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77_ConvTranspose3d_Scale_BatchNorm_GlobalAvgPool • 77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem_base

Level 2 • Task 77
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    eps: float,
    momentum: float,
    scale_factor: float,
    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,
) -> torch.Tensor:
    """
    Applies 3D transposed convolution, scaling, batch normalization and global average pooling.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_channels, depth, height, width)
        eps (float): Small constant for numerical stability in batch norm
        momentum (float): Momentum for batch norm running stats
        conv_transpose (torch.Tensor): Transposed conv weights
        conv_transpose_bias (torch.Tensor): Transposed conv bias
        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

    Returns:
        torch.Tensor: Output tensor after applying operations
    """
    x = F.conv_transpose3d(x, conv_transpose, bias=conv_transpose_bias)
    x = x * scale_factor
    x = F.batch_norm(
        x,
        bn_running_mean,
        bn_running_var,
        bn_weight,
        bn_bias,
        training=True,
        momentum=momentum,
        eps=eps,
    )
    x = F.adaptive_avg_pool3d(x, (1, 1, 1))
    return x


class Model(nn.Module):
    """
    Model that performs a 3D transposed convolution, scales the output, applies batch normalization,
    and then performs global average pooling.
    """

    def __init__(
        self, in_channels, out_channels, kernel_size, scale_factor, eps, momentum
    ):
        super(Model, self).__init__()
        conv = nn.ConvTranspose3d(in_channels, out_channels, kernel_size)
        self.conv_transpose_parameter = nn.Parameter(conv.weight)
        self.conv_transpose_bias = nn.Parameter(conv.bias)

        bn = nn.BatchNorm3d(out_channels)
        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,
        )

    def forward(self, x, eps, momentum, scale_factor, fn=module_fn):
        return fn(
            x,
            eps,
            momentum,
            scale_factor,
            self.conv_transpose_parameter,
            self.conv_transpose_bias,
            self.bn_weight,
            self.bn_bias,
            self.bn_running_mean,
            self.bn_running_var,
        )


batch_size = 16
in_channels = 64
out_channels = 32
depth, height, width = 16, 32, 32
kernel_size = 3
scale_factor = 2.0
eps = 1e-5
momentum = 0.1


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


def get_init_inputs():
    return [in_channels, out_channels, kernel_size, scale_factor, eps, momentum]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Model that performs a 3D transposed convolution, scales the output, applies batch normalization, 
    and then performs global average pooling. 
    """
    def __init__(self, in_channels, out_channels, kernel_size, scale_factor, eps=1e-5, momentum=0.1):
        super(Model, self).__init__()
        self.conv_transpose = nn.ConvTranspose3d(in_channels, out_channels, kernel_size)
        self.scale_factor = scale_factor
        self.batch_norm = nn.BatchNorm3d(out_channels, eps=eps, momentum=momentum)
        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.global_avg_pool = nn.AdaptiveAvgPool3d((1, 1, 1))

    def forward(self, x):
        x = self.conv_transpose(x)
        x = x * self.scale_factor
        x = self.batch_norm(x)
        x = self.global_avg_pool(x)
        return x

batch_size = 16
in_channels = 64
out_channels = 32
depth, height, width = 16, 32, 32
kernel_size = 3
scale_factor = 2.0

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

def get_init_inputs():
    return [in_channels, out_channels, kernel_size, scale_factor]
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Kernel Information

Operation Name 77_ConvTranspose3d_Scale_BatchNorm_GlobalAvgPool
Level ID 2
Task ID 77
Kernel Name 77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem_base
CUDA Speedup (Native) 1.000x
CUDA Speedup (Compile) 0.718x
CUDA Runtime 0.786 ms
PyTorch Runtime (Native) 0.786 ms
PyTorch Runtime (Compile) 0.565 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 77 • 77_ConvTranspose3d_Scale_BatchNorm_GlobalAvgPool)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 ldg_memory_alignment_optimization_edit_1 0.78 1.01 0.72
🥈 77_convtranspose3d_scale_batchnorm_globalavgpool_unrolled_scale_edit_1 0.78 1.00 0.72
🥉 optimized_global_avg_pool_edit_1 0.79 1.00 0.72
🥉 optimized_global_avg_pool_base 0.79 1.00 0.72
🥉 77_convtranspose3d_scale_batchnorm_globalavgpool_coalesced_base 0.79 1.00 0.72
6 77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem_base 0.79 1.00 0.72
6 77_ConvTranspose3d_Scale_BatchNorm_GlobalAvgPool 0.79 1.00 0.72
8 optimized_global_avg_pool_base 0.79 1.00 0.72
9 memory_coalescing_optimization_base 0.79 1.00 0.72
9 77_convtranspose3d_scale_batchnorm_globalavgpool_unrolled_scale_base 0.79 1.00 0.72
9 memory_coalescing_optimization_edit_1 0.79 1.00 0.72
12 77_convtranspose3d_scale_batchnorm_globalavgpool_min_warp_div_base 0.79 1.00 0.71
12 77_convtranspose3d_scale_batchnorm_globalavgpool_stride_optimized_edit_1 0.79 1.00 0.71
14 shared_mem_warp_reduction_edit_1 0.79 0.99 0.71
14 77_convtranspose3d_scale_batchnorm_globalavgpool_ldg_optimized_edit_1 0.79 0.99 0.71
14 77_convtranspose3d_scale_batchnorm_globalavgpool_stride_optimized_base 0.79 0.99 0.71
14 ldg_memory_alignment_optimization_base 0.79 0.99 0.71
18 memory_coalescing_optimization_base 0.79 0.99 0.71
18 77_convtranspose3d_scale_batchnorm_globalavgpool_shared_memory_edit_1 0.79 0.99 0.71
18 77_convtranspose3d_scale_batchnorm_globalavgpool_ldg_optimized_base 0.79 0.99 0.71 
#include <torch/extension.h>
#include <cuda_runtime.h>

#define BLOCK_SIZE 256
#define TILE_SIZE (BLOCK_SIZE * 4)

__global__ void shared_mem_scale_kernel(
    const float* __restrict__ input,
    float* output,
    const int size,
    const float scale_factor
) {
    __shared__ float shared_data[TILE_SIZE];
    
    const int tid = threadIdx.x;
    const int bid = blockIdx.x;
    const int gid = bid * TILE_SIZE + tid;
    
    // Collaborative loading into shared memory
    #pragma unroll
    for (int i = 0; i < 4; i++) {
        const int idx = gid + i * BLOCK_SIZE;
        if (idx < size) {
            shared_data[tid + i * BLOCK_SIZE] = __ldg(&input[idx]);
        }
    }
    
    __syncthreads();
    
    // Process data from shared memory
    #pragma unroll
    for (int i = 0; i < 4; i++) {
        const int idx = gid + i * BLOCK_SIZE;
        if (idx < size) {
            output[idx] = __fmaf_rn(shared_data[tid + i * BLOCK_SIZE], scale_factor, 0.0f);
        }
    }
}

torch::Tensor module_fn_cuda(
    torch::Tensor x,
    double eps,
    double momentum,
    double scale_factor,
    torch::Tensor 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
) {
    x = torch::conv_transpose3d(
        x,
        conv_transpose,
        conv_transpose_bias,
        /*stride=*/{1, 1, 1},
        /*padding=*/{0, 0, 0},
        /*output_padding=*/{0, 0, 0},
        /*groups=*/1,
        /*dilation=*/{1, 1, 1}
    );

    auto x_size = x.numel();
    auto x_flat = x.flatten();
    auto output_flat = torch::empty_like(x_flat);

    // Calculate grid size based on tile size
    const int num_blocks = (x_size + TILE_SIZE - 1) / TILE_SIZE;
    
    shared_mem_scale_kernel<<<num_blocks, BLOCK_SIZE>>>(
        x_flat.data_ptr<float>(),
        output_flat.data_ptr<float>(),
        x_size,
        static_cast<float>(scale_factor)
    );

    x = output_flat.view_as(x);

    x = torch::batch_norm(
        x,
        bn_weight,
        bn_bias,
        bn_running_mean,
        bn_running_var,
        /*training=*/true,
        momentum,
        eps,
        /*cudnn_enabled=*/true
    );

    x = torch::adaptive_avg_pool3d(x, {1, 1, 1});

    return x;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &module_fn_cuda, "Module function forward (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.940 inst/cycle 0.000 5
Executed Ipc Elapsed 0.832 inst/cycle 0.000 5
Issue Slots Busy 23.636 % 0.120 5
Issued Ipc Active 0.946 inst/cycle 0.000 5
SM Busy 25.326 % 0.138 5
Memory Throughput 2519327804267.642 byte/second 1386490522966044180480.000 5
Mem Busy 47.182 % 0.468 5
Max Bandwidth 75.264 % 1.321 5
L1/TEX Hit Rate 0.000 % 0.000 5
L2 Hit Rate 50.850 % 0.003 5
Mem Pipes Busy 37.212 % 0.290 5
Warp Cycles Per Issued Instruction 60.210 cycle 0.151 5
Warp Cycles Per Executed Instruction 60.522 cycle 0.154 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 31.410 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 20.000 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 89.816 % 0.021 5
Achieved Active Warps Per SM 57.482 warp 0.008 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 (89.7%) can be the result of warp scheduling overheads or workload imbalances during the kernel execution. Load imbalances can occur between warps within a block as well as across blocks of the same kernel. See the CUDA Best Practices Guide (https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html#occupancy) for more details on optimizing occupancy.
Operation / Metric Value Unit
aten::conv_transpose3d
CPU Time 3506420.30 μs
Device Time 3422624.99 μs
Self CPU Time 12663.77 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 3493756.53 μs
Device Time 3422624.99 μs
Self CPU Time 16821.64 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
aten::_convolution
CPU Time 3476934.88 μs
Device Time 3422624.99 μs
Self CPU Time 35081.15 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::cudnn_convolution_transpose
CPU Time 887715.32 μs
Device Time 3176214.37 μs
Self CPU Time 192518.69 μs
Self Device Time 3176214.37 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 3255475.51 μs
Device Time 36056.05 μs
Self CPU Time 3255475.51 μs
Self Device Time 36056.05 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
sm90_xmma_dgrad_implicit_gemm_f32f32_tf32f32_f32_nhwckrsc_nhwc_tilesize256x32x32_warpgroupsize1x1x1_g1_execute_segment_k_off_kernel__5x_cudnn
CPU Time 0.00 μs
Device Time 2641339.71 μs
Self CPU Time 0.00 μs
Self Device Time 2641339.71 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::add_
CPU Time 2544863.39 μs
Device Time 246410.61 μs
Self CPU Time 32740.01 μs
Self Device Time 246410.61 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 
45293 warnings generated when compiling for host.
Suppressed 45330 warnings (45283 in non-user code, 47 NOLINT).
Use -header-filter=.* to display errors from all non-system headers. Use -system-headers to display errors from system headers as well.
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:10:5 bugprone-easily-swappable-parameters
10 | const int size,
| ^~~~~~~~~~~~~~~
11 | const float scale_factor
| ~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:10:15: note: the first parameter in the range is 'size'
10 | const int size,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:11:17: note: the last parameter in the range is 'scale_factor'
11 | const float scale_factor
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:11:5: note: 'const int' and 'const float' may be implicitly converted: 'const int' (as 'int') -> 'const float' (as 'float'), 'const float' (as 'float') -> 'const int' (as 'int')
11 | const float scale_factor
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:15:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
15 | const int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:16:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
16 | const int bid = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:43:5: warning: 2 adjacent parameters of 'module_fn_cuda' of similar type ('double') are easily swapped by mistake [bugprone-easily-swappable-parameters]
43 | double momentum,
| ^~~~~~~~~~~~~~~~
44 | double scale_factor,
| ~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:43:12: note: the first parameter in the range is 'momentum'
43 | double momentum,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:44:12: note: the last parameter in the range is 'scale_factor'
44 | double scale_factor,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:45:19: 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]
45 | torch::Tensor conv_transpose,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:46:5: warning: 2 adjacent parameters of 'module_fn_cuda' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
46 | torch::Tensor conv_transpose_bias,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
47 | torch::Tensor bn_weight,
| ~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:46:19: note: the first parameter in the range is 'conv_transpose_bias'
46 | torch::Tensor conv_transpose_bias,
| ^~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:47:19: note: the last parameter in the range is 'bn_weight'
47 | torch::Tensor bn_weight,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:68:28: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
68 | const int num_blocks = (x_size + TILE_SIZE - 1) / TILE_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:68:38: 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]
68 | const int num_blocks = (x_size + TILE_SIZE - 1) / TILE_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:5:20: note: expanded from macro 'TILE_SIZE'
5 | #define TILE_SIZE (BLOCK_SIZE * 4)
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:4:20: note: expanded from macro 'BLOCK_SIZE'
4 | #define BLOCK_SIZE 256
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:68:38: note: make conversion explicit to silence this warning
68 | const int num_blocks = (x_size + TILE_SIZE - 1) / TILE_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:5:20: note: expanded from macro 'TILE_SIZE'
5 | #define TILE_SIZE (BLOCK_SIZE * 4)
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:4:20: note: expanded from macro 'BLOCK_SIZE'
4 | #define BLOCK_SIZE 256
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:68:38: note: perform multiplication in a wider type
68 | const int num_blocks = (x_size + TILE_SIZE - 1) / TILE_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:5:20: note: expanded from macro 'TILE_SIZE'
5 | #define TILE_SIZE (BLOCK_SIZE * 4)
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:4:20: note: expanded from macro 'BLOCK_SIZE'
4 | #define BLOCK_SIZE 256
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:68:55: 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]
68 | const int num_blocks = (x_size + TILE_SIZE - 1) / TILE_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:5:20: note: expanded from macro 'TILE_SIZE'
5 | #define TILE_SIZE (BLOCK_SIZE * 4)
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:4:20: note: expanded from macro 'BLOCK_SIZE'
4 | #define BLOCK_SIZE 256
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:68:55: note: make conversion explicit to silence this warning
68 | const int num_blocks = (x_size + TILE_SIZE - 1) / TILE_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:5:20: note: expanded from macro 'TILE_SIZE'
5 | #define TILE_SIZE (BLOCK_SIZE * 4)
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:4:20: note: expanded from macro 'BLOCK_SIZE'
4 | #define BLOCK_SIZE 256
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:68:55: note: perform multiplication in a wider type
68 | const int num_blocks = (x_size + TILE_SIZE - 1) / TILE_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:5:20: note: expanded from macro 'TILE_SIZE'
5 | #define TILE_SIZE (BLOCK_SIZE * 4)
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:4:20: note: expanded from macro 'BLOCK_SIZE'
4 | #define BLOCK_SIZE 256
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_77/b5_s2_77_convtranspose3d_scale_batchnorm_globalavgpool_shared_mem/base/base.cu:73:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
73 | x_size,
| ^