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43_Max_Pooling_3D • coalesced_maxpool3d_ldg_base

Level 1 • Task 43
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    kernel_size: int,
    stride: int,
    padding: int,
    dilation: int,
    return_indices: bool,
    ceil_mode: bool,
) -> torch.Tensor:
    """
    Functional implementation of Max Pooling 3D.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, channels, dim1, dim2, dim3).
        kernel_size (int): Size of the kernel for the max pooling operation.
        stride (int): Stride of the pooling operation.
        padding (int): Padding applied to the input tensor.
        dilation (int): Spacing between kernel elements.
        return_indices (bool): Whether to return indices of the maximum values.
        ceil_mode (bool): When True, the output size is ceil(input_size / stride) instead of floor.

    Returns:
        torch.Tensor: Output tensor with Max Pooling 3D applied.
    """
    return F.max_pool3d(
        x,
        kernel_size=kernel_size,
        stride=stride,
        padding=padding,
        dilation=dilation,
        return_indices=return_indices,
        ceil_mode=ceil_mode,
    )


class Model(nn.Module):
    """
    Simple model that performs Max Pooling 3D.
    """

    def __init__(
        self,
        kernel_size: int,
        stride: int,
        padding: int,
        dilation: int,
        return_indices: bool,
        ceil_mode: bool,
    ):
        """
        Initializes the Max Pooling 3D layer.

        Args:
            kernel_size (int): Size of the kernel for the max pooling operation.
            stride (int): Stride of the pooling operation.
            padding (int): Padding applied to the input tensor.
            dilation (int): Spacing between kernel elements.
            return_indices (bool): Whether to return indices of the maximum values.
            ceil_mode (bool): When True, the output size is ceil(input_size / stride) instead of floor.
        """
        super(Model, self).__init__()
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.return_indices = return_indices
        self.ceil_mode = ceil_mode

    def forward(self, x: torch.Tensor, fn=module_fn) -> torch.Tensor:
        """
        Applies Max Pooling 3D to the input tensor.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, channels, dim1, dim2, dim3).

        Returns:
            torch.Tensor: Output tensor with Max Pooling 3D applied.
        """
        return fn(
            x,
            self.kernel_size,
            self.stride,
            self.padding,
            self.dilation,
            self.return_indices,
            self.ceil_mode,
        )


batch_size = 16
channels = 32
dim1 = 64
dim2 = 64
dim3 = 64
kernel_size = 3
stride = 2
padding = 1
dilation = 3
return_indices = False
ceil_mode = False


def get_inputs():
    x = torch.randn(batch_size, channels, dim1, dim2, dim3)
    return [x]


def get_init_inputs():
    return [kernel_size, stride, padding, dilation, return_indices, ceil_mode]

import torch
import torch.nn as nn


class Model(nn.Module):
    """
    Simple model that performs Max Pooling 3D.
    """

    def __init__(
        self,
        kernel_size: int,
        stride: int = None,
        padding: int = 0,
        dilation: int = 1,
        return_indices: bool = False,
        ceil_mode: bool = False,
    ):
        """
        Initializes the Max Pooling 3D layer.

        Args:
            kernel_size (int): Size of the kernel for the max pooling operation.
            stride (int, optional): Stride of the pooling operation. Defaults to None, which means stride is equal to kernel_size.
            padding (int, optional): Padding applied to the input tensor. Defaults to 0.
            dilation (int, optional): Spacing between kernel elements. Defaults to 1.
            return_indices (bool, optional): Whether to return indices of the maximum values. Defaults to False.
            ceil_mode (bool, optional): When True, the output size is ceil(input_size / stride) instead of floor. Defaults to False.
        """
        super(Model, self).__init__()
        self.maxpool = nn.MaxPool3d(
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            return_indices=return_indices,
            ceil_mode=ceil_mode,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Applies Max Pooling 3D to the input tensor.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, channels, dim1, dim2, dim3).

        Returns:
            torch.Tensor: Output tensor with Max Pooling 3D applied.
        """
        return self.maxpool(x)


batch_size = 16
channels = 32
dim1 = 64
dim2 = 64
dim3 = 64
kernel_size = 3
stride = 2
padding = 1
dilation = 3
return_indices = False
ceil_mode = False


def get_inputs():
    x = torch.randn(batch_size, channels, dim1, dim2, dim3)
    return [x]


def get_init_inputs():
    return [kernel_size, stride, padding, dilation, return_indices, ceil_mode]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 43_Max_Pooling_3D
Level ID 1
Task ID 43
Kernel Name coalesced_maxpool3d_ldg_base
CUDA Speedup (Native) 1.309x
CUDA Speedup (Compile) 2.459x
CUDA Runtime 0.366 ms
PyTorch Runtime (Native) 0.479 ms
PyTorch Runtime (Compile) 0.900 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model o3-mini-2025-01-31
Temperature 1.00
View Experiment Progress Details

Related Kernels (Level 1, Task 43 • 43_Max_Pooling_3D)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 maxpool3d_unrolled_base_base 0.25 1.91 3.59
🥈 divergence_free_maxpool3d_base_base 0.30 1.59 2.99
🥉 max_pool3d_manual_unroll_full_unrolling_base 0.31 1.55 2.90
4 max_pool3d_manual_unroll_full_unrolling_edit_1 0.31 1.53 2.87
5 coalesced_maxpool3d_ldg_base 0.37 1.31 2.46
6 streamed_maxpool3d_base_base 0.38 1.25 2.35
6 max_pool3d_combined_base 0.38 1.25 2.35
6 combined_maxpool3d_base 0.38 1.25 2.35
9 max_pool3d_optimized_base 0.39 1.24 2.33
10 optimized_maxpool3d_kernel_base 0.39 1.23 2.32
10 modular_max_pool3d_optimized_base 0.39 1.23 2.32
12 optimized_max_pooling_3d_base 0.39 1.23 2.31
13 aligned_maxpool3d_ldg_base_base 0.39 1.23 2.31
14 block_size_experimentation_base 0.40 1.19 2.24
15 pipelined_max_pooling_3d_base 0.41 1.18 2.22
16 max_pool3d_optimized_sync_base_base 0.43 1.12 2.11
16 max_pool3d_optimized_blocksize_base 0.43 1.12 2.11
16 max_pool3d_unroll_loops_base 0.43 1.12 2.11
19 max_pool3d_optimized_sync_base_edit_1 0.43 1.12 2.11
20 even_workload_maxpool3d_base 0.43 1.11 2.09 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <vector>
#include <limits>
#include <cmath>
#include <algorithm>

// This kernel uses a 3D grid configuration and 2D block mapping for the (h, w) dimensions.
// Threads in the same warp correspond to consecutive output width positions, assuring that global memory accesses for both
// output and input (via __ldg) are coalesced. The kernel computes the pooling region boundaries and uses __ldg to read
// from global memory using the read-only cache. This optimization aligns accesses in memory so that threads in a warp
// load consecutive elements when possible, boosting effective memory bandwidth.


template <typename scalar_t>
__global__ void coalesced_maxpool3d_kernel(
    const scalar_t* __restrict__ input,
    scalar_t* __restrict__ output,
    int64_t* __restrict__ indices,
    const int batch_size,
    const int channels,
    const int input_d, const int input_h, const int input_w,
    const int output_d, const int output_h, const int output_w,
    const int kernel_size,
    const int stride,
    const int padding,
    const int dilation) {

    // Using a 3D grid: gridDim.x and gridDim.y map the output width and height;
    // gridDim.z covers (batch_size * channels * output_d). 
    int w_out = blockIdx.x * blockDim.x + threadIdx.x;
    int h_out = blockIdx.y * blockDim.y + threadIdx.y;
    int linear_idx = blockIdx.z;  // Encodes batch, channel, and depth index for the output

    // Decode d_out, channel, and batch from linear_idx
    int d_out = linear_idx % output_d;
    int tmp = linear_idx / output_d;
    int c = tmp % channels;
    int b = tmp / channels;

    if (w_out >= output_w || h_out >= output_h) return;

    // Compute starting positions in the input tensor for each dimension
    int d_start = d_out * stride - padding;
    int h_start = h_out * stride - padding;
    int w_start = w_out * stride - padding;

    // Precompute valid pooling window bounds to avoid unnecessary iterations
    int k_d_start = (d_start < 0) ? ((-d_start + dilation - 1) / dilation) : 0;
    int k_d_end = std::min(kernel_size, (input_d - d_start + dilation - 1) / dilation);

    int k_h_start = (h_start < 0) ? ((-h_start + dilation - 1) / dilation) : 0;
    int k_h_end = std::min(kernel_size, (input_h - h_start + dilation - 1) / dilation);

    int k_w_start = (w_start < 0) ? ((-w_start + dilation - 1) / dilation) : 0;
    int k_w_end = std::min(kernel_size, (input_w - w_start + dilation - 1) / dilation);

    scalar_t max_val = -std::numeric_limits<scalar_t>::infinity();
    int max_index = -1;

    // Iterate over the pooling window
    #pragma unroll
    for (int kd = k_d_start; kd < k_d_end; kd++) {
        int d_in = d_start + kd * dilation;
        #pragma unroll
        for (int kh = k_h_start; kh < k_h_end; kh++) {
            int h_in = h_start + kh * dilation;
            #pragma unroll
            for (int kw = k_w_start; kw < k_w_end; kw++) {
                int w_in = w_start + kw * dilation;
                int input_idx = (((b * channels + c) * input_d + d_in) * input_h + h_in) * input_w + w_in;
                // Use __ldg to load read-only data in a coalesced, cache-friendly way
                scalar_t val = __ldg(&input[input_idx]);
                if (val > max_val) {
                    max_val = val;
                    max_index = input_idx;
                }
            }
        }
    }

    // Compute the flattened output index for tensor of shape (batch, channels, output_d, output_h, output_w)
    int output_idx = (((b * channels + c) * output_d + d_out) * output_h + h_out) * output_w + w_out;
    output[output_idx] = max_val;
    if (indices != nullptr) {
        indices[output_idx] = max_index;
    }
}


// Host function that sets up the CUDA kernel launch with a 3D grid and 2D block configuration

torch::Tensor max_pool3d_cuda_forward(
    torch::Tensor input,
    int kernel_size,
    int stride,
    int padding,
    int dilation,
    bool return_indices,
    bool ceil_mode) {

    auto input_sizes = input.sizes();
    int batch_size = input_sizes[0];
    int channels = input_sizes[1];
    int input_d = input_sizes[2];
    int input_h = input_sizes[3];
    int input_w = input_sizes[4];

    // Compute output dimensions
    float d_out_f = (input_d + 2 * padding - dilation * (kernel_size - 1) - 1) / float(stride) + 1;
    float h_out_f = (input_h + 2 * padding - dilation * (kernel_size - 1) - 1) / float(stride) + 1;
    float w_out_f = (input_w + 2 * padding - dilation * (kernel_size - 1) - 1) / float(stride) + 1;

    int output_d = ceil_mode ? std::ceil(d_out_f) : std::floor(d_out_f);
    int output_h = ceil_mode ? std::ceil(h_out_f) : std::floor(h_out_f);
    int output_w = ceil_mode ? std::ceil(w_out_f) : std::floor(w_out_f);

    auto output = torch::empty({batch_size, channels, output_d, output_h, output_w}, input.options());
    torch::Tensor indices = return_indices ?
        torch::empty({batch_size, channels, output_d, output_h, output_w}, input.options().dtype(torch::kLong)) :
        torch::Tensor();

    // Launch configuration: using 2D blocks for (w, h) and a 3D grid where grid.z covers (batch*channels*output_d)
    dim3 block(32, 8); // 256 threads per block; 32 ensures full warp for consecutive w indices
    dim3 grid(
        (output_w + block.x - 1) / block.x,
        (output_h + block.y - 1) / block.y,
        batch_size * channels * output_d
    );

    AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "max_pool3d_forward_cuda", ([&] {
        coalesced_maxpool3d_kernel<scalar_t><<<grid, block>>>(
            input.data_ptr<scalar_t>(),
            output.data_ptr<scalar_t>(),
            return_indices ? indices.data_ptr<int64_t>() : nullptr,
            batch_size, channels,
            input_d, input_h, input_w,
            output_d, output_h, output_w,
            kernel_size, stride, padding, dilation
        );
    }));

    if (return_indices) {
        return torch::stack({output, indices}, 0);
    }
    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &max_pool3d_cuda_forward, "Coalesced Max Pool 3D forward using __ldg and aligned accesses (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 3.140 inst/cycle 0.000 5
Executed Ipc Elapsed 3.110 inst/cycle 0.000 5
Issue Slots Busy 78.438 % 0.000 5
Issued Ipc Active 3.140 inst/cycle 0.000 5
SM Busy 78.438 % 0.000 5
Memory Throughput 1262231085688.180 byte/second 1597796460468787456.000 5
Mem Busy 29.974 % 0.000 5
Max Bandwidth 37.654 % 0.001 5
L1/TEX Hit Rate 72.802 % 0.000 5
L2 Hit Rate 33.924 % 0.000 5
Mem Pipes Busy 28.834 % 0.000 5
Warp Cycles Per Issued Instruction 17.260 cycle 0.000 5
Warp Cycles Per Executed Instruction 17.260 cycle 0.000 5
Avg. Active Threads Per Warp 29.330 0.000 5
Avg. Not Predicated Off Threads Per Warp 26.470 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 84.784 % 0.001 5
Achieved Active Warps Per SM 54.262 warp 0.000 5
Analysis Rules
Rule Description
WRN HighPipeUtilization ALU is the highest-utilized pipeline (62.0%) based on active cycles, taking into account the rates of its different instructions. It executes integer and logic operations. The pipeline is well-utilized, but might become a bottleneck if more work is added. Based on the number of executed instructions, the highest utilized pipeline (62.0%) is ALU. It executes integer and logic operations. Comparing the two, the overall pipeline utilization appears to be caused by frequent, low-latency instructions. See the Kernel Profiling Guide (https://docs.nvidia.com/nsight-compute/ProfilingGuide/index.html#metrics-decoder) or hover over the pipeline name to understand the workloads handled by each pipeline. The Instruction Statistics section shows the mix of executed instructions in this kernel. Check the Warp State Statistics section for which reasons cause warps to stall.
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 (84.8%) 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::randn
CPU Time 609695.39 μs
Device Time 0.00 μs
Self CPU Time 88.80 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::normal_
CPU Time 609572.08 μs
Device Time 0.00 μs
Self CPU Time 609572.08 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
CPU Time 270689.42 μs
Device Time 55918.00 μs
Self CPU Time 36.64 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 2549854.12 μs
Device Time 17545.98 μs
Self CPU Time 2549854.12 μs
Self Device Time 17545.98 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void coalesced_maxpool3d_kernel<float>(float const*, float*, long*, int, int, int, int, int, int, int, int, int, int, int, int)
CPU Time 0.00 μs
Device Time 2319240.81 μs
Self CPU Time 0.00 μs
Self Device Time 2319240.81 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 2187454.36 μs
Device Time 486928.65 μs
Self CPU Time 13728.42 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 2173727.66 μs
Device Time 486928.65 μs
Self CPU Time 15639.81 μs
Self Device Time 486928.65 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 486928.65 μs
Self CPU Time 0.00 μs
Self Device Time 486928.65 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 
45306 warnings generated when compiling for host.
Suppressed 45329 warnings (45282 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/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:21:5 bugprone-easily-swappable-parameters
21 | const int batch_size,
| ^~~~~~~~~~~~~~~~~~~~~
22 | const int channels,
| ~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:21:15: note: the first parameter in the range is 'batch_size'
21 | const int batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:22:15: note: the last parameter in the range is 'channels'
22 | const int channels,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:23:43: warning: 2 adjacent parameters of 'coalesced_maxpool3d_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
23 | const int input_d, const int input_h, const int input_w,
| ^~~~~~~~~~~~~~~~~~
24 | const int output_d, const int output_h, const int output_w,
| ~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:23:53: note: the first parameter in the range is 'input_w'
23 | const int input_d, const int input_h, const int input_w,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:24:15: note: the last parameter in the range is 'output_d'
24 | const int output_d, const int output_h, const int output_w,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:24:45: warning: 3 adjacent parameters of 'coalesced_maxpool3d_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
24 | const int output_d, const int output_h, const int output_w,
| ^~~~~~~~~~~~~~~~~~~
25 | const int kernel_size,
| ~~~~~~~~~~~~~~~~~~~~~~
26 | const int stride,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:24:55: note: the first parameter in the range is 'output_w'
24 | const int output_d, const int output_h, const int output_w,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:26:15: note: the last parameter in the range is 'stride'
26 | const int stride,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:27:5: warning: 2 adjacent parameters of 'coalesced_maxpool3d_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
27 | const int padding,
| ^~~~~~~~~~~~~~~~~~
28 | const int dilation) {
| ~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:27:15: note: the first parameter in the range is 'padding'
27 | const int padding,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:28:15: note: the last parameter in the range is 'dilation'
28 | const int dilation) {
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:32:17: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
32 | int w_out = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:33:17: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
33 | int h_out = blockIdx.y * blockDim.y + threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:34:22: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
34 | int linear_idx = blockIdx.z; // Encodes batch, channel, and depth index for the output
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:104:22: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
104 | int batch_size = input_sizes[0];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:105:20: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
105 | int channels = input_sizes[1];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:106:19: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
106 | int input_d = input_sizes[2];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:107:19: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
107 | int input_h = input_sizes[3];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:108:19: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
108 | int input_w = input_sizes[4];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:111:21: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
111 | float d_out_f = (input_d + 2 * padding - dilation * (kernel_size - 1) - 1) / float(stride) + 1;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:112:21: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
112 | float h_out_f = (input_h + 2 * padding - dilation * (kernel_size - 1) - 1) / float(stride) + 1;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:113:21: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
113 | float w_out_f = (input_w + 2 * padding - dilation * (kernel_size - 1) - 1) / float(stride) + 1;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:115:32: warning: narrowing conversion from 'float' to 'int' [bugprone-narrowing-conversions]
115 | int output_d = ceil_mode ? std::ceil(d_out_f) : std::floor(d_out_f);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:115:53: warning: narrowing conversion from 'float' to 'int' [bugprone-narrowing-conversions]
115 | int output_d = ceil_mode ? std::ceil(d_out_f) : std::floor(d_out_f);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:116:32: warning: narrowing conversion from 'float' to 'int' [bugprone-narrowing-conversions]
116 | int output_h = ceil_mode ? std::ceil(h_out_f) : std::floor(h_out_f);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:116:53: warning: narrowing conversion from 'float' to 'int' [bugprone-narrowing-conversions]
116 | int output_h = ceil_mode ? std::ceil(h_out_f) : std::floor(h_out_f);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:117:32: warning: narrowing conversion from 'float' to 'int' [bugprone-narrowing-conversions]
117 | int output_w = ceil_mode ? std::ceil(w_out_f) : std::floor(w_out_f);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:117:53: warning: narrowing conversion from 'float' to 'int' [bugprone-narrowing-conversions]
117 | int output_w = ceil_mode ? std::ceil(w_out_f) : std::floor(w_out_f);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_43/b7_s2_coalesced_maxpool3d_ldg/base/base.cu:132:5: warning: inside a lambda, '__func__' expands to the name of the function call operator; consider capturing the name of the enclosing function explicitly [bugprone-lambda-function-name]
132 | AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "max_pool3d_forward_cuda", ([&] {
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:237:34: note: expanded from macro 'AT_DISPATCH_FLOATING_TYPES'
237 | AT_DISPATCH_SWITCH(TYPE, NAME, AT_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:233:3: note: expanded from macro 'AT_DISPATCH_CASE_FLOATING_TYPES'
233 | AT_DISPATCH_CASE(at::ScalarType::Double, __VA_ARGS__) \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:74:3: note: expanded from macro 'AT_DISPATCH_CASE'
74 | AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__)
| ^
note: (skipping 1 expansions in backtrace; use -fmacro-backtrace-limit=0 to see all)
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:58:7: note: expanded from macro 'AT_PRIVATE_CHECK_SELECTIVE_BUILD'
58 | AT_ERROR( \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Exception.h:711:32: note: expanded from macro 'AT_ERROR'
711 | C10_EXPAND_MSVC_WORKAROUND(TORCH_CHECK(false, ::c10::str(__VA_ARGS__))); \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Exception.h:536:9: note: expanded from macro 'TORCH_CHECK'
536 | __func__, \
| ^