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48_Conv3d_Scaling_Tanh_Multiply_Sigmoid • warp_broadcast_tile_edit_1

Level 2 • Task 48
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,
    scaling_factor: torch.Tensor,
    bias: torch.Tensor,
) -> torch.Tensor:
    """
    Applies 3D convolution, scaling, tanh, bias multiplication and sigmoid.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_channels, depth, height, width)
        conv_weight (torch.Tensor): 3D convolution weight tensor
        conv_bias (torch.Tensor): 3D convolution bias tensor
        scaling_factor (torch.Tensor): Scaling factor tensor of shape (out_channels, 1, 1, 1)
        bias (torch.Tensor): Bias tensor of shape (out_channels, 1, 1, 1)

    Returns:
        torch.Tensor: Output tensor after applying convolution, scaling, tanh, bias and sigmoid
    """
    x = F.conv3d(x, conv_weight, bias=conv_bias)
    x = x * scaling_factor
    x = torch.tanh(x)
    x = x * bias
    x = torch.sigmoid(x)
    return x


class Model(nn.Module):
    """
    Model that performs a 3D convolution, scales the output, applies tanh, multiplies by a scaling factor, and applies sigmoid.
    """

    def __init__(
        self, in_channels, out_channels, kernel_size, scaling_factor, bias_shape
    ):
        super(Model, self).__init__()
        conv = nn.Conv3d(in_channels, out_channels, kernel_size)
        self.conv_weight = nn.Parameter(conv.weight)
        self.conv_bias = nn.Parameter(
            conv.bias
            + torch.randn(
                conv.bias.shape, device=conv.bias.device, dtype=conv.bias.dtype
            )
            * 0.02
        )
        self.scaling_factor = nn.Parameter(torch.randn(bias_shape) * 0.02)
        self.bias = nn.Parameter(torch.randn(bias_shape) * 0.02)

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


batch_size = 128
in_channels = 3
out_channels = 16
depth, height, width = 16, 32, 32
kernel_size = 3
scaling_factor = 2
bias_shape = (out_channels, 1, 1, 1)


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


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

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Model that performs a 3D convolution, scales the output, applies tanh, multiplies by a scaling factor, and applies sigmoid.
    """
    def __init__(self, in_channels, out_channels, kernel_size, scaling_factor, bias_shape):
        super(Model, self).__init__()
        self.conv = nn.Conv3d(in_channels, out_channels, kernel_size)
        self.conv.bias = nn.Parameter(self.conv.bias + torch.randn(self.conv.bias.shape, device=self.conv.bias.device, dtype=self.conv.bias.dtype) * 0.02)
        self.scaling_factor = nn.Parameter(torch.randn(bias_shape) * 0.02)
        self.bias = nn.Parameter(torch.randn(bias_shape) * 0.02)

    def forward(self, x):
        x = self.conv(x)
        x = x * self.scaling_factor 
        x = torch.tanh(x)
        x = x * self.bias
        x = torch.sigmoid(x)
        return x

batch_size = 128
in_channels = 3
out_channels = 16
depth, height, width = 16, 32, 32
kernel_size = 3
scaling_factor = 2
bias_shape = (out_channels, 1, 1, 1)

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

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

Kernel Information

Operation Name 48_Conv3d_Scaling_Tanh_Multiply_Sigmoid
Level ID 2
Task ID 48
Kernel Name warp_broadcast_tile_edit_1
CUDA Speedup (Native) 1.274x
CUDA Speedup (Compile) 0.666x
CUDA Runtime 0.792 ms
PyTorch Runtime (Native) 1.009 ms
PyTorch Runtime (Compile) 0.527 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model o3-mini-2025-01-31
Temperature 0.00
View Experiment Progress Details

Related Kernels (Level 2, Task 48 • 48_Conv3d_Scaling_Tanh_Multiply_Sigmoid)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 optimized_hybrid_conv3d_base 0.78 1.29 0.68
🥈 streamlined_syncthreads_conv3d_base_edit_1 0.78 1.29 0.67
🥉 aligned_coalesced_conv3d_edit_1 0.78 1.29 0.67
🥉 aligned_memory_access_conv3d_base 0.78 1.29 0.67
🥉 streamlined_syncthreads_conv3d_base_base 0.78 1.29 0.67
6 unrolled_conv3d_opt_edit_1 0.79 1.28 0.67
7 unrolled_conv3d_opt_base 0.79 1.28 0.67
8 block_size_experimentation_edit_1 0.79 1.27 0.67
8 warp_broadcast_tile_edit_1 0.79 1.27 0.67
10 modular_device_functions_edit_1 0.79 1.27 0.67
11 block_size_experimentation_base 0.79 1.27 0.66
12 constant_mem_optimization_base 0.80 1.27 0.66
12 constant_memory_optimization_base 0.80 1.27 0.66
12 constant_mem_opt_base 0.80 1.27 0.66
12 constant_mem_optimization_edit_1 0.80 1.27 0.66
12 strided_loops_conv3d_edit_1 0.80 1.27 0.66
17 constant_memory_optimization_edit_1 0.80 1.27 0.66
18 modular_device_functions_base 0.80 1.26 0.66
18 optimized_shared_mem_edit_1 0.80 1.26 0.66
18 unroll_optimization_base 0.80 1.26 0.66 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <vector>
#include <math.h>

// Device inline function for sigmoid
__device__ inline float sigmoidf(float x) {
    return 1.0f / (1.0f + expf(-x));
}

// Kernel: Each warp is assigned to one (batch, channel) tile. Using warp-level primitives to broadcast the per-channel parameters
__global__ void conv3d_warp_kernel(
    const float* __restrict__ output,
    const float* __restrict__ scaling_factor,
    const float* __restrict__ bias,
    float* __restrict__ result,
    const int batch,
    const int channels,
    const int spatial_size) {

    // Identify warp within block
    int warp_in_block = threadIdx.x / 32;
    int lane = threadIdx.x % 32;
    int warps_per_block = blockDim.x / 32;
    
    // Global warp index over (batch, channel) pairs
    int global_warp_id = blockIdx.x * warps_per_block + warp_in_block;

    // Total number of (batch, channel) pairs
    int total_tiles = batch * channels;
    
    if (global_warp_id < total_tiles) {
        // Determine which batch and channel this warp is processing
        int b = global_warp_id / channels;
        int c = global_warp_id % channels;

        // Use lane 0 to load the parameters from global memory and then broadcast them to the entire warp
        float s_val, b_val;
        if (lane == 0) {
            s_val = scaling_factor[c];
            b_val = bias[c];
        }
        s_val = __shfl_sync(0xffffffff, s_val, 0);
        b_val = __shfl_sync(0xffffffff, b_val, 0);

        // Compute the base offset for this (b, c) tile
        int base_idx = (b * channels + c) * spatial_size;

        // Each thread in the warp processes a subset of the spatial elements
        for (int i = lane; i < spatial_size; i += 32) {
            float val = output[base_idx + i];
            val = val * s_val;
            val = tanhf(val);
            val = val * b_val;
            val = sigmoidf(val);
            result[base_idx + i] = val;
        }
    }
}

// Forward function: Performs conv3d then applies scaling, tanh, bias multiplication and sigmoid
// The output tensor is processed in a tiled manner by warps, using warp-level broadcast to fetch per-channel parameters

torch::Tensor forward(
    torch::Tensor x,
    torch::Tensor conv_weight,
    torch::Tensor conv_bias,
    torch::Tensor scaling_factor,
    torch::Tensor bias) {

    // Run the built-in conv3d
    auto conv_out = torch::conv3d(x, conv_weight, conv_bias);
    
    int batch = conv_out.size(0);
    int channels = conv_out.size(1);
    int depth = conv_out.size(2);
    int height = conv_out.size(3);
    int width = conv_out.size(4);
    int spatial_size = depth * height * width;

    auto result = torch::empty_like(conv_out);

    // Each warp works on one (batch, channel) tile
    int total_tiles = batch * channels;

    // Set up block dimensions: having, for example, 8 warps per block (8 * 32 = 256 threads)
    int warps_per_block = 8;
    int threads_per_block = warps_per_block * 32; // 32 is 32
    int num_blocks = (total_tiles + warps_per_block - 1) / warps_per_block;

    conv3d_warp_kernel<<<num_blocks, threads_per_block>>>(
        conv_out.data_ptr<float>(),
        scaling_factor.data_ptr<float>(),
        bias.data_ptr<float>(),
        result.data_ptr<float>(),
        batch,
        channels,
        spatial_size
    );

    return result;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Conv3d scale tanh bias sigmoid forward using warp-level broadcast tiling");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 1.032 inst/cycle 0.000 5
Executed Ipc Elapsed 0.970 inst/cycle 0.000 5
Issue Slots Busy 25.848 % 0.001 5
Issued Ipc Active 1.032 inst/cycle 0.000 5
SM Busy 27.864 % 0.001 5
Memory Throughput 1853513899528.024 byte/second 278514787244199215104.000 5
Mem Busy 30.598 % 0.073 5
Max Bandwidth 55.306 % 0.247 5
L1/TEX Hit Rate 18.262 % 0.000 5
L2 Hit Rate 51.282 % 0.000 5
Mem Pipes Busy 7.508 % 0.004 5
Warp Cycles Per Issued Instruction 14.410 cycle 0.003 5
Warp Cycles Per Executed Instruction 14.422 cycle 0.003 5
Avg. Active Threads Per Warp 31.890 0.000 5
Avg. Not Predicated Off Threads Per Warp 23.590 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 10.000 block 0.000 5
Block Limit Shared Mem 32.000 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 23.270 % 0.000 5
Achieved Active Warps Per SM 14.894 warp 0.000 5
Analysis Rules
Rule Description
WRN HighPipeUtilization All compute pipelines are under-utilized. Either this kernel is very small or it doesn't issue enough warps per scheduler. Check the Launch Statistics and Scheduler Statistics sections for further details.
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 ThreadDivergence Instructions are executed in warps, which are groups of 32 threads. Optimal instruction throughput is achieved if all 32 threads of a warp execute the same instruction. The chosen launch configuration, early thread completion, and divergent flow control can significantly lower the number of active threads in a warp per cycle. This kernel achieves an average of 31.9 threads being active per cycle. This is further reduced to 23.6 threads per warp due to predication. The compiler may use predication to avoid an actual branch. Instead, all instructions are scheduled, but a per-thread condition code or predicate controls which threads execute the instructions. Try to avoid different execution paths within a warp when possible. In addition, ensure your kernel makes use of Independent Thread Scheduling, which allows a warp to reconverge after a data-dependent conditional block by explicitly calling __syncwarp().
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 (23.3%) can be the result of warp scheduling overheads or workload imbalances during the kernel execution. Load imbalances can occur between warps within a block as well as across blocks of the same kernel. See the CUDA Best Practices Guide (https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html#occupancy) for more details on optimizing occupancy.
Operation / Metric Value Unit
aten::conv3d
CPU Time 575222.70 μs
Device Time 4139209.83 μs
Self CPU Time 10940.85 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 564281.85 μs
Device Time 4139209.83 μs
Self CPU Time 14278.03 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 550003.82 μs
Device Time 4139209.83 μs
Self CPU Time 28164.56 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::cudnn_convolution
CPU Time 454765.13 μs
Device Time 3592629.11 μs
Self CPU Time 150096.61 μs
Self Device Time 3592629.11 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
sm80_xmma_fprop_implicit_gemm_indexed_f32f32_f32f32_f32_nchwkcrs_nchw_tilesize32x32x8_stage3_warpsize1x2x1_g1_ffma_aligna4_alignc4_execute_kernel__5x_cudnn
CPU Time 0.00 μs
Device Time 3592627.44 μs
Self CPU Time 0.00 μs
Self Device Time 3592627.44 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 4006203.50 μs
Device Time 69660.44 μs
Self CPU Time 4006203.50 μs
Self Device Time 69660.44 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 643059.51 μs
Device Time 466374.72 μs
Self CPU Time 13458.70 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 629602.94 μs
Device Time 466374.72 μs
Self CPU Time 21199.49 μs
Self Device Time 466374.72 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 
45294 warnings generated when compiling for host.
Suppressed 45325 warnings (45278 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/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:14:5 bugprone-easily-swappable-parameters
14 | const float* __restrict__ output,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
15 | const float* __restrict__ scaling_factor,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
16 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:14:31: note: the first parameter in the range is 'output'
14 | const float* __restrict__ output,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:16:31: note: the last parameter in the range is 'bias'
16 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:23:25: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
23 | int warp_in_block = threadIdx.x / 32;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:24:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
24 | int lane = threadIdx.x % 32;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:25:27: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
25 | int warps_per_block = blockDim.x / 32;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:28:26: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
28 | int global_warp_id = blockIdx.x * warps_per_block + warp_in_block;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:44:17: warning: 2nd function call argument is an uninitialized value [clang-analyzer-core.CallAndMessage]
44 | s_val = __shfl_sync(0xffffffff, s_val, 0);
| ^ ~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:33:9: note: Assuming 'global_warp_id' is < 'total_tiles'
33 | if (global_warp_id < total_tiles) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:33:5: note: Taking true branch
33 | if (global_warp_id < total_tiles) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:39:15: note: 's_val' declared without an initial value
39 | float s_val, b_val;
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:40:13: note: Assuming 'lane' is not equal to 0
40 | if (lane == 0) {
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:40:9: note: Taking false branch
40 | if (lane == 0) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:44:17: note: 2nd function call argument is an uninitialized value
44 | s_val = __shfl_sync(0xffffffff, s_val, 0);
| ^ ~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:66:19: 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]
66 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:67:19: 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]
67 | torch::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:68:5: warning: 2 adjacent parameters of 'forward' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
68 | torch::Tensor conv_bias,
| ^~~~~~~~~~~~~~~~~~~~~~~~
69 | torch::Tensor scaling_factor,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:68:19: note: the first parameter in the range is 'conv_bias'
68 | torch::Tensor conv_bias,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:69:19: note: the last parameter in the range is 'scaling_factor'
69 | torch::Tensor scaling_factor,
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:69:19: warning: the parameter 'scaling_factor' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
69 | torch::Tensor scaling_factor,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:70:19: 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]
70 | torch::Tensor bias) {
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:75:17: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
75 | int batch = conv_out.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:76:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
76 | int channels = conv_out.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:77:17: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
77 | int depth = conv_out.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:78:18: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
78 | int height = conv_out.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250211_optimize_b5_s4_e1_v2/level_2/task_48/b4_s3_warp_broadcast_tile/edit_1/edit_1.cu:79:17: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
79 | int width = conv_out.size(4);
| ^