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72_ConvTranspose3d_BatchNorm_AvgPool_AvgPool • fully_unrolled_avgpool_base_base

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 fully_unrolled_avgpool_base_base
CUDA Speedup (Native) 1.043x
CUDA Speedup (Compile) 1.060x
CUDA Runtime 23.683 ms
PyTorch Runtime (Native) 24.704 ms
PyTorch Runtime (Compile) 25.103 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
Temperature 0.50
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 256

__global__ void fully_unrolled_avgpool_kernel(const float* __restrict__ input,
                                             float* __restrict__ output,
                                             int N, int C, int D, int H, int W,
                                             int pooled_D, int pooled_H, int pooled_W) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    int total = N * C * pooled_D * pooled_H * pooled_W;

    if (idx < total) {
        // Decode index
        int pw = idx % pooled_W;
        int ph = (idx / pooled_W) % pooled_H;
        int pd = (idx / (pooled_W * pooled_H)) % pooled_D;
        int c = (idx / (pooled_W * pooled_H * pooled_D)) % C;
        int n = idx / (pooled_W * pooled_H * pooled_D * C);

        // Calculate base index for input
        int base_d = pd * 4;
        int base_h = ph * 4;
        int base_w = pw * 4;
        int batch_offset = n * C * D * H * W;
        int channel_offset = c * D * H * W;
        
        // Fully unrolled 4x4x4 pooling
        float sum = 
            // d=0, h=0
            input[batch_offset + channel_offset + (base_d+0)*H*W + (base_h+0)*W + (base_w+0)] +
            input[batch_offset + channel_offset + (base_d+0)*H*W + (base_h+0)*W + (base_w+1)] +
            input[batch_offset + channel_offset + (base_d+0)*H*W + (base_h+0)*W + (base_w+2)] +
            input[batch_offset + channel_offset + (base_d+0)*H*W + (base_h+0)*W + (base_w+3)] +
            // d=0, h=1
            input[batch_offset + channel_offset + (base_d+0)*H*W + (base_h+1)*W + (base_w+0)] +
            input[batch_offset + channel_offset + (base_d+0)*H*W + (base_h+1)*W + (base_w+1)] +
            input[batch_offset + channel_offset + (base_d+0)*H*W + (base_h+1)*W + (base_w+2)] +
            input[batch_offset + channel_offset + (base_d+0)*H*W + (base_h+1)*W + (base_w+3)] +
            // d=0, h=2
            input[batch_offset + channel_offset + (base_d+0)*H*W + (base_h+2)*W + (base_w+0)] +
            input[batch_offset + channel_offset + (base_d+0)*H*W + (base_h+2)*W + (base_w+1)] +
            input[batch_offset + channel_offset + (base_d+0)*H*W + (base_h+2)*W + (base_w+2)] +
            input[batch_offset + channel_offset + (base_d+0)*H*W + (base_h+2)*W + (base_w+3)] +
            // d=0, h=3
            input[batch_offset + channel_offset + (base_d+0)*H*W + (base_h+3)*W + (base_w+0)] +
            input[batch_offset + channel_offset + (base_d+0)*H*W + (base_h+3)*W + (base_w+1)] +
            input[batch_offset + channel_offset + (base_d+0)*H*W + (base_h+3)*W + (base_w+2)] +
            input[batch_offset + channel_offset + (base_d+0)*H*W + (base_h+3)*W + (base_w+3)] +
            // d=1
            input[batch_offset + channel_offset + (base_d+1)*H*W + (base_h+0)*W + (base_w+0)] +
            input[batch_offset + channel_offset + (base_d+1)*H*W + (base_h+0)*W + (base_w+1)] +
            input[batch_offset + channel_offset + (base_d+1)*H*W + (base_h+0)*W + (base_w+2)] +
            input[batch_offset + channel_offset + (base_d+1)*H*W + (base_h+0)*W + (base_w+3)] +
            input[batch_offset + channel_offset + (base_d+1)*H*W + (base_h+1)*W + (base_w+0)] +
            input[batch_offset + channel_offset + (base_d+1)*H*W + (base_h+1)*W + (base_w+1)] +
            input[batch_offset + channel_offset + (base_d+1)*H*W + (base_h+1)*W + (base_w+2)] +
            input[batch_offset + channel_offset + (base_d+1)*H*W + (base_h+1)*W + (base_w+3)] +
            input[batch_offset + channel_offset + (base_d+1)*H*W + (base_h+2)*W + (base_w+0)] +
            input[batch_offset + channel_offset + (base_d+1)*H*W + (base_h+2)*W + (base_w+1)] +
            input[batch_offset + channel_offset + (base_d+1)*H*W + (base_h+2)*W + (base_w+2)] +
            input[batch_offset + channel_offset + (base_d+1)*H*W + (base_h+2)*W + (base_w+3)] +
            input[batch_offset + channel_offset + (base_d+1)*H*W + (base_h+3)*W + (base_w+0)] +
            input[batch_offset + channel_offset + (base_d+1)*H*W + (base_h+3)*W + (base_w+1)] +
            input[batch_offset + channel_offset + (base_d+1)*H*W + (base_h+3)*W + (base_w+2)] +
            input[batch_offset + channel_offset + (base_d+1)*H*W + (base_h+3)*W + (base_w+3)] +
            // d=2, d=3 (similar pattern continues)
            input[batch_offset + channel_offset + (base_d+2)*H*W + (base_h+0)*W + (base_w+0)] +
            input[batch_offset + channel_offset + (base_d+2)*H*W + (base_h+0)*W + (base_w+1)] +
            input[batch_offset + channel_offset + (base_d+2)*H*W + (base_h+0)*W + (base_w+2)] +
            input[batch_offset + channel_offset + (base_d+2)*H*W + (base_h+0)*W + (base_w+3)] +
            input[batch_offset + channel_offset + (base_d+2)*H*W + (base_h+1)*W + (base_w+0)] +
            input[batch_offset + channel_offset + (base_d+2)*H*W + (base_h+1)*W + (base_w+1)] +
            input[batch_offset + channel_offset + (base_d+2)*H*W + (base_h+1)*W + (base_w+2)] +
            input[batch_offset + channel_offset + (base_d+2)*H*W + (base_h+1)*W + (base_w+3)] +
            input[batch_offset + channel_offset + (base_d+2)*H*W + (base_h+2)*W + (base_w+0)] +
            input[batch_offset + channel_offset + (base_d+2)*H*W + (base_h+2)*W + (base_w+1)] +
            input[batch_offset + channel_offset + (base_d+2)*H*W + (base_h+2)*W + (base_w+2)] +
            input[batch_offset + channel_offset + (base_d+2)*H*W + (base_h+2)*W + (base_w+3)] +
            input[batch_offset + channel_offset + (base_d+2)*H*W + (base_h+3)*W + (base_w+0)] +
            input[batch_offset + channel_offset + (base_d+2)*H*W + (base_h+3)*W + (base_w+1)] +
            input[batch_offset + channel_offset + (base_d+2)*H*W + (base_h+3)*W + (base_w+2)] +
            input[batch_offset + channel_offset + (base_d+2)*H*W + (base_h+3)*W + (base_w+3)] +
            // d=3
            input[batch_offset + channel_offset + (base_d+3)*H*W + (base_h+0)*W + (base_w+0)] +
            input[batch_offset + channel_offset + (base_d+3)*H*W + (base_h+0)*W + (base_w+1)] +
            input[batch_offset + channel_offset + (base_d+3)*H*W + (base_h+0)*W + (base_w+2)] +
            input[batch_offset + channel_offset + (base_d+3)*H*W + (base_h+0)*W + (base_w+3)] +
            input[batch_offset + channel_offset + (base_d+3)*H*W + (base_h+1)*W + (base_w+0)] +
            input[batch_offset + channel_offset + (base_d+3)*H*W + (base_h+1)*W + (base_w+1)] +
            input[batch_offset + channel_offset + (base_d+3)*H*W + (base_h+1)*W + (base_w+2)] +
            input[batch_offset + channel_offset + (base_d+3)*H*W + (base_h+1)*W + (base_w+3)] +
            input[batch_offset + channel_offset + (base_d+3)*H*W + (base_h+2)*W + (base_w+0)] +
            input[batch_offset + channel_offset + (base_d+3)*H*W + (base_h+2)*W + (base_w+1)] +
            input[batch_offset + channel_offset + (base_d+3)*H*W + (base_h+2)*W + (base_w+2)] +
            input[batch_offset + channel_offset + (base_d+3)*H*W + (base_h+2)*W + (base_w+3)] +
            input[batch_offset + channel_offset + (base_d+3)*H*W + (base_h+3)*W + (base_w+0)] +
            input[batch_offset + channel_offset + (base_d+3)*H*W + (base_h+3)*W + (base_w+1)] +
            input[batch_offset + channel_offset + (base_d+3)*H*W + (base_h+3)*W + (base_w+2)] +
            input[batch_offset + channel_offset + (base_d+3)*H*W + (base_h+3)*W + (base_w+3)];

        output[idx] = 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
) {
    TORCH_CHECK(x.is_cuda(), "x must be a CUDA tensor");
    TORCH_CHECK(conv_transpose.is_cuda(), "conv_transpose must be a CUDA tensor");
    TORCH_CHECK(conv_transpose_bias.is_cuda(), "conv_transpose_bias must be a CUDA tensor");
    TORCH_CHECK(bn_weight.is_cuda(), "bn_weight must be a CUDA tensor");
    TORCH_CHECK(bn_bias.is_cuda(), "bn_bias must be a CUDA tensor");
    TORCH_CHECK(bn_running_mean.is_cuda(), "bn_running_mean must be a CUDA tensor");
    TORCH_CHECK(bn_running_var.is_cuda(), "bn_running_var must be a CUDA tensor");
    TORCH_CHECK(bn_eps.is_cuda(), "bn_eps must be a CUDA scalar tensor");
    TORCH_CHECK(bn_momentum.is_cuda(), "bn_momentum must be a CUDA scalar tensor");

    const double eps_val = bn_eps.item<double>();
    const double momentum_val = bn_momentum.item<double>();

    std::vector<int64_t> stride_3d = {stride, stride, stride};
    std::vector<int64_t> pad_3d = {padding, padding, padding};
    
    auto y = at::conv_transpose3d(x, conv_transpose, conv_transpose_bias, stride_3d, pad_3d);
    
    y = at::batch_norm(y, bn_weight, bn_bias, bn_running_mean, bn_running_var,
                      true, momentum_val, eps_val, true);

    auto sizes = y.sizes();
    int N_val = sizes[0];
    int C_val = sizes[1];
    int D_val = sizes[2];
    int H_val = sizes[3];
    int W_val = sizes[4];

    int pooled_D = D_val / 4;
    int pooled_H = H_val / 4;
    int pooled_W = W_val / 4;

    auto output = at::empty({N_val, C_val, pooled_D, pooled_H, pooled_W}, y.options());

    int total_elements = N_val * C_val * pooled_D * pooled_H * pooled_W;
    int threads = BLOCK_SIZE;
    int blocks = (total_elements + threads - 1) / threads;

    fully_unrolled_avgpool_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(
        y.data_ptr<float>(),
        output.data_ptr<float>(),
        N_val, C_val, D_val, H_val, W_val,
        pooled_D, pooled_H, pooled_W
    );

    AT_CUDA_CHECK(cudaGetLastError());
    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &module_fn_forward, "Fully unrolled 3D conv transpose + batch norm + avg pool (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.610 inst/cycle 0.000 5
Executed Ipc Elapsed 0.600 inst/cycle 0.000 5
Issue Slots Busy 15.236 % 0.001 5
Issued Ipc Active 0.610 inst/cycle 0.000 5
SM Busy 15.236 % 0.001 5
Memory Throughput 2972328140435.056 byte/second 8366229821247029248.000 5
Mem Busy 56.786 % 0.010 5
Max Bandwidth 88.666 % 0.008 5
L1/TEX Hit Rate 75.250 % 0.000 5
L2 Hit Rate 9.882 % 0.001 5
Mem Pipes Busy 10.718 % 0.000 5
Warp Cycles Per Issued Instruction 96.890 cycle 0.073 5
Warp Cycles Per Executed Instruction 96.906 cycle 0.072 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 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 92.130 % 0.001 5
Achieved Active Warps Per SM 58.964 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.
INF Occupancy This kernel's theoretical occupancy is not impacted by any block limit.
Operation / Metric Value Unit
aten::to
CPU Time 223385.72 μs
Device Time 5070.79 μs
Self CPU Time 70.56 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 223315.16 μs
Device Time 5070.79 μs
Self CPU Time 135.45 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
cudaStreamSynchronize
CPU Time 9592391.37 μs
Device Time 0.00 μs
Self CPU Time 9592391.37 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 9604977.37 μs
Device Time 1637.24 μs
Self CPU Time 1866.26 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 9603111.12 μs
Device Time 1637.24 μs
Self CPU Time 5048.63 μs
Self Device Time 1637.24 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 219241.00 μs
Device Time 3403389.92 μs
Self CPU Time 1210.80 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::batch_norm
CPU Time 33154.55 μs
Device Time 6025108.03 μs
Self CPU Time 1093.44 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 32061.11 μs
Device Time 6025108.03 μs
Self CPU Time 1206.03 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 30855.09 μs
Device Time 6025108.03 μs
Self CPU Time 11940.54 μs
Self Device Time 6025108.03 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 6025108.03 μs
Self CPU Time 0.00 μs
Self Device Time 6025108.03 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 
45311 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/b4_s1_fully_unrolled_avgpool_base/base/base.cu:10:74 bugprone-easily-swappable-parameters
10 | int N, int C, int D, int H, int W,
| ^~~~~~
11 | 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/b4_s1_fully_unrolled_avgpool_base/base/base.cu:10:78: note: the first parameter in the range is 'W'
10 | int N, int C, int D, int H, int W,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b4_s1_fully_unrolled_avgpool_base/base/base.cu:11:50: note: the last parameter in the range is 'pooled_D'
11 | 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/b4_s1_fully_unrolled_avgpool_base/base/base.cu:12:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
12 | int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b4_s1_fully_unrolled_avgpool_base/base/base.cu:109:16: 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]
109 | at::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b4_s1_fully_unrolled_avgpool_base/base/base.cu:110:5: warning: 2 adjacent parameters of 'module_fn_forward' of similar type ('int64_t') are easily swapped by mistake [bugprone-easily-swappable-parameters]
110 | int64_t stride,
| ^~~~~~~~~~~~~~~
111 | int64_t padding,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b4_s1_fully_unrolled_avgpool_base/base/base.cu:110:13: note: the first parameter in the range is 'stride'
110 | int64_t stride,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b4_s1_fully_unrolled_avgpool_base/base/base.cu:111:13: note: the last parameter in the range is 'padding'
111 | int64_t padding,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b4_s1_fully_unrolled_avgpool_base/base/base.cu:112:16: 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]
112 | at::Tensor conv_transpose,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b4_s1_fully_unrolled_avgpool_base/base/base.cu:118:16: 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]
118 | at::Tensor bn_eps,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b4_s1_fully_unrolled_avgpool_base/base/base.cu:119:16: 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]
119 | at::Tensor bn_momentum
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b4_s1_fully_unrolled_avgpool_base/base/base.cu:143:17: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
143 | int N_val = sizes[0];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b4_s1_fully_unrolled_avgpool_base/base/base.cu:144:17: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
144 | int C_val = sizes[1];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b4_s1_fully_unrolled_avgpool_base/base/base.cu:145:17: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
145 | int D_val = sizes[2];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b4_s1_fully_unrolled_avgpool_base/base/base.cu:146:17: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
146 | int H_val = sizes[3];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_72/b4_s1_fully_unrolled_avgpool_base/base/base.cu:147:17: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
147 | int W_val = sizes[4];
| ^