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21_Conv2d_Add_Scale_Sigmoid_GroupNorm • unrolled_fused_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 unrolled_fused_kernel_base_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) 0.007000
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 unrolled_fused_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) {

    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;

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

    extern __shared__ float shared_mem[];
    float* shared_sum = shared_mem;
    float* shared_sum_sq = shared_mem + blockDim.x;

    // Initialize local accumulators
    float local_sum = 0.0f;
    float local_sum_sq = 0.0f;

    // Process elements in chunks of 4 to enable better instruction-level parallelism
    #pragma unroll 4
    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 = x[idx];
        float b_val = bias_broadcast ? bias[0] : bias[c];
        float s_val = scale_broadcast ? scale[0] : scale[c];
        
        // Compute elementwise operations
        float pre_act = (in_val + b_val) * s_val;
        float v = 1.0f / (1.0f + expf(-pre_act));

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

    // Store local results in shared memory
    int tid = threadIdx.x;
    shared_sum[tid] = local_sum;
    shared_sum_sq[tid] = local_sum_sq;
    __syncthreads();

    // Manually unrolled reduction for power-of-2 sized thread blocks
    if (blockDim.x >= 512) {
        if (tid < 256) {
            shared_sum[tid] += shared_sum[tid + 256];
            shared_sum_sq[tid] += shared_sum_sq[tid + 256];
        }
        __syncthreads();
    }
    if (blockDim.x >= 256) {
        if (tid < 128) {
            shared_sum[tid] += shared_sum[tid + 128];
            shared_sum_sq[tid] += shared_sum_sq[tid + 128];
        }
        __syncthreads();
    }
    if (blockDim.x >= 128) {
        if (tid < 64) {
            shared_sum[tid] += shared_sum[tid + 64];
            shared_sum_sq[tid] += shared_sum_sq[tid + 64];
        }
        __syncthreads();
    }

    // Final warp reduction (unrolled, no sync needed within a warp)
    if (tid < 32) {
        volatile float* vsum = shared_sum;
        volatile float* vsum_sq = shared_sum_sq;
        if (blockDim.x >= 64) {
            vsum[tid] += vsum[tid + 32];
            vsum_sq[tid] += vsum_sq[tid + 32];
        }
        vsum[tid] += vsum[tid + 16];
        vsum_sq[tid] += vsum_sq[tid + 16];
        vsum[tid] += vsum[tid + 8];
        vsum_sq[tid] += vsum_sq[tid + 8];
        vsum[tid] += vsum[tid + 4];
        vsum_sq[tid] += vsum_sq[tid + 4];
        vsum[tid] += vsum[tid + 2];
        vsum_sq[tid] += vsum_sq[tid + 2];
        vsum[tid] += vsum[tid + 1];
        vsum_sq[tid] += vsum_sq[tid + 1];
    }

    // Compute statistics
    if (tid == 0) {
        float group_mean = shared_sum[0] / group_size;
        float group_var = shared_sum_sq[0] / group_size - group_mean * group_mean;
        shared_sum[0] = group_mean;
        shared_sum_sq[0] = rsqrtf(group_var + eps);  // Pre-compute inverse sqrt
    }
    __syncthreads();

    float group_mean = shared_sum[0];
    float inv_std = shared_sum_sq[0];

    // Normalize and apply affine transform
    #pragma unroll 4
    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;
    }
}

void unrolled_fused_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) {

    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;  // Power of 2 for optimized reduction
    size_t shared_mem_size = 2 * threads * sizeof(float);

    unrolled_fused_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));
    }
}

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;

    unrolled_fused_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, "Unrolled fused kernel (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 2.328 inst/cycle 0.000 5
Executed Ipc Elapsed 1.832 inst/cycle 0.000 5
Issue Slots Busy 58.316 % 0.010 5
Issued Ipc Active 2.334 inst/cycle 0.000 5
SM Busy 58.316 % 0.010 5
Memory Throughput 494723785501.954 byte/second 40186364196986224640.000 5
Mem Busy 23.746 % 0.091 5
Max Bandwidth 28.904 % 0.157 5
L1/TEX Hit Rate 65.562 % 0.000 5
L2 Hit Rate 70.500 % 0.001 5
Mem Pipes Busy 20.948 % 0.064 5
Warp Cycles Per Issued Instruction 20.868 cycle 0.038 5
Warp Cycles Per Executed Instruction 20.912 cycle 0.040 5
Avg. Active Threads Per Warp 31.630 0.000 5
Avg. Not Predicated Off Threads Per Warp 27.670 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 75.818 % 0.023 5
Achieved Active Warps Per SM 48.524 warp 0.009 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (44.6%) 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.
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 (75.9%) 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 159884.77 μs
Device Time 1145811.00 μs
Self CPU Time 30534.31 μs
Self Device Time 1145811.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::zero_
CPU Time 185018.55 μs
Device Time 1145811.00 μs
Self CPU Time 25154.75 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 1258042.82 μs
Device Time 408833.27 μs
Self CPU Time 23317.95 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 1234724.87 μs
Device Time 408833.27 μs
Self CPU Time 29540.58 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 1205184.29 μs
Device Time 408833.27 μs
Self CPU Time 60001.32 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 1003293.22 μs
Device Time 291943.29 μs
Self CPU Time 224736.25 μs
Self Device Time 291943.29 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 1005850.82 μs
Device Time 29752.74 μs
Self CPU Time 1005850.82 μs
Self Device Time 29752.74 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 1145811.00 μs
Self CPU Time 0.00 μs
Self Device Time 1145811.00 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 
45313 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/b5_s3_unrolled_fused_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/b5_s3_unrolled_fused_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/b5_s3_unrolled_fused_kernel_base/base/base.cu:14:5: warning: 4 adjacent parameters of 'unrolled_fused_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,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
17 | const float* __restrict__ gn_bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b5_s3_unrolled_fused_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/b5_s3_unrolled_fused_kernel_base/base/base.cu:17:31: note: the last parameter in the range is 'gn_bias'
17 | const float* __restrict__ gn_bias,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b5_s3_unrolled_fused_kernel_base/base/base.cu:18:5: warning: 2 adjacent parameters of 'unrolled_fused_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/b5_s3_unrolled_fused_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/b5_s3_unrolled_fused_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/b5_s3_unrolled_fused_kernel_base/base/base.cu:18:26: warning: 2 adjacent parameters of 'unrolled_fused_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/b5_s3_unrolled_fused_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/b5_s3_unrolled_fused_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/b5_s3_unrolled_fused_kernel_base/base/base.cu:24:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
24 | int group_idx = blockIdx.x % num_groups;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b5_s3_unrolled_fused_kernel_base/base/base.cu:25:22: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
25 | int sample_idx = blockIdx.x / num_groups;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b5_s3_unrolled_fused_kernel_base/base/base.cu:42:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
42 | 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/b5_s3_unrolled_fused_kernel_base/base/base.cu:42:52: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
42 | 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/b5_s3_unrolled_fused_kernel_base/base/base.cu:62:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
62 | int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b5_s3_unrolled_fused_kernel_base/base/base.cu:112:44: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
112 | 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/b5_s3_unrolled_fused_kernel_base/base/base.cu:113:46: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
113 | 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/b5_s3_unrolled_fused_kernel_base/base/base.cu:124:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
124 | 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/b5_s3_unrolled_fused_kernel_base/base/base.cu:124:52: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
124 | 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/b5_s3_unrolled_fused_kernel_base/base/base.cu:139:16: 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]
139 | at::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b5_s3_unrolled_fused_kernel_base/base/base.cu:140:16: 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]
140 | at::Tensor bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b5_s3_unrolled_fused_kernel_base/base/base.cu:141:16: 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]
141 | at::Tensor scale,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b5_s3_unrolled_fused_kernel_base/base/base.cu:142:16: 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]
142 | at::Tensor y,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b5_s3_unrolled_fused_kernel_base/base/base.cu:143:16: 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]
143 | at::Tensor gn_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b5_s3_unrolled_fused_kernel_base/base/base.cu:144:16: 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]
144 | at::Tensor gn_bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b5_s3_unrolled_fused_kernel_base/base/base.cu:150:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
150 | int N = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b5_s3_unrolled_fused_kernel_base/base/base.cu:151:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
151 | int C = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b5_s3_unrolled_fused_kernel_base/base/base.cu:152:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
152 | int H = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b5_s3_unrolled_fused_kernel_base/base/base.cu:153:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
153 | int W = x.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b5_s3_unrolled_fused_kernel_base/base/base.cu:155:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
155 | int total_blocks = N * num_groups;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b5_s3_unrolled_fused_kernel_base/base/base.cu:157:30: warning: performing an implicit widening conversion to type 'unsigned long' of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
157 | 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/b5_s3_unrolled_fused_kernel_base/base/base.cu:157:30: 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/b5_s3_unrolled_fused_kernel_base/base/base.cu:157:30: note: perform multiplication in a wider type
157 | 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/b5_s3_unrolled_fused_kernel_base/base/base.cu:167:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
167 | num_groups,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b5_s3_unrolled_fused_kernel_base/base/base.cu:180:16: 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]
180 | at::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b5_s3_unrolled_fused_kernel_base/base/base.cu:182:16: 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]
182 | at::Tensor bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b5_s3_unrolled_fused_kernel_base/base/base.cu:183:16: 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]
183 | at::Tensor scale,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b5_s3_unrolled_fused_kernel_base/base/base.cu:184:16: 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]
184 | at::Tensor gn_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_21/b5_s3_unrolled_fused_kernel_base/base/base.cu:185:16: 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]
185 | at::Tensor gn_bias,
| ^
| const &