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8_Conv3d_Divide_Max_GlobalAvgPool_BiasAdd_Sum • fused_pooling_uniform_base

Level 2 • Task 8
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    divisor: float,
    pool_size: tuple,
    sum_dim: int,
    conv_weight: torch.Tensor,
    conv_bias: torch.Tensor,
    bias: torch.Tensor,
) -> torch.Tensor:
    """
    Applies 3D convolution, division, max pooling, global average pooling, bias addition and sum.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_channels, depth, height, width)
        divisor (float): Constant to divide by
        pool_size (tuple): Size for max pooling (depth, height, width)
        sum_dim (int): Dimension to sum over
        conv_weight (torch.Tensor): 3D convolution weights
        conv_bias (torch.Tensor): 3D convolution bias
        bias (torch.Tensor): Bias tensor for addition

    Returns:
        torch.Tensor: Output tensor after applying all operations
    """
    x = F.conv3d(x, conv_weight, bias=conv_bias)
    x = x / divisor
    x = F.max_pool3d(x, pool_size)
    x = F.adaptive_avg_pool3d(x, (1, 1, 1))
    x = x + bias
    x = torch.sum(x, dim=sum_dim)
    return x


class Model(nn.Module):
    """
    Model that performs a 3D convolution, divides by a constant, applies max pooling,
    global average pooling, adds a bias term, and sums along a specific dimension.
    """

    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        divisor,
        pool_size,
        bias_shape,
        sum_dim,
    ):
        super(Model, self).__init__()
        conv_shape = (out_channels, in_channels, *kernel_size)
        conv = nn.Conv3d(in_channels, out_channels, kernel_size)
        self.bias = nn.Parameter(torch.randn(bias_shape) * 0.02)

        self.conv_weight = conv.weight
        self.conv_bias = conv.bias
        self.bias = self.bias

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


batch_size = 128
in_channels = 3
out_channels = 16
depth, height, width = 16, 32, 32
kernel_size = (3, 3, 3)
divisor = 2.0
pool_size = (2, 2, 2)
bias_shape = (out_channels, 1, 1, 1)
sum_dim = 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,
        divisor,
        pool_size,
        bias_shape,
        sum_dim,
    ]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Model that performs a 3D convolution, divides by a constant, applies max pooling,
    global average pooling, adds a bias term, and sums along a specific dimension.
    """
    def __init__(self, in_channels, out_channels, kernel_size, divisor, pool_size, bias_shape, sum_dim):
        super(Model, self).__init__()
        self.conv = nn.Conv3d(in_channels, out_channels, kernel_size)
        self.divisor = divisor
        self.max_pool = nn.MaxPool3d(pool_size)
        self.global_avg_pool = nn.AdaptiveAvgPool3d((1, 1, 1))
        self.bias = nn.Parameter(torch.randn(bias_shape) * 0.02)
        self.sum_dim = sum_dim

    def forward(self, x):
        x = self.conv(x)
        x = x / self.divisor
        x = self.max_pool(x)
        x = self.global_avg_pool(x)
        x = x + self.bias
        x = torch.sum(x, dim=self.sum_dim)
        return x

batch_size = 128
in_channels = 3
out_channels = 16
depth, height, width = 16, 32, 32
kernel_size = (3, 3, 3)
divisor = 2.0
pool_size = (2, 2, 2)
bias_shape = (out_channels, 1, 1, 1)
sum_dim = 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, divisor, pool_size, bias_shape, sum_dim]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 8_Conv3d_Divide_Max_GlobalAvgPool_BiasAdd_Sum
Level ID 2
Task ID 8
Kernel Name fused_pooling_uniform_base
CUDA Speedup (Native) 1.191x
CUDA Speedup (Compile) 0.900x
CUDA Runtime 0.758 ms
PyTorch Runtime (Native) 0.903 ms
PyTorch Runtime (Compile) 0.682 ms
Correct True
Max Diff (vs. Reference) 0.139000
Model o3-mini-2025-01-31
Temperature 1.00
View Experiment Progress Details

Related Kernels (Level 2, Task 8 • 8_Conv3d_Divide_Max_GlobalAvgPool_BiasAdd_Sum)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 fused_stride_loops_base 0.75 1.21 0.91
🥇 fused_pooling_warp_uniform_base 0.75 1.21 0.91
🥇 fused_divide_maxpool_avg_base 0.75 1.21 0.91
🥇 fused_divide_maxpool_avg_edit_1 0.75 1.21 0.91
5 fused_stride_loops_edit_1 0.75 1.21 0.91
5 fused_pooling_warp_uniform_edit_1 0.75 1.21 0.91
5 fused_stride_loops_base 0.75 1.21 0.91
8 block_size_tuned_base_base 0.75 1.20 0.91
8 fused_stride_loops_edit_1 0.75 1.20 0.91
10 fused_pooling_shared_memory_base 0.75 1.20 0.91
10 optimized_stride_boundary_base_base 0.75 1.20 0.91
10 fused_pooling_min_sync_base 0.75 1.20 0.91
13 fused_pooling_min_sync_opt_base 0.75 1.20 0.91
14 fused_pooling_opt_sync_edit_1 0.76 1.19 0.90
15 fused_pooling_uniform_edit_1 0.76 1.19 0.90
15 fused_pooling_uniform_base 0.76 1.19 0.90
15 fused_pooling_opt_sync_base 0.76 1.19 0.90
18 fused_pooling_stride_boundaries_base 0.76 1.19 0.90
18 fused_pooling_unroll_edit_1 0.76 1.19 0.90
20 fused_pooling_unroll_base 0.76 1.19 0.90 
#include <torch/extension.h>
#include <vector>
#include <cfloat>
#include <cuda_runtime.h>

// This kernel assumes that D, H, and W are exactly divisible by the corresponding pool sizes to avoid boundary checks.
// It uses fmaxf to compute the maximum value without divergent branching and precomputes the inverse of divisor to fuse division.
// Warp-level reduction with __shfl_down_sync is used to aggregate results uniformly and minimize warp divergence.

__global__ void fusedPoolingAndReductionKernel(
    const float* __restrict__ conv_out,  // (N, C, D, H, W)
    int N,
    int C,
    int D,
    int H,
    int W,
    int poolD,
    int poolH,
    int poolW,
    const float* __restrict__ bias,      // (C)
    int sum_dim,                         // if 1, reduce across channels
    float* __restrict__ output,          // if sum_dim==1, shape (N), else (N, C)
    float divisor
) {
    // Identify (n, c) slice by flattening blockIdx.x
    int nc = blockIdx.x;  // in [0, N*C)
    int n = nc / C;
    int c = nc % C;

    // Compute output sizes for pooling
    int outD = D / poolD;
    int outH = H / poolH;
    int outW = W / poolW;
    int numPools = outD * outH * outW;

    const float invDiv = 1.0f / divisor;  // Precompute inverse divisor
    float thread_sum = 0.0f;

    // Each thread processes multiple pooling windows in a strided loop with no divergent bounds
    for (int idx = threadIdx.x; idx < numPools; idx += blockDim.x) {
        int d = idx / (outH * outW);
        int rem = idx % (outH * outW);
        int h = rem / outW;
        int w = rem % outW;

        int startD = d * poolD;
        int startH = h * poolH;
        int startW = w * poolW;

        float max_val = -FLT_MAX;
        // Loop over pooling window; no boundary checks due to exact divisibility
        for (int pd = 0; pd < poolD; pd++) {
            for (int ph = 0; ph < poolH; ph++) {
                for (int pw = 0; pw < poolW; pw++) {
                    int curD = startD + pd;
                    int curH = startH + ph;
                    int curW = startW + pw;
                    int index = (((n * C + c) * D + curD) * H + curH) * W + curW;
                    // Fuse division via multiplication with invDiv
                    float val = conv_out[index] * invDiv;
                    max_val = fmaxf(max_val, val);
                }
            }
        }
        thread_sum += max_val;
    }

    // Intra-warp reduction without divergent branching
    unsigned int lane = threadIdx.x & 31;
    float sum_val = thread_sum;
    for (int offset = 16; offset > 0; offset /= 2) {
        sum_val += __shfl_down_sync(0xFFFFFFFF, sum_val, offset);
    }

    // Shared memory for inter-warp reduction
    extern __shared__ float shared[];
    if (lane == 0) {
        shared[threadIdx.x >> 5] = sum_val;
    }
    __syncthreads();

    float block_sum = 0.0f;
    int numWarps = (blockDim.x + 31) >> 5;
    if (threadIdx.x < numWarps) {
        block_sum = shared[threadIdx.x];
    }
    if (threadIdx.x < 32) {
        for (int offset = 16; offset > 0; offset /= 2) {
            block_sum += __shfl_down_sync(0xFFFFFFFF, block_sum, offset);
        }
    }

    // Final result: compute average and add bias in uniform control flow
    if (threadIdx.x == 0) {
        float avg = block_sum / numPools;
        avg = avg + bias[c];
        // Write either per (n,c) element or atomically reduce over channels
        if (sum_dim == 1) {
            atomicAdd(&output[n], avg);
        } else {
            output[n * C + c] = avg;
        }
    }
}

// Forward function: performs conv3d using cuDNN then the fused pooling and reduction

torch::Tensor forward_cuda(
    torch::Tensor x,
    double divisor,
    std::vector<int64_t> pool_size,  // pool_size: [poolD, poolH, poolW]
    int64_t sum_dim,
    torch::Tensor conv_weight,
    torch::Tensor conv_bias,
    torch::Tensor bias
) {
    TORCH_CHECK(x.is_cuda(), "x must be a CUDA tensor.");
    TORCH_CHECK(conv_weight.is_cuda(), "conv_weight must be a CUDA tensor.");
    TORCH_CHECK(conv_bias.is_cuda(), "conv_bias must be a CUDA tensor.");
    TORCH_CHECK(bias.is_cuda(), "bias must be a CUDA tensor.");

    // Use PyTorch's optimized conv3d (cuDNN) without fusion
    auto conv_out = at::conv3d(x, conv_weight, conv_bias);
    int N = conv_out.size(0);
    int C = conv_out.size(1);
    int D = conv_out.size(2);
    int H = conv_out.size(3);
    int W = conv_out.size(4);

    // Launch configuration: each block handles one (n, c) slice
    int totalBlocks = N * C;
    int threadsPerBlock = 128;
    int sharedMemSize = ((threadsPerBlock + 31) / 32) * sizeof(float);

    torch::Tensor output;
    if (sum_dim == 1) {
        // Sum over channel dimension, output shape: (N)
        output = torch::zeros({N}, conv_out.options());
    } else {
        // Output per (n, c) slice: shape (N, C)
        output = torch::empty({N, C}, conv_out.options());
    }

    fusedPoolingAndReductionKernel<<<totalBlocks, threadsPerBlock, sharedMemSize>>>(
        conv_out.data_ptr<float>(),
        N, C, D, H, W,
        pool_size[0], pool_size[1], pool_size[2],
        bias.data_ptr<float>(),
        sum_dim,
        output.data_ptr<float>(),
        static_cast<float>(divisor)
    );

    cudaDeviceSynchronize();
    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward_cuda, "Fused 3D Conv, Pooling, GlobalAvgPool, BiasAdd, and Sum (CUDA) with uniform control flow");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 2.238 inst/cycle 0.000 5
Executed Ipc Elapsed 1.966 inst/cycle 0.000 5
Issue Slots Busy 55.960 % 0.093 5
Issued Ipc Active 2.238 inst/cycle 0.000 5
SM Busy 55.960 % 0.093 5
Memory Throughput 1981107821152.326 byte/second 296375889172882391040.000 5
Mem Busy 36.052 % 0.111 5
Max Bandwidth 59.136 % 0.267 5
L1/TEX Hit Rate 60.840 % 0.000 5
L2 Hit Rate 13.000 % 0.001 5
Mem Pipes Busy 18.920 % 0.020 5
Warp Cycles Per Issued Instruction 25.834 cycle 0.004 5
Warp Cycles Per Executed Instruction 25.848 cycle 0.005 5
Avg. Active Threads Per Warp 31.480 0.000 5
Avg. Not Predicated Off Threads Per Warp 26.150 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 16.000 block 0.000 5
Block Limit Shared Mem 28.000 block 0.000 5
Block Limit Warps 16.000 block 0.000 5
Theoretical Active Warps per SM 64.000 warp 0.000 5
Theoretical Occupancy 100.000 % 0.000 5
Achieved Occupancy 89.900 % 0.001 5
Achieved Active Warps Per SM 57.538 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (38.6%) 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 (89.9%) 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::to
CPU Time 344926.24 μs
Device Time 8162.36 μs
Self CPU Time 72.25 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::conv3d
CPU Time 562008.61 μs
Device Time 7971185.78 μs
Self CPU Time 21626.76 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 540381.85 μs
Device Time 7971185.78 μs
Self CPU Time 28108.95 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 512272.90 μs
Device Time 7971185.78 μs
Self CPU Time 56547.87 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 332921.17 μs
Device Time 6919074.30 μs
Self CPU Time 238418.81 μs
Self Device Time 6919074.30 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 6919073.02 μs
Self CPU Time 0.00 μs
Self Device Time 6919073.02 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaDeviceSynchronize
CPU Time 8831045.94 μs
Device Time 118391.66 μs
Self CPU Time 8831045.94 μs
Self Device Time 118391.66 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 
45300 warnings generated when compiling for host.
Suppressed 45327 warnings (45280 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_8/b3_s1_fused_pooling_uniform/base/base.cu:12:5 bugprone-easily-swappable-parameters
12 | int N,
| ^~~~~~
13 | int C,
| ~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_8/b3_s1_fused_pooling_uniform/base/base.cu:12:9: note: the first parameter in the range is 'N'
12 | int N,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_8/b3_s1_fused_pooling_uniform/base/base.cu:13:9: note: the last parameter in the range is 'C'
13 | int C,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_8/b3_s1_fused_pooling_uniform/base/base.cu:16:5: warning: 2 adjacent parameters of 'fusedPoolingAndReductionKernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
16 | int W,
| ^~~~~~
17 | int poolD,
| ~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_8/b3_s1_fused_pooling_uniform/base/base.cu:16:9: note: the first parameter in the range is 'W'
16 | int W,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_8/b3_s1_fused_pooling_uniform/base/base.cu:17:9: note: the last parameter in the range is 'poolD'
17 | int poolD,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_8/b3_s1_fused_pooling_uniform/base/base.cu:26:14: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
26 | int nc = blockIdx.x; // in [0, N*C)
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_8/b3_s1_fused_pooling_uniform/base/base.cu:40:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
40 | for (int idx = threadIdx.x; idx < numPools; idx += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_8/b3_s1_fused_pooling_uniform/base/base.cu:40:56: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
40 | for (int idx = threadIdx.x; idx < numPools; idx += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_8/b3_s1_fused_pooling_uniform/base/base.cu:83:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
83 | int numWarps = (blockDim.x + 31) >> 5;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_8/b3_s1_fused_pooling_uniform/base/base.cu:95:33: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
95 | float avg = block_sum / numPools;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_8/b3_s1_fused_pooling_uniform/base/base.cu:109:19: 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]
109 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_8/b3_s1_fused_pooling_uniform/base/base.cu:113:19: 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]
113 | torch::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_8/b3_s1_fused_pooling_uniform/base/base.cu:115:19: 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]
115 | torch::Tensor bias
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_8/b3_s1_fused_pooling_uniform/base/base.cu:124:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
124 | int N = conv_out.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_8/b3_s1_fused_pooling_uniform/base/base.cu:125:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
125 | int C = conv_out.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_8/b3_s1_fused_pooling_uniform/base/base.cu:126:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
126 | int D = conv_out.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_8/b3_s1_fused_pooling_uniform/base/base.cu:127:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
127 | int H = conv_out.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_8/b3_s1_fused_pooling_uniform/base/base.cu:128:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
128 | int W = conv_out.size(4);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_8/b3_s1_fused_pooling_uniform/base/base.cu:133:25: warning: narrowing conversion from 'unsigned long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
133 | int sharedMemSize = ((threadsPerBlock + 31) / 32) * sizeof(float);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_8/b3_s1_fused_pooling_uniform/base/base.cu:147:9: warning: narrowing conversion from 'value_type' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
147 | pool_size[0], pool_size[1], pool_size[2],
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_8/b3_s1_fused_pooling_uniform/base/base.cu:147:23: warning: narrowing conversion from 'value_type' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
147 | pool_size[0], pool_size[1], pool_size[2],
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_8/b3_s1_fused_pooling_uniform/base/base.cu:147:37: warning: narrowing conversion from 'value_type' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
147 | pool_size[0], pool_size[1], pool_size[2],
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_8/b3_s1_fused_pooling_uniform/base/base.cu:149:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
149 | sum_dim,
| ^