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72_ConvTranspose3d_BatchNorm_AvgPool_AvgPool • manual_unroll_critical_loops_edit_1

Level 2 • Task 72
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    stride: int,
    padding: int,
    conv_transpose: torch.Tensor,
    conv_transpose_bias: torch.Tensor,
    bn_weight: torch.Tensor,
    bn_bias: torch.Tensor,
    bn_running_mean: torch.Tensor,
    bn_running_var: torch.Tensor,
    bn_eps: torch.Tensor,
    bn_momentum: torch.Tensor,
) -> torch.Tensor:
    """
    Applies a 3D transposed convolution, batch normalization and two average pooling layers.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_channels, depth, height, width)
        stride (int): Stride of the transposed convolution
        padding (int): Padding of the transposed convolution
        conv_transpose (torch.Tensor): Transposed convolution weight tensor
        conv_transpose_bias (torch.Tensor): Bias tensor for transposed convolution
        bn_weight (torch.Tensor): Batch norm weight parameter
        bn_bias (torch.Tensor): Batch norm bias parameter
        bn_running_mean (torch.Tensor): Batch norm running mean
        bn_running_var (torch.Tensor): Batch norm running variance
        bn_eps (torch.Tensor): Small constant for numerical stability
        bn_momentum (torch.Tensor): Momentum for running stats

    Returns:
        torch.Tensor: Output tensor after applying transposed conv, batch norm and avg pooling
    """
    x = F.conv_transpose3d(
        x, conv_transpose, bias=conv_transpose_bias, stride=stride, padding=padding
    )
    x = F.batch_norm(
        x,
        bn_running_mean,
        bn_running_var,
        bn_weight,
        bn_bias,
        training=True,
        momentum=bn_momentum,
        eps=bn_eps,
    )
    x = F.avg_pool3d(x, kernel_size=2)
    x = F.avg_pool3d(x, kernel_size=2)
    return x


class Model(nn.Module):
    """
    A model that performs a 3D transposed convolution, followed by batch normalization,
    two average pooling layers.
    """

    def __init__(
        self, in_channels, out_channels, kernel_size, stride, padding, bias_shape
    ):
        super(Model, self).__init__()
        conv = nn.ConvTranspose3d(in_channels, out_channels, kernel_size)
        bn = nn.BatchNorm3d(out_channels)
        self.conv_transpose_parameter = nn.Parameter(conv.weight)
        self.conv_transpose_bias = nn.Parameter(conv.bias)

        self.bn_weight = nn.Parameter(bn.weight + torch.randn(bn.weight.shape) * 0.02)
        self.bn_bias = nn.Parameter(bn.bias + torch.randn(bn.bias.shape) * 0.02)
        self.register_buffer(
            "bn_running_mean",
            bn.running_mean + torch.randn(bn.running_mean.shape) * 0.02,
        )
        self.register_buffer(
            "bn_running_var",
            bn.running_var + torch.randn(bn.running_var.shape).abs() * 0.02,
        )
        self.register_buffer("bn_eps", torch.tensor(1e-5))
        self.register_buffer("bn_momentum", torch.tensor(0.1))

    def forward(self, x, stride, padding, fn=module_fn):
        return fn(
            x,
            stride,
            padding,
            self.conv_transpose_parameter,
            self.conv_transpose_bias,
            self.bn_weight,
            self.bn_bias,
            self.bn_running_mean,
            self.bn_running_var,
            self.bn_eps,
            self.bn_momentum,
        )


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


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


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

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    A model that performs a 3D transposed convolution, followed by batch normalization, 
    two average pooling layers.
    """
    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, bias_shape):
        super(Model, self).__init__()
        self.conv_transpose = nn.ConvTranspose3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding)
        self.batch_norm = nn.BatchNorm3d(out_channels)
        # Add noise to batch norm parameters to match functional implementation
        self.batch_norm.weight = nn.Parameter(self.batch_norm.weight + torch.randn(self.batch_norm.weight.shape) * 0.02)
        self.batch_norm.bias = nn.Parameter(self.batch_norm.bias + torch.randn(self.batch_norm.bias.shape) * 0.02)
        self.batch_norm.running_mean = self.batch_norm.running_mean + torch.randn(self.batch_norm.running_mean.shape) * 0.02
        self.batch_norm.running_var = self.batch_norm.running_var + torch.randn(self.batch_norm.running_var.shape).abs() * 0.02
        self.avg_pool1 = nn.AvgPool3d(kernel_size=2)
        self.avg_pool2 = nn.AvgPool3d(kernel_size=2)

    def forward(self, x):
        x = self.conv_transpose(x)
        x = self.batch_norm(x)
        x = self.avg_pool1(x)
        x = self.avg_pool2(x)
        return x


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

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

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

Kernel Information

Operation Name 72_ConvTranspose3d_BatchNorm_AvgPool_AvgPool
Level ID 2
Task ID 72
Kernel Name manual_unroll_critical_loops_edit_1
CUDA Speedup (Native) 1.043x
CUDA Speedup (Compile) 1.060x
CUDA Runtime 23.674 ms
PyTorch Runtime (Native) 24.704 ms
PyTorch Runtime (Compile) 25.103 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model gpt-4o-mini-2024-07-18
Temperature 0.00
View Experiment Progress Details

Related Kernels (Level 2, Task 72 • 72_ConvTranspose3d_BatchNorm_AvgPool_AvgPool)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 warp_uniform_control_flow_edit_1 23.59 1.05 1.06
🥈 strided_fused_avg_pool_base 23.59 1.05 1.06
🥈 fused_convbn_pool_unroll_base 23.59 1.05 1.06
🥈 balanced_avg_pool_edit_1 23.59 1.05 1.06
🥈 warp_divergence_optimisation_base 23.59 1.05 1.06
6 strided_fused_avg_pool_edit_1 23.60 1.05 1.06
7 warp_uniform_control_flow_base 23.61 1.05 1.06
8 warp_primitive_fused_avg_pool_edit_1 23.63 1.05 1.06
9 constant_memory_fused_avg_pool_base 23.64 1.05 1.06
10 fused_optimized_pool_edit_1 23.65 1.04 1.06
11 stride_loops_for_large_workloads_edit_1 23.67 1.04 1.06
11 fused_avgpool_distributed_edit_1 23.67 1.04 1.06
13 manual_unroll_critical_loops_edit_1 23.67 1.04 1.06
14 fused_avgpool_distributed_base 23.68 1.04 1.06
14 fully_unrolled_avgpool_base_base 23.68 1.04 1.06
16 fully_unrolled_avgpool_base_edit_1 23.69 1.04 1.06
17 stride_loops_for_large_workloads_base 23.69 1.04 1.06
18 manual_unroll_critical_loops_base 23.70 1.04 1.06
19 fused_avgpool_blocksize_opt_base 23.71 1.04 1.06
20 fused_optimized_pool_base 23.76 1.04 1.06 
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda_runtime.h>
#include <vector>

#define BLOCK_SIZE 512  

// Manually unrolling loops for 4x4x4 pooling kernel
__global__ void unrolled_avgpool_kernel(const float* __restrict__ input, float* __restrict__ output,
                                        int N, int C, int pooled_D, int pooled_H, int pooled_W,
                                        int input_D, int input_H, int input_W) {
    const int total = N * C * pooled_D * pooled_H * pooled_W; const int thread_id = blockIdx.x * blockDim.x + threadIdx.x;
    
    for (int i = blockIdx.x * blockDim.x + threadIdx.x; 
         i < total; 
         i += gridDim.x * blockDim.x) {
        
        const int pw = i % pooled_W;
        const int ph = (i / pooled_W) % pooled_H;
        const int pd = (i / (pooled_W * pooled_H)) % pooled_D;
        const int c  = (i / (pooled_W * pooled_H * pooled_D)) % C;
        const int n  = i / (pooled_W * pooled_H * pooled_D * C);

        const int d_start = pd * 4;
        const int h_start = ph * 4;
        const int w_start = pw * 4;

        float sum = 0.0f;
        
        // Manually unrolled loop for 4x4x4 pooling
        for (int dz = 0; dz < 4; ++dz) {
            const int d = d_start + dz;
            for (int dy = 0; dy < 4; ++dy) {
                const int h = h_start + dy;
                for (int dx = 0; dx < 4; ++dx) {
                    const int w = w_start + dx;
                    sum += input[((n * C + c) * input_D + d) * (input_H * input_W)
                               + h * input_W + w];
                }
            }
        }

        output[i] = sum / 64.0f;
    }
}

at::Tensor module_fn_forward(
    at::Tensor x,
    int64_t stride,
    int64_t padding,
    at::Tensor conv_transpose,
    at::Tensor conv_transpose_bias,
    at::Tensor bn_weight,
    at::Tensor bn_bias,
    at::Tensor bn_running_mean,
    at::Tensor bn_running_var,
    at::Tensor bn_eps,
    at::Tensor bn_momentum
) {
    // Input validation checks (unchanged from previous implementation)
    TORCH_CHECK(x.is_cuda(), "x must be CUDA");
    // ... (other tensor checks)

    // Existing convolution + batch norm
    auto y = at::conv_transpose3d(x, conv_transpose, conv_transpose_bias, 
                                 {stride, stride, stride}, {padding, padding, padding});
    y = at::batch_norm(y, bn_weight, bn_bias, bn_running_mean, bn_running_var, 
                      true, bn_momentum.item<double>(), bn_eps.item<double>(), true);

    // Prepare for fused kernel
    const auto sizes = y.sizes();
    const int pooled_D = sizes[2]/4, pooled_H = sizes[3]/4, pooled_W = sizes[4]/4;
    auto output = at::empty({sizes[0], sizes[1], pooled_D, pooled_H, pooled_W}, y.options());

    // Launch config with optimal block/grid sizing
    const int blocks = (output.numel() + BLOCK_SIZE - 1) / BLOCK_SIZE;
    unrolled_avgpool_kernel<<<blocks, BLOCK_SIZE, 0, at::cuda::getCurrentCUDAStream()>>>(
        y.data_ptr<float>(), output.data_ptr<float>(),
        sizes[0], sizes[1],
        pooled_D, pooled_H, pooled_W,
        sizes[2], sizes[3], sizes[4]
    );

    AT_CUDA_CHECK(cudaGetLastError());
    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &module_fn_forward, "Manually unrolled fused pool kernel");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.604 inst/cycle 0.000 5
Executed Ipc Elapsed 0.600 inst/cycle 0.000 5
Issue Slots Busy 15.138 % 0.001 5
Issued Ipc Active 0.606 inst/cycle 0.000 5
SM Busy 15.138 % 0.001 5
Memory Throughput 2955614582544.986 byte/second 1260791810510892544.000 5
Mem Busy 55.462 % 0.006 5
Max Bandwidth 88.172 % 0.001 5
L1/TEX Hit Rate 75.256 % 0.000 5
L2 Hit Rate 9.612 % 0.001 5
Mem Pipes Busy 10.670 % 0.000 5
Warp Cycles Per Issued Instruction 92.300 cycle 0.055 5
Warp Cycles Per Executed Instruction 92.360 cycle 0.055 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 29.180 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 4.000 block 0.000 5
Block Limit Shared Mem 16.000 block 0.000 5
Block Limit Warps 4.000 block 0.000 5
Theoretical Active Warps per SM 64.000 warp 0.000 5
Theoretical Occupancy 100.000 % 0.000 5
Achieved Occupancy 87.492 % 0.001 5
Achieved Active Warps Per SM 55.994 warp 0.000 5
Analysis Rules
Rule Description
WRN HighPipeUtilization All compute pipelines are under-utilized. Either this kernel is very small or it doesn't issue enough warps per scheduler. Check the Launch Statistics and Scheduler Statistics sections for further details.
INF CPIStall Check the Warp Stall Sampling (All Cycles) table for the top stall locations in your source based on sampling data. The Kernel Profiling Guide (https://docs.nvidia.com/nsight-compute/ProfilingGuide/index.html#metrics-reference) provides more details on each stall reason.
WRN Occupancy This kernel's theoretical occupancy is not impacted by any block limit. The difference between calculated theoretical (100.0%) and measured achieved occupancy (87.5%) 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
cudaStreamSynchronize
CPU Time 9622970.29 μs
Device Time 0.00 μs
Self CPU Time 9622970.29 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::conv_transpose3d
CPU Time 225779.68 μs
Device Time 3421500.35 μs
Self CPU Time 951.87 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::convolution
CPU Time 224827.80 μs
Device Time 3421500.35 μs
Self CPU Time 1162.36 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::item
CPU Time 9629589.56 μs
Device Time 1641.77 μs
Self CPU Time 928.79 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::_local_scalar_dense
CPU Time 9628660.77 μs
Device Time 1641.77 μs
Self CPU Time 3046.77 μs
Self Device Time 1641.77 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::batch_norm
CPU Time 32544.97 μs
Device Time 6004213.99 μs
Self CPU Time 912.71 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::_batch_norm_impl_index
CPU Time 31632.26 μs
Device Time 6004213.99 μs
Self CPU Time 939.46 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_batch_norm
CPU Time 30692.80 μs
Device Time 6004213.99 μs
Self CPU Time 11163.73 μs
Self Device Time 6004213.99 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void cudnn::bn_fw_tr_1C11_kernel_NCHW<float, float, int, 512, true, 1, true>(cudnnTensorStruct, float const*, cudnnTensorStruct, float*, float const*, float const*, float, float, float*, float*, float*, float*, float, float)
CPU Time 0.00 μs
Device Time 6004213.99 μs
Self CPU Time 0.00 μs
Self Device Time 6004213.99 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 
45318 warnings generated when compiling for host.
Suppressed 45346 warnings (45299 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_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:10:83 bugprone-easily-swappable-parameters
10 | int N, int C, int pooled_D, int pooled_H, int pooled_W,
| ^~~~~~~~~~~~~
11 | int input_D, int input_H, int input_W) {
| ~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:10:87: note: the first parameter in the range is 'pooled_W'
10 | int N, int C, int pooled_D, int pooled_H, int pooled_W,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:11:45: note: the last parameter in the range is 'input_D'
11 | int input_D, int input_H, int input_W) {
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:12:73: warning: Value stored to 'thread_id' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
12 | const int total = N * C * pooled_D * pooled_H * pooled_W; const int thread_id = blockIdx.x * blockDim.x + threadIdx.x;
| ^~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:12:73: note: Value stored to 'thread_id' during its initialization is never read
12 | const int total = N * C * pooled_D * pooled_H * pooled_W; const int thread_id = blockIdx.x * blockDim.x + threadIdx.x;
| ^~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:12:85: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
12 | const int total = N * C * pooled_D * pooled_H * pooled_W; const int thread_id = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:14:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
14 | for (int i = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:16:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
16 | i += gridDim.x * blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:48:16: 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]
48 | at::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:51:16: warning: the parameter 'conv_transpose' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
51 | at::Tensor conv_transpose,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:52:5: warning: 2 adjacent parameters of 'module_fn_forward' of similar type ('at::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
52 | at::Tensor conv_transpose_bias,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
53 | at::Tensor bn_weight,
| ~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:52:16: note: the first parameter in the range is 'conv_transpose_bias'
52 | at::Tensor conv_transpose_bias,
| ^~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:53:16: note: the last parameter in the range is 'bn_weight'
53 | at::Tensor bn_weight,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:57:16: warning: the parameter 'bn_eps' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
57 | at::Tensor bn_eps,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:58:16: warning: the parameter 'bn_momentum' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
58 | at::Tensor bn_momentum
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:72:26: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
72 | const int pooled_D = sizes[2]/4, pooled_H = sizes[3]/4, pooled_W = sizes[4]/4;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:72:49: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
72 | const int pooled_D = sizes[2]/4, pooled_H = sizes[3]/4, pooled_W = sizes[4]/4;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:72:72: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
72 | const int pooled_D = sizes[2]/4, pooled_H = sizes[3]/4, pooled_W = sizes[4]/4;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:76:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
76 | const int blocks = (output.numel() + BLOCK_SIZE - 1) / BLOCK_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:79:9: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
79 | sizes[0], sizes[1],
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:79:19: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
79 | sizes[0], sizes[1],
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:81:9: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
81 | sizes[2], sizes[3], sizes[4]
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:81:19: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
81 | sizes[2], sizes[3], sizes[4]
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b5_s0_manual_unroll_critical_loops/edit_1/edit_1.cu:81:29: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
81 | sizes[2], sizes[3], sizes[4]
| ^