // Scale for elementwise op
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:106:16: warning: the parameter 'y' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
106 | at::Tensor y, // Output tensor
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:107:16: warning: the parameter 'gn_weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
107 | at::Tensor gn_weight, // Group normalization weight
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:108:16: warning: the parameter 'gn_bias' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
108 | at::Tensor gn_bias, // Group normalization bias
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:114:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
114 | int N = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:115:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
115 | int C = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:116:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
116 | int H = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:117:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
117 | int W = x.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:119:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
119 | int total_blocks = N * num_groups;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:121:30: warning: performing an implicit widening conversion to type 'unsigned long' of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
121 | size_t shared_mem_size = 2 * threads * sizeof(float);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:121:30: note: make conversion explicit to silence this warning
6 | size_t shared_mem_size = 2 * threads * sizeof(float);
| ^~~~~~~~~~~
| static_cast<unsigned long>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:121:30: note: perform multiplication in a wider type
121 | size_t shared_mem_size = 2 * threads * sizeof(float);
| ^
| static_cast<long>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:131:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
131 | num_groups,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:145:16: warning: the parameter 'conv_weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
145 | at::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:147:16: warning: the parameter 'bias' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
147 | at::Tensor bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:148:16: warning: the parameter 'scale' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
148 | at::Tensor scale,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:149:16: warning: the parameter 'gn_weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
149 | at::Tensor gn_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b7_s1_optimized_memory_access_kernel/base/base.cu:150:16: warning: the parameter 'gn_bias' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
150 | at::Tensor gn_bias,
| ^
| const &