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21_Conv2d_Add_Scale_Sigmoid_GroupNorm • fused_stride_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 fused_stride_kernel_base
CUDA Speedup (Native) 1.704x
CUDA Speedup (Compile) 1.459x
CUDA Runtime 0.044 ms
PyTorch Runtime (Native) 0.075 ms
PyTorch Runtime (Compile) 0.064 ms
Correct True
Max Diff (vs. Reference) 0.008000
Model o3-mini-2025-01-31
Temperature 1.00
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)

// This fused kernel performs elementwise bias addition, scaling, sigmoid activation, and group normalization.
// It uses stride loops to handle workloads larger than the number of available threads, ensuring proper boundary handling.
// Each block processes one group from one sample. The kernel computes a reduction (sum and sum of squares) using shared memory,
// and then applies normalization in a second pass with stride loops for complete coverage of all elements.

__global__ void fused_stride_kernel(
    const float* __restrict__ x,    // Input tensor (output of conv2d), shape [N, C, H, W]
    float* __restrict__ y,          // Output tensor, same shape as input
    const float* __restrict__ bias, // Bias for elementwise operation (size 1 or C)
    const float* __restrict__ scale,// Scale for elementwise operation (size 1 or C)
    const float* __restrict__ gn_weight, // Gamma for group normalization, shape [C]
    const float* __restrict__ gn_bias,   // Beta for group normalization, shape [C]
    int N, int C, int H, int W,
    int num_groups,
    bool bias_broadcast,
    bool scale_broadcast,
    float eps) {

    // Each block processes one group per sample
    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 number of elements in this group

    // Compute offsets into the global tensor
    int sample_offset = sample_idx * C * H * W;
    int group_channel_offset = group_idx * channels_per_group;

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

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

    // First pass: elementwise computation using a stride loop
    // When group_size exceeds blockDim.x, the loop ensures full coverage.
    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;  // Global channel index within this sample
        int idx = sample_offset + c * (H * W) + hw;

        // Load input value and apply broadcast if needed
        float in_val = x[idx];
        float b_val = bias_broadcast ? bias[0] : bias[c];
        float s_val = scale_broadcast ? scale[0] : 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;  // Save intermediate activation
        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();

    // Reduction loop in shared memory to calculate sum and sum of squares
    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();
    }

    // Compute mean and variance for the group
    float group_mean = shared_sum[0] / group_size;
    float group_var = shared_sum_sq[0] / group_size - group_mean * group_mean;
    float inv_std = 1.0f / sqrtf(group_var + eps);

    // Second pass: normalize and apply group normalization affine transform using another stride loop
    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 = gn_weight[c];
        float beta = gn_bias[c];
        y[idx] = gamma * normalized + beta;
    }
}

// Launcher for the fused kernel
void fused_stride_cuda(
    at::Tensor x,          // Input tensor from conv2d
    at::Tensor bias,       // Bias tensor for elementwise op
    at::Tensor scale,      // Scale tensor for elementwise op
    at::Tensor y,          // Output tensor
    at::Tensor gn_weight,  // Group normalization weight (gamma)
    at::Tensor gn_bias,    // Group normalization bias (beta)
    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);

    // Each block processes one group's data from one sample
    int total_blocks = N * num_groups;
    int threads = 256;  // Number of threads per block
    size_t shared_mem_size = 2 * threads * sizeof(float);

    fused_stride_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: performs convolution then applies the fused kernel
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);

    // Perform convolution using PyTorch's conv2d
    x = at::conv2d(x, conv_weight, conv_bias);

    // Allocate output tensor
    at::Tensor y = at::empty_like(x);

    bool bias_broadcast = (bias.numel() == 1);
    bool scale_broadcast = (scale.numel() == 1);
    float eps = 1e-5;

    fused_stride_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, "Fused kernel with stride loops (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 2.436 inst/cycle 0.000 5
Executed Ipc Elapsed 1.910 inst/cycle 0.001 5
Issue Slots Busy 61.086 % 0.032 5
Issued Ipc Active 2.444 inst/cycle 0.000 5
SM Busy 61.086 % 0.032 5
Memory Throughput 492176579755.110 byte/second 42655493802729406464.000 5
Mem Busy 23.386 % 0.107 5
Max Bandwidth 28.532 % 0.151 5
L1/TEX Hit Rate 65.500 % 0.001 5
L2 Hit Rate 70.378 % 0.000 5
Mem Pipes Busy 18.816 % 0.069 5
Warp Cycles Per Issued Instruction 20.278 cycle 0.028 5
Warp Cycles Per Executed Instruction 20.336 cycle 0.028 5
Avg. Active Threads Per Warp 31.410 0.000 5
Avg. Not Predicated Off Threads Per Warp 28.390 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 77.166 % 0.014 5
Achieved Active Warps Per SM 49.386 warp 0.006 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (47.6%) 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 (77.1%) 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.
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.
Operation / Metric Value Unit
aten::fill_
CPU Time 159434.97 μs
Device Time 1091297.91 μs
Self CPU Time 33095.02 μs
Self Device Time 1091297.91 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 185559.84 μs
Device Time 1091297.91 μs
Self CPU Time 26144.56 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::conv2d
CPU Time 1159130.39 μs
Device Time 388295.88 μs
Self CPU Time 24521.74 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 1134608.64 μs
Device Time 388295.88 μs
Self CPU Time 30624.90 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 1103983.74 μs
Device Time 388295.88 μs
Self CPU Time 61592.02 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 906763.07 μs
Device Time 277311.33 μs
Self CPU Time 240319.93 μs
Self Device Time 277311.33 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 909167.47 μs
Device Time 28228.05 μs
Self CPU Time 909167.47 μs
Self Device Time 28228.05 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 1091297.91 μs
Self CPU Time 0.00 μs
Self Device Time 1091297.91 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/b6_s1_fused_stride_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/b6_s1_fused_stride_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/b6_s1_fused_stride_kernel/base/base.cu:19:5: warning: 4 adjacent parameters of 'fused_stride_kernel' of similar type ('const float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
19 | const float* __restrict__ bias, // Bias for elementwise operation (size 1 or C)
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
20 | const float* __restrict__ scale,// Scale for elementwise operation (size 1 or C)
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
21 | const float* __restrict__ gn_weight, // Gamma for group normalization, shape [C]
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
22 | const float* __restrict__ gn_bias, // Beta for group normalization, shape [C]
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_kernel/base/base.cu:19:31: note: the first parameter in the range is 'bias'
19 | const float* __restrict__ bias, // Bias for elementwise operation (size 1 or C)
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_kernel/base/base.cu:22:31: note: the last parameter in the range is 'gn_bias'
22 | const float* __restrict__ gn_bias, // Beta for group normalization, shape [C]
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_kernel/base/base.cu:23:5: warning: 2 adjacent parameters of 'fused_stride_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
23 | 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/b6_s1_fused_stride_kernel/base/base.cu:23:9: note: the first parameter in the range is 'N'
23 | 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/b6_s1_fused_stride_kernel/base/base.cu:23:16: note: the last parameter in the range is 'C'
23 | 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/b6_s1_fused_stride_kernel/base/base.cu:23:26: warning: 2 adjacent parameters of 'fused_stride_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
23 | int N, int C, int H, int W,
| ^~~~~~
24 | int num_groups,
| ~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_kernel/base/base.cu:23:30: note: the first parameter in the range is 'W'
23 | 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/b6_s1_fused_stride_kernel/base/base.cu:24:9: note: the last parameter in the range is 'num_groups'
24 | int num_groups,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_kernel/base/base.cu:30:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
30 | int group_idx = blockIdx.x % num_groups;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_kernel/base/base.cu:31:22: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
31 | int sample_idx = blockIdx.x / num_groups;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_kernel/base/base.cu:49:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
49 | 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/b6_s1_fused_stride_kernel/base/base.cu:49:52: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
49 | 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/b6_s1_fused_stride_kernel/base/base.cu:68:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
68 | int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_kernel/base/base.cu:74:23: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
74 | 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/b6_s1_fused_stride_kernel/base/base.cu:83:40: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
83 | float group_mean = shared_sum[0] / group_size;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_kernel/base/base.cu:84:42: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
84 | float 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/b6_s1_fused_stride_kernel/base/base.cu:88:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
88 | 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/b6_s1_fused_stride_kernel/base/base.cu:88:52: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
88 | 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/b6_s1_fused_stride_kernel/base/base.cu:104:16: 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]
104 | at::Tensor x, // Input tensor from conv2d
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_kernel/base/base.cu:105:16: 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]
105 | at::Tensor bias, // Bias tensor for elementwise op
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_kernel/base/base.cu:106:16: 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]
106 | at::Tensor scale, // Scale tensor for elementwise op
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_kernel/base/base.cu:107:16: 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]
107 | at::Tensor y, // Output tensor
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_kernel/base/base.cu:108:16: 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]
108 | at::Tensor gn_weight, // Group normalization weight (gamma)
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_kernel/base/base.cu:109:16: 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]
109 | at::Tensor gn_bias, // Group normalization bias (beta)
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_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 N = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_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 C = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_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 H = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_kernel/base/base.cu:118:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
118 | int W = x.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_kernel/base/base.cu:121:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
121 | int total_blocks = N * num_groups;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_kernel/base/base.cu:123:30: warning: performing an implicit widening conversion to type 'unsigned long' of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
123 | 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/b6_s1_fused_stride_kernel/base/base.cu:123:30: 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/b6_s1_fused_stride_kernel/base/base.cu:123:30: note: perform multiplication in a wider type
123 | 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/b6_s1_fused_stride_kernel/base/base.cu:133:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
133 | num_groups,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_kernel/base/base.cu:147:16: 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]
147 | at::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_kernel/base/base.cu:149:16: 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]
149 | at::Tensor bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_kernel/base/base.cu:150:16: 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]
150 | at::Tensor scale,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_kernel/base/base.cu:151:16: 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]
151 | at::Tensor gn_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b6_s1_fused_stride_kernel/base/base.cu:152:16: 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]
152 | at::Tensor gn_bias,
| ^
| const &