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89_ConvTranspose3d_MaxPool_Softmax_Subtract_Swish_Max • balanced_thread_block_distribution_base

Level 2 • Task 89
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    stride: int,
    padding: int,
    output_padding: int,
    pool_kernel_size: int,
    pool_stride: int,
    pool_padding: int,
    conv_transpose: torch.Tensor,
    conv_transpose_bias: torch.Tensor,
    subtract: torch.Tensor,
) -> torch.Tensor:
    """
    Applies sequence of operations:
        - ConvTranspose3d
        - MaxPool3d
        - Softmax
        - Subtract
        - Swish
        - Max

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_channels, depth, height, width)
        stride (int): Stride for conv transpose
        padding (int): Padding for conv transpose
        output_padding (int): Output padding for conv transpose
        pool_kernel_size (int): Kernel size for max pooling
        pool_stride (int): Stride for max pooling
        pool_padding (int): Padding for max pooling
        conv_transpose (torch.Tensor): Weight tensor for transposed convolution
        conv_transpose_bias (torch.Tensor): Bias tensor for transposed convolution
        subtract (torch.Tensor): Subtraction parameter tensor
    """
    x = F.conv_transpose3d(
        x,
        conv_transpose,
        bias=conv_transpose_bias,
        stride=stride,
        padding=padding,
        output_padding=output_padding,
    )
    x = F.max_pool3d(
        x, kernel_size=pool_kernel_size, stride=pool_stride, padding=pool_padding
    )
    x = F.softmax(x, dim=1)
    x = x - subtract.view(1, -1, 1, 1, 1)
    x = torch.sigmoid(x) * x  # Swish
    x = torch.max(x, dim=1)[0]
    return x


class Model(nn.Module):
    """
    A model that performs a sequence of operations:
        - ConvTranspose3d
        - MaxPool3d
        - Softmax
        - Subtract
        - Swish
        - Max
    """

    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        stride,
        padding,
        output_padding,
        pool_kernel_size,
        pool_stride,
        pool_padding,
    ):
        super(Model, self).__init__()
        conv_transpose = nn.ConvTranspose3d(in_channels, out_channels, kernel_size)
        self.conv_transpose_parameter = conv_transpose.weight
        self.conv_transpose_bias = conv_transpose.bias
        self.subtract_parameter = nn.Parameter(torch.randn(out_channels) * 0.02)

    def forward(
        self,
        x,
        stride,
        padding,
        output_padding,
        pool_kernel_size,
        pool_stride,
        pool_padding,
        fn=module_fn,
    ):
        return fn(
            x,
            stride,
            padding,
            output_padding,
            pool_kernel_size,
            pool_stride,
            pool_padding,
            self.conv_transpose_parameter,
            self.conv_transpose_bias,
            self.subtract_parameter,
        )


batch_size = 128
in_channels = 3
out_channels = 16
depth, height, width = 16, 32, 32
kernel_size = 3
stride = 2
padding = 1
output_padding = 1
pool_kernel_size = 2
pool_stride = 2
pool_padding = 0


def get_inputs():
    return [
        torch.randn(batch_size, in_channels, depth, height, width),
        stride,
        padding,
        output_padding,
        pool_kernel_size,
        pool_stride,
        pool_padding,
    ]


def get_init_inputs():
    return [
        in_channels,
        out_channels,
        kernel_size,
        stride,
        padding,
        output_padding,
        pool_kernel_size,
        pool_stride,
        pool_padding,
    ]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    A model that performs a sequence of operations:
        - ConvTranspose3d
        - MaxPool3d
        - Softmax
        - Subtract
        - Swish
        - Max
    """
    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, output_padding, pool_kernel_size, pool_stride, pool_padding):
        super(Model, self).__init__()
        self.conv_transpose = nn.ConvTranspose3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, output_padding=output_padding)
        self.max_pool = nn.MaxPool3d(kernel_size=pool_kernel_size, stride=pool_stride, padding=pool_padding)
        self.subtract = nn.Parameter(torch.randn(out_channels)*0.02) # Assuming subtraction is element-wise across channels

    def forward(self, x):
        x = self.conv_transpose(x)
        x = self.max_pool(x)
        x = torch.softmax(x, dim=1) # Apply softmax across channels (dim=1)
        x = x - self.subtract.view(1, -1, 1, 1, 1) # Subtract across channels
        x = torch.sigmoid(x) * x # Swish activation
        x = torch.max(x, dim=1)[0] # Max pooling across channels
        return x

batch_size = 128
in_channels = 3
out_channels = 16
depth, height, width = 16, 32, 32
kernel_size = 3
stride = 2
padding = 1
output_padding = 1
pool_kernel_size = 2
pool_stride = 2
pool_padding = 0

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

def get_init_inputs():
    return [in_channels, out_channels, kernel_size, stride, padding, output_padding, pool_kernel_size, pool_stride, pool_padding]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 89_ConvTranspose3d_MaxPool_Softmax_Subtract_Swish_Max
Level ID 2
Task ID 89
Kernel Name balanced_thread_block_distribution_base
CUDA Speedup (Native) 1.137x
CUDA Speedup (Compile) 0.994x
CUDA Runtime 5.027 ms
PyTorch Runtime (Native) 5.715 ms
PyTorch Runtime (Compile) 4.998 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model azure-gpt-4o-2024-08-06
Temperature 1.00
View Experiment Progress Details

Related Kernels (Level 2, Task 89 • 89_ConvTranspose3d_MaxPool_Softmax_Subtract_Swish_Max)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 balanced_thread_block_distribution_base 5.03 1.14 0.99
🥈 convtranspose3d_fused_unroll_edit_1 5.05 1.13 0.99
🥉 convtranspose3d_fused_unroll_base 5.05 1.13 0.99
4 convtranspose3d_fused_unroll_blocksize_edit1_edit_1 5.06 1.13 0.99
5 convtranspose3d_fused_unroll_blocksize_edit1_base 5.07 1.13 0.99
6 convtranspose3d_fused_stream_pipeline_edit_1 5.09 1.12 0.98
7 fused_unroll_edit_1 5.55 1.03 0.90
8 89_ConvTranspose3d_MaxPool_Softmax_Subtract_Swish_Max_stride_loop_edit_1 5.63 1.01 0.89
9 89_ConvTranspose3d_MaxPool_Softmax_Subtract_Swish_Max_stride_loop_base 5.63 1.01 0.89
10 89_ConvTranspose3d_aligned_memory_base 5.68 1.01 0.88
11 89_ConvTranspose3d_MaxPool_Softmax_Subtract_Swish_Max 5.71 1.00 0.88
12 constant_memory_optimization_base 5.73 1.00 0.87
13 constant_memory_optimization_edit_1 5.74 1.00 0.87
14 constant_memory_optimization_base 5.75 0.99 0.87
15 89_convtranspose3d_maxpool_softmax_subtract_swish_max_sync_optimized_base 6.25 0.91 0.80
16 89_convtranspose3d_maxpool_softmax_subtract_swish_max_sync_optimized_edit_1 6.25 0.91 0.80
17 89_convtranspose3d_maxpool_softmax_subtract_swish_max_optimized_base 6.88 0.83 0.73
18 89_convtranspose3d_maxpool_softmax_subtract_swish_max_sync_optimized_base 6.88 0.83 0.73
19 fused_warpshuffle_nodivergence_base 7.04 0.81 0.71
19 fused_warpshuffle_nodivergence_edit_1 7.04 0.81 0.71 
#include <torch/extension.h>
#include <pybind11/pybind11.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cfloat>
#include <cmath>

namespace py = pybind11;

// This CUDA kernel distributes workloads evenly across threads and blocks.
// Each thread processes one spatial location for a given n, d, h, w.
// The loops over channels are manually unrolled to reduce loop overhead.

__global__ void balanced_fusion_kernel(
    const float* __restrict__ input,        // pooled output: shape [N, C, D, H, W]
    const float* __restrict__ subtract_tensor, // subtract tensor: shape [C] (broadcast over n, d, h, w)
    float* __restrict__ output,               // final output: shape [N, D, H, W]
    int N, int C, int D, int H, int W) {

    // Compute a linear index for each spatial element n, d, h, w
    int index = blockIdx.x * blockDim.x + threadIdx.x;
    int NDHW = N * D * H * W;
    if (index >= NDHW) return;

    // Calculate each dimension from the linear index
    int w_idx = index % W;
    int h_idx = (index / W) % H;
    int d_idx = (index / (H * W)) % D;
    int n_idx = index / (D * H * W);

    int strideC = D * H * W;
    int base0 = n_idx * C * strideC + d_idx * H * W + h_idx * W + w_idx;

    // 1. Compute maximum value over channels
    float max_val = -FLT_MAX;
    #pragma unroll
    for (int c = 0; c < C; c++) {
        max_val = max(max_val, input[base0 + c * strideC]);
    }

    // 2. Compute sum of exponentials for softmax normalization
    float sum_exp = 0.0f;
    #pragma unroll
    for (int c = 0; c < C; c++) {
        sum_exp += expf(input[base0 + c * strideC] - max_val);
    }

    // 3. Calculate softmax, subtract, apply swish and find the max value over the channels
    float final_max = -FLT_MAX;
    #pragma unroll
    for (int c = 0; c < C; c++) {
        float sm_val = expf(input[base0 + c * strideC] - max_val) / sum_exp;
        float y = sm_val - subtract_tensor[c];
        float swish = y / (1.0f + expf(-y)); // swish activation
        final_max = max(final_max, swish);
    }

    // Write to output
    output[index] = final_max;
}

// The forward function calls optimized ATen operations and the new kernel

torch::Tensor forward(
    torch::Tensor x,
    int64_t stride,
    int64_t padding,
    int64_t output_padding,
    int64_t pool_kernel_size,
    int64_t pool_stride,
    int64_t pool_padding,
    torch::Tensor conv_transpose_weight,
    torch::Tensor conv_transpose_bias,
    torch::Tensor subtract_tensor
) {
    auto conv_out = at::conv_transpose3d(
        x,
        conv_transpose_weight,
        conv_transpose_bias,
        {stride, stride, stride},
        {padding, padding, padding},
        {output_padding, output_padding, output_padding},
        1,
        {1, 1, 1}
    );

    auto pool_out = at::max_pool3d(
        conv_out,
        {pool_kernel_size, pool_kernel_size, pool_kernel_size},
        {pool_stride, pool_stride, pool_stride},
        {pool_padding, pool_padding, pool_padding}
    );

    int N = pool_out.size(0);
    int C = pool_out.size(1);
    int D = pool_out.size(2);
    int H = pool_out.size(3);
    int W = pool_out.size(4);

    auto output = at::empty({N, D, H, W}, pool_out.options());
    int NDHW = N * D * H * W;
    // Optimal thread and block size for uniform workload distribution
    const int threads = 256;
    const int blocks = (NDHW + threads - 1) / threads;

    balanced_fusion_kernel<<<blocks, threads>>>(
        pool_out.data_ptr<float>(),
        subtract_tensor.data_ptr<float>(),
        output.data_ptr<float>(),
        N, C, D, H, W);

    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Balanced CUDA forward pass with optimized workload distribution");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 2.376 inst/cycle 0.000 5
Executed Ipc Elapsed 2.272 inst/cycle 0.000 5
Issue Slots Busy 59.458 % 0.140 5
Issued Ipc Active 2.380 inst/cycle 0.000 5
SM Busy 63.852 % 0.163 5
Memory Throughput 1673690493553.476 byte/second 179794972988697346048.000 5
Mem Busy 31.444 % 0.065 5
Max Bandwidth 49.948 % 0.157 5
L1/TEX Hit Rate 58.576 % 0.023 5
L2 Hit Rate 22.348 % 0.184 5
Mem Pipes Busy 24.258 % 0.035 5
Warp Cycles Per Issued Instruction 22.482 cycle 0.018 5
Warp Cycles Per Executed Instruction 22.494 cycle 0.019 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 30.300 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 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 84.254 % 0.227 5
Achieved Active Warps Per SM 53.922 warp 0.091 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (31.1%) 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 (84.6%) 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::conv_transpose3d
CPU Time 8842255.74 μs
Device Time 6953630.01 μs
Self CPU Time 3950.97 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 8838304.77 μs
Device Time 6953630.01 μs
Self CPU Time 5629.13 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 8832675.64 μs
Device Time 6953630.01 μs
Self CPU Time 13522.70 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::cudnn_convolution_transpose
CPU Time 8783706.24 μs
Device Time 5507327.20 μs
Self CPU Time 129352.32 μs
Self Device Time 5507327.20 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaMemsetAsync
CPU Time 5077423.67 μs
Device Time 0.00 μs
Self CPU Time 5077423.67 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
sm90_xmma_dgrad_implicit_gemm_indexed_f32f32_tf32f32_f32_nhwckrsc_nhwc_tilesize256x64x32_warpgroupsize1x1x1_g1_strided_execute_kernel__5x_cudnn
CPU Time 0.00 μs
Device Time 3888718.62 μs
Self CPU Time 0.00 μs
Self Device Time 3888718.62 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 
45296 warnings generated when compiling for host.
Suppressed 45330 warnings (45283 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/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s3_balanced_thread_block_distribution/base/base.cu:15:5 bugprone-easily-swappable-parameters
15 | const float* __restrict__ input, // pooled output: shape [N, C, D, H, W]
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
16 | const float* __restrict__ subtract_tensor, // subtract tensor: shape [C] (broadcast over n, d, h, w)
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s3_balanced_thread_block_distribution/base/base.cu:15:31: note: the first parameter in the range is 'input'
15 | const float* __restrict__ input, // pooled output: shape [N, C, D, H, W]
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s3_balanced_thread_block_distribution/base/base.cu:16:31: note: the last parameter in the range is 'subtract_tensor'
16 | const float* __restrict__ subtract_tensor, // subtract tensor: shape [C] (broadcast over n, d, h, w)
| ^~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s3_balanced_thread_block_distribution/base/base.cu:18:5: warning: 2 adjacent parameters of 'balanced_fusion_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
18 | int N, int C, int D, int H, int W) {
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s3_balanced_thread_block_distribution/base/base.cu:18:9: note: the first parameter in the range is 'N'
18 | int N, int C, int D, int H, int W) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s3_balanced_thread_block_distribution/base/base.cu:18:16: note: the last parameter in the range is 'C'
18 | int N, int C, int D, int H, int W) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s3_balanced_thread_block_distribution/base/base.cu:21:17: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
21 | int index = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s3_balanced_thread_block_distribution/base/base.cu:65:19: 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]
65 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s3_balanced_thread_block_distribution/base/base.cu:68:5: warning: 2 adjacent parameters of 'forward' of similar type ('int64_t') are easily swapped by mistake [bugprone-easily-swappable-parameters]
68 | int64_t output_padding,
| ^~~~~~~~~~~~~~~~~~~~~~~
69 | int64_t pool_kernel_size,
| ~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s3_balanced_thread_block_distribution/base/base.cu:68:13: note: the first parameter in the range is 'output_padding'
68 | int64_t output_padding,
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s3_balanced_thread_block_distribution/base/base.cu:69:13: note: the last parameter in the range is 'pool_kernel_size'
69 | int64_t pool_kernel_size,
| ^~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s3_balanced_thread_block_distribution/base/base.cu:72:19: warning: the parameter 'conv_transpose_weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
72 | torch::Tensor conv_transpose_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s3_balanced_thread_block_distribution/base/base.cu:73:5: warning: 2 adjacent parameters of 'forward' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
73 | torch::Tensor conv_transpose_bias,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
74 | torch::Tensor subtract_tensor
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s3_balanced_thread_block_distribution/base/base.cu:73:19: note: the first parameter in the range is 'conv_transpose_bias'
73 | torch::Tensor conv_transpose_bias,
| ^~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s3_balanced_thread_block_distribution/base/base.cu:74:19: note: the last parameter in the range is 'subtract_tensor'
74 | torch::Tensor subtract_tensor
| ^~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s3_balanced_thread_block_distribution/base/base.cu:74:19: warning: the parameter 'subtract_tensor' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
74 | torch::Tensor subtract_tensor
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s3_balanced_thread_block_distribution/base/base.cu:94:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
94 | int N = pool_out.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s3_balanced_thread_block_distribution/base/base.cu:95:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
95 | int C = pool_out.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s3_balanced_thread_block_distribution/base/base.cu:96:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
96 | int D = pool_out.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s3_balanced_thread_block_distribution/base/base.cu:97:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
97 | int H = pool_out.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s3_balanced_thread_block_distribution/base/base.cu:98:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
98 | int W = pool_out.size(4);
| ^