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65_Conv2d_AvgPool_Sigmoid_Sum • block512_conv_pool_sigsum_edit_1

Level 2 • Task 65
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,
) -> torch.Tensor:
    """
    Performs convolution, average pooling, applies sigmoid, and sums the result.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_channels, height, width)
        conv_weight (torch.Tensor): Convolution weights of shape (out_channels, in_channels, kernel_size, kernel_size)
        conv_bias (torch.Tensor): Convolution bias of shape (out_channels)

    Returns:
        torch.Tensor: Output tensor of shape (batch_size,) containing summed values
    """
    x = F.conv2d(x, conv_weight, bias=conv_bias)
    x = F.avg_pool2d(x, pool_kernel_size)
    x = torch.sigmoid(x)
    x = torch.sum(x, dim=[1, 2, 3])
    return x


class Model(nn.Module):
    """
    This model performs a convolution, average pooling, applies sigmoid, and sums the result.
    """

    def __init__(self, in_channels, out_channels, kernel_size, pool_kernel_size):
        super(Model, self).__init__()
        conv = nn.Conv2d(in_channels, out_channels, kernel_size)
        self.conv_weight = nn.Parameter(conv.weight)
        self.conv_bias = nn.Parameter(conv.bias)

    def forward(self, x, fn=module_fn):
        return fn(x, self.conv_weight, self.conv_bias)


batch_size = 128
in_channels = 3
out_channels = 16
height, width = 32, 32
kernel_size = 3
pool_kernel_size = 2


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


def get_init_inputs():
    return [in_channels, out_channels, kernel_size, pool_kernel_size]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    This model performs a convolution, average pooling, applies sigmoid, and sums the result.
    """
    def __init__(self, in_channels, out_channels, kernel_size, pool_kernel_size):
        super(Model, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
        self.avg_pool = nn.AvgPool2d(pool_kernel_size)

    def forward(self, x):
        x = self.conv(x)
        x = self.avg_pool(x)
        x = torch.sigmoid(x)
        x = torch.sum(x, dim=[1,2,3]) # Sum over all spatial dimensions
        return x

batch_size = 128
in_channels = 3
out_channels = 16
height, width = 32, 32
kernel_size = 3
pool_kernel_size = 2

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

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

Kernel Information

Operation Name 65_Conv2d_AvgPool_Sigmoid_Sum
Level ID 2
Task ID 65
Kernel Name block512_conv_pool_sigsum_edit_1
CUDA Speedup (Native) 3.443x
CUDA Speedup (Compile) 3.544x
CUDA Runtime 0.016 ms
PyTorch Runtime (Native) 0.055 ms
PyTorch Runtime (Compile) 0.057 ms
Correct True
Max Diff (vs. Reference) 21.792000
Model o3-mini-2025-01-31
Temperature 0.00
View Experiment Progress Details

Related Kernels (Level 2, Task 65 • 65_Conv2d_AvgPool_Sigmoid_Sum)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 block512_conv_pool_sigsum_base 0.01 3.67 3.78
🥈 block512_conv_pool_sigsum_edit_1 0.02 3.44 3.54
🥉 unrolled_conv_pool_sigmoid_sum_base 0.02 3.24 3.34
4 nondivergent_conv_avgpool_sigmoid_sum_base 0.02 3.06 3.15
5 coalesced_conv_avgpool_sigmoid_sum_base_base 0.02 2.90 2.98
5 optimized_conv_pool_sigmoid_sum_base 0.02 2.90 2.98
5 opt_conv_pool_sigmoid_sum_edit_1 0.02 2.90 2.98
5 opt_conv_pool_sigmoid_sum_base 0.02 2.90 2.98
5 uniform_warp_conv_avgpool_sigmoid_sum_base 0.02 2.90 2.98
10 warp_atomic_reduction_conv_base_base 0.02 2.75 2.83
10 warp_reduction_conv_pool_sigmoid_sum_base 0.02 2.75 2.83
10 even_dist_conv_avgpool_sigmoid_sum_edit_1 0.02 2.75 2.83
10 fast_atomic_conv_avgpool_sigmoid_sum_base 0.02 2.75 2.83
10 fast_atomic_conv_avgpool_sigmoid_sum_edit_1 0.02 2.75 2.83
10 optimized_conv_pool_sigmoid_sum_base 0.02 2.75 2.83
10 even_dist_conv_avgpool_sigmoid_sum_base 0.02 2.75 2.83
10 warp_atomic_reduction_conv_base_edit_1 0.02 2.75 2.83
10 uniform_flow_optimized_base_base 0.02 2.75 2.83
10 efficient_3d_indexing_base 0.02 2.75 2.83
10 coalesced_memory_access_base_base_base 0.02 2.75 2.83 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <vector>

#define BLOCK_SIZE 512
#define POOL_SIZE 2

__global__ void conv_pool_sigmoid_sum_kernel(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    const float* __restrict__ bias, 
    float* __restrict__ output,
    const int batch_size,
    const int in_channels,
    const int out_channels,
    const int height,
    const int width,
    const int kernel_size
) {
    __shared__ float shared_mem[BLOCK_SIZE/32]; // Reduced shared memory footprint

    const int tid = threadIdx.x;
    const int bid = blockIdx.x;
    if (bid >= batch_size) return;

    const int out_h = height - kernel_size + 1;
    const int out_w = width - kernel_size + 1;
    const int pool_h = out_h / POOL_SIZE;
    const int pool_w = out_w / POOL_SIZE;
    const int total_work = out_channels * pool_h * pool_w;

    const float pool_scale = 1.0f / (POOL_SIZE * POOL_SIZE);
    float thread_sum = 0.0f;

    // Increased parallelism with larger block size
    for (int idx = tid; idx < total_work; idx += BLOCK_SIZE) {
        const int oc = idx / (pool_h * pool_w);
        const int ph = idx % (pool_h * pool_w);
        const int pool_row = (ph / pool_w) * POOL_SIZE;
        const int pool_col = (ph % pool_w) * POOL_SIZE;
        
        float conv_val = bias[oc];

        #pragma unroll 8
        for (int ic = 0; ic < in_channels; ++ic) {
            #pragma unroll
            for (int kh = 0; kh < kernel_size; ++kh) {
                const int h_in = pool_row + kh;
                const float* input_row = &input[((bid * in_channels + ic) * height + h_in) * width];
                const float* weight_row = &weight[((oc * in_channels + ic) * kernel_size + kh) * kernel_size];
                
                #pragma unroll
                for (int kw = 0; kw < 3; ++kw) {
                    conv_val = __fmaf_rn(input_row[pool_col + kw], weight_row[kw], conv_val);
                }
            }
        }

        conv_val *= pool_scale;        
        thread_sum += __fdividef(1.0f, (1.0f + __expf(-conv_val)));
    }

    // Efficient 512-thread reduction hierarchy
    for (int offset = 16; offset > 0; offset /= 2)
        thread_sum += __shfl_down_sync(0xffffffff, thread_sum, offset);

    // Warp leaders store to shared memory
    if (tid % 32 == 0)
        shared_mem[tid/32] = thread_sum;

    __syncthreads();

    // Final reduction across warps
    if (tid < 32) {
        thread_sum = tid < (BLOCK_SIZE/32) ? shared_mem[tid] : 0.0f;
        for (int offset = 16; offset > 0; offset /= 2)
            thread_sum += __shfl_down_sync(0xffffffff, thread_sum, offset);

        if (tid == 0)
            output[bid] = thread_sum;
    }
}

torch::Tensor forward(
    torch::Tensor input,
    torch::Tensor weight,
    torch::Tensor bias
) {
    const int batch_size = input.size(0);
    const int in_channels = input.size(1);
    const int height = input.size(2);
    const int width = input.size(3);
    const int out_channels = weight.size(0);
    const int kernel_size = weight.size(2);

    auto output = torch::empty({batch_size}, input.options());

    conv_pool_sigmoid_sum_kernel<<<batch_size, BLOCK_SIZE, (BLOCK_SIZE/32)*sizeof(float)>>>(
        input.data_ptr<float>(),
        weight.data_ptr<float>(),
        bias.data_ptr<float>(),
        output.data_ptr<float>(),
        batch_size,
        in_channels,
        out_channels,
        height,
        width,
        kernel_size
    );

    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "BLOCK512 Conv+Pool+Sigmoid+Sum");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 1.768 inst/cycle 0.000 5
Executed Ipc Elapsed 1.416 inst/cycle 0.000 5
Issue Slots Busy 44.288 % 0.086 5
Issued Ipc Active 1.772 inst/cycle 0.000 5
SM Busy 44.288 % 0.086 5
Memory Throughput 99634575828.330 byte/second 281491879705090880.000 5
Mem Busy 49.664 % 0.092 5
Max Bandwidth 25.974 % 0.026 5
L1/TEX Hit Rate 98.650 % 0.000 5
L2 Hit Rate 47.258 % 0.108 5
Mem Pipes Busy 25.108 % 0.024 5
Warp Cycles Per Issued Instruction 8.850 cycle 0.000 5
Warp Cycles Per Executed Instruction 8.880 cycle 0.000 5
Avg. Active Threads Per Warp 31.860 0.000 5
Avg. Not Predicated Off Threads Per Warp 29.800 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 3.000 block 0.000 5
Block Limit Shared Mem 7.000 block 0.000 5
Block Limit Warps 4.000 block 0.000 5
Theoretical Active Warps per SM 48.000 warp 0.000 5
Theoretical Occupancy 75.000 % 0.000 5
Achieved Occupancy 24.696 % 0.000 5
Achieved Active Warps Per SM 15.806 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (34.6%) 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 (75.0%) is limited by the number of required registers. The difference between calculated theoretical (75.0%) and measured achieved occupancy (24.7%) 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::to
CPU Time 175896.16 μs
Device Time 80.51 μs
Self CPU Time 52.86 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::_to_copy
CPU Time 175843.31 μs
Device Time 80.51 μs
Self CPU Time 106.17 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::empty_strided
CPU Time 175410.85 μs
Device Time 0.00 μs
Self CPU Time 108.46 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
cudaDeviceGetStreamPriorityRange
CPU Time 174913.81 μs
Device Time 0.00 μs
Self CPU Time 174913.81 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaLaunchKernel
CPU Time 504365.89 μs
Device Time 16222.81 μs
Self CPU Time 504365.89 μs
Self Device Time 16222.81 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
conv_pool_sigmoid_sum_kernel(float const*, float const*, float const*, float*, int, int, int, int, int, int)
CPU Time 0.00 μs
Device Time 93967.68 μs
Self CPU Time 0.00 μs
Self Device Time 93967.68 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaEventRecord
CPU Time 15988.32 μs
Device Time 31249.14 μs
Self CPU Time 15988.32 μs
Self Device Time 31249.14 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 75735.71 μs
Device Time 584120.13 μs
Self CPU Time 13899.42 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::fill_
CPU Time 61838.06 μs
Device Time 584120.13 μs
Self CPU Time 16530.79 μs
Self Device Time 584120.13 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 584120.13 μs
Self CPU Time 0.00 μs
Self Device Time 584120.13 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 
45291 warnings generated when compiling for host.
Suppressed 45323 warnings (45276 in non-user code, 47 NOLINT).
Use -header-filter=.* to display errors from all non-system headers. Use -system-headers to display errors from system headers as well.
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_65/b5_s3_block512_conv_pool_sigsum/edit_1/edit_1.cu:10:5 bugprone-easily-swappable-parameters
10 | const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
11 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
12 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_65/b5_s3_block512_conv_pool_sigsum/edit_1/edit_1.cu:10:31: note: the first parameter in the range is 'input'
10 | const float* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_65/b5_s3_block512_conv_pool_sigsum/edit_1/edit_1.cu:12:31: note: the last parameter in the range is 'bias'
12 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_65/b5_s3_block512_conv_pool_sigsum/edit_1/edit_1.cu:14:5: warning: 3 adjacent parameters of 'conv_pool_sigmoid_sum_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
14 | const int batch_size,
| ^~~~~~~~~~~~~~~~~~~~~
15 | const int in_channels,
| ~~~~~~~~~~~~~~~~~~~~~~
16 | const int out_channels,
| ~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_65/b5_s3_block512_conv_pool_sigsum/edit_1/edit_1.cu:14:15: note: the first parameter in the range is 'batch_size'
14 | const int batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_65/b5_s3_block512_conv_pool_sigsum/edit_1/edit_1.cu:16:15: note: the last parameter in the range is 'out_channels'
16 | const int out_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_65/b5_s3_block512_conv_pool_sigsum/edit_1/edit_1.cu:23:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
23 | const int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_65/b5_s3_block512_conv_pool_sigsum/edit_1/edit_1.cu:24:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
24 | const int bid = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_65/b5_s3_block512_conv_pool_sigsum/edit_1/edit_1.cu:50:43: warning: result of multiplication in type 'int' is used as a pointer offset after an implicit widening conversion to type 'ptrdiff_t' [bugprone-implicit-widening-of-multiplication-result]
50 | const float* input_row = &input[((bid * in_channels + ic) * height + h_in) * width];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_65/b5_s3_block512_conv_pool_sigsum/edit_1/edit_1.cu:50:49: note: make conversion explicit to silence this warning
4 | const float* input_row = &input[((bid * in_channels + ic) * height + h_in) * width];
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_65/b5_s3_block512_conv_pool_sigsum/edit_1/edit_1.cu:50:49: note: perform multiplication in a wider type
50 | const float* input_row = &input[((bid * in_channels + ic) * height + h_in) * width];
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_65/b5_s3_block512_conv_pool_sigsum/edit_1/edit_1.cu:51:44: warning: result of multiplication in type 'int' is used as a pointer offset after an implicit widening conversion to type 'ptrdiff_t' [bugprone-implicit-widening-of-multiplication-result]
51 | const float* weight_row = &weight[((oc * in_channels + ic) * kernel_size + kh) * kernel_size];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_65/b5_s3_block512_conv_pool_sigsum/edit_1/edit_1.cu:51:51: note: make conversion explicit to silence this warning
51 | const float* weight_row = &weight[((oc * in_channels + ic) * kernel_size + kh) * kernel_size];
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_65/b5_s3_block512_conv_pool_sigsum/edit_1/edit_1.cu:51:51: note: perform multiplication in a wider type
51 | const float* weight_row = &weight[((oc * in_channels + ic) * kernel_size + kh) * kernel_size];
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_65/b5_s3_block512_conv_pool_sigsum/edit_1/edit_1.cu:86:19: warning: the parameter 'input' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
86 | torch::Tensor input,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_65/b5_s3_block512_conv_pool_sigsum/edit_1/edit_1.cu:87:19: warning: the parameter 'weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
87 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_65/b5_s3_block512_conv_pool_sigsum/edit_1/edit_1.cu:88:19: 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]
88 | torch::Tensor bias
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_65/b5_s3_block512_conv_pool_sigsum/edit_1/edit_1.cu:90:28: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
90 | const int batch_size = input.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_65/b5_s3_block512_conv_pool_sigsum/edit_1/edit_1.cu:91:29: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
91 | const int in_channels = input.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_65/b5_s3_block512_conv_pool_sigsum/edit_1/edit_1.cu:92:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
92 | const int height = input.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_65/b5_s3_block512_conv_pool_sigsum/edit_1/edit_1.cu:93:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
93 | const int width = input.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_65/b5_s3_block512_conv_pool_sigsum/edit_1/edit_1.cu:94:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
94 | const int out_channels = weight.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_65/b5_s3_block512_conv_pool_sigsum/edit_1/edit_1.cu:95:29: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
95 | const int kernel_size = weight.size(2);
| ^