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21_Conv2d_Add_Scale_Sigmoid_GroupNorm • atomic_optimized_kernel_base_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 atomic_optimized_kernel_base_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) 0.007000
Model bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
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)

__global__ void atomic_optimized_kernel(
    const float* __restrict__ x,
    float* __restrict__ y,
    const float* __restrict__ bias,
    const float* __restrict__ scale,
    const float* __restrict__ gn_weight,
    const float* __restrict__ gn_bias,
    int N, int C, int H, int W,
    int num_groups,
    bool bias_broadcast,
    bool scale_broadcast,
    float eps) {

    extern __shared__ float shared[];
    float* shared_sum = shared;
    float* shared_sum_sq = &shared[blockDim.x];
    float* shared_bias = &shared[2 * blockDim.x];
    float* shared_scale = &shared[2 * blockDim.x + C/num_groups];

    const int group_idx = blockIdx.x % num_groups;
    const int sample_idx = blockIdx.x / num_groups;
    const int channels_per_group = C / num_groups;
    const int group_size = channels_per_group * H * W;
    const int sample_offset = sample_idx * C * H * W;
    const int group_offset = group_idx * channels_per_group;

    if (threadIdx.x < channels_per_group) {
        int c = group_offset + threadIdx.x;
        shared_bias[threadIdx.x] = bias_broadcast ? bias[0] : bias[c];
        shared_scale[threadIdx.x] = scale_broadcast ? scale[0] : scale[c];
    }
    __syncthreads();

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

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

        const float val = x[idx];
        const float b_val = shared_bias[c_local];
        const float s_val = shared_scale[c_local];
        const float activated = 1.0f / (1.0f + expf(-(val + b_val) * s_val));
        
        y[idx] = activated;
        local_sum += activated;
        local_sum_sq += activated * activated;
    }

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

    for (int stride = blockDim.x/2; stride > 0; stride >>= 1) {
        if (threadIdx.x < stride) {
            shared_sum[threadIdx.x] += shared_sum[threadIdx.x + stride];
            shared_sum_sq[threadIdx.x] += shared_sum_sq[threadIdx.x + stride];
        }
        __syncthreads();
    }

    if (threadIdx.x == 0) {
        const float mean = shared_sum[0] / group_size;
        const float variance = (shared_sum_sq[0] / group_size) - (mean * mean);
        shared_sum[0] = mean;
        shared_sum_sq[0] = rsqrtf(variance + eps);
    }
    __syncthreads();

    const float mean = shared_sum[0];
    const float inv_std = shared_sum_sq[0];

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

        const float val = y[idx];
        const float normalized = (val - mean) * inv_std;
        y[idx] = normalized * gn_weight[c] + gn_bias[c];
    }
}

void atomic_optimized_cuda(
    at::Tensor x,
    at::Tensor bias,
    at::Tensor scale,
    at::Tensor y,
    at::Tensor gn_weight,
    at::Tensor gn_bias,
    int64_t num_groups,
    bool bias_broadcast,
    bool scale_broadcast,
    float eps) {

    const int N = x.size(0);
    const int C = x.size(1);
    const int H = x.size(2);
    const int W = x.size(3);
    
    const int threads = 256;
    const int blocks = N * num_groups;
    const int channels_per_group = C / num_groups;
    
    const size_t shared_mem_size = 
        (2 * threads + 2 * channels_per_group) * sizeof(float);

    atomic_optimized_kernel<<<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));
    }
}

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;

    atomic_optimized_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, "Atomic optimized kernel (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 2.492 inst/cycle 0.000 5
Executed Ipc Elapsed 1.990 inst/cycle 0.001 5
Issue Slots Busy 62.482 % 0.004 5
Issued Ipc Active 2.500 inst/cycle 0.000 5
SM Busy 62.482 % 0.004 5
Memory Throughput 509245993923.580 byte/second 47506615920007421952.000 5
Mem Busy 24.400 % 0.108 5
Max Bandwidth 29.686 % 0.153 5
L1/TEX Hit Rate 61.522 % 0.002 5
L2 Hit Rate 70.236 % 0.001 5
Mem Pipes Busy 19.614 % 0.057 5
Warp Cycles Per Issued Instruction 20.480 cycle 0.011 5
Warp Cycles Per Executed Instruction 20.536 cycle 0.011 5
Avg. Active Threads Per Warp 31.180 0.000 5
Avg. Not Predicated Off Threads Per Warp 28.200 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 20.000 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 80.256 % 0.216 5
Achieved Active Warps Per SM 51.362 warp 0.087 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (44.3%) 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.
INF CPIStall Check the Warp Stall Sampling (All Cycles) table for the top stall locations in your source based on sampling data. The Kernel Profiling Guide (https://docs.nvidia.com/nsight-compute/ProfilingGuide/index.html#metrics-reference) provides more details on each stall reason.
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 (80.5%) 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 163378.85 μs
Device Time 1079075.32 μs
Self CPU Time 31315.33 μs
Self Device Time 1079075.32 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 187362.41 μs
Device Time 1079075.32 μs
Self CPU Time 24001.52 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::conv2d
CPU Time 1182412.26 μs
Device Time 386982.86 μs
Self CPU Time 22844.56 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 1159567.71 μs
Device Time 386982.86 μs
Self CPU Time 28173.98 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 1131393.73 μs
Device Time 386982.86 μs
Self CPU Time 56544.77 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 943073.94 μs
Device Time 274694.85 μs
Self CPU Time 217639.68 μs
Self Device Time 274694.85 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 974372.55 μs
Device Time 27989.80 μs
Self CPU Time 974372.55 μs
Self Device Time 27989.80 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 1079075.32 μs
Self CPU Time 0.00 μs
Self Device Time 1079075.32 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 
45315 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/b10_s3_atomic_optimized_kernel_base/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/b10_s3_atomic_optimized_kernel_base/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/b10_s3_atomic_optimized_kernel_base/base/base.cu:14:5: warning: 3 adjacent parameters of 'atomic_optimized_kernel' of similar type ('const float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
14 | const float* __restrict__ bias,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
15 | const float* __restrict__ scale,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
16 | const float* __restrict__ gn_weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:14:31: note: the first parameter in the range is 'bias'
14 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:16:31: note: the last parameter in the range is 'gn_weight'
16 | const float* __restrict__ gn_weight,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:18:5: warning: 2 adjacent parameters of 'atomic_optimized_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
18 | 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/b10_s3_atomic_optimized_kernel_base/base/base.cu:18:9: note: the first parameter in the range is 'N'
18 | 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/b10_s3_atomic_optimized_kernel_base/base/base.cu:18:16: note: the last parameter in the range is 'C'
18 | 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/b10_s3_atomic_optimized_kernel_base/base/base.cu:18:26: warning: 2 adjacent parameters of 'atomic_optimized_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
18 | int N, int C, int H, int W,
| ^~~~~~
19 | int num_groups,
| ~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:18:30: note: the first parameter in the range is 'W'
18 | 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/b10_s3_atomic_optimized_kernel_base/base/base.cu:19:9: note: the last parameter in the range is 'num_groups'
19 | int num_groups,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:27:27: warning: result of multiplication in type 'unsigned int' is used as a pointer offset after an implicit widening conversion to type 'size_t' [bugprone-implicit-widening-of-multiplication-result]
27 | float* shared_bias = &shared[2 * blockDim.x];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:27:34: note: make conversion explicit to silence this warning
6 | float* shared_bias = &shared[2 * blockDim.x];
| ^~~~~~~~~~~~~~
| static_cast<size_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:27:34: note: perform multiplication in a wider type
27 | float* shared_bias = &shared[2 * blockDim.x];
| ^
| static_cast<size_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:30:27: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
30 | const int group_idx = blockIdx.x % num_groups;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:31:28: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
31 | const int sample_idx = blockIdx.x / num_groups;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:38:17: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
38 | int c = group_offset + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:47:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
47 | 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/b10_s3_atomic_optimized_kernel_base/base/base.cu:47:52: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
47 | 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/b10_s3_atomic_optimized_kernel_base/base/base.cu:67:23: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
67 | for (int stride = blockDim.x/2; stride > 0; stride >>= 1) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:76:44: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
76 | const float mean = shared_sum[0] / group_size;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:77:52: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
77 | const float variance = (shared_sum_sq[0] / group_size) - (mean * mean);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:86:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
86 | 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/b10_s3_atomic_optimized_kernel_base/base/base.cu:86:52: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
86 | 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/b10_s3_atomic_optimized_kernel_base/base/base.cu:99:16: 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]
99 | at::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:100:16: 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]
100 | at::Tensor bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:101:16: 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]
101 | at::Tensor scale,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:102:16: 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]
102 | at::Tensor y,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:103:16: 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]
103 | at::Tensor gn_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:104:16: 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]
104 | at::Tensor gn_bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:110:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
110 | const int N = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:111:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
111 | const int C = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:112:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
112 | const int H = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:113:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
113 | const int W = x.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:116:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
116 | const int blocks = N * num_groups;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:117:36: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
117 | const int channels_per_group = C / num_groups;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:130:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
130 | num_groups,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:143:16: 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]
143 | at::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:145:16: 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]
145 | at::Tensor bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:146:16: 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]
146 | at::Tensor scale,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:147:16: 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]
147 | at::Tensor gn_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b10_s3_atomic_optimized_kernel_base/base/base.cu:148:16: 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]
148 | at::Tensor gn_bias,
| ^
| const &