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42_Max_Pooling_2D • fully_unrolled_maxpool2d_base_base

Level 1 • Task 42
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,
) -> torch.Tensor:
    """
    Applies Max Pooling 2D using functional interface.

    Args:
        x (torch.Tensor): Input tensor
        kernel_size (int): Size of pooling window
        stride (int): Stride of pooling window
        padding (int): Padding to be applied
        dilation (int): Spacing between kernel elements

    Returns:
        torch.Tensor: Output tensor after max pooling
    """
    return F.max_pool2d(
        x, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation
    )


class Model(nn.Module):
    """
    Simple model that performs Max Pooling 2D.
    """

    def __init__(self, kernel_size: int, stride: int, padding: int, dilation: int):
        """
        Initializes the model parameters.
        """
        super(Model, self).__init__()
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.dilation = dilation

    def forward(self, x: torch.Tensor, fn=module_fn) -> torch.Tensor:
        """
        Forward pass that calls module_fn.
        """
        return fn(
            x,
            self.kernel_size,
            self.stride,
            self.padding,
            self.dilation,
        )


batch_size = 16
channels = 32
height = 128
width = 128
kernel_size = 2
stride = 2
padding = 1
dilation = 3


def get_inputs():
    x = torch.randn(batch_size, channels, height, width)
    return [x]


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

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that performs Max Pooling 2D.
    """
    def __init__(self, kernel_size: int, stride: int, padding: int, dilation: int):
        """
        Initializes the Max Pooling 2D layer.

        Args:
            kernel_size (int): Size of the pooling window.
            stride (int): Stride of the pooling window.
            padding (int): Padding to be applied before pooling.
            dilation (int): Spacing between kernel elements.
        """
        super(Model, self).__init__()
        self.maxpool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Applies Max Pooling 2D to the input tensor.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, channels, height, width).

        Returns:
            torch.Tensor: Output tensor after Max Pooling 2D, shape (batch_size, channels, pooled_height, pooled_width).
        """
        return self.maxpool(x)

batch_size = 16
channels = 32
height = 128
width = 128
kernel_size = 2
stride = 2
padding = 1
dilation = 3

def get_inputs():
    x = torch.randn(batch_size, channels, height, width)
    return [x]

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

Kernel Information

Operation Name 42_Max_Pooling_2D
Level ID 1
Task ID 42
Kernel Name fully_unrolled_maxpool2d_base_base
CUDA Speedup (Native) 1.375x
CUDA Speedup (Compile) 2.910x
CUDA Runtime 0.024 ms
PyTorch Runtime (Native) 0.033 ms
PyTorch Runtime (Compile) 0.070 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
Temperature 1.00
View Experiment Progress Details

Related Kernels (Level 1, Task 42 • 42_Max_Pooling_2D)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 textured_modular_unroll_base_base 0.02 1.43 3.04
🥇 max_pool2d_optimized_strided_base 0.02 1.43 3.04
🥇 tuned_block_size_maxpool2d_base_base 0.02 1.43 3.04
🥇 modularized_unroll_base_base 0.02 1.43 3.04
🥇 fused_unroll_constmem_pool_base 0.02 1.43 3.04
🥇 base_unrolled_combo_base 0.02 1.43 3.04
🥇 warp_divergence_optimized_unroll_base 0.02 1.43 3.04
8 max_pool2d_strided_base_base 0.02 1.37 2.91
8 streams_unroll_pipelined_batch_base_base 0.02 1.37 2.91
8 optimized_max_pool2d_base 0.02 1.37 2.91
8 42_Max_Pooling_2D_manual_unroll_base 0.02 1.37 2.91
8 coalesced_1d_unroll_base_base 0.02 1.37 2.91
8 max_pool2d_kernel_manually_unrolled_base_base 0.02 1.37 2.91
8 coalesced_ldg_unrolled_base_base 0.02 1.37 2.91
8 unrolled_coalesced_maxpool_base 0.02 1.37 2.91
8 max_pool2d_combined_base 0.02 1.37 2.91
8 max_pool2d_combined_optimized_base 0.02 1.37 2.91
8 unroll_base_optimized_base 0.02 1.37 2.91
8 max_pool2d_combined_base 0.02 1.37 2.91
8 fully_unrolled_maxpool2d_base_base 0.02 1.37 2.91 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>

template <typename scalar_t, int KERNEL_SIZE>
__global__ void max_pool2d_unrolled_kernel(
    const scalar_t* __restrict__ input,
    scalar_t* __restrict__ output,
    const int batch_size,
    const int channels,
    const int input_height,
    const int input_width,
    const int output_height,
    const int output_width,
    const int stride,
    const int padding,
    const int dilation
) {
    const int output_idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (output_idx >= batch_size * channels * output_height * output_width) return;

    const int ow = output_idx % output_width;
    const int oh = (output_idx / output_width) % output_height;
    const int c = (output_idx / (output_width * output_height)) % channels;
    const int b = output_idx / (output_width * output_height * channels);

    const int input_batch_offset = b * (channels * input_height * input_width);
    const int input_channel_offset = c * (input_height * input_width);
    
    scalar_t max_val = -std::numeric_limits<scalar_t>::infinity();

    if constexpr (KERNEL_SIZE == 2) {
        // Fully unrolled 2x2 kernel
        const int ih_base = oh * stride - padding;
        const int iw_base = ow * stride - padding;
        
        if (ih_base >= 0 && ih_base < input_height && iw_base >= 0 && iw_base < input_width) {
            const int idx = input_batch_offset + input_channel_offset + ih_base * input_width + iw_base;
            max_val = __ldg(&input[idx]);
        }
        if (ih_base >= 0 && ih_base < input_height && iw_base + dilation >= 0 && iw_base + dilation < input_width) {
            const int idx = input_batch_offset + input_channel_offset + ih_base * input_width + (iw_base + dilation);
            max_val = max(max_val, __ldg(&input[idx]));
        }
        if (ih_base + dilation >= 0 && ih_base + dilation < input_height && iw_base >= 0 && iw_base < input_width) {
            const int idx = input_batch_offset + input_channel_offset + (ih_base + dilation) * input_width + iw_base;
            max_val = max(max_val, __ldg(&input[idx]));
        }
        if (ih_base + dilation >= 0 && ih_base + dilation < input_height && iw_base + dilation >= 0 && iw_base + dilation < input_width) {
            const int idx = input_batch_offset + input_channel_offset + (ih_base + dilation) * input_width + (iw_base + dilation);
            max_val = max(max_val, __ldg(&input[idx]));
        }
    }
    else if constexpr (KERNEL_SIZE == 3) {
        const int ih_base = oh * stride - padding;
        const int iw_base = ow * stride - padding;
        
        #pragma unroll
        for (int i = 0; i < 3; i++) {
            const int ih = ih_base + i * dilation;
            if (ih >= 0 && ih < input_height) {
                const int ih_offset = ih * input_width;
                #pragma unroll
                for (int j = 0; j < 3; j++) {
                    const int iw = iw_base + j * dilation;
                    if (iw >= 0 && iw < input_width) {
                        const int idx = input_batch_offset + input_channel_offset + ih_offset + iw;
                        max_val = max(max_val, __ldg(&input[idx]));
                    }
                }
            }
        }
    }
    else {
        for (int kh = 0; kh < KERNEL_SIZE; kh++) {
            const int ih = oh * stride - padding + kh * dilation;
            if (ih >= 0 && ih < input_height) {
                const int ih_offset = ih * input_width;
                for (int kw = 0; kw < KERNEL_SIZE; kw++) {
                    const int iw = ow * stride - padding + kw * dilation;
                    if (iw >= 0 && iw < input_width) {
                        const int idx = input_batch_offset + input_channel_offset + ih_offset + iw;
                        max_val = max(max_val, __ldg(&input[idx]));
                    }
                }
            }
        }
    }

    output[output_idx] = max_val;
}

torch::Tensor max_pool2d_cuda_forward(
    torch::Tensor input,
    int kernel_size,
    int stride,
    int padding,
    int dilation
) {
    const auto batch_size = input.size(0);
    const auto channels = input.size(1);
    const auto input_height = input.size(2);
    const auto input_width = input.size(3);

    const auto output_height = ((input_height + 2 * padding - dilation * (kernel_size - 1) - 1) / stride) + 1;
    const auto output_width = ((input_width + 2 * padding - dilation * (kernel_size - 1) - 1) / stride) + 1;

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

    const int threads = 256;
    const int blocks = (batch_size * channels * output_height * output_width + threads - 1) / threads;

    AT_DISPATCH_FLOATING_TYPES(input.type(), "max_pool2d_cuda_forward", ([&] {
        if (kernel_size == 2) {
            max_pool2d_unrolled_kernel<scalar_t, 2><<<blocks, threads>>>(
                input.data_ptr<scalar_t>(), output.data_ptr<scalar_t>(),
                batch_size, channels, input_height, input_width,
                output_height, output_width, stride, padding, dilation);
        }
        else if (kernel_size == 3) {
            max_pool2d_unrolled_kernel<scalar_t, 3><<<blocks, threads>>>(
                input.data_ptr<scalar_t>(), output.data_ptr<scalar_t>(),
                batch_size, channels, input_height, input_width,
                output_height, output_width, stride, padding, dilation);
        }
        else {
            max_pool2d_unrolled_kernel<scalar_t, -1><<<blocks, threads>>>(
                input.data_ptr<scalar_t>(), output.data_ptr<scalar_t>(),
                batch_size, channels, input_height, input_width,
                output_height, output_width, stride, padding, dilation);
        }
    }));

    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &max_pool2d_cuda_forward, "Max Pool 2D forward with unrolled kernels (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 3.012 inst/cycle 0.000 5
Executed Ipc Elapsed 2.668 inst/cycle 0.000 5
Issue Slots Busy 75.474 % 0.003 5
Issued Ipc Active 3.020 inst/cycle 0.000 5
SM Busy 75.474 % 0.003 5
Memory Throughput 1672022907238.172 byte/second 84759722360461213696.000 5
Mem Busy 30.004 % 0.021 5
Max Bandwidth 49.938 % 0.065 5
L1/TEX Hit Rate 47.290 % 0.000 5
L2 Hit Rate 24.870 % 0.002 5
Mem Pipes Busy 28.956 % 0.010 5
Warp Cycles Per Issued Instruction 18.182 cycle 0.008 5
Warp Cycles Per Executed Instruction 18.216 cycle 0.009 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 27.750 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 86.062 % 0.001 5
Achieved Active Warps Per SM 55.080 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (59.9%) 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 (86.1%) 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 635689.40 μs
Device Time 3433.05 μs
Self CPU Time 40.52 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::_to_copy
CPU Time 635648.88 μs
Device Time 3433.05 μs
Self CPU Time 103.89 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::empty_strided
CPU Time 631859.81 μs
Device Time 0.00 μs
Self CPU Time 90.02 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaDeviceGetStreamPriorityRange
CPU Time 630814.85 μs
Device Time 0.00 μs
Self CPU Time 630814.85 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
cudaLaunchKernel
CPU Time 483955.19 μs
Device Time 17040.72 μs
Self CPU Time 483955.19 μs
Self Device Time 17040.72 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void max_pool2d_unrolled_kernel<float, 2>(float const*, float*, int, int, int, int, int, int, int, int, int)
CPU Time 0.00 μs
Device Time 136697.19 μs
Self CPU Time 0.00 μs
Self Device Time 136697.19 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaEventRecord
CPU Time 14664.05 μs
Device Time 33834.27 μs
Self CPU Time 14664.05 μs
Self Device Time 33834.27 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 109720.87 μs
Device Time 508726.94 μs
Self CPU Time 10390.16 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 99332.20 μs
Device Time 508726.94 μs
Self CPU Time 14698.94 μs
Self Device Time 508726.94 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 508726.94 μs
Self CPU Time 0.00 μs
Self Device Time 508726.94 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
Status: Completed 
45291 warnings generated when compiling for host.
Suppressed 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/20250201_optimize_b10_s4_e0_sweep/level_1/task_42/b6_s2_fully_unrolled_maxpool2d_base/base/base.cu:12:5 bugprone-easily-swappable-parameters
12 | const int input_width,
| ^~~~~~~~~~~~~~~~~~~~~~
13 | const int output_height,
| ~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_42/b6_s2_fully_unrolled_maxpool2d_base/base/base.cu:12:15: note: the first parameter in the range is 'input_width'
12 | const int input_width,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_42/b6_s2_fully_unrolled_maxpool2d_base/base/base.cu:13:15: note: the last parameter in the range is 'output_height'
13 | const int output_height,
| ^~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_42/b6_s2_fully_unrolled_maxpool2d_base/base/base.cu:14:5: warning: 2 adjacent parameters of 'max_pool2d_unrolled_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
14 | const int output_width,
| ^~~~~~~~~~~~~~~~~~~~~~~
15 | const int stride,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_42/b6_s2_fully_unrolled_maxpool2d_base/base/base.cu:14:15: note: the first parameter in the range is 'output_width'
14 | const int output_width,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_42/b6_s2_fully_unrolled_maxpool2d_base/base/base.cu:15:15: note: the last parameter in the range is 'stride'
15 | const int stride,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_42/b6_s2_fully_unrolled_maxpool2d_base/base/base.cu:19:28: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
19 | const int output_idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_42/b6_s2_fully_unrolled_maxpool2d_base/base/base.cu:105:49: warning: performing an implicit widening conversion to type 'int64_t' (aka 'long') of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
105 | const auto output_height = ((input_height + 2 * padding - dilation * (kernel_size - 1) - 1) / stride) + 1;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_42/b6_s2_fully_unrolled_maxpool2d_base/base/base.cu:105:49: note: make conversion explicit to silence this warning
105 | const auto output_height = ((input_height + 2 * padding - dilation * (kernel_size - 1) - 1) / stride) + 1;
| ^~~~~~~~~~~
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_42/b6_s2_fully_unrolled_maxpool2d_base/base/base.cu:105:49: note: perform multiplication in a wider type
105 | const auto output_height = ((input_height + 2 * padding - dilation * (kernel_size - 1) - 1) / stride) + 1;
| ^
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_42/b6_s2_fully_unrolled_maxpool2d_base/base/base.cu:105:63: warning: performing an implicit widening conversion to type 'int64_t' (aka 'long') of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
105 | const auto output_height = ((input_height + 2 * padding - dilation * (kernel_size - 1) - 1) / stride) + 1;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_42/b6_s2_fully_unrolled_maxpool2d_base/base/base.cu:105:63: note: make conversion explicit to silence this warning
4 | const auto output_height = ((input_height + 2 * padding - dilation * (kernel_size - 1) - 1) / stride) + 1;
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_42/b6_s2_fully_unrolled_maxpool2d_base/base/base.cu:105:63: note: perform multiplication in a wider type
105 | const auto output_height = ((input_height + 2 * padding - dilation * (kernel_size - 1) - 1) / stride) + 1;
| ^~~~~~~~
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_42/b6_s2_fully_unrolled_maxpool2d_base/base/base.cu:106:47: warning: performing an implicit widening conversion to type 'int64_t' (aka 'long') of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
106 | const auto output_width = ((input_width + 2 * padding - dilation * (kernel_size - 1) - 1) / stride) + 1;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_42/b6_s2_fully_unrolled_maxpool2d_base/base/base.cu:106:47: note: make conversion explicit to silence this warning
106 | const auto output_width = ((input_width + 2 * padding - dilation * (kernel_size - 1) - 1) / stride) + 1;
| ^~~~~~~~~~~
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_42/b6_s2_fully_unrolled_maxpool2d_base/base/base.cu:106:47: note: perform multiplication in a wider type
106 | const auto output_width = ((input_width + 2 * padding - dilation * (kernel_size - 1) - 1) / stride) + 1;
| ^
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_42/b6_s2_fully_unrolled_maxpool2d_base/base/base.cu:106:61: warning: performing an implicit widening conversion to type 'int64_t' (aka 'long') of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
106 | const auto output_width = ((input_width + 2 * padding - dilation * (kernel_size - 1) - 1) / stride) + 1;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_42/b6_s2_fully_unrolled_maxpool2d_base/base/base.cu:106:61: note: make conversion explicit to silence this warning
106 | const auto output_width = ((input_width + 2 * padding - dilation * (kernel_size - 1) - 1) / stride) + 1;
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_42/b6_s2_fully_unrolled_maxpool2d_base/base/base.cu:106:61: note: perform multiplication in a wider type
106 | const auto output_width = ((input_width + 2 * padding - dilation * (kernel_size - 1) - 1) / stride) + 1;
| ^~~~~~~~
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_42/b6_s2_fully_unrolled_maxpool2d_base/base/base.cu:111:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
111 | const int blocks = (batch_size * channels * output_height * output_width + threads - 1) / threads;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_42/b6_s2_fully_unrolled_maxpool2d_base/base/base.cu:113:5: 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]
113 | AT_DISPATCH_FLOATING_TYPES(input.type(), "max_pool2d_cuda_forward", ([&] {
| ^
/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__, \
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_42/b6_s2_fully_unrolled_maxpool2d_base/base/base.cu:113:5: warning: 'scalar_type' is deprecated: passing at::DeprecatedTypeProperties to an AT_DISPATCH macro is deprecated, pass an at::ScalarType instead [clang-diagnostic-deprecated-declarations]
113 | AT_DISPATCH_FLOATING_TYPES(input.type(), "max_pool2d_cuda_forward", ([&] {
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:237:3: 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:218:36: note: expanded from macro 'AT_DISPATCH_SWITCH'
218 | at::ScalarType _st = ::detail::scalar_type(the_type); \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:106:1: note: 'scalar_type' has been explicitly marked deprecated here
106 | C10_DEPRECATED_MESSAGE(
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Deprecated.h:24:43: note: expanded from macro 'C10_DEPRECATED_MESSAGE'
24 | #define C10_DEPRECATED_MESSAGE(message) [[deprecated(message)]]
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_42/b6_s2_fully_unrolled_maxpool2d_base/base/base.cu:113:38: warning: 'type' is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [clang-diagnostic-deprecated-declarations]
113 | AT_DISPATCH_FLOATING_TYPES(input.type(), "max_pool2d_cuda_forward", ([&] {
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/core/TensorBody.h:224:3: note: 'type' has been explicitly marked deprecated here
224 | C10_DEPRECATED_MESSAGE("Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device().")
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Deprecated.h:24:43: note: expanded from macro 'C10_DEPRECATED_MESSAGE'
24 | #define C10_DEPRECATED_MESSAGE(message) [[deprecated(message)]]
| ^